What does digital maturity look like? In this episode, host Allison Hartsoe talks about department alignment and analytic leadership: key traits and exit criteria for each stage, plus insight into the Pit of Technology Despair. These are the stages where all of the data preparation and organizational alignment start to pay off: data is interlocked, goals are aligned across all departments, and the entire company has embraced a customer centric attitude. The organization is running at the speed of the customer, and customer equity accelerated bears financial fruit.
Who should listen: Chief analytics officers, C-suite, Marketing, Analytics, Digital Transformation, Customer Transformation, Data Insights, Customer Experience, eCommerce, Digital Marketing, and Customer Satisfaction professionals.
Key Concepts: CAO leadership, data keys and data interlocking, customer voice, customer lifetime value, descriptive vs. predictive value, running at the speed of the customer, customer centricity, customer equity.
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 wins on how to improve your bottom line while creating happier more valuable customers. Ready to accelerate? Let's go!
Today we're going to continue building on the stages of the customer centricity maturity curve and we'll lightly dig into stage 4 which is department alignment, stage 5 which is CAO Leadership. CAO stands for Chief Analytics Officer. And finally, the pit of technology despair for all of you Princess Bride fans. Now as I did in the last segment in case you missed it, we will be talking at each stage about the classic people process technology framework. But I use this framework a little bit differently. I've always felt like this framework was missing something so one of the elements that I include is leadership in addition to process technology. And then I also define people a little bit differently. So, let me give you some definitions and we can use that as the framework going in. Around a leadership, we're looking at leadership as measured by organizational alignment around the customer portfolio. With people it's not just people and their skills it's the actions that they can take. So, people as measured by their behavior, how much are they using tools? What's the output of their customer-centric decisions? With the process, we're thinking about that as measured by the ability to execute optimizations around the customer. And with technology that's measured by the organization's agility and the enablement of the technology to help the organization achieve its business goals.
So, with that framing let's dig into Stage Three - Department Alignment.
The key question at this stage is - Are all your marketing channels, do they all appear in one report using the grain of the customer or proxies of customers for both identified and unidentified people. This is a question that starts to happen at the director level so we're moving up the organization. And the key activity here is around governance, process, breaking up the data silos especially across business units and that's what governance does is it starts to give you keys to interlock the data. Our activities also include visualization and adding the customer ID. So really this stage in order to get to alignment, we've got to have keys and keys are simply a piece of matching data. It could be a name. It could be an email address. It could be a random series of numbers. That is identical in two different datasets. That's all a key is. So oftentimes e-mail is used as a key and an e-mail address can be somewhat unique. You can see a problem with this right. I give you my first name, last name, and my email address; and then I use a different email address. Now maybe your customer records aren't that clean.
So, what a lot of people do is they'll create a specific customer I.D. and they'll try to match all the different bits of customer data into one ID. So that aside it sounds like a very technical level but it's actually not. It's more of a process level. I mean there are some technical pieces. There's no doubt about it but we're no longer stuck on the foundations of governance. Now we're really looking at the outputs of - How do I optimize? How do I personalize? Testing oftentimes takes off at this stage and changes that happen tend to be low risk and somewhat tactical. That's okay because what we're really trying to do is get these optimizations, this personalization synchronized around customer value. And that's oftentimes where segmentation spring up and you know you may have 12 different segmentation across 12 different tools but that's just part of working through the department alignment, so for example paid search may have a segmentation and then the brand may have a segmentation and then there may be an email segmentation. All of that is just process that is very critical in this stage and the keys that we use to bring the data together help unite those different segmentations so that we start to get a sense for what meaning is.
Now with people's actions at this particular stage, internal influencers are critical because you're starting to get a new normal going, you're starting to get a sense of what you know and that something's changing. And having those internal influencers is it's key to getting people to adopt different uses of the data and to understand how to use things like self-service tools. Which Tableau is a great example. So, there's a lot more of how to use this training. There are improved visualizations. Storytelling is really hot here. Self-service tools, better compliance with standards and a sense of the future accountability really starts to take hold.
For leadership, our discussion and the discussion and clarity of who the customer is starts to take root. Because we're as we unite the data it tends to have the green of the customer and the initiation of department-wide alignment on measurement goals oftentimes called a measurement framework is key from the leadership. So, they're saying we really want to measure an improvement in performance this year. And then the department starts to think about okay how are we going to measure that across all of our different channels across all of our different groups. So, this is the early part of customer centricity, organizations probably not thinking a lot about the value of each customer at this point but they're starting to talk in customer terms which is great. On the technology level, there is a shared governance of standards with I.T. which means that there's a lot more smoothness happening and a strong push for tools to start to capture customer voice and ID if they didn't happen earlier. So oftentimes what I see at this stage is people who set an anonymous hash when you first enter a website which allows them to later connect all of your behavior into one particular ID once they know who you are. So, first, that happens in pockets and then it starts to happen across all business units. Now the critical blocker to overcome here, as I've alluded to earlier is there's a fragmentation of segmentation, segments of segments of segments.
I am not a fan of micro-segmentation and I don't find much value in that.
And the other blocker to overcome is getting the organization to think about the virtue of lifetime value. When they start thinking about customer lifetime value it changes the denominator of all the measurements. So, if you think about it as three stages the first part is I'm going to measure the volume that I get and then the conversion from that volume. Then the second part is, what I'm going to measure is the volume and the conversion of that more volume. But I'm going to think about it as I got a person that's a valuable customer so maybe I actually know that you're a customer, a new customer, or repeating customer and I start to get a stronger sense that this is quality. But the third part the one that's hardest to get to it has the most value, particularly for customer centricity, is about the value of that customer. And remember earlier when I was in the other podcast when I talked about descriptive metrics when we deal with customer lifetime value we've actually shifted from descriptive into predictive metrics because the way that's calculated is all on future revenue dollars.
So, we expect that based on particular patterns and propensities is there is a likelihood that we'll get .056 revenue of today's dollars from this person in the future. So, the virtue of customer lifetime value really has to get across at this point and people have to start seeing, oh, there's another level we can get to that really moves the bottom line. The beauty of that is you can, of course, tie it into your marketing budget. Now you can have a good argument for why those multiple millions of dollars that you've been driving should result in multiple million-dollar marketing budgets or bigger. So our exit criteria for this stage is, is the department centralizing the data from all the different vendors and all the different sources to produce results or insights. Now, I don't really mean sources I mean vendors and collecting all the customer signals that it can because oftentimes at this stage the organization needs help and we're getting different vendors with different data. And the real challenge is to try to get it to sing to get it to come together.
Moving on we have Stage Four which is CAO leadership or chief analytics officer. The key question here is do you know who your good customers are and what they do in sales, marketing, business intelligence, finance, call center, and support? And how much revenue they represent based on individually calculated not averaged lifetime value. Now that's a long and tricky but it's basically saying that when you think about the CAO stage, we're not thinking about the marketing department anymore. We're thinking about the holistic part of the customer. So, when a customer looks at your brand, they don't think oh this is the marketing department. This is the sales department. This is well hopefully they don't think that they think this is one brand I'm going to have an experience with one brand. Now a great example of that and as an aside a good story, I heard was about a guy who was interviewing for an e-commerce role, a very senior e-commerce role and what he did is he bought a product online and then he took it to the store and he tried to return it. And this was his test. Now, this was years ago. This was his test for determining how customer-centric the organization was. Were they actually able to accept a return from an online purchase? I still know lots of companies that can't quite do that, but it's gotten a lot better. So, this chief analytics officer is really working on finding those quick cross-department wins. Like when you combine marketing and sales and supply chain or marketing can you find some great areas of optimization. You know there's some nice low hanging fruit here that they can usually knock out right away. So, leadership is a key element here. It's somewhat important that a neutral chief analytics officer is hired because they have to stretch across all departments and this is a highly political role. You've got to make good friends across the areas, but you've also got to be a leader. So more precise customer centric calculation is important and alignment across the organization is really their first task. You know they have to get all the boats rowing in the same direction.
The second part is technology. This is not to be underestimated. And that's why you see a lot of organizations putting up a chief data officer instead of a chief analytics officer. I'm an advocate of the analytics officer because I think that speaks more to business impact and output. But there is a solid argument for a CDO and I don't think it's bad to have both. Because the CDO or the technology stage here is really about finding a way to put the wealth of the business in one place. So, you need an analytics landing zone in a new or separate technology and you need certain types of data sharing and rules to be able to give data and get data in a flexible format. Those silos that are native to data systems can run alongside the core systems, but they need to be integrated and broken down and this is where speed becomes a major consideration. If you have like a lot of major retailers do, multiple terabytes of data grinding through every day then it's a big process to move data from one place to another. So, if it takes you a full day or more to move the data so that you can run an analysis. Well, then how fast can your analysis be you know you may only be able to move with the weekly level when you need to move at the daily level. So, this is a really big deal at this point.
Process is another area that is of consideration. And at this point, we're looking at cross business units and the more acceptable medium risk optimizations that start to become tolerable. So, you know whereas before we were optimizing based on maybe smaller bits of things. Now we have a bigger tolerance for high impact. A bigger tolerance for failure. A bigger understanding that it might be one out of 10 Tests that has a significant, that makes a significant difference. So, this tolerance for optimization and changes is growing in the organization and that also means that customer-centric optimizations are now possible and business algorithms start to emerge to help knock down certain pieces of low hanging fruit about business impact. I oftentimes think that once we collect the data and then we report, and we analyze that really the next step that starts to come out is algorithms. Which is just a fancy way of saying we put together rules, high powered business rules that help us understand what to do why and when?
Now finally in the People's Action Stage, the analytics that supports this group are self-service tools and they're operationalized across business units. So, people talk about this as data democratization. You're now able to see data from other business units. It’s that give to get paradigm and you're able to start to use more predictive customer-centric data that is a great output for people. It means that they can find more business value in their everyday activities because we're driving it back to actual dollar value change in the business. Influencers remain strong mentors now across the teams and influencers, in general, are only about 5 percent. And it does not track to title. But you all know who they are. And this is the person who's always the first one to raise their hand to say I'll try that or all do that. And they seem to learn things as if they're just breathing air. So, these influencers are really important at this stage and personal goals tend to be tied to the use of tools or the ability to get output like recommendations going. So, all good thing ends in that stage.
The other critical blocker to overcome is speed. How fast can you respond to the things that you've learned? How fast can you run the analysis? So, it's not that everybody has to be running at a daily or an intraday pace. You need to run at the speed of the customer. Now if your customer is buying something at a very low frequency and they don't tend to take a lot of time to do research and make that purchase may be your timing for customer data analysis is more weekly or monthly. But if your customer is making an expensive purchase they do a lot of research and it happens quickly or it happens in a certain specific timeframe then you really need to know that's happening right away so that you can take advantage otherwise lots of money is gone quickly. So, this stage has an exit criteria, I phrase it this way I say "Is the organization aligned around the customer?" Now you know from listening to my podcasts that doesn't mean that we just say we put the customer at the heart of the business. That means that we're really looking at the lifetime value and we're really weaving the data in at the individual grain of each and every customer. Because just like a party you know you want to speak to every person individually. You don't want to give them the same message or even cluster the same message you really want to start to align around the individual nature of each customer.
Now finally we're going to look at the pullback which is the Pit of Technology Despair. So, this is what happens when things don't go correctly as they did in the previous stages or you know you ran through these two stages but then something went awry. The key question here is, is there a fast-connected data system that pulls each piece of customer data together from every corner of the organization? What happens here is you often have splintered groups; like maybe your chief analytics officer doesn't cover all the parts of the organization they report into a technology leader or they report into a marketing leader or something else. That creates this splintering that can make it difficult to get customer centricity to extend across the full organization. And so, the key activity here is to really build fast connected Data Systems at the customer grain and we know this is a technology problem. We know that the organization needs to be thinking about how can we move this forward when we've got an overwhelming volume of data our frameworks might not be aligned, influencers are probably not empowered, and it could be that our leadership doesn't even understand the financial impact of customer-centric data. Overall this Pit of Technology Despair is really signaled by dysfunctional sometimes combative teams. A heavy tribal knowledge that is not invented here that's not the way we used to do it. Low data-driven decisions and so no matter what the vision of a CAO or a CDO. If we can't bring the full breadth of people along with us if we can't share the vision of how beautiful it is to empower customers and how that benefits both the business and the customer in the long run if we can't be thinking of ourselves as, of service to the customer, not you trying to get the customer to buy yet another product. But how can we enhance or improve their lives if we can't get that vision across? We sometimes end up in this Pit of Technology Despair and the critical blocker here is really, why do this? You know why look at the value of customer equity through the customer-centric thinking? Why does it matter? Well you know so the critical blocker to overcome here is, why do this? And the answer to that is because there's real value behind customer equity and we get to customer equity through customer-centric thinking.
Now our exit criteria are not only the technology but the internal attitude that this is the dawn of a new normal and that takes a lot of executive leadership. So as always when you use your data effectively you really can build customer equity and that's bottom line value for your company. This is just not magic. It is a very specific journey that you can follow to get tangible results. Now when you use your data effectively you can build customer equity and that's bottom line value for your company. This is not magic. It is a very specific journey that you can follow to get results. So as always, links to everything we discussed today can be found at ambitiondata.com/podcast and I look forward to having you join the next show where we will talk about the final stages. Stage 5 and beyond which is the ultimate vision for customer centricity. Thanks, everyone.
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 e-mail The Signal. Things I include could be smart tools I've run across, articles I've shared, cool statistics where 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.