New customer acquisition is costly. And customers are increasingly demanding, fickle, and empowered with endless options — new and old — to spend their dollars. So brands are rightly focused on increasing retention and share of wallet to maximize customer value.
Brands know that data holds the key to making the customer value gains they want to see. But they struggle to leverage that data in the right way. Here are 3 fresh approaches many brands are not using, but should consider, to improve customer value.
1. The More Data, The Merrier
You are collecting some data — likely even a lot of data — about your customers. You’ve got some demographics, geography/location, and purchase history. You may have their customer service history and website behavior as well.
Don’t stop there. Do you know their marital status, education level, income? How about the words they said when talking to a customer service rep? How about their tweets? How loyal to your brand are their friends, family, and coworkers in their social networks?
And don’t stop with your customers. What about the enterprise itself? You’ve got a wealth of data about every aspect of the business, including sales data, ops data, and much more.
Why is this important?
Many things (in endless combination) drive customer value, and what drives customer value is what matters when trying to improve it. For example, when a customer makes $100K+ income, follows you on Twitter, and purchases less than 1x per week, their value is high, but not the highest, and you should email a 10% off coupon every Sunday to drive more sales and move them to a higher value bracket.
Income, social media activity, and purchase frequency are driving customer value in this example, and the solution (coupon to increase purchase frequency) is derived from the drivers.
This kind of information — exactly what to do with which customers, when, to improve an outcome like customer value — is the gold at the end of the analytics rainbow. Full stop.
But you’d never have this incredibly useful information unless you were collecting this important data.
2. Segment by Outcomes
Many brands that want to increase value understand the importance of segmenting their customers by value. But they frequently start a value-based segmentation with their customer base already segmented…by something other than value — firmographics, needs, or some other meaningful grouping. Then they determine the value of that segment. For example, they have a “small business” segment then determine that customers in that segment have an average customer value of $80K.
If you’re going to improve customer value, you need to segment by that outcome, and that outcome only. Start with a clean slate – no pre-existing segmentation. Then let your data segment your customers for you, into differing levels of customer value.
Only then can you ensure that every action taken to influence that customer — every offer, resolution, price — is firmly anchored to their actual value and tailored to taking it to the next level.
3. Stop Searching for Insight
Many brands that are leveraging data are cranking it through some analytical process with data scientists and analysts or tool like visualization or modeling software.
They are hoping to find out how to improve customer value. What they often get is “insight” or slide-ware or pretty charts. It’s not immediately apparent how all of this this translates into the specific actions — the offers, resolutions, prices, etc. — that should be applied to each customer at every single touchpoint to increase their value to the business. Many brands have the common, frustrating experience of digging through reports and looking at trend lines, and still not being sure what steps to take to improve value.
There’s a better way to solve for value and that’s to stop searching for insight, and start insisting on the technology that gives you what you need. You need technology that will cut to the chase, telling you exactly what tasks to perform, and when, to improve every customer’s value.
A new breed of analytics — prescriptive analytics — is up to the task. Whereas other analytics tools and methods may simply tell you what has happened in the past, or what will happen in the future, prescriptive analytics can tell you why it’s going to happen and what to do about it.
Smart brands trying to improve customer value should focus on gathering as much data as possible, segmenting their customers by outcomes, and looking for the right technology to tell them exactly what tasks will do the trick.
How are you using data to increase customer value?