Digging Deeper Into CRM Data
The really revealing use of data comes when you can make correlations between activities that happened in multiple touchpoints throughout your company. These are hard to understand, because we've always kept the data siloed -- which is what CRM is supposed to remedy. The tough part in all of this is that what works best can't be dictated by you.
CRM is sometimes described as the place where all your customer data resides. That's a nice image -- all that helpful data, chilling in its own hangout. That data works for you, though -- if you're just storing it, it isn't doing you any good.
That's why it's important to think about what you want to do with and learn from that data. We talk a lot about what we want to do with data: We want to give sales people additional tools in their arsenals; we want to track service interactions; we want to identify the most profitable customers so we can redouble efforts to keep them loyal; and so on.
We talk a lot less about what we should be learning from customer data -- and that's because that takes some imagination.
Digging in the Wrong Place?
Marketing automation vendors have taken a step toward that in their own limited realm: If an offer gets a great response from potential customers, create more offers like it. What types of approaches generate responses to calls to action? How many contacts does it take before a customer takes a desired activity?
These are similar ideas to what you could be looking for in your customer and lead data, and responses to marketing and ongoing engagement efforts should certainly be part of the mix. However, there are other details buried in the data. It's like an archaeological dig -- if you're not aware that something is important, you may discard it while looking for something else.
First decide what you want to learn. Most of this will be retrospective in nature, since it's based on data you've already collected.
For example, is there a sequence of events that is common between customers you've lost that led to their departure? Perhaps their response to nurturing messages dropped off just before they left. If that's the case, that behavior may be an alert to you to reach out in a more personal way to reengage with them.
Is there a pattern of customers leaving after they've contacted your support staff -- or a pattern of departures after a price increase and a service issue? If so, it may be a good practice to review service processes and beef up service staff training before any price increases are announced.
The Golden Path
It's not all bad news to be discovered. You can do the same analysis for customers who turn into repeat buyers. What pattern of activity and interaction did they exhibit before returning to buy from you? Did they register for a webinar or white paper? Did they call service and have a problem resolved? Did the sales rep working with them do anything unique to foster the relationship? Did all or some of these things happen in sequence? And is there something you're trying that didn't have an impact on any of your closed sales?
On the sales side, is there a pathway through your content that pays off an inordinate amount of the time in a closed sale? If you can identify it, your sales team can help steer potential customers down that path.
These are all pretty simple examples. The really revealing use of data comes when you can make correlations between activities that happened in multiple touchpoints throughout your company. These are hard to understand, because we've always kept the data siloed -- which is what CRM is supposed to remedy.
The tough part in all of this is that what works best can't be dictated by you. Your customers will tell you what they prefer by their actions and their responses to your actions. Your role is to pay close enough attention to the data to see what those customer responses are, and to use that information to replicate what works and to fix what doesn't work.