Customer Equity Accelerator Podcast

Ep. 28 | What CAOs are Thinking Now with Allison Hartsoe

What are Chief Analytics Officers (CAOs) thinking today? In this episode host Allison Hartsoe summarizes a meeting of CAOs in Miami. She includes poll results from audience questions to provide a sense of who is in the room, and then seven things CAOs wish their analysts knew. Finally, she wraps up with three insightful nuggets that address the questions everyone was asking.

 


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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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Show Transcript

Allison Hartsoe - 00:01 - This is the Customer Equity Accelerator, a weekly show for marketing executives who need to accelerate customer centric thinking and digital maturity. I'm your host, Allison Hartsoe of Ambition Data. This show features innovative guests who share quick winds on how to improve your bottom line while creating happier, more valuable customers. Ready to accelerate. Let's go!

Welcome everyone. In today's show, we're going to talk about the modern chief analytics officer and in fact, I'm going to summarize for you an event that I attended earlier this year in Miami, perhaps save you a few thousand dollars in conference travel and ticket expenses, and the first part, we're going to talk a little bit about who goes to these events, who was present, and I think you'll be surprised that it's not just Silicon Valley insiders and second, we're going to talk about seven things you should already know, but probably don't do that came out of the conference.

Allison Hartsoe - 01:09 - Third, I'm going to give you three insightful nuggets that were really the value pieces from the conference and then wrap it up with one final, very interesting tip at the end. So, thank you for joining the customer accelerator today. Let's dig in. So first off, I want to talk about who comes to these events. Now, this event was in Miami and not in Silicon Valley, which surprisingly perhaps not surprisingly, I changed the mix of who was in attendance. So, this had a lot of brand-new chief analytics officers that came out of government law, healthcare and other industries that were not typically regarded as aggressive data leaders. That in itself is very interesting and of course there were some industry leaders that were speakers, notably from healthcare, from direct to consumer and other amazing places, but most of the folks had been in their roles less than a year and they were really here to listen and learn.

Allison Hartsoe - 02:20 - So they did a little bit of profiling of the audience. And these three pieces came out of that profiling. First, the audience was asked, how much time do you or your team spend cleaning data? And they responded with about a third spent 50 percent or more of their time trying to perfect data. About a third spent 20 to 50 percent refining it and about a third only spent 10 to 20 percent of their time spiffing up the data. Now I think it's important to note that the lower amount of time spent cleaning the data does necessarily indicate sophistication. It could simply mean that the companies are just starting and haven't realized how messy it is. So, a third, a third, a third, okay. You know? Yes. People spend time cleaning data. What was more interesting was the organizational structure and the second question was within the organization, what level is responsible for data and analytics?

Allison Hartsoe - 03:25 - And surprisingly 40 percent did not report to the C level further when it came to which C level, the people who did report to C levels, the type of C level they reported to was all over the board. So, it was CMO, CXOs, CFO, CFO, CTO. I mean it was, it was literal alphabet soup. But I think what's interesting about that is it's natural to report to whomever has the most passion about analytics and the influence to get it done. So, it doesn't really matter what the title is, what matters is the ability to execute. And in fact, in in one of our other shows where Jose Murillo from Banorte talks about the progress he made with analytics in his organization, he actually reports to a combination COO and CTO, which I thought was fascinating in terms of the ability to get things done.

Allison Hartsoe - 04:32 - Now, the third piece they asked the audience was about analytics ROI. And it was interesting that in 2017, the year prior to this conference, a lot of people were saying that they really weren't measured on it, but they were trying to figure out ways to be measured on it. And, and so the answer from the audience was, okay, we're, we're not measured largely on analytics ROI. And part of the conversation was an interesting twist that I didn't actually expect from analytics. And they said, what would be the output of a physical office? How much does that contribute to the bottom line? And the idea is that analytics like operations is a sunk cost and it doesn't necessarily need to put out an ROI. And I'm not sure whether I agree with that or not, but I can definitely see there is a fundamental component that is an operational cost, certainly in the earlier stages when you deal with cleaning data and landing data and all of that stuff.

Allison Hartsoe - 05:40 - And then they asked a little bit more about what the primary KPI was for those who were measuring the ROI and the number one answer was direct financial contribution. Twenty three percent. Customer satisfaction was 22 percent, cost efficiency was 21 percent and what surprised me the most was new customer acquisition did not even score. So, I guess we're still at the early stages of evaluating customer equity when it comes to brand new CAO's who are really just trying to get their hands around the data and trying to figure out what is meaningful. So overall there's plenty of room for growth in the chief analytics officer space. If you are thinking that you might be a good candidate for this space, if you're thinking that a hey, I only have some experience but not enough experience, my message to you is this space is still young.

Allison Hartsoe - 06:42 - Yes, there are plenty of people out there with really great rich depth of experience, but it's still open to people who want to learn and grow and move along in this direction. So, feel free to jump right in. So ultimately the other piece here is that there are quite a few people who are coming from areas that were, let's say nontraditional analytics areas, so they're not necessarily moving in from marketing or they're not moving in from finance. Um, they're coming from other areas that weren't as intense for analytics. But what that also means is that they're bringing along the rich data sets that they had in these other pockets. Maybe it's around sales or around customer service or around production. All of that is great news for us because those data sets can now be married to other data sets, bringing their perspectives along with them. So good for them.

Allison Hartsoe - 07:40 - Now, let's move into the section about what ca o's are probably thinking now. Oh, I break this section into two parts. A seven things you should already know but probably don't do, which I also sometimes called tips for analysts. And three insightful nuggets which were really the heart of my takeaways from the event. The first part, things you should already do. I'm going to fire through this list. It's not in any particular order, but I'm going to move through it pretty quickly and I, I love the first one. No one wants to know how your sausage was made. So, this basically means that data scientists need to be able to tell a story without loading it up with jargon. Nobody wants to know what the p value is or what a proper ER is ever. And have to say when, when I do presentations, I actually test them in front of my nine-year-old to see if he gets them, if he understands them, that I'm good.

Allison Hartsoe - 08:40 - And that's not to disparage anybody at the executive levels. Not In any way, but it is to say that there's a certain getting to the point necessity that we have to convey when we look at data and we start to make recommendations. And, and that's actually leading me right into the second point, which is buy, sell, or hold. Just tell me what you want me to do if you ever see that George Carlin skit a, I love the skin. It's been years, years since it was out, but he talks about how his wife is not so easy to understand and he just wants her to tell him what she wants. And since I'm married to an engineer, I, I, I totally get that. So, I'm here. You know, imagine that you're telling your spouse something about their daily score. You're telling your stakeholder, you know, a daily score hunting today you're 50, but last week you're a 38.

Allison Hartsoe - 09:42 - What does that mean? What should I do? Bottom line here, don't play games. Just take a stand. Tell your stakeholders what you want them to do. Now, what's, what's also interesting about that is when we deal with data and we mined the data and we start to understand it, we are not necessarily subject matter experts and that's our third point is data miners versus subject matter experts. Data miners tend to interpret the data without enough subject matter expert context. It means you might not know what's reasonable. You might not be able to see some of the nuances in the data, so it's incredibly important to take your, your insights to the subject matter experts prior to creating that final presentation so that you can make that buy, sell, or hold recommendation, and you know what's reasonable to ask. Within organizations, we oftentimes have kind of a a partnership at the early stages where you've got the data miner on one hand, you've got the subject matter on the other hand.

Allison Hartsoe - 10:51 - But it actually tends to evolve to be that the best case may be training your subject matter experts to become data miners or conversely and betting your data miners deep in the business. So, either way those are stronger choices then a two-person partnership, but the two-person partnership can be a great way to start. Now that brings me to point number four, which is the single source of truth. If you've ever dealt with multiple sources of data, multiple definitions of the customer, you will know this pain. So, this is both in data sources. As in formulas, People tend to rework the data to display whatever they think is correct and what tends to make them look good until they can't. So, there's a definite need to teach people how to pull the data together to, to set certain rules, to set certain definitions, ideally blessed by your CFO's office if you're trying to connect it to cost or revenue that you can get real impact, but to put that single source single definition together.

Allison Hartsoe - 12:07 - And that also leads me to the next point, which is number five, about messaging your data. If you're going to make a single source of truth, you have a responsibility to create a data dictionary or a user guide or some way to explain the subtle nuances of what's actually known about the data so that people had a chance to stand on the shoulders of your greatness and know what logically goes together or what's likely to roll up with what. That can be very powerful, particularly for companies. And in fact, this tip comes from a, uh, uh, an organization in New York that shares their data publicly. This helps you get ahead of the story and protect the data and avoid some of those willy nilly conclusions. Now, point number six is to start with business value.

Allison Hartsoe - 13:07 - What we've heard this about, I don't know how many times everybody says this, but again, remember the title of this section is seven things you should already know, but probably don't do. I like to think about business value as really just three things. Does it increase growth by helping you find rich new types of customers or rich new acquisition sources? Does it lower costs through optimization or efficiency, or does it reduce risk through stronger predictive modeling problems we're solving generally trace their roots back to these fundamental business drivers, and remember this is still an iterative process of learning and trying based on data, but if you can pass one of those gates as you frame the problem, you're well on your way to starting with business value.

Allison Hartsoe - 13:59 - And finally point number seven, democratize the data carefully out. Giving stakeholders access to data with multiple sources of truth is like spilling a bag of sugar in a sandbox of toddlers. No one is going to be happy with the end result. Instead Bake your data into business ready nuggets and monitor how much people are consuming or consider teaching some of the more advanced skills of using data, pulling data, and certifying those higher levels of access, so whether it's self-service or whether it's certification one way or another, democratize your data carefully. Now that wraps up the first part of insights that came out of this chief analytics officer conference and these were just, you know, nuggets that people spread across their presentations across the board.

Allison Hartsoe - 15:02 - Next, I'm going to go into some of the insights that were, I think fairly new or, or let's say they had more depth. Uh, the reason why you go to a conference and you'd pull back this nugget. So, let's look at the three insightful nuggets that came out of the chief analytics officer event. The first insight comes around gaining budget. The ability to gain budget when your department is seen as a cost center is a clear challenge. One speaker said it was a three plus year process that tracked the buildup of value. That was what he used in order to gain budget and he outlined the process this way. First, demonstrate value in specific projects, use that ROI to get more funds for your team. Then create scalable infrastructure that can be reused, so your efficiencies, your operations efficiencies.

Allison Hartsoe - 16:08 - Third, demonstrate value across multiple use cases by leveraging that infrastructure and then fourth, create new businesses around those outcomes, new PNL owners data monetized a billion dollar value revenue lines and this not this same speaker, but a different speaker added onto this by saying that they actually lead a cross functional center of excellence to first examine what was needed in order to deliver value to the organization. And this happened to be a health care organization and when asked about redeploying existing teams, he said, quote, we addressed this specifically in the ask, can we bring these people into this function? No, these people are already used for stuff and we atrophy their knowledge, which we need. Leave them alone.

Allison Hartsoe - 17:10 - All of our ask was for net new resources and we got it. So, a very powerful way of looking at the process that we go through to gain budget. The second nugget is about measuring ROI. Everyone at the conference, I wanted to know how to measure the ROI of the data analytics team. That question probably came up five times now, the way they answered this was really interesting. I said this question is often muddied by mixing the ROI of the project outcome with the ROI of operational purchases. The latter, meaning the technology, the operational piece pieces should be seen as a sunk cost. It's necessary just to get in the game for project ROI, however it gets back to revenue, cost, risk, balance sheet, human capital, and your contribution to this and that's what we talked about earlier on.

Allison Hartsoe - 18:16 - The seven points about starting with business value. So, you need to quantify the upside. However, it's not easy if your analytics team is decentralized. So, the common thought was organizations often do not give the data analytics person enough real estate on which to operate. One speaker actually said, quote, if the CEO were the buyer, you would get access to every data warehouse in the company and quote, and that is true, so rarely do we have universal access to all the data we want and that intern gates, how long the journey takes us as well as how easily you can connect to ROI. It does create a chicken and the egg situation and that's just not easy to resolve. So, another way to look at ROI was to model impact. Instead other words to project it.

Allison Hartsoe - 19:13 - For example, you could run discrete events, simulations to optimize something pre and post and then attach the impact assessment. It might not be revenue, it might be more cost optimization, but as the bar of analytics continues to rise, your teams have to be less about shiny dashboards and much more about value and impact. Now, the third nugget is about the rise of the data product manager. Now, I have heard this before, uh, at a similar CAO conference and Silicon Valley probably three years ago, but that was from a very cutting-edge team and now it seems to be more pervasive. And the and the insight goes like this CAO teams that are cranking out the best results are very courageously building on top of their data platforms, whether it's Apps, tools, products of all sizes and shapes are designed to answer worthy business questions based on use cases from the stakeholders and to expedite the easy adoption of data.

Allison Hartsoe - 20:28 - The best teams that do this usually bring a product manager on this helps them separate what might be longer term build work from urgent short-term project work and the function of the product management team can be to track and run those projects according to the capacity that they have. Now, here are a couple of examples from the healthcare space and some of the product portfolios they cranked out. They cranked out tools on mortality reduction, capacity management, readmission prediction, cost analytics, service line analytics, healthcare utilization explorer, clinical variation, which is how are we treating similar patients differently? Risk segmentation and through put simulation which is about efficiently moving around in physical spaces.

Allison Hartsoe - 21:33 - Now that's all from just healthcare space and I might note they did this themselves. They did not put a Martech tool or any kind of analytics tool in the, in the way to craft this together for them. They basically landed the raw data and then started building the products the way they wanted on top of it. I think that's also what Amazon did, if you go back 10 years and why they have so much custom data in house, uh, so many of the leaders have this in common. They don't put tools in the way they create ways that they can get close to their own data and then manipulate it in the ways that they need to provide the business impact for their stakeholders.

Allison Hartsoe - 22:24 - Now finally, I've got one last tip to thank you for staying all the way to the end and this was probably the most valuable and the most non-obvious tip for people who are in analytics and data space. And this comes from a speaker who was also a neuroscientist and he said, quote, we are human, which means we make emotional decisions first. Then justify them with logic and quote. And what that means is you cannot underestimate the value of people liking you first in order to actually believe the data and the recommendations you are making in order to take action on it. If you need an example of this, just consider the classic case of similar ways. Google it. If you don't know who some of voices or the summer wise effect, I think we all like to think that we're rational about data, but we're actually not. We emotionally decide what to believe.

Allison Hartsoe - 23:30 - So therefore to get your message across, to have real impact in an organization, the feeling must be, hey, Allison's got my back. I may not understand this data completely, but I her and her judgment. So, to when you have to be seen as a brilliant advisor, not the geeky nerd in the corner. And I think we try to capture this as an industry when we talk about storytelling, but what it really means is we all need a whole lot more EQ to go with all that IQ. Now that wraps it up this week. Thank you for joining. We'll look forward to seeing you next week on the Customer Equity Accelerator.

Allison Hartsoe - 24:20 - Thank you for joining today's show. This is Allison. Just a few things before you head out. Every Friday I put together a short-bulleted list of three to five things I've seen that represent customer equity signal, not noise, and believe me, there's a lot of noise out there. I actually call this email The Signal things I include could be smart tools. I've run across articles, I've shared cool statistics or people and companies I think are doing amazing work, building customer equity. If you'd like to receive this nugget of goodness each week, you can sign up at ambitiondata.com and you'll get the very next one. I hope you enjoy The Signal. See you next week on the Customer Equity Accelerator.

 

Ep. 29 | Finding the Customer State of Mind with guest Brooks Bell Ep. 27 | Customer Centric Product Development with Twitch’s June Dershewitz
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