Ep. 6 | Reaching Centricity – Payoff Stages of the Customer Centricity Maturity Curve
The final stages of the customer centricity maturity curve are where all of a company’s hard work starts to bear fruit. In this episode, host Allison Hartsoe talks about the key traits for the final stages of customer centricity maturity: Innovation, Operationalization, and Integration. She shares how work spearheaded by the C-suite in earlier stages is pushed out to the entire organization; every team member has access to information and tools needed to move tactically in alignment on company goals. Customer data has become the company’s competitive advantage, and people are measured by their impact to the organization.
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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 Alison 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!
In today’s episode of the Customer Equity Accelerator, we’ll cover the final stages of the customer-centric maturity curve. Stage 5 is innovation. Stage 6 is operationalization, just more syllables than I can almost pronounce. And finally, the holy grail is Full Integration. We’ll talk a little bit about what is involved in each of those sections and just like the podcast before we’re going to use the same kind of framework that I’ll define again here. Leadership as measured by the organizational alignment around the customer portfolio. Peoples actions which are measured by their ability to use tools or the outputs of those tools for customer-centric decision-making. Process which is the ability to execute optimizations around the customer. And finally, technology which is measured by the agility and the enablement to get to business goals. So those are our definitions.
Now let’s dig into Stage 5 Innovation.�
In the innovation stage, the key question is having each member of the executive team agreed to run by a small set of specific metrics usually about four that interlock with company strategy and align to customer-centric tactics. This is a big focus. It sounds like, Oh, we’ve just set up a couple of metrics and aligned with them. But what it’s really saying is that the company takes the time to think about what those right metrics are, whether they can be measured, how they’ll be measured, and what that means at all the layers of the organization. That’s a big ask and that’s why the stakeholders here involve the management team as well as the chief analytics officer, sometimes the chief data officer which are usually reporting into a CTO, a COO, sometimes a CFO and occasionally they report directly into the CEO.
It’s interesting to see how organizations move that position around. It’s very much in flux. There’s no stable definition of where the CAO or CDO should go yet.
Then the key activity here is really around interlocking a lot of disparate datasets. So, your CRM, your support, your supply chain, your marketing interlocks, you’re optimizing customer relationships, and your view of them around all of these different datasets. And at this point, you’re able to pretty much stand up a rapid analysis platform. Well, that’s a big deal and one of the things that surprised me recently I was watching some sessions from the Google cloud summit. I think it was Google next actually. Watching some sessions from Google next and they talked about why systems like Hadoop, that I thought was the fastest thing out there, were actually too slow for the kind of decision-making processes that some of the more modern companies have. And so, they talked about using newer technologies that were available on Google cloud. So, at this stage the organization is really starting to get a feeling for hey this stuff is paying off its bearing fruit and that’s why I call it the innovation stage as it’s the first time that the organization starts to knock down a few really good pieces of low hanging fruit. So, in this stage, the most important section is process. What was previously High-Risk is starting to be perceived as lower risk due to all that supporting data. And that means that cross business unit teams can start to emerge around optimization for CLV.
That also means that machine learning starts to step into the process. So, this is the first time we start to get a sense for better data and task-focused automation coming in and taking over parts of the process and the organization starts to trust, and the organization starts to say “yes” we want more of this. We like what we’re getting. Behind that you have leadership which is again still centralized around the chief analytics officer and their guidance is incredibly important. So, they’re giving some guidance around the management of how you can align customer equity driving goals. You’re trying to get interlocking measures stood up and centralizing the definitions and formulas are a big part of this stage which in one way or another eventually have to be blessed or perhaps managed by the CFO. That basically means that if you’re going to claim a win you’re going to say that as, say you’re the chief analytics officer you’re going to say, that this thing that you did had X impact. You’re going to have to have a financial person bless that and say “Yep” that’s indeed the impact that we saw, and our numbers agree and we all aligned.
So that’s an important part of that process. The third piece of people actions at this stage people are really moving into the self-service kind of model and this is more than just self-service. What happens here is there’s better data available and better data sometimes has more complex ways that it needs to be pulled. So, what some organizations are doing is kind of a rank testing. In other words, if you pass this test then you qualify for a certain group of data and once you can prove that you can get in there and use it reliably and credibly then you get access. That’s a really interesting approach because it does get away from the whole data democratization zoo which can happen if you give everybody full range access.
�So. at this level, the strategic insight is easier because you’ve got cleaner combined data and you’re starting to see more prescriptive and predictive datasets widely available. Governance is starting to lock in. What’s more important is people are actually aligning to those definitions of what it, what an impact actually is. There’s a lot of discussion and that means that the wikis that hold information start to become really critical for knowledge sharing. What do we mean by that? What’s a wiki? Well, wiki is just a place that can be a wiki, it could be SharePoint, it could be some other type of organizational knowledge sharing system and what it’s designed to do is to help the organization understand that over here in the call center, we found this kind of impact but over there in the marketing area they found that kind of impact. Here’s a way for all of us to understand what is being done and to start to row in the same direction so it’s heavily used by analysts. This is a knowledge sharing that is now perhaps owned by the CAO, and all the analysts start to get interconnected across the organization and their various areas of expertise come in and are shared with other parts of the organization where different areas of expertise are in place and can imagine why that’s important. You know we basically have to get the organization to know how much goodness is going on and all that analysis and to start to take hold of it.�
Finally, the last piece is technology and here the organization does recognize that data is an asset. Security becomes a stronger concern within balance need for speed and business agility. So, you know you don’t want to slow things down with security, but you want the business to be agile and responsive. So, then we start to realize that if analysis is our competitive advantage should the underlying data ever be sold, or should we be locking that down. That’s an interesting area because there are some organizations that sell data to third parties because they found that it can be another revenue source. It’s a very interesting question that occurs at this point. The critical blocker to overcome in Stage 5 is that we have to embrace the customer as the business equity driver. When you’re pulling all those datasets together something has to matter the most. What is it that you’re solving for? And the key piece here is you’re solving for equity. What kind of equity customer equity. Why. Because customers have more power than they ever had before. And that’s what your business should be based on not brand equity. To a lesser extent operational equity but customer equity is really the way we’re trying to innovate. The exit criteria for this stage is – is the customer data our competitive advantage?
And the answer there should be yes.
Moving on to Stage 6 Operationalization. Now here the key question is – Does everyone in every department understand and are they individually accountable for the same set of up to four specific company-wide metrics that are continuously measured and specifically ladder from tactic to customer equity evaluation or a similar goal. Now operationalization is about what was previously known and handled at the c-levels starts to push down a level the centralized teams that are supporting the c-level are now getting it and they move tactically more in alignment with what the c-levels want. So, c-levels aren’t driving as hard at this stage. We’re starting to pass off certain known quantities down the chain and say yes go after this. That doesn’t mean we know everything. It just means that more people are able to drive by the company-wide metrics that are set, and these metrics are measured and aligned in a very logical way across the board. So, our key activity here is something that I haven’t actually seen exist yet although I have seen some organizations make it on kind of a customized format. I call this network operations center, a NOC. Let me describe this to you because this is going backward in technology a little bit. It used to be at the dawn of the internet and probably a little bit before that there were these SOCs, security operation centers and NOCs, network operations centers and a person would basically sit in a cockpit and they would be surrounded by 5-10 screens. And then in front of them in almost in a war room style format there would be another 5-10 screens monitoring what was going on and what this allowed a person to do was to see network or security breaches coming in in real time comparing them to other things that were going on and then make decisions about what should be done at that particular moment. It was a very fast process. A lot of data a lot of knowledge coming together. And so, you can see the application to analysis.�
I’ve only heard of one company think that was Procter and Gamble that did this kind of NOC or SOCs set up for all of their analysts. Well actually it wasn’t for all of their analysts they did it for their senior team and then the problem that they discovered was that when they held the data back from the rest of the organization and only the top 200 managers could get hold of it the rest of the organization wasn’t aligned. And so, people didn’t really understand how whatever was deemed to be important related to them. So, remember how in the last step we really hit on the fact that the metrics in the alignment have to happen across the organization. Well, that’s particularly from learning from that story of what happens when you don’t do that. So again, operationalization is about pushing the data down in a format that people can get hold of and understand. So, our number one piece in this section is going to be people’s actions. They need to access the master customer repository which has prepared high-value low noise data sets perhaps reports and self-service tools available to those managers. And again, that’s beyond the top 100. That’s beyond the top 200. That’s way down into the organization. The process here is almost equally important where multiple departments are contributing to the same wiki knowledge sharing system for insights pre and post optimizations. So, the department organization shifts to customer teams for faster execution. So, I don’t want to breeze over that. The process here is the organization is shifting to align around the customer. It might start out as a matrix shift and then it moves into a full-blown reorg around the customer for the sheer need of speed. So, process here again has two pieces. It’s the knowledge sharing and the ability to take action and it’s also the organizational alignment that is like the grease in the wheels, so people can take that action. They have the authority. They have the obligation to.�
Our leadership piece shows that the drive for customer equity has become fairly clear. We understand that through the metrics that are measured, and our decision making is decentralized around customer portfolio goals. It all lines into the tactics. Technology starts to be about advancements that continue around balancing emerging technologies for speed, security, and the need to extend customer knowledge. There’s always more to know from different datasets. How does that come into our technology stack?�
Now in Stage 6, our critical blocker to overcome is the ability to reorganize the company around the customer. I don’t know that this always needs to be a full-on reorg. It depends on how fast the organization moves. It depends on how people are responsive to different areas of responsibility that are given to them. Are they accountable? If an organization has very slow traditional processes, then a full reorg may be the way to get that accomplished. But if an organization has pretty natural fast-moving processes that it may simply need to be a matrix that reorganizes the company around the customer.�
The exit criteria here is – are company-specific customer-centric algorithms and machine learning now entrenched as our competitive advantage. It’s not just the presence of data but what we do with that data that’s our advantage.�
Now finally the last stage, Full Integration Stage 7, this is not so much a stage as it is really the dream. At this point we have full company alignment. It is essentially the new normal and the critical achievement has become that the company is organized around the customer. It runs very sensitively and intelligent. What I’d almost call sense and response systems to take advantage of changes as they happen in real time and a move with customers preferences, to move with changes in the market, to move along very quickly, and be of service to their customer base. Internally you have everyone rowing in the same direction. So, you have a lot of internal collaboration up and down the chain for the good of the customer. If you were to ask someone how they contributed to the customer and to the equity that’s generated from each customer, they would know, and they would have an answer. So here our key activities are really aligned intelligent data is just part of doing your job. And wouldn’t that be nice? People understand their direct impact and what you’re measured on may determine whether you want to work there.
So for example if we just draw out this idea a little bit if you have a lot of data about your customer base you probably also have a lot of data about your employees because remember as we were growing up in the customer journey we were actually starting to extend across the rest of the organization, that includes people. So, the optimization of our people for the benefit of the customer becomes of critical importance and that means that what we’re measured on. So, what you and I are measured on in our everyday day work starts to become very quantitative. Now I think some organizations will measure perhaps more effectively than others. So, an organization that’s able to trace your work all the way through to impact could be a very good place to work because they’re not measuring every minute that you put on the clock. They’re looking at how much those minutes mattered. So, we’ve all seen examples of people who learn things really quickly and people who take a little bit longer to learn. It�s no fault one way or the other. But the fact that I learned something quickly doesn’t necessarily mean I should be penalized.
So, if our purpose is to measure hours instead of impact like a lot of consulting organizations do then perhaps I am inadvertently rewarding people based on the time they work instead of their impact. So that’s what I mean by you may decide whether you want to work for an organization depending on how they measure you personally and how they measure their success to the customer. Because ultimately, I think this is a big part of the B-corporations that are coming up, the collaboration with customers that are becoming a big deal. Ultimately the corporations should be of service to the customer. That’s kind of the end game here. It’s almost like the 1950s has come back and everyone is just finding a way to operate at scale in a very close – “Hey, what can I do for you, Joe?”, kind of partnership. You know I would like an organization or a big brand to relate to me in that personal fashion. We’ve all had experiences where somebody inside a big company takes interest in us and gives us that warm personal touch. That’s great but we should be doing that at scale. And I think ultimately, we can. That is our goal at the end of customer centricity to relate to people individually, personally, meaningfully at scale with the tools that we have so that we can create a better environment and a less frustrating environment that ultimately moves our entire world forward. And why not make a customer centricity be a way to make the world a better place. That’s certainly a goal I wouldn’t mind.�
Today for almost every company 17 percent or less of customers are driving more than 80 percent of the future revenue. In a world where that’s opposite 80 percent of my customers are driving the majority of my value in a world like that every customer matters and I have a need to speak to them personally and meaningfully and that’s the goal. That’s what we want out of customer centricity at the end of the day.
So today we looked at the last three stages of the customer centricity curve. We looked at Stage 5 Innovation. Stage 6 Operationalization. And finally, the holy grail of Full Integration.
As always, anything that I talked about that has a pertinent link is available at ambitiondata.com/podcast. And remember in every single one of these podcasts what we’re trying to get to is to help you use data effectively so that you can build customer equity. It is not magic. It’s a very specific journey that you can follow to get results. 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, or people and companies I think who 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.