How does a company go about deciding when to give up the ghost?
The idea is that there are these simple customer models that have been used for decades in businesses that have always been very customer-centric, like the catalog, insurance and credit card businesses. These industries have always collected a lot of customer data because it was the nature of the business. They're the ones who have developed these simple models -- ranking customers against each other for periods of time. If some period of time goes by and there is no interaction, then they are no longer customers. What is the amount of time? The most effective way to [judge] that is to look at your customer base itself. Let's say you ranked your customers by how long it's been since they last contacted you. Twenty percent, say, haven't contacted you in more than two years. So you chose that as the [cutoff] number. But before giving up on these customers, make an effort to contact them to validate that you're in the zone. Customer retention guru Jim Novo says you don't have to spend a fortune to learn more about your customers -- you just have to direct your energies wisely. Novo, the consultant and author who turned around the Home Shopping Network, says that most companies have been measuring CRM success all wrong. He told SearchCRM.com news editor Jon Panker that maximizing CRM begins with understanding the customer life cycle.
SearchCRM.com: Why is the customer life cycle so
I really see this as the crux of the whole issue. I'll say, 'if someone hasn't had contact with a company in two years, do you consider them a customer?' The company will say, 'yes, they're still a customer.' But are they still a customer after five years? Ten years? You end up with this infinite customer life, and you can't measure the lifetime value. It comes down to having to start deciding when a customer is no longer a customer. Companies have to give up the ghost, so to speak. My focus is to get people to understand it's not such a bad thing to say you lost a customer; it leads to other benefits.
A couple of maxims that you share with your clients are 'Don't act until you have to' and 'Act
only at the point of maximum impact.' What type of customer actions are you talking about?
It's usually contact of some kind. Let's say in banking, if there's a high network customer, they'll have a one-to-one relationship with the salesperson. With Web retailing, then it's probably an e-mail campaign that offers some kind of discount. So why wait until the last minute? This is the whole ROI question. For example, in retailing, people lose a lot of money because they give discounts to customers who would have bought anyway, without a discount. The ROI of any campaign is higher if you provide discounts that are attractive -- to the right people at the right time. Let's say you have a budget of $1 per customer, per month for promotion. Typically you send an e-mail with a 5% discount to every customer. If you identified which customers were most likely to defect and that number was half of your customers, you could spend $2 per customer instead of $1. You reallocate the funding to only the customers who will defect. And you could boost the discount to 10%. So, you've upped the offer and delivered it to people who are specifically at risk to not becoming a customer. So, are the well-publicized CRM failures and low ROI figures exaggerated because we've been calculating retention wrong?
I think that's a really good possibility. That's what I mean by this whole jump to lifetime value. Lifetime value is very complex and difficult to deal with in a lot of ways because people fail to call an end to the life cycle. If people went back through a lot of these 'failures' and set up the right metrics to measure them, you would see then that they would turn into successes. What kind of tools can you use to gauge these customer life cycle measurements? Which are the most effective?
I think virtually all analytics packages do this kind of stuff. I just don't think people realize how they should be used very well. If you talk about analytics in general, you have people who are convinced it's good to have analytics, but nobody has said 'here's what you do with it and how you use it.' You have a lot of people creating reports, but it's never really action-oriented.
For example, with the RFM scoring model, which works in larger enterprises, you end up attaching a digital score of value to the customer. You can use that number to trigger a screen pop in a call center. So if you have a customer with the highest score, and then in the next month you see [the score drop], that's an action element. That says to you 'we have this customer who used to be high value, and now it looks like they're beginning to defect.' The title of your book is Drilling Down: Turning Customer Data into Profits with a Spreadsheet. Can you really do this customer scoring with just a spreadsheet?
Sure you can. An Excel spreadsheet holds 65,000 records. So you have to be a company with less than 65,000 customers. You can do any simple scoring in a spreadsheet or in Access. From an ROI perspective, it's a lot easier starting with a spreadsheet than with a $10 million software package. I would say it is true that there are many companies who spend much more on analytics packages than they have to because they [want] to start at the top. In the case of analytics, in every case I can think of, it's better to start at the bottom. Any moderately skilled IT staff has easily built these kinds of simple scoring applications in two or three hours. Can you give me an example of a company that is really good at managing customer life cycles and achieves ROI on CRM initiatives as a result?
One of my clients is MBNA -- the big credit card company. They are in the business of predicting customer behavior. They deal with this whole 'act at the right time' and 'act only when you have to' kind of idea. So they get high returns on their marketing investments. Cellular One is another company that did a tremendous job before they were bought out on predicting customer defection and acting. They had a 252% return on investment for every dollar they put in their customer loyalty program. There was no special software purchased. We did all the analytic stuff on just an Oracle database using query tools.
Learn more about the models used for customer life cycle measurement and get specific examples of how they work. Tune in to an on-demand webcast with Jim Novo.