In this post we do a deep dive on what LTV is in marketing, and how to leverage it to improve performance.
🛍️ What is LTV in marketing and does a customer's "lifetime" ever really end?
Customer Lifetime Value or LTV in marketing is your customer’s total value to your business. In the traditional world of eCommerce, we think of this in terms of revenue. “How much revenue has this customer spent with us since their first purchase?”
But, LTV can and should be thought of as more than just revenue. If you start to think about a customer’s “worth”as it relates to your business, a few metrics come to mind:
NPS score: How many of your customers have participated in an Net Promoter Score survey? How many are Promoters, Detractors, or Passive customers? Promoters can be worth much more to your business than can be attributed back to their order history.
Product Feedback Participant: Which of your customers have been active in giving feedback on future products or the products they currently own? Feedback like this can be worth its weight in gold if utilized effectively.
Returns & CX Tickets: This is more of a “negative LTV” metric, but does this customer regularly create tickets with the customer service team, and/or frequently return items?
♻️ Will you really have to wait a “lifetime” to measure customer lifetime value?
No, and you shouldn’t. When you hear the word “lifetime”, you tend to think about a beginning and an end. However, a customer’s relationship with your brand may never really end if you can keep them coming back in perpetuity. Which is why it’s important to focus on 30, 60 & 90 day LTV metrics. Let’s dig into the all important question, why?
Why we rely on 30, 60, 90 day LTV (projections and average historical data) to make decisions today?
Your business needs cash to survive. You don’t need me to tell you this but cash pays the bills and provides you with leverage to grow your business.
Increasing both the percentage of customers that come back to reorder, as well as the frequency in which they do so increases your cash position and can mean the difference between running a great business or going under.
Key metrics that you’ll want to pay attention to:
Percent of orders from repeat customers: This metric can help you understand the health of your retention marketing efforts and be a leading indicator for how good your product is perceived or how well it solves your customer’s problem.
Frequency in which customers purchase: How long does it take your customers to make that second purchase? Third purchase? Understanding the average time between orders ( 👈 more on this in a future Whale Mail) and where customers drop off can help you make impactful changes to your marketing efforts in order to speed up that second purchase and keep customers longer (think, dynamic post-purchase flows).
Marginal Value: How much additional value, on average, are your new customers worth after their first purchase?
An easy way to do this in your 60-90 day LTV analysis is to simply subtract the two data points from each other.
Additionally, you can find these values in the “LTV Cohorts” tab (under Analytics) within your Triple Whale dashboard.
Cumulative Value: How much total value (add the marginal value above to your AOV) are your new customers worth over time (since they were acquired)?
How much value can you expect your customers to be worth on average, 90 days after they first ordered from your store?
Understanding all of these metrics for your store will literally keep your marketing team busy in perpetuity. Once you can measure these data points (👋 hello, Triple Whale), you can start improving them.
🧑 LTV in marketing examples
Here are a few examples how you can use these data points in your decision making:
How much can you afford to spend on new customer acquisition? Are you okay with breaking even on your customer’s initial order, because historical data tells you they will be profitable within 30 or even 60 days?
Subscription businesses can typically afford to acquire customers at break-even or even a loss on the first purchase if their retention is strong enough (think about how much you’ve spent on your Netflix subscription over your “lifetime” as a customer 🤯 )
Are you serving your customers the right post-purchase messages based on the first product they purchased? Tailoring this message to specific cohorts of customers could result in more customers making that second purchase (frequency).
Are the customers you acquired last holiday (Black Friday/Cyber Monday) more or less valuable to your business compared to those acquired in other months? Are your Black Friday deals attracting the wrong type of customers?
Once you have these data points (found in Triple Whale, of course) start listing out questions you want answered for each of them.
Then, turn those questions into hypotheses -> which turn into tests -> which yield learnings for your business (note that I used the word “learnings”, because running tests doesn’t always mean you get the results you want(ed)).
.png)
