Customer loyalty is an idea that’s as old as the business itself. Everyone knows that having loyal customers makes fundamental business sense, and everyone talks about it a lot. But a question often bothered me: how do we categorize a customer (or user) as loyal or not?
The answer is not in customer behavior alone.
Some of you may think that it’s a very trivial question, and your answer might be something like this:
I will look into the user’s relationship history with my company and decide whether they can be called loyal or not. If they have been buying a lot from me, for instance, they are loyal. Or, if they’ve been engaging with the mobile app a lot, in such a case too, they are loyal.
In other words, you rely on customer behavior to decide whether a customer is loyal or not. Often this is the approach businesses take. While it makes sense and sounds obvious, it leaves a couple of questions unanswered.
A) Is the past behaviour a guarantee of future behaviour?
- What if your business changed its ways/policies of dealing with customers over time (intentionally or otherwise)?
- Just in case, what if your products have been changing over time in little or significant ways, as they always do?
- Similarly, what if your competitor released a powerful product that suddenly attracts your customers?
- What if you had a PR disaster?
As you can see, there are many factors at play here, which may or may not be in your direct control.
B) What about the customer’s emotions (intent)?
Being human, customers aren’t always rational and do make emotional decisions. Behavioral economics has done an excellent job by teaching us that our classic economic model of a customer as a rational, profit-maximizing creature is not entirely accurate except in the case of B2B where decisions are relatively more logical than in B2C.
A customer’s intent can’t be deduced or inferred from their behavior. Think when a company’s customers are disgruntled with their products but put up with it as a necessary evil due to lack of alternates.
The complete picture: the customer loyalty equation
The point I’m making is that behavior isn’t enough to categorize a customer as loyal confidently. It needs to be corroborated with the customer’s emotions towards your business.
This is because, as humans, our behavior isn’t always synchronized with our thoughts and emotions. We can think one way while acting very differently, even contradictorily, and we usually have excellent reasons to behave in this manner.
Think of it as a simple equation:
Customer Loyalty = Past Performance of Desired Behaviours + Intentions To Continue Such Behaviour In the Future.
Both positive behavior and positive intent are needed to categorize a customer as loyal.
The three dimensions of customer loyalty
Loyalty can be detected in any customer by tracking three critical dimensions of behavior & their corresponding intents.
DIMENSION 1 – RETENTION Behavior: Is the customer using your product or service continually, or not?
Intent: What’s the customer’s response to the question: How likely are you to continue using our offering? (0 – Not at all likely, 10 – Extremely likely)
DIMENSION 2 – ADVOCACY Behavior: Is the customer advertising your offering in their circles in ways like referrals, sharing, participation in open customer forums, word of mouth, etc.?
Intent: What’s the customer’s response to the question: How likely are you to recommend us to your friends, colleagues, and family? (0 – Not at all likely, 10 – Extremely likely)
DIMENSION 3 – REPEAT & DIVERSIFIED PURCHASE Behavior: Is the customer buying more of the same stuff, and are they trying out new offerings from you?
Intent: What’s the customer’s response to the question: How likely are you to buy more offerings from us in the future? (0 – Not at all likely, 10 – Extremely likely)
Challenges in tracking loyalty & what to watch out for
Of course, the behavior is tracked by user analytics & transaction data, whereas customer intent is obtained via targeted surveys.
Commonly, I’ve seen companies use different tools for each of these purposes, but they always run into a problem. Although when confronted with it, most of them deny that it’s a problem. What are we talking about here? It’s that they waste enormous time and effort in-.
- trying to bring together the analytics and survey responses from their diverse toolsets, then
- to make sense of the joint information
- and to create actionable customer segments on whom they can plan appropriate marketing activities
Such exercises are long, tedious, and a lousy use of one’s time. A useful, powerful tool such as Upshot.ai will let you track user analytics and survey responses in a single place and store all of the user’s information (clickstream + survey responses) in a unified profile.
Why does this matter?
Well, you can use all that unified information to run powerful segmentation models and partition your customer/user base into smaller segments. Finally, you can plan what kind of marketing treatment to give to each segment and run those experiments from within that same tool. It’s hard to beat convenience at this level!
Example, customer segmentation & how to market to them
Using the above three dimensions of loyalty, we can arrive at hypothetical segments such as:
Declared loyal users:
- # of product uses per month >= 5
- Intent to be retained >= 8
Buyers about to churn:
- Total purchases so far >= USD 200
- Intent to buy more products < 5
Regular users that turned bitter:
- # of product uses per month >= 5
- Intent to be retained <= 4
- # of referrals per year > 5
- # of referrals in last year = 0
- Intent to recommend = 2
To each of these hypothetical segments, you can create appropriate plans of action such as:
- Declared loyal users: you can send out personalized thank you emails, with special discounts on new products that you’d like them to try.
- Buyers about to churn: you can call them directly and find out what’s going wrong, then (this is pretty important) close the loop by telling them what you’re going to do to give them a better experience in the future.
- Regular users that turned bitter: you can dig deeper into product logs insights and reach out to them to gather full context for their bitterness towards your brand. Possible hypotheses for their negative attitude:
- Product not delivering on its promise
- Rough or rude service experience
- Inability to get through to support
- Consistent product troubles or breakdown during usage
After gathering this information, you can involve product and service teams to determine what happened and ensure the root causes are identified and fixed for good. You can then reach out to these users with the fixes you have put in place, and as an apology, you can give them free subscriptions or some other privileges to attempt to reactivate them.
About Upshot.ai :
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