Part 2 of a 3-part series on customer retention.
Keeping the customers you've worked hard to acquire is essential to your company's financial performance. Currently, there are various approaches to customer retention. Though attracting new customers will always be of critical importance, it is 6-7 times more costly than retaining existing customers. The benefits to retention are obvious. However, the best approach is not.
Many loyalty and retention programs exist. Though customer loyalty and retention technically are different, the programs themselves often tend to have the same goal of retaining more customers. Typically, there is a level of merit to each type of program or tactic and some degree of success as a result. Companies often use a combination of tactics, which is wise. Premiums, discounted prices, and rebates for renewals are common. Unfortunately, some are not deployed effectively and many are not truly data-driven. Worse yet, companies sometimes lose money unnecessarily, have trouble measuring results, and attribution is unclear.
Here are a few examples.
1. Point System
One common example of a loyalty program is a point system. Customers earn these points with each purchase to redeem them later for discounts, free products, or other rewards. Examples of these can be points associated with miles flown with certain airlines, frequency of transactions with banks, or tickets tracking burritos purchased at a specific burrito chain. The reward can be a free trip, cash back, or a free burrito, respectively.
A point system can be effective in some ways for a number of businesses, but it is less than ideal for others. It is best suited for companies with many small transactions per customer or many points are awarded at each purchase. If you're marketing a local restaurant in a small town, this might be one of your best options. For many others, it's not.
The reasons for ineffectiveness can vary industry to industry, business to business. Some companies may not track how many points each customer has and collect no other data. As a result, these companies gain little to no customer insight. Other companies may track these points very closely and gain insight into customer preferences, but without further analysis they have no idea of whether a customer will likely defect to the competition for a lower price.
Companies can incorporate a point system into their retention programs, but will fall short on their ability to predict defectors. Prediction takes additional data and analytics.
2. Customer Service/Experience
Good customer service and experience is another way that companies try to retain customers. You would be hard-pressed to find a good company that does not regard customer service and experience as critical. Clearly, ensuring customers receive good service from you will help you keep them and make you more profitable. In fact, companies that prioritize customer experience are 60% more profitable than their competitors.
Unfortunately, some customers may be happy with the customer service and experience, but still leave for another reason. These could include price sensitivity, perceived value, or life events, to name a few. If they don’t tell you, then you simply don’t know if they are at-risk for defecting.
This technique also does not take into account customer profitability or Customer Lifetime Value (CLV).
3. Broadcasted Special Offers
Another approach companies use is creating special promotions where they offer discounts to their customers for service renewals. Magazines that offer subscriptions costing $30+ per year may promote special offers of $5 per year for a two-year renewal. This can be very effective for a company. However, it can be costly if executed in a sub-optimal manner.
The shortcoming here is a company can waste money offering this type of promotion to too many of the wrong customers. Extending a discount to every customer would be wasteful since the most loyal customers would stay anyway. Therefore, a non-targeted offer would yield low ROI.
Many marketers use simple segmentation for these efforts. For example, a customer of Service A who has been a customer for three years will get a different renewal notice than those of Service B who became a customer a year ago.
This is a step in the right direction, but still runs into the same problem—treating people who are likely to stay the same as those who are likely to leave.
Broadcasting the same special offer to everyone or even everyone in a specific segment is ineffective. These approaches do not reveal the customers who are most at-risk for leaving. Offering the same promotion to customers likely to stay and those likely to leave wastes resources and decreases marketing ROI.
4. The "Do Data Science without a Data Scientist" approach
As technology, Big Data, and Predictive Analytics advance, more and more are entering the Marketing Technology (MarTech) field. They focus on things like customer acquisition and retention. The technology is remarkable. Of those in this sector, many add true value to other businesses, offering software for statistical modelers to use. Some also make bold claims about their software that sound too good to be true.
Oftentimes, that’s because they are.
Some businesses that create and sell predictive analytics software and services sometimes claim they offer a solution that anyone can use to get the best results possible. Simply upload a file, click a button, and their modeling software will return you a scored file. They claim you don’t even need a statistician or data scientist. The honest ones will tell you there are shortcomings with this approach.
In some cases, your results will improve. In others, they will not. In fact, they can possibly be worse. It then comes down to how much you are willing to risk being wrong.
Though convenient, self-service predictive analytics software and generic models ignore the art of modeling. They do not account for consumer behavior or cultural nuances. They have no business background or marketing experience. They will not understand your strategic plans or specific goals. They can easily “overstate” a model, leading to inaccurate results. The “machine” cannot ask “why.” The individual working with the software needs to understand the right questions to ask.
What this means for you
Many companies focus on customer experience, service, and special offers and other tactics to retain more customers. All can be beneficial, but will fall short in effectiveness and targeting the right customers. This can result in avoidable financial losses.
If you want to improve customer retention, improve customer loyalty. To improve customer loyalty, focus on your individual customers, not your program.
As Seth Godin put it, "Loyalty can be rewarded, but loyalty usually comes from within..."
Predictive modeling allows marketers to use data and proven scientific techniques to retain the right customers. It enables you to understand the level of loyalty of your individual customers. It is also measurable, scalable, and well established in direct marketing.
If you haven't already, read the first part of this 3-part series on customer retention. Also, be sure to check out our next blog post on customer retention where we'll talk about how to use predictive modeling to target the right customers for retention.
If you're interested in learning more, download our special report by clicking the link below.