Podcasts about ibm analytics

Marketers know that Artificial Intelligence is being integrated into our work, but many are unsure how they can apply it to their daily work. Our guest is Christopher S. Penn, the author of AI for Marketers: A Primer and Introduction, just out in its 3rd edition. Our guest is an authority on analytics, digital marketing, and marketing technology. A recognized thought leader, best-selling author, and keynote speaker. He has been named by IBM as a Champion in IBM Analytics. Chris is a cofounder and Chief Data Scientist of Trust Insights, a Boston -based digital analytics firm. He is co-host of the Marketing Over Coffee podcast. He has also run the marketing for a series of startups in the financial services, SaaS software, and public relations industries. People, products and concepts mentioned in the episode: Seven steps of AI Maturity: Data Foundation Measurement & analytics Insights & Research Process Automation Data Science Machine learning AI-Powered Microsoft's Tay Bot Jay Baer's book Youtility 'When E.F. Hutton talks, people listen' commercial American Airlines SABRE system  Common Biases in AI Ann Handley Google's MUM algorithm and short video explainer GPT J6B algorithm (for a publicly accessible chat-enabled AI, see Replika) Dunkin donuts Brookings Institution Research on Jobs under threat from AI Chris' Social Profiles YouTube Instagram LinkedIn Twitter Facebook For complete show notes, please visit: https://funnelreboot.com/episode-47-ai-for-marketers-by-chris-penn-summer-books/

Kinsa Durst interviews John Colthart. John has made clients successful in every major market worldwide during his 17-year career in technology leading world-class sales and professional services organizations. This started after his departure as a corporate finance and accounting practitioner in 2000 so he could grow a startup to +425 employees and exit to IBM with a role of VP Sales Operations in 2010. During his stay at IBM, John held global roles running sales enablement, offering management and design leadership within the IBM Analytics division. He strives to lead clients to effectively use analytics to change the course of their business. Sponsors: Otter Labs www.hireotter.com - Hire great and inexpensive developer with staff augmentation through Otter. DeFi Code www.defi-code.com Go-to-market agency for cryptocurrency companies. Make your project stand out and gain maximum impact in the industry.

Christopher Penn is the co-founder/Chief Data Scientist at Trust Insights, co-host of Marketing Over Coffee, and three-time IBM Champion in IBM Analytics. In this Tech Bound podcast, Christopher Penn lays it all out: the impact of GPT-3 on SEO, what you can do with machine learning and what not, and the tools he uses for the AI-powered SEO process. Timestamps 0:00 Introduction 0:59 The impact of GPT-3 on SEO 8:46 The AI-powered SEO process 13:04 Using Markov Chains for analytics and conversion attribution 18:25 Tools SEOs can use for machine learning 21:24 Do keywords still matter in an AI world? 28:09 Challenges that come with AI-generated content 36:56 Old knowledge is dangerous 40:32 Decay in Marketing 44:29 Optimization vs. Innovation 47:23 What Christopher is curious about at the moment Subscribe to the channel for more videos: https://www.youtube.com/channel/UCoQ5uxfxcnObjzLAk1lmM6g?sub_confirmation=1 Subscribe to the Tech Bound Newsletter for more content: https://www.kevin-indig.com/tech-bound Follow me on Twitter: https://twitter.com/Techbound2 iTunes: https://podcasts.apple.com/us/podcast/tech-bound-conversations/id1488939659 Spotify: https://open.spotify.com/show/1Ze0gqMmuh22rR8rVv0oz8?si=87cysHp3S5yEzNuuUK9Ezg Soundcloud: https://soundcloud.com/kevin-indig Show notes https://www.christopherspenn.com/ https://www.christopherspenn.com/2020/01/you-ask-i-answer-do-keywords-still-matter-in-an-ai-seo-world/ AI-powered SEO process: https://www.christopherspenn.com/2017/11/the-ai-powered-seo-process-inventory/ Marketing over coffee: https://www.marketingovercoffee.com/ IBM watson: https://www.ibm.com/watson Google AI blog: https://ai.google/ Google co-lab: https://colab.research.google.com/notebooks/intro.ipynb Quanteda: https://quanteda.io/ Stylometry: https://en.wikipedia.org/wiki/Stylometry Ethics and Data Science: https://www.amazon.com/Ethics-Data-Science-Mike-Loukides-ebook/dp/B07GTC8ZN7 Aleyda Solis: https://www.aleydasolis.com/en/ Danny Sullivan: https://twitter.com/dannysullivan Bill Slawski: https://twitter.com/bill_slawski Simo Ahava: https://twitter.com/SimoAhava Lunchtime Pandemic: https://lunchtimepandemic.substack.com/ Trustinsights: https://www.trustinsights.ai/ #marketing #ai #machinelearning #seo

Nancy Hensley is the director of technical marketing for IBM Analytics. Nancy has more than 20 years' experience working in the data business in many capacities, from development and product management to sales and marketing. Before joining IBM, Nancy was a senior project manager at McDonald's Corporation, working largely on the international side of the business. Now in the 16th year of her IBM career, Nancy has worked chiefly in sales and technical sales positions. In 2004, she became the business unit executive leading the North American architect and technical teams. In that position, Nancy was granted a patent and a technical innovation award for her work on the data warehouse architecture. In her current role, Nancy runs a global product marketing team focused on business intelligence, predictive analytics, data warehousing, data integration, and database technology. LinkedIn: https://www.linkedin.com/in/nancyhensley/ Twitter: https://twitter.com/nancykoppdw?lang=en Medium: https://medium.com/@nancykoppdw 1:44 Nancy's career trajectory from college onward 6:21 Discussing the corporate culture at IBM 7:48 How IBM's business model changed over the years 12:34 What's it like to be a female in tech? 16:48 Talking about her leadership style. What works, what doesn't work, etc. 27:26 Nancy talking about ‘Growth Hacking' 32:59 On IBM's innovation and customer research 35:43 Nancy discusses why women in STEM are so important to her 38:53 On the importance of being kind and ‘servant leadership' * (winning w data - quote) 42:24 Discussing the importance of ‘failing fast' 45:48 Talking about Simon Sinek 49:13 How Nancy addresses work/life balance in such a demanding role 52:42 Talking about Nancy's sense of humor and being authentic

Dr. Dobrin joined IBM in November 2016 as the Vice President and Chief Data Officer for IBM Analytics(now IBM Data and AI). He is responsible for the transformation of the Cloud and Cognitive Softwarebusiness operations using data and analyticsand providing his perspective and experiences to clients. Having traveled around the World speak with clients he... The post Seth Dobrin – IBM appeared first on FPG Maestro.

This week we're joined on the show by Christopher S. Penn. He considered an authority on analytics, digital marketing, and marketing technology. As well as publishing over a dozen books, he is also recognized as a keynote speaker and thought leader. He is a 2019 IBM Champion in IBM Analytics, a Brand24 Top 100 Digital Marketer, and co-founder of the PodCamp Conference. Penn is the Co-Founder and Chief Data Scientist at Trust Insights. Trust insights want to make the world a better place by helping companies unlock and transform their data into useful analysis, valuable insights, and actionable strategies. On top of all the work Chris does, he is a proficient content creator, he co-hosts one of the top marketing podcast, Marketing Over Coffee with John Wall discuss marketing tactics, methods, strategies, and much more. Topics discussed on the show: What is data science, and what does a Chief Data Scientist do How can brands better track attribution? Google Analytics and other methods Discovering trends from social platform data ‘Owned’ platforms vs ‘rented’ platforms How big of a role do you think data should play in setting your marketing strategy? Measuring a brand Links:  Christopher S. Penn on Twitter Christopher's latest book: AI For Marketers Trust Insights A big thank you to Christopher for taking the time to chat with us!  - Thanks for listening, we hope you enjoyed this episode. Make sure to follow Tara at @missrogue & Carlos @carlospache_co on Twitter. You can also check out Tara's YouTube channel; it has over 200 videos on digital strategy and online audience building.  Truly Inc. is a digital strategy and insights agency based in Toronto, Canada. Visit our website trulyinc.com.  Anatomy of a Strategy podcast is recorded in Toronto, Canada in the offices of Truly Inc. Produced by Carlos Pacheco and Tara Hunt. Podcast editing by Joe Pacheco.

Jayson Gehri is currently a worldwide marketing leader for a $3bn+ business that includes the Db2 brand within the IBM Analytics software group. He leads a team of cross-functional marketers responsible for the strategy, plan and execution of a global marketing program to support these products. As part of the role, h is also leading a transformation to agile marketing within his team to drive better market responsiveness and outcomes.

Brian T. O'Neill leads the acclaimed dual-ensemble, Mr. Ho's Orchestrotica and has performed at prestigious venues in the US including Carnegie Hall, the Kennedy Center, and the Montreal Jazz Festival. In addition to being a busy independent musician, Brian is also a product designer and founder of the consultancy, Designing for Analytics, which helps enterprise companies turn data into indispensable information products and services. For over 20 years, he has worked with companies including DELL/EMC, Tripadvisor, Fidelity, NetApp, MITRE, JP Morgan Chase, ETrade and numerous SAAS startups. Today Brian focuses on helping clients create more useful, usable, profitable, and engaging decision support software and information products. Brian is also an international speaker and podcast guest, having appeared at multiple O'Reilly Strata conferences, Predictive Analytics World in Berlin, and on the IBM Analytics podcast, Making Data Simple. He also authored the Designing for Analytics Self-Assessment Guide for Non-Designers, maintains an active mailing list, and hosts the new podcast, Experiencing Data. Earlier in 2018, Brian joined the International Institute for Analytics' Expert Network as an advisor on design and UX. Our conversation covers the intersection of music, data, and technology and I hope you find it as fascinating as I did! Episode Links: http://crashandboom.com http://orchestrotica.com/presskit | brian@orchestrotica.com | @orchestrotica https://www.designingforanalytics.com | brian@designingforanalytics.com | @rhythmspice https://www.designingforanalytics.com/podcast-subscribe/

Chris Penn Explains how AI will listen to your customers and create content for them... Automatically!   (2:40) What are Elmo, Grover, Burt and Ernie in terms of Natural Language Generation (NLG)  (4:45) What politician was mimicked on Twitter by a machine generation AI system?  (7:50) Can Mark feed the AI system a Techcrunch writer and have the AI write an article?  (8:45) When will be able to realize truly credible AI articles and Whitepapers?  (11:50) Why it cost $0.44 to create 20,000 articles  (16:11) What should marketers prepare for to prepare for the AI wave?  (19:15) AI That listens to your customers then creates content for them   (22:25) Chris Penn's favorite AI Solution  Show links:  http://www.MostValuablePages.com http://www.AIForMarketersbook.com Fanatics Bot: https://m.me/fanaticsmedia?ref=w6471331    Find Mark Fidelman www.fanaticsmedia.com @markfidelman Twitter and Instagram  @fanaticsmedia Twitter and Instagram  Facebook: www.facebook.com/fanaticsmedia LinkedIn: http://www.linkedin.com/in/fidelman Guest Bio: Christopher S. Penn is an authority on analytics, digital marketing, and marketing technology. A recognized thought leader, best-selling author, and keynote speaker, he has shaped four key fields in the marketing industry: Google Analytics adoption, data-driven marketing and PR, modern email marketing, and artificial intelligence/machine learning in marketing. As Chief Data Scientist of Trust Insights, he is responsible for the creation of products and services, creation and maintenance of all code and intellectual property, technology and marketing strategy, brand awareness, and research & development. Mr. Penn is a 2019 IBM Champion in IBM Analytics, a Brand24 Top 100 Digital Marketer, co-founder of the groundbreaking PodCamp Conference, and co-host of the Marketing Over Coffee marketing podcast. Prior to cofounding Trust Insights, he built the marketing for a series of startups with a 100% successful exit rate in the financial services, SaaS software, and public relations industries. Mr. Penn is an IBM Watson Machine Learning Certified Professional, a Google Analytics Certified Professional, a Google Ads Certified Professional, a Google Digital Sales Certified Professional, and a Hubspot Inbound Certified Professional. He is the author of over two dozen marketing books including bestsellers such as AI for Marketers: A Primer and Introduction, Marketing White Belt: Basics for the Digital Marketer, Marketing Red Belt: Connecting With Your Creative Mind, and Marketing Blue Belt: From Data Zero to Marketing Hero, and Leading Innovation.

Nancy Hensley is the Chief Digital Officer for IBM Analytics, a multi-billion dollar IBM software business focused on helping customers transform their companies with data science and analytics. Nancy has over 20 years of experience working in the data business in many facets from development, product management, sales, and marketing. Today’s episode is probably going to appeal to those of you in product management or working on SAAS/cloud analytics tools. It is a bit different than our previous episodes in that we focused a lot on what “big blue” is doing to simplify its analytics suite as well as facilitating access to those tools. IBM has many different analytics-related products and they rely on good design to make sure that there is a consistent feel and experience across the suite, whether it’s Watson, statistics, or modeling tools. She also talked about how central user experience is to making IBM’s tools more cloud-like (try/buy online) vs. forcing customers to go through a traditional enterprise salesperson. If you’ve got a “dated” analytics product or service that is hard to use or feels very “enterprisey” (in that not-so-good way), then I think you’ll enjoy the “modernization” theme of this episode. We covered: How Nancy is taking a 50-year old product such as SPSS and making it relevant and accessible for an audience that is 60% under 25 years of age The two components Nancy’s team looks at when designing an analytics product What “Metrics Monday” is all about at IBM Analytics How IBM follows-up with customers, communicates with legacy users, and how the digital market has changed consumption models Nancy’s thoughts on growth hacking and the role of simplification Why you should always consider product-market fit first and Nancy’s ideas on MVPs The role design plays in successful onboarding customers into IBM Analytics’ tools and what Brian refers to as the “honeymoon” experience Resources and Links: Nancy Hensley on Twitter Nancy Hensley on LinkedIn Quotes: “It’s really never about whether it’s a great product. It’s about whether the client thinks it’s great when they start using it.” –Nancy “Every time we add to the tool, we’re effectively reducing the simplicity of everything else around it.”–Brian “The design part of it for us is so eye-opening, because again, we’ve built a lot of best in class enterprise products for years and as we shift into this digital go-to-market, it is all about the experience…”–Nancy “Filling in that “why” piece is really important if you’re going to start changing design because you may not really understand the reasons someone’s abandoning.”–Brian “Because a lot of our products weren’t born in the cloud originally, they weren’t born to be digitally originally, doesn’t mean they can’t be digitally consumed. We just have to really focus on the experience and one of those things is onboarding.” –Nancy “If they [users] can’t figure out how to jump in and use the product, we’re not nailing it. It doesn’t matter how great the product is, if they can’t figure out how to effectively interact with it. –Nancy Episode Transcript Brian: Today on Experiencing Data, I [talked] to Nancy Hensley, the Chief Digital Officer of IBM Analytics. Nancy brings a lot of experience and has a lot to say about how user experience and design have become very integral to IBM’s success especially as they move their applications into the cloud space. They really try to bring the price point down and make their services and applications much more low touch in order to access a new base of subscribers and users. I really enjoyed this talk with her about what the designers and people focused on the product experience have been doing at IBM to keep their company relevant and keep them pushing forward in terms of delivering really good experiences to their customers. I hope you enjoy this episode with Nancy Hensley. Hello everybody. I’m super stoked to have Nancy Hensley, the Chief Digital Officer of IBM Analytics. How’s it going, Nancy? Nancy: Good. I’m happy to be here. Happy Friday. Brian: Yeah. It’s getting cold here in Cambridge, Mass ; [ you’re] in Chicago area, if I remember correctly. Nancy: Yeah, it’s a little bit chilly here as well. Brian: Nice. So it begins. You’ve done quite a bit of stuff at IBM when we had our little pre-planning call. You talked a lot about growth that’s been happening over at IBM. I wanted to talk to you specifically about the role that design and experience has played, how you guys have changed some of your products, and how you’re talking to new customers and that type of thing. Can you tell people, first of all, just a little bit about your background, what you’re currently doing, and then we could maybe […] some of those things. Nancy: Sure, happy to. Thank you for having me again. I think I’m one of those people that doesn’t fit nicely into a box of, “Are you product? Are you marketing?” I am a little bit of both. Most of my IBM career, I have moved in between product marketing and product management. That’s why I love digital so much because it really is a nice mixture. And in particular, growth hacking because it combines all the things I love, including data as a part of what we do. What I’m doing right now as a Chief Digital Officer in the Analytics Division and Hypercloud is how do we transform our products to make them more consumable, more accessible? We have best in class products in data science, in unified governments and integration, in hyper data management products, but our products and our business is built on a traditional face-to-face model. There is even a perception that we’re not as accessible to them and that’s what we’re looking to change. Creating those lower entry points, making it easier for people who didn’t have access to us before, to start small and grow through a digital channel, through a lower entry point product, and then scale up from there. That’s really what we’re trying to do and as part of a bigger mission to really democratize data science—I kind of cringe when I say that word—I think it’s really important for more clients to be able to be more data-driven, have tools that are easy to use, and leverage data science to optimize their business. Part of the way we’re doing that is to develop a digital route to market. We’re pretty excited about it. Brian: I think a lot of our listeners probably come from internal roles of companies. They might be someone that’s purchasing vendor software as opposed to a SaaS company where they may have a closer role to marketing and all that. Can you tell me what you guys are doing there? Part of the thing with my experience is that some of the legacy companies, the older companies that are out there tend to get associated with big giant enterprise installations, really crappy user experience. It’s just so powerful, you have to put up with all these stuff. People’s tendency these days to accept that poor experience as just status quo is changing. What have you guys done? Not that you’re to blame but I’m sure that opinion exist. How do you guys adapt to that and wonder if upstart analytics companies coming out with other things, what do you guys to to address the experience? Nancy: There’s certainly a perception that IBM is that big, complicated, enterprise-focused product out there. We see the data. There’s a lot of articles, there’s a lot of feedback, there’s endless report that all validate that clients are trading off complexity or features and functions for consumability, because they got to get things done, they have less people to do it. We fully recognize that. Where we started to look for that was how we first started to make things much more accessible, not just our cloud products because that’s pretty easy if you have stuff in the cloud—it’s pretty accessible—but our on-prime products as well. So, for clients that are running analysis behind the private cloud, whether it’s a statistical product, or a predictive analytics product, or data science project, or even what they’re doing on their data catalog, all of that was not something people would go to the cog to look for it. There are some things they need especially financial and health care, and there’s large and small companies on both sides. One of the things we set out to do is how do we create that cloud-like experience for clients that are running things behind their firewall. We started a project about a year ago to look at some of our on-prime products and create that experience where literally you could, within a couple of clicks, download, try, and be using a product within 15 minutes. That was our goal. As opposed to before where you would have to contact and IBM salesperson, get them to come out and meet with you, and then set-up a trial. That’s what we started to change was that at least make it accessible. As we progressed that capability, we started changing our pricing and packaging to be appropriate, to create that entry-level point, to create a shift to subscription. You want to buy everything on subscription these days, I think. The last part of that shift for us has been to really focus on the experience because a lot of these products were not born digital. We really need to make sure that when clients were coming through that channel, that it was a great experience. That’s really where design experience came into play for us. Brian: How did you know of what’s wrong beyond broad surveys or just that general feeling that like, “Oh, it’s the big giant bloated software…” the stereotype, right? How do you guys get into the meat and potatoes of like you said, sounds like there’s a benchmark there, 15 minutes on that first onboarding experience, but can you tell us a little bit of maybe if you have a specific example about how you figured it out? What do we need to change about this software application to make it easier to get value out of the analytics of the data that’s there? Nancy: I’ve got lots of examples. We’ll opt with one that clients actually are very familiar with, which is SPSS Statistics that a lot of us used back in college. That was a product that actually turns 50 years old this year. It’s been out a while, a lot of people still using it a lot, and most of our base of users for statistics, I think if you look at the demographics of it, over 60% are under the age of 25. So, their buying preferences were very different than they were when they started out in 1968. We look at the verbatims from our NPS feedback and it was clear that clients really wanted a much more simplified and flexible experience than buying SPSS Statistics and having access to it. A lot of times, students have to get it really quickly for a project because they’ve might have waited until the last minute and they wanted a much more flexible subscription-based program. They might only use it for a few months and then come back to it. That was one of the first things that we implemented was to change the buying experience for the consumption model. We didn’t actually change the product at that point. We just changed the consumption model to see if in fact that actually will help us have some growth on that product, and it absolutely did. Since then, we’ve actually gone back and change the product as well. It’s got a whole new UI for its 50th anniversary. Joke around that it’s got a face lift for it’s 50th anniversary. Brian: Does it have a green screen mode? Nancy: It is a completely different experience, not just from a buying perspective, but also from a UI perspective as well. We have other products, too, that have been around maybe not 50 years but have been very popular products like our DB2 Warehouse on Cloud and our DB2 database that clients have been buying for years to run their enterprises. We wanted to make that again, as we created a SaaS alternative of these products that it was extremely consumable. So, we’ve been looking specifically, is it easy to figure out which version to buy? How much to buy? What it’s going to do for you? Like I said, which version? How do I calculate things? We’ve been really looking at the experience of that is, if there was no salesperson at all, how do we help clients through that buying experience? Brian: I’m curious. When you decided to helping them through the buying experience, does any of that thinking or that strategy around hand-holding someone through that experience happen in the product itself? I’m guessing you’re downloading a package at some point, you’re running an installer, and at that point, did you continue that hand-holding process to get them out of the weeds of the installation and onboarding again to the actual, “Is this tool right for what I needed to do?” Everything else being friction up until that point where you’re actually working with your data, did you guys carry that through? Can you talk about that? Nancy: You’re hitting one of my favorite topics which is onboarding. Because a lot of our products weren’t born in the cloud originally, they weren’t born to be digital originally, doesn’t mean they can’t be digitally consumed. We just have to really focus on the experience and one of those things in onboarding. Let’s say, DB2 in particular, which won the process of creating onboarding experience for DB Warehouse on Cloud. For anybody who’s used DB2, we do have an updated UI for that. They can jump in and start using it. But that’s not everyone, the people that haven’t used it before. So, we just started working with a couple of different onboarding tools to create these experiences. Our goal was to be able—at least I’m offering management side alongside our partners but design—to create these experiences in a very agile way and make them measurable—my second favorite topic, which is instrumentation—but not have a burden on development, because the fact is, in almost any organization, development wants to build features and functions. Whenever we talk about this, they were prioritized lower because they want to build new capabilities. They’re less enthusiastic about building in things like onboarding experiences. With some of the tools like [.DB2..] give us, is a way to make it codeless to us. We can create these experiences, then pass the code snippet, and then measure whether those are effective or not because we actually see those flowing through segment into our amplitude as a part of the shuttle. We’ve got some great feedback as to whether they’re working or where they’re falling down. We can create checklists of things that we want the clients to do that we know makes the product sticky, and see if they actually complete that checklist. It’s giving us so much better view because before, what we would see with a client is register for trial, they downloaded the trial, they’ve created their instance, and then boom they fall off the cliff. What happened? Now we’re getting a much better view to what’s actually going on for the products that have been instrumented as well as the view we’re getting in from the onboarding experiences. Brian: For every one of these applications that you’re trying to move into a cloud model or simplify whether it’s cloud, to me the deployment model doesn’t matter. It’s really about removing the friction points whether it’s on-premise software or not. I think we all tend to use the word ‘cloud’ to kind of feel like, “Oh, is this browser-based thing? There’s no hard clients? There’s no running scripts at the terminal and all that kind of stuff?” Do you guys have a set of benchmarks or something that you establish for every one of these products that are going to go through a redesign? Nancy: We do. We’ve got a set of criteria, it’s really broken down into two pieces. Whether it’s going to be a cloud product or an on-premise product—I actually have a mix of both—there is what we call the MVP side, which might be something that’s not born in the cloud, it’s not a new product, and we’re looking to create a lower entry point, a really good trial experience, a very optimized journey. We’re even doing things like taking some of the capabilities that we used to have from a technical perspective and making those more digitally available. Online proof of concepts, hands-on labs that you do online instead of waiting for a technical salesperson to come out to see you. Tap us that can answer your questions faster even before you talk to a sales rep. All of that is included in the what we call the MVP portion of the criteria that we look at. Pricing and packaging’s got to be right for the product, for the marketplace. Got to have that right product market fit that you’ve got a good valuable product but a low-enough entry point where somebody can start small and scale up. The second part of the criteria is where the growth magic happens, where we’re dumbing down a lot more on the experimentation, where we’re making sure that we’ve got onboarding, instrumentation we want done, and the MVP phase, we don’t always get it, but our development partners really understand the value of that now, which is great. Though more often, we’re getting into the second phase of where we’re more doing the transformation. Through that, then we’re getting a lot more feedback, where we can create the onboarding experience. We can do even more on the optimized journey. We’re doing a lot of growth hacking that’s based on terms of optimizing. Things like how clear is information on the pricing page? Is it easy for the customer to figure out what they need to buy? What the pricing is for that? Can they get their questions answered quickly? Can we create a deeper technical experience for them, even outside of the trial itself? Like I mentioned, things we’re doing with our digital technical engagement, thinking that what used to be our tech sales modeling and making it more digital. Brian: That’s cool. When you guys go through this process of testing, are you primarily looking at quantitative metrics then that are coming back from the software that you guys are building, or you’re doing any type of qualitative research to go validate like, “Hey, is the onboarding working well?” Obviously, the quantitative can tell you what. It doesn’t tell you why someone might have abandoned at this point. You guys do any research there? Nancy: We do. It happens in a couple of places. We run squads that are cross-functional across marketing, product, development, and design, each product. Then every Monday we have this thing called Metrics Monday where we get the cross-functional routines together, we share the insights around the metrics. If we had a big spike or we had a big decrease, or if we had a change in engagement, or if we did some experimentation that came out with a very interesting result, we actually share that across teams. We really focus on why did things happen. We have a dashboard. Everybody is religious in using on a daily basis that tracks all of our key metrics, whether it’s visits, engage visits, trials, trial-to-win conversions, number of orders, things like that, but we also want to dive deeper into the ebbs and flows of the business itself, why things are happening, and if the experimentation we’re doing is helping or not helping. We’ve got a lot of focus on that on a daily and a weekly basis. Brian: Do you have any way to access the trial users and do one-on-one usability study or a follow-up with them that’s not so much quantitative? Nancy: Our research team and design will do that and they’ll take a very thorough approach to both recording users using the product, getting their feedback. It’s pretty thorough and also gives us some feedback. We usually don’t do that until the product’s been in the market for a little bit longer. We’ve got some hypothesis of how we think it’s doing, and then the research team will spend a couple of weeks diving a lot deeper into it. We get some great feedback from that. Honestly, as a product person, as much as I’d to think I’m focused on a beautiful experience, my lens versus our designers’ lens is completely different and they just see things we don’t. Brian: Yeah, the friction points and filling in the why’s, it takes time to go and do that, but it can tell you things, it helps you qualify the data, and makes sense especially when you’re collecting. I’m sure at the level that you guys are collecting that, you have a lot of inbound analytics coming back on what’s happening. But it’s really filling in that “why” piece that is really important if you’re going to start changing design because you may not really understand the reasons someone’s abandoning. Maybe it’s like, “I couldn’t find the installer. I don’t know where the URL is. I ended up locking the server on my thing and I don’t know how to localhost, but I forgot the port number,” and the whole product is not getting accessed because they don’t know the port number for the server they installed or whatever the heck it is, and it’s like, “Oh, they dropped off. They couldn’t figure it out how to turn it on, like load the browser…” Nancy: Right, and even behavioral things that we don’t always think of, like putting a really cool graphic in the lead space that actually takes the attention away from the callback-ends. We’re all proud of, “Hey look at this cool graphic we built.” One of our designers uses a tool that tracks eye movements and [wait a second] “We’re losing the focus here.” But again, you don’t always see from that lens. The design part of it, for us has been so eye-opening because again, we’ve built a lot of best in class enterprise products for years. As we shift into this digital go-to market, it is all about the experience. It’s all about how good the experience is, how easy the experience is, how frictionless it is, and it’s also about how consumable and accessible the product is in the marketplace. Brian: You mentioned earlier, it sounded like engineering doesn’t want to go back and necessarily add onboarding on all of this. This gets into the company culture of who’s running the ship, so to speak. Is it engineering-driven in your area? How do you guys get aligned around those objectives? I’ve seen this before with larger enterprise clients where engineering is the most dominant force and sprints are often set up around developing a feature and all the plumbing and functionality required to get that feature done, but there’s not necessarily a collective understanding of, “Hey, if someone can’t get from step A to step G, horizontally across time, then all that stuff’s a failure. Step F which you guys went in deep on is great, but no one can get from E to F, so definitely they can’t get to G.” So, that’s you’re qualifier of success. How do you guys balance that? Who’s running the ship? Does your product management oversee the engineering? Can you talk a little bit about that structure? Nancy: We call operating management aside from product management for a reason, because we really do want the operating managers to feel like they’re the CEO of their business and run the ship. Of course, development has a big say at the table, but they have a natural tendency to want to build capabilities. It’s never going to go away. It’s been that way for ages. We just don’t want to fight that tendency. We want them to focus on building, not take six months to build an onboarding experience when they could build in really valuable functionality in that six months instead. So, we really run it as a squad, just like many other companies. Operating management does leave a lot of the strategy with our products and development, but I would say that design is also a really, really chief at the table, for sure, absolutely. Brian: Tell us a little bit about your squads and is this primarily a designer or a UX professional up in your offering manager? Are they a team and then you pull in the engineering representatives as you strategize? Nancy: My team is a digital offering management. We’re a subset of offering management better known as product management. We will run the squads and the squads will be a cross-function of our product marketing team, our performance marketing team, which is demand to and type marketing. They run the campaigns, design, developments, the core product managers because we’re the digital product managers and such, and then there’s the core product managers. They have all routes to market. We’re just focused on the digital ones. With that is the cross-functional squad that gets together on a weekly basis and they run as a team. From a digital perspective, it’s led by the Digital OM for our route to market there. Brian: That’s interesting. How do you ensure that there are some kind of IBMness to all these offerings? Your UX practice and offering managers sound like they are part of one organization, but I imagine some of these tools, you might be crossing boundaries as you go from tool X to tool Y. Maybe you need to send data over like, “Oh, I have this package of stuff and I need to deploy this model,” then we have a different tool for putting the model into production and there’s some cross user experience there. Can you talk about that? Nancy: That’s really why design’s been key because their job is to keep us onus making sure that the experience is somewhat consistent across the tools so they seem familiar to us, especially within a segment data science. Some of these are using our Watson Studio tool and then moves to our statistics for our modeler tool. There should be a very familiar experience across those. That’s why design is really the lead in the experience part of it. From pricing and packaging, we try to maintain a consistency as much as possible across all the products again. Whatever level of familiarity you have and how we price and package things should be consistent across the entire segment. So we strive for that as well. On the digital side, in terms of the experience on the actual web, we partner with a team called the Digital Business Group. They are basically the host of our digital platform and they maintain a level of consistency worldwide across all the products in terms of the digital journey itself with us. Brian: That’s cool that you guys are keeping these checkpoints, so to speak, as stuff goes out the door. You’ve got the front lenses on it looking at it from different quality perspectives, I guess you could say. Earlier, you mentioned democratizing data science and we hear this a lot. Are we talking about democratizing the results of the data science, so at some point there’s maybe decision support tool or there’s some kind of outcome coming from the data science? Is that what you’re talking about democratizing? Or are you saying for a data scientist of all levels of ability, it’s more for the toolers as opposed to the [consumers..]? Nancy: It’s about the capability. The ability to put more of these products or these products in people’s hands that bought, that they might have been out of their reach, or that they were too enterprisey, or that they are for big companies. That’s one of the key things that we want to do. When you look at some of our products, they start really, really low. Cognizant Analytics is another great example where people might have had a perception that it’s really expensive but we just introduced a new version of it, and it’s less than $100 a month. You can get these powerful tools for analysis for a lot less than you think. Statistics in $99 a month, one of our pay products are significantly less, and it allows these companies that might not have considered doing business with us, to smart small and build up. That’s one of the key things we noticed as we shifted to a subscription model. With that, we started to see double digit increases in the number of clients that were new on products. Just because opened up this new route to market, doesn’t mean that we still didn’t maintain our enterprise face-to-face relationships because, of course, we did, but this allowed us to open up relationships with clients might have not gotten to before. Brian: How are the changes affecting the legacy users that you have? I imagine you probably do have some people that are used to, “Don’t change my toolset,” like, “I’ve been using DB2 for 25 years.” How are they reacting to some of the changes? I imagine at some point maybe you have some fat clients that turn to browser-based interfaces. They undergo some redesign at that point. Do you have a friction between the legacy experience and maybe do you employ the slow change mentality? Or do you say, “Nope, we’re going to cut it here. We’re jumping to the new one and we’re not going to let the legacy drag us back”? You talk about how you guys make those changes? Nancy: We’re shifting towards the subscription model. Our clients are, too. We have clients that are demanding that this is the only way that they actually want to buy software is through a subscription model. So it’s changing for them as well. I think in many ways, it’s a welcome change across the board. I can’t think of any negativity that we’ve had in both the change for the consumption models on a subscription side, as well as the new UI changes and things that we’re doing to the product that really update them and give them a modern feel. I know a lot of the onboarding is a welcome change, even for clients that are familiar with us. It helps them because they have to do less training internally to help people use the tool because now we’re building it into the product. Brian: How do you measure that they’re accepting that? Do you wait for that inbound feedback? Do you see if there’s attrition and then go talk to them? I imagine there’s some attrition that happens when you make a large tooling change. Is there a way to validate that or why that happened? Was it a result of changing too quickly? Any comment on that? Nancy: I think it’s a couple of things. We’re constantly monitoring the flow of MRR and the contraction of revenue where the attrition that we get through some of our subscription, to see if there is any anomalies there. But also we’re always were very in-tune with NPS. A lot of our product managers live and die in the verbatims and with the integration of FLAX, they get a lot of it. They’re coming right at them constantly, that they respond to. We are very, very in-tune with NPS and the feedback we’re getting there. We’re also getting a lot of reviews now on our software using tools like G2 Crowd where we keep an eye on that. I think the feedback doesn’t just come from one place. We’ll look at things like the flow through Amplitude. Our clients, when they’re coming in and during the trial, are they getting stuck someplace? Are they falling off someplace? Are they falling off either at a specific page like the pricing page? Or are they falling off as soon as they get the trial because they don’t know what to do with it? We look at things like that. We look at NPS in particular after we’ve introduced new capabilities. Did our NPS go up? What’s the feedback? Are our clients truly embracing this? I think it’s a combination of things. There is a lot of information, a lot of data that we just need to stay in-tune with. We’ve got a couple of dashboards that I know my team wakes up with everyday and takes a look at, and the product team. The core product manager stayed very focused on NPS. Brian: Do you have a way of collecting end-user feedback directly? I would imagine maybe in your newer tools, it’s easier to tool some of that in, but is there any way to provide customer feedback or something to chat or any type of interactivity that’s directly in the tools that you’re creating these days? Nancy: Sure. We are rolling out more end-product nurture capability than we ever had before. That gives them the ability to chat directly within the product, as well as schedule a time with an expert. We’re working in making that even easier through a chat bot. So if you do get stuck and you’re chatting with that bot, you can schedule the appointment with an expert right there. I think there’s lots of ways to do that. I think sometimes I worry that there’s too much data coming at us but we [didn’t have enough..] before, so I’m not going to do that. Brian: Right. It’s not about data, right? It’s, do we have information? Nancy: Do we have information? Exactly. I would say my team spends a lot of time going through that, looking at Amplitude, analyzing the flows, looking in the patterns, in the orders, in the data, and the revenue. With the NPS feedback, it’s a combination of all of that stuff that really gives us a good view. As well as looking at the chat data, and analyzing some of the keywords that’s coming across on the chat, the Watson robots are constantly learning, which is great. We’re using machine learning to get smarter about what do people ask about, and that’s giving us also some good insight into the questions they ask, the patterns of information they’re searching for by product. Brian: In terms of the net promoter score that you talked about, tell me about the fact that how do you interpret that information when not everybody is going to provide a net promoter score? You have nulls, right? Nancy: Right. Brian: How do you factor that in? That’s the argument against NPS as the leading indicator. Sometimes, it’s not having any information. So you may not be collecting positive or potentially negative stuff because people don’t even want to take the time to respond. Do you have comments on how you guys interpret that? Nancy: I think you also have to look at the NPS is going to go up and down. If you have a client who has particularly a bad experience, it’s the week of thanksgiving, there was only X amount of surveys, and one of them had a bad experience that could make your NPS score looks like it drops like a rock. [right] you’ve got to look at it like the stock market. It’s more of the patterns over the long haul, what’s coming across within those patterns of information and feedback the clients are giving you. We react but you have to look at the data set, you have to look at the environmental things that are happening, and take that all into consideration from an NPS perspective. We’re very driven by that and that comes down from our CEO. She’s very cognizant, making sure that the product teams and the development teams are getting that feedback directly from the clients. As an organization—we’re a few years old—the way we used to do that is we would have these client advisory boards. It was a small number of clients that would give us feedback on our products, roadmap, and usability of that. The reality is just that then you end up building the product for 10% of your clients. Now it’s been eye-opening for us as we really open that up. Obviously, we’re still getting feedback from a larger community and client advisory board still, but NPS comments and feedback has really widen the aperture of the feedback we’ve gotten from a broader scope of clients. Brian: You brought up a good point. I had a client who luckily was cognizant of this and they did the same things where they fly their clients, they do two-day workshops, and they gather feedback from them. I was doing some consulting there and he said, “Brian, I’d like you to just go walk around, drop in some of the conversations and just listen, but take it with a grain of salt because I hate these freaking things. All we do is invite people that are willing to come for 2–3 days and tell us how much they love our stuff, it’s a free trip, we’re not getting to the people that don’t like our stuff…” Nancy: Or don’t use it. Brian: Or don’t use it at all. I love the concept of design partners, which is new, where you might have a stable of customers who are highly engaged, but that the good ones are the ones that are engaged who will pummel you when you’re stuff is not happening. They will come down on you and they will let you know. So it’s really about finding highly communicative and people who are willing to tell it like it is. It’s not, we’ll go out and find people that rah-rah, cheerleading crowd for you. Did that inspire the changes? Nancy: Even in the client advisory councils that we had—I ran a couple of them for products like Netezza for a while—we started to change the way we even ran those. I remember the biggest aha moment was, we had a client advisory board for Netezza one year and not too long ago. We decided to run a design thinking camp as part of the agenda, so that they would actually drive what they wanted from our requirements prospectus, going through the design thinking process through that. What came out of it was truly eye-opening. You know how a design thinking process progresses. I think even they were surprised at what they ended up prioritizing across the group of requirement. I think we’re really starting using differently about that feedback from clients. I do remember that day when we were looking at those things and that was not where we thought we would end up. Brian: Do you have a specific memory about something that was surprising to the group that really stuck? Something you guys learned in particular that stuck with you? Nancy: I think we focused a lot more at that point. At the time there was a lot of issues around security and what was one of our leading things going into the next version. What clients actually were not necessarily as verbal about was that, as they were using these appliances and they were becoming more mission-critical, they were doing more mixed workloads. Yes, security was still incredibly important, but what was emerging beyond that for them was workload management because they had this mixed workload that was emerging. So many different groups were jumping in with different types of workload. They have not anticipated on their [day route?] appliance, so it was something that I think came out of the next in the design thinking process that was important to them that they actually hadn’t been able to verbalize to us. Outside of that process, which was really, really interesting to us, we were on track with the requirements that we have but beyond that, the requirements that we just hadn’t thought of and quite honestly they hadn’t verbalized. Brian: You make a good point there. Part of the job of design is to get really good clarity on what the problems are and they’re not always going to be voiced to you in words or in direct statements. It’s your job to uncover the latent problems that are already there, crystalize them, so ideally whoever your project manager in the organization and your leadership, can understand and make them concrete because then you can go and solve them. When they’re not concrete and vague, like, “We need better security.” But what does that mean specifically? If you start there and really the problem had to do with the mixed workloads and managing all that, it’s like you can go down a completely different path. You can still write a lot of code, you can build a lot of stuff, and you can do a lot of releases, but if you don’t really know what that problem is that you’re solving, then you’re just going through activity and you’re actually building debt. You’re building more technical debt, you’re wasting money and time for everybody, and you’re not really driving the experience better for the customer. I think you made a good point about the design thinking helps uncover the reality of what’s there, when it’s not being explicitly stated, support requests are not going to get that type of information. They tend to be much more tactical. You’re not going to get a, “Hey, strategically I think the project needs to go this direction.” Nancy: Right and if you would have asked of us an open-ended question, you would have gotten and answer that could have been interpreted slightly differently. I think this was when I became the biggest fan of design is that, there was this magical person who was running this design camp for me that got information that I didn’t think I could get to. I mean, I knew nothing about the product. It was pretty amazing. Brian: That can happen when you also get that fresh lens on things even when they may not be a domain expert. You get used to seeing the friction points that people have and you can ask questions in a way to extract information that’s not biased. You’re not biased by the legacy that might be coming along with that product or even that domain space. It’s sometimes having jthat almost like first grade, “Tell it to me like I’m your grandfather,” or, “Explain that to me this way,” and then you can start to see where some of those friction points are and make them real. I always enjoyed that process of when you’re really fresh. Maybe this happens for other people but especially as a designer and consultant, coming into a product and a new domain, and just having that first-grader lens on it like, “Hey, could you unpack that for me?” “What is the workload in there like?” looking at you like, “What?” and you make them unpack that but you give that full honesty there to really get them to extract out of their head into words that you [and.] everyone can understand. That’s where some of those magical things happen like, “Oh my gosh. We had no idea that this was a problem,” because he or she thought it was so obvious like, “Of course, they know this,” and it’s like, “No. No one’s ever said that.” Nancy: Right. We’re experiencing that now. We have an embedded designer into our team that’s focused on our growth products. Again, she’s coming in with a complete fresh set of eyes and her perspective that she brings on the experience is just so completely different, not completely different but there are things that she flushes out we would have never see. It’s really helping because a lot of times, too, when you’re focused on the experience as opposed to the features and functions analysis, and you come down to looking at it from that perspective. I don’t want to go to development and tell them this because it’s like calling their baby ugly. But at the end of the day, the client needs to have a great experience. They need to see the value. When they’re even just trying the product out, they don’t get to that aha experience like, “I know how this will help me within 15 minutes.” We’re just not nailing it. If they can’t figure out how to jump in and use the product, we’re not nailing it. It doesn’t matter how great the product is, if they can’t figure out how to effectively interact with it. Brian: Effectively, none of the stuff really exist in their world. It just doesn’t exist because they can’t get to it. So, effectively it’s totally worthless. Whatever that island you have on the island, if there’s no bridge to get there, it doesn’t matter because its just totally inaccessible. Nancy: Right and it’s harder sometimes for even the product managers to see it. When I was sitting down in a demo of a product that we are going to be releasing, dude was cruising through the demo, my eyes were like glazed over, I just look and I was like, “Boy, we’re going to need some onboarding with that.” Great product, amazing capabilities, very complex and dense in its capability. It’s never really about whether it’s a great product. It’s about whether the client understands that’s great, when they start using it. Brian: Yeah and I think especially for analytics tools, highly technical tools used by engineers and other people that have better working in this kind of domain. Sometimes we gloss over stuff that seems like it would be totally easy or just not important. I have this specific example I was working on a storage application. It was a tool I think for migrating storage between an old appliance and a new appliance. At some point during that workload migration, something as simple as like, “Oh, I need a list of these host names and these IP addresses,” some other information that’s just basically setup-related stuff, and all the tool needed to do was have a CSV download of a bunch of numbers to be piped into another thing so that they could talk to each other. It’s not sexy. It’s literally a CSV. It was the only technical lift required, but it was not seen as engineering. It’s not part of the product. That has to do with some other product but you have to go type it into. It’s like yes, but that bridge is never going to happen. It takes them 10 years to go figure out where all these IP addresses are listed, domain names, and all these kind of stuff. It’s not sexy but if you look at the big picture, the full end-to-end arc, and if we’re all lying around, what is that A to G workflow, there’s six steps that have to happen there. This is not sexy, it’s not a new feature but this is the blocker from getting from B to E. They’re never get to A, which is where the product begins. Nancy: We definitely had those discussions in the early days about making it more consumable instead of giving it more features and functions, and can’t we really hack growth that way? That is a mind shift that if you are a design-led organization, you get it, and we believed in every part of our being that we are. Sometimes we still have that natural resistance that we really need to add more features and functions to make this product grow, but I think we’ve really turned the corner on that. Digital really has been the task for us to do that because we build the experience in the products as if there was no IBM sales team that’s going to surround you to help make you a success. That’s a very different way that we’ve done things for so many years, and the only way you can do that is by focusing on experience. Brian: You bring up a good point and I think that it’s worth reiterating to listeners. You can add these features but they do come at a cost. The cognitive load goes up. Every time we add to the tool, we’re effectively reducing the simplicity of everything else around it. Typically as a general rule, removing choice simplifies because you’re just removing the number of things that someone has to think about. So those features don’t really come for free. It’s almost like you have a debt as soon as you add the feature and then you hope you recoup it by, “Oh there’s high engagement. People are really using that,” so that was a win. If there’s low engagement with it, you just add it. It’s like Microsoft Word 10 years ago. You just added another menu bar and another thing that no one’s gonna use, and now it’s even worse. The pile continues to grow and it’s so hard to take stuff out of software once it’s in there, because you’re going to find, “You know what? But IBM’s our client, and they’re using it. IBM makes $3 million a year. We’re not taking that button out of the tool. End of story,” and now you have that short-term like, “We can’t take that out because Nancy’s group uses this.” Nancy: That’s right and we can’t point out exactly. I think my favorite story when it comes to that is the Instagram story that people talk about, where it was launched as a tool, a product called Bourbon. It had all of these great capabilities and it was going nowhere. So they dug into the usability side of things and said, “Well, what are people actually using?” which is what we do as well from an instrumentation perspective, and found that they were really only using a couple of things. They wanted to post a picture, they wanted to comment on the picture, they want to add some sort of emojis or in like system the picture and they are like, “Let’s [do.]. Let’s just do three or four things, do them really great, and relaunch the product,” and then of course the rest is history. I think that that’s a great illustration of more features and functions. If they’re not important, relevant, and consumable, all three of those things, are not going to give you growth. It comes down to, is it easy to use? Can I get value out of it? Do I immediately see that I can get value out of it? That’s all product market fit. That’s where we shifted our focus and digital’s helped us, too. That’s why my job is so cool. Brian: Cool. This has been super fun. Can you leave us with maybe an anecdote? Do you have a big lesson learned or something you might recommend to people that are either building internal tools, internal enterprise software or even SaaS products, something like, “Hey, if I was starting fresh today, I might do this instead of doing that.” Anything from your experience you could share? Nancy: For me, the biggest thing is just really focusing on product market fit because we build something sometimes to be competitively great, but not necessarily competitively great and competitively different, or that. So to understand that you not only have something that solves somebody’s problem but does it in a way that’s unique, and that’s so valuable that they’ll pay the price that’s appropriate for whatever they’ll pay for it. You’ve got to start thinking about that upfront because oftentimes, we’ll build something we’ll see a market opportunity for, but we may not truly understand product market fit whereas we know who the target is, we know what they’ll pay for this, we know what the value is, we know how to get to them, and I think you’ve got to start with that upfront, like you really got to understand product market fit or you’re never be able to grow the product. I’ve got a lot of religion around that and we really try very, very hard to create pricing and packaging around making sure we hit that, but the product has to have that value. It can’t be too overwhelming, it can’t be too underwhelming, it’s got to hit that great value spot. Brian: Fully agree on getting that fit upfront. You save a lot of time, you could solve a lot of technical debt instead of jumping in with the projects that you going to have to change immediately because you find out after the fact and now you’re starting it like… Nancy: See you in Instagram not a Bourbon, right? Brian: Exactly. Tell us where can people find you on the interwebs out there? Nancy: I probably spend a lot of time on Twitter. Maybe not so much lately. It’s been a little bit crazy but you can find me on Twitter @nancykoppdw […] or you can find me on LinkedIn. I am going to try and do better. I am on Medium. I haven’t done as good about blogging but that’s one of my goals for trying to get back on blogging. I’m usually out there on Medium or Twitter talking about growth hacking and digital transformation. I do podcast as well. Brian: Cool. I will put those links up on the show notes for anyone. Thanks for coming to talk with us, Nancy. It’s been fun. This has been Nancy Hensley from IBM Analytics, the Chief Digital Officer. Thanks again for coming on the show and hope we get the chance to catch up again. Nancy: Thank you.

Christopher Penn is an authority on digital marketing and marketing technology. He is a 2019 IBM Champion in IBM Analytics, co-founder of the groundbreaking PodCamp Conference, and co-host of the Marketing Over Coffee marketing podcast. In this episode, Chris shares his expertise when it comes to artificial intelligence for marketers.  We explore the different ways of dealing with artificial intelligence and embracing it in your business. And how you can use artificial intelligence today to make a difference in your business right away.  Chris’s insight helps bridge the gap between marketing and technology.  You're going to learn a ton from Chris Penn. Listen and Discover The biggest misconception businesses have when it comes to artificial intelligence.  How to leapfrog your competition by embracing AI, machine learning in your business. Ways to improve your Sales Team’s performance and the sales process with voice transcription software Two or three things your businesses should be looking at to embrace artificial intelligence And much more...

In this episode, we chat with Alfredo Ruiz who is leading the design and product strategy of IBM Immersive Insights as well as the Augmented Reality Program at IBM Analytics. We discuss how to get started in AR. You can connect with Alfredo on LinkedIn: https://www.linkedin.com/in/alfredoruizc Also, if you have comments or questions from today's episode or would just like to connect, you can connect with us on LinkedIn: www.linkedin.com/in/cam-sackett-b6495567 www.linkedin.com/in/caden-damiano Pivot by Cam Sackett and Caden Damiano. Pivot Intro Music by Cam Sackett This episode was recorded on Dec. 11, 2018. --- This episode is sponsored by · Anchor: The easiest way to make a podcast. https://anchor.fm/app --- Send in a voice message: https://anchor.fm/thewayofproductdesign/message

Seth Dobrin is back to kick off season 3 and reflect on data and tech in 2018. Seth Dobrin, vice president and Chief Data Officer of IBM Analytics, gives insight to leading the data science elite team, and he details the steps and strategies required to be successful in the field. Host Al Martin and Seth also make some data science predictions for 2019, letting you know what you should be looking out for in the year ahead.Shownotes: 00:00 - Check us out on YouTube and SoundCloud. 00:10 - Connect with Producer Steve Moore on LinkedIn and Twitter. 00:15 - Connect with Producer Liam Seston on LinkedIn and Twitter. 00:20 - Connect with Producer Rachit Sharma on LinkedIn. 00:25 - Connect with Host Al Martin on LinkedIn and Twitter. 00:55 – Connect with Seth Dobrin on LinkedIn and Twitter. 02:00 – Seth Dobrin’s first podcast from January 2018. 03:30 - What is data science? 04:25 - Seth Dobrin’s Blog: Don’t let data science become a scam. 10:55 - IBM Data Science Elite Team: Kickstart, build andaccelerate 31:55 - What is AI?37:58 - What are data pipelines? 41:55 - What is Blockchain?

Seth Dobrin is back to kick off season 3 and reflect on data and tech in 2018. Seth Dobrin, vice president and Chief Data Officer of IBM Analytics, gives insight to leading the data science elite team, and he details the steps and strategies required to be successful in the field. Host Al Martin and Seth also make some data science predictions for 2019, letting you know what you should be looking out for in the year ahead.Shownotes: 00:00 - Check us out on YouTube and SoundCloud. 00:10 - Connect with Producer Steve Moore on LinkedIn and Twitter. 00:15 - Connect with Producer Liam Seston on LinkedIn and Twitter. 00:20 - Connect with Producer Rachit Sharma on LinkedIn. 00:25 - Connect with Host Al Martin on LinkedIn and Twitter. 00:55 – Connect with Seth Dobrin on LinkedIn and Twitter. 02:00 – Seth Dobrin’s first podcast from January 2018. 03:30 - What is data science? 04:25 - Seth Dobrin’s Blog: Don’t let data science become a scam. 10:55 - IBM Data Science Elite Team: Kickstart, build andaccelerate 31:55 - What is AI?37:58 - What are data pipelines? 41:55 - What is Blockchain?

Happy holidays from the Making Data Simple team! Enjoy a rebroadcast of a conversation with Seth Dobrin, Vice President and Chief Data Officer for IBM Analytics, as he and Al explore the strategies and people your company needs to disrupt and succeed in the year ahead. Do you or your team members need new credentials to work in data? Seth also discusses what you need in your toolkit to be a data scientist at IBM.Show Notes00.30 Connect with Al Martin on Twitter and LinkedIn.01.00 Connect with Seth Dobrin on Twitter and LinkedIn.01.40 Read "What IBM looks for in a Data Scientist" by Seth Dobrin and Jean-Francois Puget.06.00 Learn more about GDPR. 13.00 Learn more about master data management.13.05 Learn more about unified governance and integration. 13.25 Learn more about machine learning. 14.00 Connect and learn more about Ginni Rometty. 14.40 Learn more about cognitive computing.19.35 Connect with Rob Thomas on Twitter and LinkedIn.21.00 Connect with Jean-Francois Puget on Twitter and LinkedIn.Follow @IBMAnalytics

Happy holidays from the Making Data Simple team! Enjoy a rebroadcast of a conversation with Seth Dobrin, Vice President and Chief Data Officer for IBM Analytics, as he and Al explore the strategies and people your company needs to disrupt and succeed in the year ahead. Do you or your team members need new credentials to work in data? Seth also discusses what you need in your toolkit to be a data scientist at IBM.Show Notes00.30 Connect with Al Martin on Twitter and LinkedIn.01.00 Connect with Seth Dobrin on Twitter and LinkedIn.01.40 Read "What IBM looks for in a Data Scientist" by Seth Dobrin and Jean-Francois Puget.06.00 Learn more about GDPR. 13.00 Learn more about master data management.13.05 Learn more about unified governance and integration. 13.25 Learn more about machine learning. 14.00 Connect and learn more about Ginni Rometty. 14.40 Learn more about cognitive computing.19.35 Connect with Rob Thomas on Twitter and LinkedIn.21.00 Connect with Jean-Francois Puget on Twitter and LinkedIn.Follow @IBMAnalytics

EP154 - Turkey-5 2018 Recap Episode 154 is a quick recap of all the retail and e-commerce activity over the Turkey-5 weekend (Thanksgiving through CyberMonday). Jason interviewed by eMarketer about AmazonGo stores Jason article in Forbes "The Future of Brick-And-Mortar Retail is Mobile"   Adobe    Revenue YoY Growth Thurs $3,700,000,000 28.00% Friday $6,200,000,000 23.60% Sat $3,200,000,000 25.00% Sun $3,200,000,000 25.00% Monday $7,900,000,000 29.00% Turkey 5 $24,200,000,000 26.41% Nov-Dec $124,100,000,000* 14.8%*       * Adobe Forecast   Amazon Cyber-5 Press Release Salesforce Holiday Insights Hub Adobe Holiday 2018 predictions, actuals, and analysis IBM Analytics Outage J Crew Website Outage Google and Facebook Outages Don't forget to like our facebook page, and if you enjoyed this episode please write us a review on itunes. Episode 154 of the Jason & Scot show was recorded on Tuesday, November 27, 2018. http://jasonandscot.com Join your hosts Jason "Retailgeek" Goldberg, SVP Commerce & Content at SapientRazorfish, and Scot Wingo, Founder and Executive Chairman of Channel Advisor as they discuss the latest news and trends in the world of e-commerce and digital shopper marketing. Transcript Jason: [0:25] Welcome to the Jason and Scott show this is episode 154 being recorded on Tuesday November 27th 2018 I'm your host Jason retailgeek Goldberg and as usual I'm here with your Cohoes Scot Wingo. Scot: [0:40] Hey Jason welcome back Jason Scott show listeners we sincerely hope you had a great Thanksgiving an awesome profitable Black Friday and a record shattering Cyber Monday Jason did you get enough turkey over the break. Jason: [0:55] I did indeed it's my favorite time of the year my two favorite things shopping and eating. Scot: [1:01] I have been dying to ask you two questions what's your favorite pie if pies are the favorite Thanksgiving dessert which pie and if not what's your favorite Thanksgiving dessert. Jason: [1:13] Do you like I'm a traditionalist I go with a pumpkin pie with whipped cream on it what about yourself. Scot: [1:18] I am a southerner so it has to be pecan pie with a little scoop of ice cream. Jason: [1:28] So my grandmother's family my grandma Daisy who's no longer with us is also a southerner and we always have followed her Thanksgiving Traditions but oddly somehow somewhere along the line she added a German chocolate cake to the mix so full disclosure I sort of Miss now that she's not with us anymore the grandma Daisy's German chocolate cake in addition to my pumpkin and pecan pies. Scot: [1:54] Maybe she was Southern German. Jason: [1:56] Exactly Daisy just was at a head of her time she was just a multicultural. Scot: [2:02] And then I've been dying to know how you survive when Starbucks closed for a day. Jason: [2:07] That is a great question there have been years when that was a challenge for me but I feel like at this Advanced stage of my life I have pretty much mastered at so a Starbucks wasn't Starbucks strategically close early so I like to make a visit before they close and then Abby as a back at the Starbucks I do have a super fancy automatic espresso machine that makes my Starbucks drinks at home. Scot: [2:36] Call and I saw you have published couple articles before we jumped into the meat of the show toast us what you been putting out there into the interwebs. Jason: [2:43] Yeah I was prolific the time off I give me a chance to catch up on a few things I've been meaning to do so you and I are both Ford's contributor so I published a Forbes article today and it's about a topic we first started broke on the the podcast it's called the future brick-and-mortar is Mobile in its talking about all these new store Concepts that are opening up and and how the customers mobile phone is increasingly becoming a mandatory part of the in-store shopping experience in an increasingly you need a mobile phone to get into the store so that that was interesting to me and then I did do an interview in emarketer all about the Amazon go store soap again probably not any, opinions that would be new or surprising to Jason and Scott show listeners but nice concise article of sort of every swear I think the the Amazon go stores are going so I'll I'll post a link to both of those in the show note. Scot: [3:46] Cool this is kind of a the first of a Trilogy of shows were put together that really are looking at kind of the halftime report of where we are as far as holiday 18th concerned we're recording this the day after Cyber Monday so we've got those key 5 days out there and some data starting to roll in then our next to Gaston the show or going to come with some more proprietary data someday we're going to do kind of a hot take on what we're seeing out there from Publix results from all kinds of sources that that will try to slip note as we go through and we want to jump into that and it wouldn't be a Jason Scott show without some Amazon used to kick it off. Jason: [4:34] Amazon news new your margin is there opportunity. Scot: [4:42] So Amazon is one of the most secretive companies you'll ever come across and holiday is no exception typically they did Issue a press release which was some of it was interesting and revealing and others of it was frustrating leak looked so I just wanted to walk through some of the highlights there. First highlight is a tortoise a new term where Amazon is calling it instead of I like the Cyber 5 they're calling it The turkey-5. Jason what you think about this rebranding of the five days from Black Friday to Cyber Monday. Jason: [5:13] So I think I don't consider rebranding I think both terms are important so you were actually the first person I heard you cyber 5 and it was quite some time ago way before the podcast and so I've embraced that when I'm talking about e-commerce sales over those five day. But when I'm talking about omni-channel sales and brick-and-mortar sales then I feel like we need to talk about the turkey-5. Scot: [5:36] Okay well next time they can both look together so. What are some of the highlights of wood Amazon announced they announce the Cyber Monday was their biggest day ever and they sold millions quote-unquote millions which you know I think. Is a range between 1 and 100000. I don't know how to arrange that I know it starts at 1 and goes Tenpenny so millions and millions of Amazon devices were sold the new Echo dot was the top Amazon device, they sold Millions more this turkey-5 versus last over 18 million toys and then over 39 fashion items and I always find it interesting to Think Through. And some of this is probably much mind-reading but herbal Tea Leaf reading that goes on here you know why do they pick out these two categories I think 21 is kind of like Hey we're picking up that Toys R Us business that evaporated out there and then fashion everyone there's been a lot of negative kind of Amazon Fashion Stories where people are saying Yep they're not really doing well they're fashion designers aren't embracing Amazon so this one I thought was just a little bit of hay. Fashion industry check us out. [7:05] This is the most revealing data point they said in total and I believe this is a u.s. number over a hundred eighty million items were sold over the turkey five and when I try to do the math on that if we assume, $50 average order value that comes in kind of 9 billion now sometimes Amazon average order value is tricky because sometimes they include didn't see if this was paid or unpaid items as soon as paid items unpaid items would be free books to Kindle stuff there free apps on stuff like that. So this could be as high of us as a $75 ASP 1213 billion dollars. [7:48] So I wasn't ashamed actually kind of put a number out there that I could actually anchor off of and get to a real number that's the first time. [7:56] It's a third party sales grew 20% year-over-year Which is pretty good you know I would expect so so you know. E-commerce is going around 15% according to the US Census and Cubs score. Amazon has been growing in the mid-to-high 20s so it's actually little loaf I felt for this third-party datapoint if I understood the metric right I've only grow 20% year-over-year and then couple of highlights they said Black Friday alone or million toys and electronics are sold through the mobile app so I think they're they're highlighting you know I think there were talking a little bit about showrooming where I think they're trying to hints that people are out in the stores shopping toys Electronics on Amazon from their phones while they're out couple other highlights of the top sellers. Obviously Amazon devices everywhere you go you see that mentioned so this is Nivea. Echo Christmas there should be a lot more Echo devices out the world after this holiday looks like instapot the classic is a top seller I am now seeing it everywhere so around Prime day you only saw it at Amazon and now I can't run into a store without Nintendo switch is hot this year. [9:20] Such a platform has been around awhile but has, new Breath of Life Jason what did what did you think of Amazon's results on Turkey 5. Jason: [9:28] Yeah it's always interesting to try to parse anything out of their press releases because that you know they're the pretty expert of giving you these numbers. That we don't have any frame of reference for a right like everything's the biggest ever but you know they said all these records last year so if they were .1% growth it would be, it would still be the biggest ever and they let you know they give you this many items in these big categories you know it's obvious they're trying to paint a Rosy picture without disclosing too much real information but to me some of the things that jump out like your math is super interesting if we take that $50 aov and say they're like. You know it will you don't 9 billion to 12 billion that puts them at a third to about a half of what a Dobby predicted. Overall e-commerce sales were for the the December 5 so. Normally we think of Amazon as being about half of e-commerce so if they're only a third to a half of cyber 5 e-commerce that actually means. Other retailers are doing a better job of of grabbing a little bit of that traffic so that to me is interesting. [10:38] It does make sense to me that 3-piece sales would it be growing as fast on Cyber 5 because I think Amazon promotes the bejesus out of the Amazon owned products particular The Echoes In fires and the Rings were heavily promoted and so you know when they're selling. Not even just one p items but Amazon owned Brands so heavily and kind of crowds out that the three p a little bit. And it is interesting to me you know this these guys are increasingly becoming the largest retailer in North America get on the biggest sales days of the year the things they're able to sell the most of our the things they own which is like completely unique like you know prior to Amazon you know Craftsman was not the number one selling item at Sears on Black Friday for example so that feels like a. That's sort of interesting Trend and you know it'll be interesting when we get some of the other guests on that have some datasets to kind of get their point of view about all that. Scot: [11:39] Now Adobe has been probably the most prolific this holiday with getting their data out which is interesting because I haven't seen last couple years IBM made of big push but I didn't see them out there really pushing hard on the data so it's almost like a one kind of company show now with Adobe and I know you gathered some of the highlights what what did you see there. You don't want to spoil it for 4 we have us be coming on the next shows we don't want to spoil too much but what were some highlights. Jason: [12:11] Just a quick primer on data sources the route to write like the the most ubiquitous best data source out there that I use the most of it kind of track it is Adobe there's three big analytics platforms that e-commerce lights tend to use Adobe IBM and Google Google you know his never liked had an evangelist kind of you know posting real-time data out there IDM some years does some of your doesn't they actually had a very meaningful outage on their analytics platform this year which will talk about a little later so even if they're planning on it I suspect they they bailed when they started having problems and Adobe did a phenomenal job so it will hear specifically from Adobe tomorrow but just so listeners have sort of a frame of reference. Adobe is heavily used by the largest. Brick-and-mortar retailers in in North America so I feel like they're their data ranges a very broad set of retailers from. Serta medium size two very big you know they don't have much data from Amazon who doesn't use any of these platforms for their main main site. So to me Adobe is kind of the broadest and definitely best representation of the biggest sites that are most meaningful. [13:27] I do have some data from Shopify which I think of a sort of the long tail so it's interesting to see what's going on there and then we do get some data from Salesforce which is. Salesforce Commerce cloud is the old demandware e-commerce platform and to meet a man where is a little more than itchy it's kind of in the middle between. The really big sites and the long tail it's these like pretty darn big predominately apparel sites for example. And so it just it's interesting to see where the date of matches between those different sets and where it's different but that kind of. [14:04] Key things up adobe gives us a number for every day of cyber 5 so Thursday was 33.7 billion. Which is up 28% Friday with 6.2 billion which is up 23.6% Saturday was 3.2 billion. Which is up 25% Sunday was 3.2 billion which was up 25% Monday the biggest day ever for. Ecommerce with 7-point not in North America is 7.9 billion which was up 29% so. Hopefully you're not doing math while you're driving in the car you add up those 5 days and there were 23.1 billion dollars worth of sales during those. Those 5 days. [14:53] I just realized I had a slight air in my spreadsheet so we'll actually see Liam and then I caught 24.1 billion in in sales over those 5 days. And the gross for all 5 days was about 26% and what's interesting about that is Adobe predicts. 124 billion for the entire November December. And a growth I would like 14.8% which is. Kind of similar to all the other e-commerce estimates we see you in that 10 to 15 to 16% and so you go wait. These five days grew 26% they represent about 15% of the whole holiday right there. You know about 19% of the holiday right there so it's it's it's interesting that that it seems like these days are getting the disproportionate amount of the. The grout sensor you sometimes your talks about how promotions are stretching out longer and in that stretching out sales but the data makes it seem like people are still very habituated to shop on these Amy's 5 cyber 5 days. Scot: [15:58] So yeah. Jason: [16:01] I was just going to add to it in Shopify doesn't provide super granny or data. But they did give us an Insight that on the the four days between Thanksgiving in and Cyber Monday they sold about 1.6 billion and about 1.8 billion over the Cyber 5 so if that's true again Adobe says 24 billion sold all over the Cyber five and Shopify alone sold 1.8 billion that would put Shopify at about 7.5% of all. Cyber 5 sales witch. I kind of doubt year-round that they have that big market share again that we don't have good data to know for sure. It's believable to me that more shopping shift to do some of the Shopify sites over holiday so I don't know what what's your initial reaction on that Scott could you. Scot: [16:53] Kills High you know but. It's hard to say the thing that always confounds me about some of these things is when you add up the pie slices I always get up to like 130% so no matter whether it's holiday data or quarterly data or annual data I always get a little confused by how all the stuff adds up. The number to supposed to. Jason: [17:18] Incident if you compare that so again and that the Dobies kind of this broad look if you look at like Salesforce you see like more like a 16% growth for e-commerce to see traffic going 9% what what's interesting to me about salesforce's they share some of their mobile numbers and sales for says that like 62% of all there traffic was mobile. And a 45% of all their sales was Mobile on Cyber Monday for example and on. Black Friday mobile actually Pete that slightly over 50% of all of all sale so so you know. [18:03] In the demandware Echo System the majority a strong majority of traffic is mobile and almost half of all orders is of of Revenue is mobile orders are actually number of orders is actually even a little higher so if you think about like you got dollars in sales you got number of orders and you got traffic. The the Salesforce number to look kind of like the Shopify numbers from mobile so we have a couple of vendors that share their data that mainly live on the Shopify platform and swim it was one that talked to you on Twitter and they were claiming 73% of their Shoppers were mobile and 62% of their shop their purchases were mobile those mobile numbers are way higher than what we see from Adobe where it's going to be something like 45% of traffic was mobile and only like 30 or 30. 5% of of orders were mobile so it's going to be interesting to talk to Adobe about about that if it is. In their mind true that this a longer tail is more mobile than than their whole user base or what were missing their. Scot: [19:09] And I also wonder if it Dobie is including any Magento data because historically it was mostly kind of that omniture I did it right that they were effectively tapping into so I wonder if they're able to pull in the Magento data analysis. Jason: [19:26] Yeah that will be interesting I'm going to Mike Hess is going to be no for this year the murderer wasn't that long ago and remember. Magento can't see the majority of their data so most people are running the gento on-prem they they have the software but magenta wouldn't necessarily know whether the revenue was unless they happen to be either one of the the minority of Magento customers that's hosted. Scot: [19:50] I forgot that they were more of an assault versus has. Bottom line this it feels like online were definitely kind of more in the 20 to 30% Reigns versus kind of the traditional 15 so that feels good and then it feels like a brick-and-mortar is a little slow. Jason: [20:10] Yep so well it depends on what you mean by slow so. Scot: [20:18] They have been drawn at kind of like three or four percent right kind of mid single-digit. Jason: [20:22] Some MasterCard says that like on Black Friday brick-and-mortar sales were up 9% there. Scot: [20:32] What's good. Jason: [20:33] Yeah that sounds great MasterCard is predicting that November December sales will be up. 5% right and of course you know you can imagine what MasterCards dataset is they have like like a 1/3 of all the credit card sales. [20:47] So so those are good numbers there's a couple companies that rent Hardware to retailers that gets installed on the front door to the store to measure traffic in the store so one of those companies is called shoppertrak and the other is called retailnext they aggregate all their data as traffic data shoppertrak says that traffic was down 1% over the holiday over by Friday retail next said the traffic was down five to 9%. [21:20] They do tend to attract slightly different customer so as I sit here I can't tell you exactly what categories they tend to be strong in butt that sounds to me like fewer people are going to the store then they have in years past and yet we still see higher sales in the store and we have in your past which just means the conversion rate of Shoppers to buyers is higher and the amount they're spending when they're in the store is higher and so you add all that up I think before the holiday started people were kind of forecasting they're like 16% e-commerce growth like in a three to 4% brick-and-mortar gross I think it's possible we're going to see like 5% he, brick-and-mortar drugs which would be the biggest year since 2011 so we may see some big numbers on the revenue side when I'm more worried about is that it it potentially was super promotional revenue and said the earnings may may suffer that that commonly is the yin and yang of of holiday sales. Scot: [22:23] Yeah unfortunately won't have a read on that until some of the January did I start to come out right before results. Jason: [22:29] Exactly it mostly shows up in earnings where people are like oh we had our Revenue goals but we missed our earnings goals Adobe does get to see some interesting promotional data so when they're on we will definitely get their perspective about whether the holiday felt online more. Lesser the same promotional is past years. Scot: [22:49] Call a couple other seems I saw out there there was this kind of is Black Friday dead a lot of this circles around somatic Mall while getting your everyone is there's a whole controversy around the opening on Thanksgiving Day itself and. Some retailers like REI stand on that others are kind of messaging that up even more and more and they're doing their doorbusters around that so there's a dinner I had some day. [23:20] Where they actually had several buckets of Shoppers they reported on so they said 41.4 million people shot online only from Thanksgiving to Cyber Monday so the I guess I'll call that cyber 5 and that 6.4 million more who's been shot exclusively in stores so they have an online exclusive bucket stories lusive bucket and then they have an omni-channel bucket and then the omni-channel bucket they say 89.7 million hour Shoppers. So I'll see you in a day or calling a reference so so that was interesting and there's an article that was kind of saying because more people shopped online than offline during that. It was kind of the end of the traditional Black Friday. Brick-and-mortar holiday discount Holiday in your reaction to that. Jason: [24:22] Yeah it was it yummy you here to Reasons by Friday is going away because people are shifting online and because black Fridays you know keeping earlier tough to Thursday night it didn't feel like, more stores open on Thursdays then did years pass so I don't think that really affected by Friday I do think / that interested anymore yeah if you're only going to do one of the other there more people that are opting for the convenience of online shopping and that that seems reflected in the the few datasets we had that show store traffic was down so I could lie but honestly it doesn't feel like. [25:03] A dramatic shift to me over your you know it seems very windy or change versus sort of an exponential change and I think all this is happening in a climate in which like most of the consumer macroeconomic factors are really favorable and particularly for the first time in a long time the macroeconomic factors for low-income Shoppers are favorable in those are the Shoppers that are least likely to shop online right like so that's the the low-income Walmart Shopper probably feels like they have more money in their pockets and they have the last several holidays and until I feel like you know some of that the trends that we try to predict do I get office gated by the fact that there probably just are more people shopping and more people spending money this year than they then we've seen in the last couple years so that's that's a good problem but it makes it hard to really. [25:59] Really have a strong opinion I have I will say one of their data points from UW data is that buy online pickup in-store orders were up 50% over your past so I think like driving with an RF data what we're seeing is that the this notion of these being separate channels is going away and people are increasingly using digital tools whether they. Go to the store or not and you know you tease me for talking too much about grocery on the show I feel like this is the first year when people could potentially buy online curbside pickup their their ingredients for their Thanksgiving dinner and so I haven't seen any data on that yet but I but I know as an amenity digital grocery shopping was available to many more consumers this year than ever before and so I'm going to be really interested to see if Shoppers take advantage of that and if that you know changes behavior and all of those are two things so I think there's going to be a lot of fun fall out to follow from this holiday. For the next several months we should keep doing the podcast. Scot: [27:01] We we will another theme that was interesting and you kind of touched on this little bit with outages Amazon seems to be pretty robust Facebook had an outage kind of like right around Thanksgiving so happy for freaking out. It was kind of a yeah that was interesting and then you would mention coremetrics an outage Amazon I've been kind of rough for Prime day then they did Cyber Monday and end so that's interesting it seems like they must have tweet whatever they had some lips on on Prime day good warm-up for Cyber Monday. I did see some articles that highlighted some other stuff it's hard in today's news climate to know kind of what exactly is going on and how bad it is with what did you see as far as I was just. Jason: [27:47] Yeah I think there were some partial out I mean the number retard saw some partial added outages for you know for some subset of all the people that tried to get to the site for some small. Of time during the day and it's really hard size how significant those are I would slide side note on the Facebook one when Facebook has an outage it has an impact on e-commerce in an unexpected way there's a fair amount of eCommerce sites that let you use your Facebook credential as the keys to your eCommerce account in your stored payment information so it is possible when Facebook's down that the people can't log into their account on an e-commerce site and and Shop so it has the potential to have that impact and Facebook tags are all over these e-commerce sites so even. If the outage and I don't think this was such an outage causes those tags to not respond it can really have a material impact on the page load speed on all these e-commerce side so there's. [28:46] Town of best practices that site should do to mitigate those risks and and frankly wait way too many sites do do to those best practices so it's always surprising to me. But you reference IBM and their analytics package which I think officially is now called IBM Analytics all ice Old-Timers would know it is coremetrics and from my perspective they had a pretty catastrophic outage so I think they were out for most of Black Friday and then, I were able to resolve the issues and then they have another significant outage for most of. [29:24] Cyber Monday so that's a huge deal if you're running a site in your using the analytics to make decisions about you know whether you should get more less promotional you know what what marketing you should be doing on the side how much additional email you should send you know that. Data from that from your analytics platform is critical and you know some heavy rain Deluxe platform out is a huge deal I will tell you I had several clients that that had an outage and the only reason that they weren't Furious is because many of them are now using more than one analytics package so pretty, and that you had a IBM and Google which meant you were exclusively. Using Google for the holiday and it probably is not going to bode well the next time you know IBM comes to have Yuri up your your contract and you had this outage in the most important time and you were forced to use Google and probably found out that that that Google Map most your needs so I think from a business standpoint that that IBM analytics at outage is going to be pretty catastrophic I think they were probably struggling to maintain market share against Adobe and Google anyway and so this is probably going to be a. Another black eye to them in that regard. Scot: [30:42] Quick question did this. So I know that mosites almost every page of a site while will reference to the underlying analytic system and there's a lost tag and stuff did this cause the retailer sites to either go down or be sluggish or to those guys. All currently have that kind of asynchronous so that if it's down it doesn't really. Jason: [31:02] Exactly so what you want and what the in finished all these vendors at least the analects vendors sort of beg you to do is have these tags load asynchronous way which essentially means it doesn't block anything from happening lower on the page from still happening while that tag loads and so it's still even when you load it asynchronously there still you know some ways in which its it's hurt hurting your rendering budget but but the impact is much lower than having the screen be white while you wait for that tag to fail and so for analytics most people have them well implemented and I didn't hear about any anyone that had coremetrics tags blocking the whole site. You do like to also put all these vendors at the end of the page but for Analytics. [31:50] That means you miss out on a lot more analytics if someone like interrupt a page from completely loading or their clicks away from the page before for renters and so you know the analytics vendors like to have their their tags at the very top of the page so when there's an outage. It's even a little more prominent so. Bad deal all around but to my knowledge there were no there were no sites that were like literally WhitePages while they're waiting to find out that the idiom tags didn't work. I don't think Google analytics had any problems but Google had a problem with her at platform over the holiday and I know that impacted a lot of people's promotional plans that you know I felt like the number of clients that had. Plan spins and open the spins that they you know we're going to make over holiday depending on how things went and they literally were like locked out of their AdWords account and weren't able to make some of the adjustments that they intended to make. Scot: [32:46] Yeah that the Facebook ad platform was down during their outages well so it could be some pretty material. Jason: [32:54] Yep that digital marketing tends to be about the third-highest source of traffic to the site so that's a big deal and then the retailer that I think at the most news for having a complete outage unfortunately it was at J crew had a very aggressive sale they were they were offering 50% off on everything and I think they were there down for like the majority of of. Black Black Friday so that's a pretty tough outage I know that they probably offered to extend that sale. Extra days to try to catch that that Revenue but in most cases you know a shopper. Likely move to some other other site and spent that money somewhere else so so you know that that's. Pretty material impact to be down that long on this holiday so sorry for my friends at J.Crew and that's probably going to be a perfect place to wrap it we promise to keep this is a short concise show just about the holiday folks have any comments or questions as always we encourage you to jump on her Facebook page and will continue the dialogue there as always if you found the show useful we'd love it if you jump on the iTunes and give us that 5-star review. Scot: [34:10] Makes everyone we hope that you're cyber 5 / turkey-5 crushed all your expectations and that you are going to finish strong in Holiday 18. Jason: [34:23] Absolutely and then till next time happy commercing.

Dr. Seth Dobrin, Chief Data Officer at IBM Analytics, shares steps for launching successful data science projects in an organization. He also discusses the types of leaders and data scientists you need to hire to drive innovation.

Sam Lightstone is an IBM Fellow and Master Inventor in the IBM Analytics group. He leads a number of technical teams in product development for relational databases, data warehousing & big data, cloud computing, analytics for IoT, data virtualization, ground to cloud data movement, and machine learning. He co-founded the IEEE Data Engineering Workgroup on Self-Managing Database Systems. Sam has more than 60 patents issued and pending and has authored four books and over 30 papers. Sam’s books have been translated into Chinese, Japanese and Spanish. You can follow Sam on social media. His Twitter handle is "samlightstone".

In this episode, learn how IBM uses analytics to build intelligence into iOS apps. Explore the four categories of analytics that benefit enterprise users, including descriptive analytics, predictive analytics, perscriptive analytics, and cognitive analytics.

Nessa série de podcasts, você está entendendo um pouco mais sobre como tirar proveito dos dados dentro da sua empresa. A grande dificuldade é: como fazer isso da melhor maneira, ou como tirar o máximo de proveito desses dados. Um dos principais pontos a ser resolvido é a democratização dos dados. Quem nos conta mais a respeito é Luiz Libório, platform sales specialist, IBM Analytics.

Olá! Sejam bem vindos à série de podcasts Desvendando o Big Data, com o apoio da IBM, aqui no Canaltech. Dando início a essa série, conversamos com Beatriz Correa, platform sales specialist da IBM Analytics, para entender como organizar os dados dentro de uma empresa.

Na França, uma nova lei proíbe que empresas enviem e-mails para seus funcionários fora do horário comercial, das 9h às 18h. A medida visa dar aos empregados o direito de se desconectar do trabalho, em um mundo cada vez mais online e "escravo" do smartphone. No Braincast 194, Carlos Merigo, Luiz Yassuda e Luiz Hygino se perguntam se uma lei assim funciona. Qual o impacto na produtividade das pessoas? O que muda para as empresas? Discutem ainda as ideias por trás do equilíbrio entre trabalho e vida pessoal. Isso existe? Quais as melhores práticas? OUÇA ======== Você tem perguntas. Nós temos respostas. Conheça nossos caminhos e decisões #DaquiPraFrente< Quando o assunto é Petrobras tem um monte de questões que passam pela cabeça da gente. Foi pensando nisso que a Petrobras criou um espaço onde responde as principais perguntas que as pessoas têm feito a respeito da empresa. O site está disponível desde o final do ano passado e nele você vai encontrar respostas para as principais dúvidas, incluindo vídeos e informações dadas por profissionais do corpo técnico da empresa. Muita coisa já foi feita depois de tudo que aconteceu e muita coisa vem sendo feita todos os dias. Uma série de mudanças internas, com o objetivo de deixar a Petrobras mais transparente, com mais controles internos e mais focada em dar resultado. Então, não alimente dúvidas, passe lá e fique bem informado. > SAIBA MAIS EM: petrobras.com.br/daquiprafrente ======== As soluções IBM Analytics podem transformar a sua relação com seus clientes Em um mundo de pessoas cada vez mais conectadas, informadas e exigentes, o relacionamento entre uma empresa e seus clientes começa muito antes da primeira venda. É preciso entender os desejos e necessidades individuais de cada um, para que a sua interação seja personalizada e capaz de conquistar. Pensando nisso, a IBM oferece soluções de Big Data e Analytics que podem ajudar nessa transformação da experiência com o consumidor. Com o poder dos dados em tempo real, sua empresa estará preparada para prever o comportamento das pessoas, e dessa forma otimizar campanhas específicas para cada indivíduo. > SAIBA MAIS EM: goo.gl/yNz9iY ======== PATREON DO BRAINCAST Saiba como se tornar um patrono do Braincast aqui e ganhar lindas recompensas. ======== Críticas, comentários, sugestões para braincast@b9.com.br ou nos comentários desse post. > Edição: Caio Corraini > Sound Design: Caco Teixeira > Arte da Capa: Johnny Brito

Entender o que se passa dentro da mente do consumidor sempre foi o El Dorado das grandes marcas. Mas isso nunca pode ser alcançado de maneira tão profunda e efetiva como agora. Sim, é ele, o famoso Big Data. No Braincast 175, Carlos Merigo, Cris Dias, Luiz Yassuda e Adriano Brandão discutem como através de análise de dados em grande escala é possível compreender o comportamento, opinião, gostos e padrões de compra de cada pessoa, e como isso pode ser usado para criar campanhas específicas e promover experiências de marca certeiras. — Esse episódio do Braincast é um oferecimento de IBM As soluções IBM Analytics podem transformar a sua relação com seus clientes Em um mundo de pessoas cada vez mais conectadas, informadas e exigentes, o relacionamento entre uma empresa e seus clientes começa muito antes da primeira venda. É preciso entender os desejos e necessidades individuais de cada um, para que a sua interação seja personalizada e capaz de conquistar. Pensando nisso, a IBM oferece soluções de Big Data e Analytics que podem ajudar nessa transformação da experiência com o consumidor. Com o poder dos dados em tempo real, sua empresa estará preparada para prever o comportamento das pessoas, e dessa forma otimizar campanhas específicas para cada indivíduo. Conheça as ferramentas IBM Analytics e transforme a relação de sua marca com seus clientes. — Ouça! 03m10 Comentando os Comentários 21m43 Pauta principal 1h05m32 Qual é a Boa? ======== Críticas, elogios, sugestões para braincast@b9.com.br ou nos comentários desse post. Edição: Caio Corraini Sound Design: Caco Teixeira Arte da Capa: Johnny Brito