Customer Equity Accelerator Podcast

Ep. 104 | 2020 Customer-Centric Predictions

 

"There’s no better way to accelerate than through the deep understanding of your customer base." - Allison Hartsoe

 

This week Allison Hartsoe, CEO of Ambition Data, makes her predictions for 2020 – and scores her 2019 predictions - in the Accelerator. What forces are driving customer-centric change? How will companies react to them? If you don’t know then be sure to catch this future-facing episode.

Please help us spread the word about building your business’ customer equity through effective customer analytics. Rate and review the podcast on Apple Podcast, Stitcher, Google Play, Alexa’s TuneIn, iHeartRadio or Spotify. And do tell us what you think by writing Allison at info@ambitiondata.com or ambitiondata.com. Thanks for listening! Tell a friend! See the full transcriptView all episodes.

 

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Show Transcript

Allison Hartsoe: 00:01 This is the Customer Equity Accelerator. If you are a marketing executive who wants to deliver bottom-line impact by identifying and connecting with revenue-generating customers, then this is the show for you. I'm your host, Allison Hartsoe, CEO of Ambition Data. Each week I bring you the leaders behind the customer-centric revolution who share their expert advice. Are you ready to accelerate? Then let's go! Welcome everybody. Today's show is about 2020 predictions for customer-centric marketing. I am Allison Hartsoe, the host of the podcast and also the CEO of ambition data. Now, as you know, I love CLV. It's always a connection between customer empowerment, predictive data, and the companies who realize that the future is really about using that data to figure out how they can be of service to their customer base. And so marketing in this way not only puts the customer at the heart of the business, but it also provides that clear connection between business value and marketing actions.

Allison Hartsoe: 01:12 So you eventually get an ROI on marketing. Ambition data is a consulting company that helps companies make this connection. We don't sell platforms or lock you into technology, but we can tell you how to pick up the right data to properly hear your customers, how to learn from them, and how to lead with customer lifetime value-driven strategies, happier customers, happier company, happier life. So at the beginning of 2019, I made five predictions, and I think I came in about three for five for those predictions. But some I'm going to kind of say are half credit. So here's how 2019 shaped up. Prediction number one was insights proliferate like rabbits. And I think the answer for that prediction was sort of half and half. Yes and no meaningful insights resulting from consolidated data did gain traction, but they also raised a lot of privacy concerns, and I think that essentially put the brakes on a lot of excess proliferation of insights.

Allison Hartsoe: 02:22 Prediction number two, the data operations become more of the cloud fabric. And that was, yes, that was spot on. Microsoft, Amazon, Google, pushing really hard here with deeper services, more services to help you operate, not just your IT style operations in the cloud, but all of your services, marketing services and more. So very cool that they are pushing the technology so fast because when it knits together, it creates an easier way for us to take action on the information that we know. Now, prediction number three was about personalization is injected with smarter, more contextual nuance, so basically taking personalization up a notch, and that was partially right. I think the desire is there, but many companies are still wrestling with how to get more sophisticated through the technology and the integrations that enable personalization. You remember they're more than 7,000 just in the MarTech space alone.

Allison Hartsoe: 03:31 So when we talk about technology and integrations to enable personalization, we're not talking about a small action here. We're talking about a pretty heavy lift, which brings me back to why prediction number two is so powerful. When Amazon, Microsoft, and Google are knitting this together for you, it creates a lot more velocity. Prediction number four needed a special name. I called it hollowgail, and this was based on the idea that the structure of an organization was not really built to help the organization move as quickly as it needed to, particularly around customer data, which we know spreads across all these different cross-functional areas. So we didn't really see the change there that I thought we might see. We didn't really see the changes that I thought we would see there, and that's probably because it just takes time to do that kind of massive infrastructure reorg.

Allison Hartsoe: 04:33 So I'm going to say that that was a miss, but we did see some references to cross-functional SWAT teams for problem-solving that are becoming more and more popular ways to pull the right knowledge together, the right resources together to execute on particularly customer-centric changes. Finally, we have prediction number five, which was the channel goes out of style, and I'm going to give that a yes because I did see a lot more customer language, particularly on CLV last year. Customer lifetime value and a lot less on channel or omnichannel. It's not, the channel isn't used, it's just that it goes out of style as a way for measuring success. So very happy with that. Now, what does 2020 hold? I've got three predictions for you for 2020. Prediction number one is that artificial intelligence becomes more clearly defined. At an AI conference last year, I was surprised when the speaker on the stage asked people in the audience whether they were doing artificial intelligence or machine learning.

Allison Hartsoe: 05:51 And the fact was that most of the audience really wasn't even sure if what they were doing qualified as AI or machine learning. So, therefore, I was hesitant last year to put AI in any of my predictions because the meaning was so vague. But as we reached the hype peak, I think the definition becomes much clearer, and people stop equating AI with this magic black box, and they start asking more detailed questions. So instead of a company saying, Oh, because of AI, you know, it works because AI people start asking questions such as, well, is it deep learning? Is the model just running a decision tree? I've even seen customer lifetime value positioned as AI. So that's a, you know, is it, is it really just a model? How does the model update itself? What are the features, and how did that feature selection introduce or mitigate bias?

Allison Hartsoe: 06:53 So lots of good questions start to spin out of a more clear, detailed understanding of artificial intelligence. That's my prediction number one. Prediction number two, which is related to the first one, is that model security is going to become a real concern. It is such a heavy lift to move toward more machine learning and other models to basically run the show for us that we don't often spend time to think about the security of those models. So you finally get the data, you get the executives approval, you find a good business problem to solve, you get the team to do it, you run your hypotheses, you get the broader company to buy in on the findings, and then finally you get a positive return. That's what I mean by it's a heavy lift. It's a big process. So after all that journey, now you've got to worry about somebody messing with your models.

Allison Hartsoe: 07:55 Yes. Not only is that human crosscheck required, but I imagine it won't be long before we have machines checking other machines, just like we do today in our homes. Think about it, your fire alarm basically checks your stove, right? Or the carbon monoxide detector in your house crosschecks the car in your garage. Now we don't think about that as machines checking machines, but it's just a faster pace with models, and it's something that I think we'll get used to. So prediction number two, model security is a real concern, and we need to pay attention to it when we start to operate more and more with really good models. Prediction number three, the Martech consolidation begins. This has been percolating for a while, but basically, investors generally want to see their money returned. And so when we have something like 7,000 MarTech tools, which is what we hit last year, who have received funding over say the past five years, there's going to be a comeuppance.

Allison Hartsoe: 08:58 Some companies will pivot toward AI, and we're already seeing that in many ways. Some companies will merge, some companies will fail, and since the patients of investors is finite, and the business models are changing and moving faster than ever, we know there's a mass consolidation ahead. It just takes a tipping point, like a major market pullback or a recession to kick it off in earnest. Now, a lot of people last year started to cite the yield curve as a reason to anticipate a recession in 2020. Now, I will be the last person to say that I can anticipate the future of wall street. It's not my skill or my territory, but I will say that the conditions seem bright to kick off a consolidation in 2020 and beyond. And that's going to happen fast. Now, prediction number four, I think there's going to be a stronger pull from the research and academic world into the corporate world.

Allison Hartsoe: 10:02 I've always been amazed by how many ideas kind of languish in academic research. Now, if you look at the early copies of, um, the Fader Hardy research papers, they were years, years ago, and, and I think the, the book, the original book that Pete Fader put out was 11 years ago. So it's almost as if there was this Gandalf inspired chasm where one pool of insight from the academic world simply did not transcend into the corporate world, at least it used to be that way. And I think there's a new marketing hashtag it's called a marketing ACAD. I don't really know how to say that. It's marketing with academic kind of truncated. And what it's doing is it's shining more light on the academics in the marketing field. And I think that's important because it's no longer just a bunch of nerds in the corner. And at the same time, what we had on the corporate side, there's a stronger desire to test and try new approaches.

Allison Hartsoe: 11:09 There's a hunger for it because corporations have moved beyond the idea that you can just use stuff by gut feel. They've moved beyond the idea that they really don't need to listen to their customers. All of that is passe. They know they need to test and learn, and that that creates this need for more data coming in, more insights, more academically tested approaches for companies to try. So nowhere else is that seen as clearly as the customer-based corporate valuation models that I think Pete Fader and Dan McCarthy at they'd equity have been so good in promoting and that did come out a couple of years ago, but it is gaining traction, and I think in tandem with the direct listing trend, companies seem to be turning the tide so that they are dictating to wall street what's important to them rather than being dictated to what metrics wall street thinks they should care about.

Allison Hartsoe: 12:10 All of that power begins with really good data, particularly customer data, and data equity is at the heart of that valuation. So kudos to them and prediction number four, a stronger pull from academic research to corporate reality. So 2020 is the start of a new accelerated decade. I think there is no better way to accelerate then through the deep understanding of your customer base. So thank you for joining me on my podcast as we continue to explore that topic. As always, links to everything we discussed are here at ambitiondata.com/podcast. Remember, when you use your data effectively you and build customer equity, it is not magic. It's just a very specific journey that you can follow to get results. See you next time on the customer equity accelerator.


Key Concepts: Customer Lifetime Value, Marketing, Digital Data, Customer Centricity, Long-Term Customer Value, Marketing Leaders, Analytics, Creativity, Product Development, Audience Research

Who Should Listen: CAOs, CCOs, CSOs, CDOs, Digital Marketers, Business Analysts, C-suite professionals, Entrepreneurs, eCommerce, Data Scientists, Analysts, CMOs, Customer Insights Leaders, CX Analysts, Data Services Leaders, Data Insights Leaders, SVPs or VPs of Marketing or Digital Marketing, SVPs or VPs of Customer Success, Customer Advocates, Product Managers, Product Developers

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