Turn Data Into Information
TL,DR: We have tons upon tons of data, but to create actionable insights that you can truly execute against, you don’t need data, you need information.
We strive to know our customers because we want to align our product and service offerings with customers’ interests, and knowing precisely who the audience is can help us ensure alignment, identify gaps in our offerings, or show us anomalies for which we should explore creating something entirely new.
So we need more data, right?
We’ve all heard the calls from our organizations: “We want more data!” “Give us all the data!” I’m pretty sure I’ve even made those asks myself.
The problem starts when the worst happens: we get that data we requested. Oops! Now we are drowning in data, because there’s no logical filter. When we get some data, we want more data, and so on. Too much data takes up dashboard “real estate”, can overly complicate decision making, and can lead to analysis paralysis.
It turns out that we are actually just making the wrong request. Here’s what you should be requesting:
“Give us more information!”
Of course I’m referring to more and better information about our customers. That information can generate the keystone: insights.
Using better information and insights, we can make better business decisions about individual customers, and we can create new offerings if we know our audience (or perhaps who’s not in the audience).
So if your company is keeping up with the (Dow) Joneses, you log every interaction with your customer-facing teams, with your products, and with your communities, right?
DATA: The Gathering
Most companies gather that information about their customers, including various demographics, product telemetry (e.g. how customers use your product), and perhaps information about segmentation or business area. Many companies employ enriched customer data from external sources, creating vast “lakes” of customer data that are challenging to use well.
That’s the data needs to be turned into information.
Whoa. How do we do that? Isn’t that simply “enrichment”?
Sort of. Enrichment typically involves data that comes from external sources, though depending on your definition of “external”, that could be a ‘yes’ I suppose. But I digress again.
One answer today is certainly enrichment through Artificial Intelligence (AI). The premise being that untrained humans might miss some relevant data relationship that an AI model would catch, so an AI is obviously better. The notion of AI is getting easier for Joe Sixpack to comprehend, as these types of insights tools are included natively in platform packages (think Bard, ChatGPT4, etc.).
However, creating, training and integrating a model still takes time and expertise and perhaps an integrated product to implement an AI solution that can correctly direct your teams’ actions. Perhaps this will become commonplace, but there are simpler methods available to you and your “I”, the human (i.e. not artificial) intelligence you already hired on your teams.
Besides, AI is super busy trying to achieve The Singularity, so it can’t (or won’t) really be bothered to help you, Dave. (ref. 2001 A Space Odyssey, kids.)
We need more dashboards!
No, actually, you don’t. And my colleagues can confirm: I’m kind of a dashboard guy.
What you need is for the information to be better.
In most cases, because we don’t lack data, what we lack is data in context. What I mean is certain data points connected to other data points and yet other data points to create some relative points of understanding. That’s information.
For example, if you know your customer’s renewal date and size of contract, then you can perhaps prioritize them based on that. But how many of them have active Sales pipelines that you might want to coordinate with? That’s second-level data that becomes information. Further, how many are actively trying to self-serve in your learning portal? How many are struggling — or not struggling — with adoption?
As you can see, the more data that you can collate into a decision point, the more ways you can segment or subdivide that customer list into smaller, discrete, specifically-actionable segments.
Said another way, we can now create individualized solutions or approaches for those customers based on the nuance of their situation.
Unlock Your Approach
What becomes readily apparent when you turn your data into information is that there are many approaches that you can unlock. Perhaps you can use this mass customization approach to make headway across many accounts at once via a self-service offering or program.
Is there a group of customers struggling with one aspect of your product? Maybe there’s a webinar you could record and publish to help aid a whole suite of customers manage a specific situation.
Our Marketing brethren have known about this for years. They want everything in context so that they can refine their offer strategy.
Stated in English, they want to show you the right shoes when you’re near the shoe store.
It’s really that simple. And no, it’s not just shoes, it’s every identifiable situation you might find yourselves in: needing a mortgage, buying insurance, better razor blades, or manufacturing sensors.
The Point.
We aren’t used to combining disparate sets of data quickly and easily. Every silo wants to own their world (and their data). Frankly, that’s bad for business.
Putting the data in the customers’ frame of reference and adding as much additional context as you can will unlock your ability to see trends and serve your customers.
Account + Renewal Info is orders of magnitude less helpful than:
Account + Renewal + Sales Opportunity + Support History + Purchasing History + Call History + etc.
You can see where I’m going here.
Whatever the customer outcomes you see ahead, the notion of finding a newly addressable market within your current portfolio means that you can now quantify your programs, measure the outcomes, and perhaps even create a lifecycle of segments that you see your customers move through.
It’s enlightening, isn’t it? Try it.
If you’re curious what drives my passion for this topic, please check out the story of my experience that helped me put a version of this into practice:
