How Data Driven Marketing Can Help With Customer Retention
It’s a generally accepted fact among data driven marketing managers that it’s far less expensive to keep an existing customer than to acquire a new one. Cost estimates vary, but acquiring a new customer can cost several times more than retaining an existing one does.
It’s not unusual for companies to find that acquisition is ten times more expensive than retention. For this reason, even relatively modest improvements in customer retention rates can pay big dividends.
No matter what business you’re in, customer retention is important. Event triggers are not a replacement for a broader retention strategy, but they can be a useful tactic. Ask yourself what you know or can learn about when and why customers stop doing business with you. Knowing beforehand gives you the chance to do something about it.
Sometimes you create the customer-retention risk. You sometimes need to increase prices for example. Or you may have oversold services to your customer — services they don’t really need. The key is to recognize when a customer is at risk and intervene before they leave you. How you do this is obviously highly dependent on what business you’re in.
The financial-services industry, particularly retail banking, provides a good context for a discussion of event triggers. For one thing, bankers have the luxury of having a large amount of data on their customers. The very nature of banking requires that they keep track of everything.
For another thing, bankers have been aware of the value of their customer data for a very long time — the banking industry was one of the first to really embrace the use of customer databases in marketing.
The basic idea of banking is to take in deposits at a low rate of interest (don’t you just love the three cents a month you get on your checking account?). These deposits are then loaned out at a significantly higher rate of interest. For this reason, among others, keeping deposit customers is very important.
The approach that banks might take is to monitor the average balances in large deposit accounts. For example, they may keep an eye on customers who keep at least $10,000 in a savings account. What they look for is a sudden drop in the size of the account.
A customer can have any number of reasons for withdrawing money from such an account. They may be making a large purchase — a house or car, for example. They may be paying off a debt. They may be moving the money to a higher-yielding investment like a CD or a mutual fund.
Here’s where having a large amount of guest data can come in handy. The bank knows what other accounts the customer has. So the bank can check to see if this withdrawal was immediately deposited in another account. The analytics here can get very advanced, but the basic idea is to predict what the customer has in mind for the money.
If the money went from savings into a checking account then the customer is probably getting ready to spend it. Because this was a significant amount of money, they may be getting ready to spend it on something big. But large purchases are often partially financed.
The money from the savings account might be intended to cover a down payment. In that case, this would be a good time to communicate with the customer about the various loans offered by the bank. This gives the bank a chance to replace the lost deposit with a loan and not only keep the customer but actually deepen the relationship.
Another simple train of thought might revolve around how the money left the savings account. If the money was transferred out via a wire transfer, it becomes more likely that it may have gone into some sort of investment account.
In this case, it might be a very good time to have an investment advisor contact the customer to talk about the bank’s products in that area. This may seem futile if the customer has already invested the money elsewhere. But remember, even a small percentage recovered is very valuable.
These examples all relate to banking, but similar thought processes can be applied to any number of different businesses. Resort hotels have customers that come back every year. They can be on the lookout for folks who haven’t made their usual reservation. Automobile dealers know how long people usually drive their cars before buying a new one. They can contact them pro-actively; extolling the virtues of this year’s new model.