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How Discounts Affect Customer Lifetime Value

Discounts can influence customer LTV but also impact customer behavior patterns. Identifying optimal discount levels will allow you to address both aspects, and deliver the optimal outcome

Knowing your customers is key to effective marketing — we all agree on that. And one of the most common ways to attract new customers and retain legacy customers is to offer discounts.

Once you get to know each type of customer segment better, choosing the optimal discount amount becomes easier. But the question is, what discount to offer and how will it affect your customers’ lifetime value (LTV)?

Different levels of discounts will influence not only the customer LTV directly but also specific customer behavior patterns. By identifying the optimal discount level, you can address both these aspects, that ultimately intertwine, to deliver the optimal outcome.

How to Segment Customers Based on Discount Affinity

The first step towards identifying the effect discounts have is to examine the historical data of your customers. By clustering customers based on their discount affinity, you can view how they react to different levels of discounts, and what behavior each range cultivates.

Although you could divide your customers into many granular segments based on their discount affinity, in most cases splitting them into these three tiers will work:

  1. Very low or no discount customers: Customers who tend to purchase due to necessity or impulse, and not necessarily as a result of brand familiarity.
  2. Medium discount customers — “Charmed Customers”: Customers who are charmed by your brand and will potentially become your loyal customers. They are less conscious about maximizing discounts but appreciate the gesture (enough that even a small discount could encourage them to make additional orders).
  3. High discount customers — “Cherry Pickers”: Customers who enjoy high discounts and will rarely purchase when discounts are low. They usually purchase during high-sales and holiday seasons.

The Effect of Discounts on New Customers

My firm, Optimove, analyzed the likelihood of a new customer making an additional order based on the discount claimed on their first order. Results showed that the possibility of a newly acquired customer making subsequent orders and developing brand loyalty is significantly higher when the discount claimed on the first offer is between 5% and 20%.

The same research showed little to no difference between offering a 10% or 20% discount on customer’s likelihood to purchase again and their average future value. While providing high discounts may increase the number of orders you generate, it will typically lower the average order value. Counterintuitively, it also tends to reduce customer LTV.

The Effect of Discounts on Customer LTV

When further examining the average future value of customers based on their discount affinity, you can see that offering a medium-sized discount can increase the average future value of a customer by 20% to 25%, compared to offering high or no discounts.

Furthermore, the average future value of customers who receive no discount and those who receive a high discount is nearly identical. So, are high discounts even necessary or desirable? Probably not.

One retailer divided its customers based on the following discount range:

  • 0%–5% discount — very low or no discounts
  • 5%–10% discount — medium discount
  • Above 10% — high discount

The graph below demonstrates the average future value and customer LTV of customers based on their discount affinity:

Leverage Discount Affinity Data to Increase Customer LTV

Although we’ve seen that offering high or no discounts is not ideal for customer LTV, there are ways for marketers to utilize this data in their marketing efforts:

  1. Invest in brand awareness: Customers who claimed high discounts shouldn’t receive campaigns offering them high discounts on their next orders. Instead, these customers should receive campaigns that educate them on your brand, creating brand awareness to encourage full price purchases.
  2. Prioritize marketing efforts: By grouping customers according to discount affinity, you can evaluate which customers are worth the marketing investment. Channel more of your resources towards your “charmed” customers who are likely to be more valuable to your brand in the long-run, generating higher average future value.
  3. Normalize discounts: While you shouldn’t get into the habit of offering high discounts to acquire new customers if you do choose to, you should normalize these discounts going forward by gradually reducing the discount range without eliminating it.

Our research showed the likelihood of making additional orders increases with every subsequent order. By gradually reducing discounts, you encourage the customer to make other purchases, while slowly educating them to purchase with optimal discount levels.

Remember: Less Is More

As in many other aspects of life, less is more. To create brand awareness and loyalty, you want to be selective with the discounts you offer to ensure you’re encouraging brand-positive customer behaviors.

Our research identified the optimum discount value as 5% to 10%, so hovering around that range whenever possible will probably be most beneficial to your brand.

Furthermore, it’s essential to realize that while offering high discounts and lower prices can bring an influx of customers. They will not necessarily be the customer you want to invest in. As seen, driving high customer LTV counterintuitively requires moderate incentives.

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    Moshe

    How did you check the likelihood of a new customer to make an additional order based on the discount claimed on their first order?

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      Pini Yakuel

      Hi Moshe,
      Thank you for the question. I’m glad you found this interesting.
      To identify this likelihood, we performed a historical analysis of all customers for a number of brands, identifying their first purchase, the discount level claimed at that purchase and whether each customer returned for a second purchase. By aggregating the results of the analysis you can identify an average across the industry or for each particular brand.
      Best regard,
      Pini

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