This article is part of an occasional series from leading voices about key issues facing marketing today.
Customers. Everyone cares about them. Every team in your company is customer-focused. And everyone is likely working diligently to understand how your customers think. But what if all that effort is for naught?
What if, despite investments in customer platforms, your understanding of your customer is only marginally better.
For marketing teams, that should be a terrifying thought.
For many companies, even those that tout themselves as being customer-first, detailed customer insight can be hard to come by. And it's not for a lack of trying. More importantly, and more likely, it's because of a lack of coordination and integration.
Doing the Same Thing Over and Over Again...
Albert Einstein is apocryphally attributed with having said "Insanity is doing the same thing over and over again and expecting different results." Whoever coined it, it's a great maxim to keep in mind, especially when we're tackling difficult challenges.
Ironically, however, "the same thing" doesn't always look like "the same thing."
Consider customer analysis. Years ago, a customer service team might have informally built its "customer model" through accumulated experience that came from repeated one-on-one interactions with customers. Today, that same team will take advantage of Cloud-based customer service tools that can record chatbot interactions and transcribe phone calls. What hasn't changed, however, is the underlying data source: the potentially irate customer calling in to complain. And, naturally, a team that is exposed to the same type of interaction over and over and over will begin to develop a bias.
The same holds true for other teams in your company. The sales team, with its shiny new CRM will view customers through the lens of potential deal size when (and if) they enter data into the system. The marketing team, through its social media monitoring, will get to see a very, very positive and curated view of their customers. The finance team sees each customer as ARPU and LTV. And don't even ask what the collections team thinks of customers...
Each time one of those teams embarks on a new customer-focused initiative, they unwittingly bring their positive and negative biases along with them. Regardless of the tools they use.
What If We Could Connect the Data?
The big issue, it would seem, is that the various teams aren't connected with one another. If only the sales team had access to customer service logs, or the marketing team had access to Finance's data.
Not so fast.
The issue isn't that these teams can't view data from other sources; it's that these teams aren't refining their customer insights through application of cross-department data. Marketing, for example, might not be concerned about the exact details behind each customer call, but they do care about which customer category these people are calling from.
Proper data aggregation requires effort. Different departments naturally develop siloes. Left unchecked, those data siloes lead to fragmentation, tribalism, and an us-vs.-them mentality. To counteract that natural tendency, a company needs to develop a formal data strategy.
That strategy must incorporate data security, governance, and privacy. Integration of data from different teams requires well-planned data architecture and business processes to regulate the proper sharing of information.
If all this sounds like a big commitment, in a way it is. Companies that make the mistake of running a small data project often fail because they try to apply analytics within a limited scope. The moment they need to work with teams outside the scope of their project, they run into the same barriers that were preventing collaboration between those teams in the first place.
So, What Does More Integrated Data Buy Me?
A richer customer model, for one thing. Increasing the number of dimensions that you can use for viewing a customer means more refined segmentation, which in turn can feed multisegment marketing efforts.
Those unique customer segments should be easily differentiated (even if they are multidimensional) and measurable in terms of their impact on the larger organization. Furthermore, when deploying campaigns against those segments, you should be able to measure the specific results against each segment. Building hypothetical customer segments that you can't subsequently measure against is of little practical value.
Contrasted against data-poor customer models, or models that are limited to data gathered by a specific team (like customer surveys), a customer model that incorporates Finance, Support, Sales, Operations, and of course Marketing data is a huge step forward.
Barriers that prevent companies from developing rich customer models come in many forms. In some cases, they arise due to siloes that have developed among teams. Other organizations cite cost of integration as an issue. Most disturbingly, some organizations are simply satisfied with the limited insight they gather, or perhaps they are lulled into a false sense of security by fancier versions of the same old tools they've used in the past.
Regardless of the reason, it's always worth re-examining your assumptions, especially regarding something so critical to success as understanding your customer.
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