6 Tactics that Transform Learners to Leaders

The following excerpt is from The Age of Customer Equity: Data-Driven Strategies to Build a Sustainable Company. Available at Amazon, Barnes & Noble, Porchlight, and your local bookstore.

The transformation from Learner to Leader is all about initializing and then spinning the customer-centric flywheel of the business. As it begins to turn, there are lots of fits and starts. One brand or product line might move faster than another. Politics and turf wars may erupt internally as the power of customer data—and ultimately customer equity—becomes clear. The goal is to keep moving forward and gaining momentum. Listen to each other internally, build, and grow. Here are six tactics you can use to transform your team from Learners to Leaders:

Transform with Technology

As companies enter the Learning Zone, they may substitute digital tracking systems like Adobe or Google as their customer database. Gradually, the technology needs to put the wealth of the business in one place, at the grain of the customer. To do that, technology must identify the customer and match all interactions and behaviors to the customer. Speed and flexibility are fundamental needs. You may also want an analytics landing zone as you work out data sharing and security rules to be able to give data and get data. If you have multiple terabytes of data grinding through every day like a lot of major retailers do, then it is a time-consuming process to move that much data from one place to another. Analysis will only be able to run as fast as the data is available, so you may only be able to initially move at the weekly level when you need to move at the daily level. Build for speed and scale and get ahead of it. Data is not getting any slower or smaller. 

Take Action with Influencers

A good way to solve for low adoption is to train a handful of internal influencers. Influencers are strong internal mentors across teams. In any company, they make up about five percent of employees. Influence does not track to title, but you know who these people are. They are the person who’s always the first one to raise their hand to say, “I’ll try that,” or “I know how that works.” And they seem to learn things as if they’re just breathing air. Influencers trained on self-service data tools create a “seed” within the company culture that allows one person to lean over and ask their neighbor how to get a report instead of creating a backlog of service tickets for the analysis team. 

What happens next? Visualizations improve. Live data appears on the wall and teams begin to use it to support decisions in meetings. Knowledge libraries form to document learnings from the experiments that are beginning to take root. And ultimately, data creates better compliance with standards. A sense of the future accountability takes hold. 

Ramp Up Leadership

Leadership is critical in the Learner stage both at the entry and exit points. That’s why you see a lot of organizations adding a chief data officer (CDO), a chief analytics officer (CAO) or a combination of the two. Leaders should be learning 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. We are not thinking about only the marketing department anymore. We’re thinking about the holistic customer. Leaders are working hard to find those quick cross-department wins. There is usually some nice low-hanging fruit that once seen through the data, can be knocked out right away. These early data leaders must stretch across all departments, and these are highly political roles. They often work at making friends across the departments to gain alignment and get at least some boats rowing in the same direction.

Implement Processes That Support Learners

The Learning Zone is a process-heavy time. At this point, we’re looking at cross-business units and the more acceptable medium-risk experiments start to become tolerable. As the company progresses in customer-centric transformation, tolerance increases to try, learn, and sometimes fail. Business impact increases as customer-centric tests are tied to CLV. As a result, more optimizations (the result of a test) become internalized knowledge, and that knowledge begins to appear in business algorithms. To maintain the progress, Learners need to create and maintain a data dictionary including the source of record. Experimentation should be backed up by a knowledge library where previous tests and findings are recorded. Finally, a trackable list of tests including the expected amount of revenue and actual ROI they generated creates a nice insurance policy when the inevitable new executive questions whether customer-centric transformation is working. 

Two more processes that will help you defend early momentum in this zone are the report rate and recommendation adoption rate. The report rate simply tracks how often reports are accessed and by whom. The recommendation adoption rate is a simple but powerful process. As new analytics recommendations are made (usually through extensive presentations), track each recommendation on a separate list and who is responsible for taking action. 

Monitor Metrics 

In the Learning Zone we monitor report and adoption rates, track data sources of record, and build a data dictionary to prevent companies from sliding back down the curve. These are defensive measures that protect your progress. Within existing dashboards or at least in context with experiments or analyses, we monitor customer voice and behavior, watch the flow of behavioral segments (including CLV) and message resonance as well as the customer acquisition cost (CAC) to CLV ratio to help expedite your progress. These are offensive measures that accelerate progress.

Create Exit Criteria 

The exit criteria for Learners 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. Ask yourself: 

• Is the organization aligned around the customer? That means we’re really looking at the lifetime value as a measure of performance quality.

• Is the data at the individual grain of every customer? Because just like you would connect with friends at a party, 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.

When we use customer data effectively, we build customer equity and that’s bottom-line value for your company.