Portrait of author Catherine Frame
Catherine Frame

Customer Intelligence Lead

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How to deal with bad customers

By Catherine Frame on April 18, 2023 - 5 Minute Read

You've read countless articles on finding good customers. Great. Everybody wants them. But what if finding good customers isn’t the only thing that matters? What if bad customers are just as important? How do you find bad customers? And what do you do when you find them?

Remorseless returners, constant complainers, late payers. These are some of the behaviors that might come to mind when we think of bad customers. These customers can cause a lot of trouble for most businesses, but the truth is there is no universal definition of a bad customer. 

A bad customer is simply a customer that’s bad for your business; a customer whose behavior holds your business back from achieving its goals. In this article, we’ll tell you how to identify and act on customers that are bad for your business. 

So how do you identify the customers holding your business back? The first step is truly understanding your business.



What’s your business about?

A bad customer is one that gets in the way of us reaching our targets. So if you want to find your bad customers, you’ll need to know what our business targets are and how you’re tracking against them. These business targets should be specific and measurable.

For example, you might look at things like: 

  • Revenue
  • Transactions
  • Profits
  • Repeat purchase rate
  • Average order value
  • Cost per acquisition 
  • Customer acquisition cost
  • Lifetime value
  • Net promoter score
  • Return rate

Let’s begin our search for bad customers. We’ll start with an easy target, like profitability. Let’s say your business has a profitability margin of 40%. To find your bad customers, you’ll need to look for customers who are getting in the way of delivering this target. Here’s how. 



Bad customer #1: Fails to Pay Philippa

This is Fails to Pay Philippa. Philippa loves shopping, but hates paying — and Philippa’s not alone. She’s part of a whole segment of customers who are nowhere to be seen when the bill comes. No business wants Philippa and her spendthrift friends as a customer. 

Fails to Pay Philippa has got what she wants and she’s on the move.

Thankfully, customers like Philippa are pretty easy to find. Every business has a list of customers with a credit account, one that flags whether or not payments for their orders have been settled. So, you know where to find them. But what should you do with them? 

For starters, I wouldn’t suggest offering Philippa credit in the future. Make sure Philippa pays up front (she’s shown she can’t be trusted with a credit line). So, bad customer found and dealt with. Easy. In this case, Philippa is a straightforwardly bad customer, one that no business wants. But not all cases are this cut and dry…



Bad customer #2: Coupon Karen

Let’s look at another example, those customers hooked on discounts and coupons. Customers like Coupon Karen.

Coupon Karen

Coupon Karen smells a discount, and she’s not buying until she gets one.

Karen is keen to buy, but she’s a master negotiator. You’ll have to budge on price before she shows you the money. Karen’s highly engaged, regularly searching through your range, saving items for later and scouring every one of your marketing channels for sales or price-cutting coupon codes.

You’ve been sending her great content for weeks, but it seems like she just won’t buy. It’s clear Karen’s not blinking first. But then, as soon as it’s sale season, Karen whips out her credit card and puts her plastic to work.

Finally! You’ve made a sale and Karen’s bagged her discount — everyone’s happy, right? Maybe not. Customers like Karen have a lower lifetime value and increase your cost per sale because they need so much persuasion to purchase. 

But does it really matter? Money’s money, right? Unfortunately, it’s not that simple. Relying on discounts and sales has consequences for customers like Karen. You see, she hasn’t always been this cut-throat about cut-prices. 

You created the discount diva that Karen is today. The discounts you’ve been using to trigger a purchase from Karen got her hooked on the thrill of thrift. It’s changed her perception of your brand. Before, she’d be happy to pay RRP if she saw the right product — but now she feels ripped off unless she’s getting a discount. 



Bad customer #3: Randy the Returner

Next up, meet Randy the Returner. Randy loves shopping with you, but he returns 90% of everything he orders.

Randy the Returner can’t wait to clog up your supply chain with returns.

Let’s say you’re a fast fashion retailer that relies on a high volume of low margin purchases. It may cost you more to administer their returns than you bag in profits for the few items your customers keep.  

But, what if you’re a high-end fashion retailer that targets affluent customers? You’ll rely on fewer transactions at a high margin — and the profits earned from the high margin items those customers keep may far exceed any administrative cost of return? 

This is why knowing your business inside out is so important. A certain customer might not be ideal in the long term, but they might give your business the boost (in this case, cash flow) it needs right now. 

The key here is to know what your business needs now, and to understand which customers are delivering or distracting from it.


Segmentation: the bad customer search engine

People look at segmentation as a tool for marketing personalization and they’re right, it’s key to nailing personalization. But segmentation can do so much more than enable highly-targeted, personalized emails and ads. It can help you find your best and worst customers.

Segmentation tools that use standard approaches, which look only at predefined metrics (e.g. number of purchases or age) can’t quite get you there. Segmentation needs to incorporate a broad range of behavioral, transactional and historical attributes to help you find (and fix!) your bad customers. 

As an example, let’s catch up with Randy the Returner. We said different businesses might deal with him differently. But not every returner is created equal. Some will have a higher lifetime value and cost more to initially acquire than others.

That might mean losses incurred from their return frequency are outweighed by the value of retaining them as a customer. In other words, the short-term losses incurred by their bad customer behavior might be tolerable now, if they could be highly profitable in the long term. 



How to banish bad customers 

Why do we need to look for bad customers? There are two reasons; either we want to turn them into a good customer, or we want to prevent them from interacting with us in the future. How we deal with customers needs to be as data-driven as the approach we use to find them. 

Sometimes it can be our processes or policies that are actually making customers bad. Let’s return to Coupon Karen. Why does she need a discount before buying? 

The first place you could look is churn risk. Are you applying a single churn risk threshold across your entire customer base? If you are, you could be creating your own Coupon Karens. Churn risk should be based on a specific customer segment. You don’t need to act if a customer’s not made a purchase after an arbitrary period. Instead, you need to act when they fail to purchase within their usual buying pattern. 

For instance, Karen might’ve previously only made a purchase from you every six months.. But you’ve got a blanket business rule that says anyone who’s not purchased in three months is a churn risk. 

So you make sure Karen’s first in line for a coupon code, and it works — she purchases! But she might’ve made the same purchase at RRP only three months later. You’ve repeated this cycle and, eventually, she’s come to expect discounts, and she won’t buy without them. 

How about Randy the Returner — why is he returning so many items?

If we create a segment of customers who purchase specific SKUs across multiple sizes, we might find that the true cause of their returning habit is inconsistent sizing practices between the brands you stock. 

Without this insight, you might decide the best way to deal with the problem is to charge your customers for returns, or penalize customers who return items frequently. But this may cause valuable customers to buy elsewhere.

If we know size variability is causing returns, we can try another strategy. ASOS is a great example of this problem. In 2018, ASOS was experiencing a meteoric rise. But with the fashion market exerting downward pressure on price, its profitability was taking a hit.

One factor impacting ASOS’ margins was the high rate of returns. After looking for answers in the data, ASOS discovered size variability was generating legitimate customer returns; customers often found their usual sizes were either too large or small, depending on which brand they bought.

To tackle the issue at its root, ASOS launched Fit Assistant. Fit Assistant uses artificial intelligence (AI) to learn customer size using the customer’s previous retained orders as data. It then advises customers on what size will fit them in each brand.

‘Fit Assistant’ from ASOS

It was a huge win for ASOS and its customers. Customers were more likely to get their size right the first time, meaning less frustration for customers and fewer costly returns for ASOS. 

The truth is that there are always going to be tradeoffs when it comes to business decisions. The key is to tip the balance of those tradeoffs in your favor. It’s important not to just understand who your bad customers are, but why they’re a bad customer. You might be surprised at what’s really driving bad customer behavior.



Beat bad customers with game-changing AI segmentation

So how do you find and fix bad customers? The answer is in your data, and the most efficient way of looking at your customer data is through segmentation. But often segmentation tools are built into existing marketing platforms (e.g. social media or email marketing platforms). To truly understand customer behavior and take strategic decisions, you need to see their actions across the whole customer journey. 

Peak’s application, Audiences, can combine customer data from several sources or directly from your customer data platform (CDP), to give comprehensive segments across your customers’ entire journey. 

It uses AI to give you highly accurate customer segments based on not just historical data, but AI-driven predictions of their future behavior. 

If you want to learn more about how you can get started, you can read about the concept of ‘headless segmentation’ here. Or book a call below, and see how Peak can help you get started. 




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