I recently had a client tell me that his company needs to do a better job of understanding the needs and expectations of different cohorts of customers. As he so astutely put it, “if a customer tells us he has a headache, we give him a pedicure because that’s what we’re currently offering. One size fits all”. We all inherently know that’s a bad CX strategy, but most companies are helpless to change. Why? Because it’s just too damn hard to get all the right data into one place to create cohorts of data and learn what’s different about them. And there are stark differences.
Let’s take a minute to understand the creation of cohorts before we talk about what can be learned. The first step in cohort-centric CX is establishing “who”. Most companies have mountains of information about their customers – demographic info, purchase information, contact history and more. It can be as simple as identifying subsets of customers in certain regions/markets, or who have bought a certain product, or even male/female. It can also be a group of customers who are seemingly affected by the same issue such as a billing error or a defective product. Start simple. Isolate enough customer interactions to have critical mass so that you can look for patterns. Once you have established those patterns, you can determine how to modify your approach to that specific cohort.
Identifying the patterns in behavior, friction points and needs of a cohort is the next step. Leveraging an omnichannel conversation analytics platform such as Topbox provides the ability to look across all customer communication channels simultaneously. That’s important because trying to look at each channel independently can be extremely difficult due to the nature of disparate data formats and analytic reports from individual platforms (call recording, social aggregator, chat, email, etc). There’s also the issue of data ownership and navigating the organizational silos to get access to the platforms and data. Good luck with that! All that said, once you have the data in one analytic platform and your cohorts created with simple queries, you can begin to leverage conversation analytics to look for trends and patterns.
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Here’s a very simple example from the cable and broadband space. A basic gender query may show that females tend to focus more on price and customer service while males may focus more on data allotment, speed, and programming. With that info in hand, companies can steer telesales, cross-sales, promotions and retention offers in a general direction that may be more appealing than a one-size-fits-all approach. Even a modest improvement in performance in these areas can yield big results in new and retained revenue.
There are obviously many other great examples of cohort analysis and the application of the findings for business improvement. Expect some trial and error when doing the analysis, but more often than not the insights will begin to pile up. Then it’s a matter of prioritizing your team’s focus.