You are wasting retention advertising dollars, and you might not know it. Marketers often look for low cost per click (CPC) and high conversion rates on their paid search and social advertising campaigns. These numbers look great on reports and seemingly showcase a successful campaign.
We certainly don’t recommend ignoring these metrics, but basing campaign success only upon them is inherently flawed. If you are targeting your ads to a wide audience, you are likely advertising to existing customers, including your highest value customers. These “best” customers are already highly engaged with your brand, very likely to purchase again, leave positive reviews, and so on.
So the question is: Would these high-value customers make another purchase even if they hadn’t seen your ad? The answer is likely yes.
Stop Wasting Ad Spend
Consider a customer that makes a purchase from your beauty brand. Let’s call her Tina. She made her first purchase after seeing an Instagram ad and then visited your website to purchase two days later. Tina leaves a positive review on your website and shares the new makeup with her Instagram followers. Perhaps you split the sale attribution between Instagram ads and organic search.
A month later, Tina is running low on her makeup products and makes a mental note to repurchase. She gets targeted by another Instagram ad that reminds her to buy. She makes the purchase, and you attribute the sale to Instagram.
But did she need the ad to push her to purchase? Probably not. Should you stop retargeting ads? Not necessarily.
Imagine a second customer, Karen. She purchased perfume from your brand after seeing a discount ad on Facebook. After a few months, she makes a makeup purchase with a 20% off coupon from an Instagram ad.
The difference between Karen and Tina is that the former would not have purchased again without the retargeted ad. Karen is a lower value customer that is more price and promotion sensitive.
Determine the Value of Your Customers
The next question is: how do you know which customers are high value? Which customers will purchase again without a promotional ad?
You can stop wasting ad spend by calculating the customer lifetime value of each of your customers. To make this metric actionable, your data science team should choose a model that predicts CLV early in the customer journey with low individual error rates.
CLV models take into account numerous customer behaviors and product features that may affect CLV. With the example of Tina and Karen above, these characteristics might include:
- Purchase category: makeup, fragrance, etc.
- Purchase made via mobile or desktop
- Coupon or discount used
- Positive or negative product review
If available, CLV models can also take into account information about customers. For a beauty brand, this might be:
- Skin sensitivity
- Skincare concerns
- Hair type
Not all of these variables will impact CLV, and some may be present in both high and low CLV customers. In our example, both Tina and Karen purchased after seeing an Instagram ad, so that channel may not impact CLV.
However, the model might reveal that purchasing makeup first, as opposed to fragrance, is associated with higher lifetime value customers. In that case, it makes sense to promote makeup products to prospects.
For an overview on how to calculate customer lifetime value, download our whitepaper.
Change Your Ad Strategy
Once you know the customer lifetime value of each of your customers, you can adjust your advertising strategy accordingly. Based on our example, here are a few recommendations:
- Stop retargeting ads to your highest value customers (like Tina). They will likely convert again without an ad or promotion.
- Instead, use email as a channel to engage with high value customers to save costs
- Retarget your mid-level LTV customers to incite a repeat purchase
- Create lookalike audiences based upon your high value customers and target that group with social media and display ads
You can exclude current high-value customers from your retargeting ads if your model predicts that they will make another purchase. For example, if your CLV model predicts that Tina will repurchase, consider excluding her from ad campaigns. On the other hand, Karen has a low probability of repurchase, so include her in retargeting campaigns soon after her first purchase.
Because this strategy requires different tactics based on individual customer lifetime value, it’s important for your data science team to choose a model that predicts CLV at the individual level, not at the cohort or aggregate level.
Ultimately, the best strategy is to acquire the right, high-value customers in the first place.
Once you’ve removed wasteful ad spend, it’s important to keep testing. Find out what ads resonate with your high value lookalike audiences. Tailor your ad copy and design to attract high CLV customers, instead of producing generic ads that will appeal to a wider audience. Update your CLV model with new behavioral and demographic data from your customers.
As your business grows, keep track of your customer journeys. What paths are high CLV customers taking? What types of products are they purchasing first? What ads are they engaging with? A sophisticated customer lifetime value model can track and learn from these changes. If you need help implementing a model like this, we’re happy to chat.
At Retina, our mission is to help you connect the dots from individual customer lifetime value metrics to advertising strategy. Chat with us about how we can help your business eliminate wasteful ad spend and optimize your advertising campaigns with customer lifetime value.