Tell me if you see your business in what follows:

You sell some type of regularly consumed product or service. You have a lot of competition, your customers have a lot of choices who to buy from, and the switching costs for them are minimal.

So you aren’t necessarily surprised when some customers stop being your customers. Maybe you’ve even built a lot of churn into your business model. Maybe you just run faster and spend more to acquire new customers faster than you lose them.

But why do customers stop being customers? Is it possible to know why after the fact? Even if you could, how useful is that rear view mirror understanding?

Or is it also possible to somehow know the warning signs before they stop buying, to actually predict which customers are more likely to stop buying, and intervene before that happens?

If you embrace your data and an Analytics process, the answer is “Yes”.

You can understand the warning signs of an impending and likely customer departure by looking to your data and using Analytics to take preemptive action.

For example, if you looked at your data from the last 12 months for

  1. the demographics of your “Best” customers
  2. the buying patterns of your Best customers
  3. the selling patterns of your sales reps that manage those accounts

you might discover that, 7 times out of 10, customers who went more than 90 days between purchases never bought from you again. However, if they had been engaged by your sales reps in some way before that 90 day period transpired, the only 3 out of 10 customers stopped buying. And based on what your data tells you, only 10% of your sales reps seem to know this and engage their customers at the right time.

Armed with this knowledge, shouldn’t you have all your sales reps proactively contacting your customers every 6-8 weeks, offering something of value, paying a personal visit, etc., to stay top of mind and engaged with your customers? Why let 90% of your sales team underperform by not sharing what your best 10% have discovered through experience, trial and error?

For most businesses, the cost of customer retention will be far less than the cost of acquiring a new customer. Why not use data and Analytics to spend less and get more?

If you’d like to run through a simple ROI calculation for cost vs benefits of using a good predictive model, see my blog post on the topic here

Having improved customer retention, are you done? What else can you do to maximize the lifetime value of your customers once you embrace Analytics and use your data as a business asset? “Test and Learn”, that’s what.

Know what you need to do by learning what you need to do, and when, to get more value from your relationships with your customers.

  • What makes your customers more likely to buy additional products and services from you? Test and Learn.
  • What can you do to make it more likely that your customers refer a friend to your business? Test and Learn.
  • What can you learn from applying Analytics to your data about the Best Customers so that you can effectively target “lookalikes” when marketing and prospecting for new customers? Test and Learn.

With each Test comes a hypothesis, a “business experiment” and a specific outcome you are testing for and collecting data on. By analyzing the data generated from your business experiment to confirm or reject your hypotheses, you Learn …and then start the process all over again with a new hypothesis. It never ends. And you always get better at doing what you need to do better. And when you can do that well, you get competitive advantage.

So don’t look at reports next month that will tell you about all the customers you lost last month. How is that actionable? With data and Analytics you have the opportunity to keep more of your hard won customers, deepen their engagement and loyalty, and make all your customers your “Best Customers”.

 

Finally, I’d like to offer a big note of thanks to Karl Becker with The Carruthers Group for his collaboration with me on the content for this blog post.