Influencing CAC and AOV for better customer retention and sales

– The author does not like using CAC:LTV as a metric and instead focuses on CAC:1st time AOV.
– To influence CAC and LTV, the author suggests understanding margins, creating offers for paid channels, boosting conversion rates, and matching price to experience for customers with an OK or poor experience.
– The author believes that predicting LTV accurately is difficult due to external factors that determine product use, and instead emphasizes the importance of focusing on the customer journey and building positive experiences.Focus on building the best experiences on websites and through channels to allow the most people possible to feel good about making a purchase.I don’t like CAC:LTV.

I know how to influence both, but I don’t use it as a metric.

Instead, I focus on CAC:1st time AOV. Yes, it’s a blend, but it’s a blend that tells me more than LTV does. So, here’s a primer on how to influence these two things. Understand your margins. Create an offer for a paid channel, boost your conversion rate, and lower your CAC. Get more product into hands. For LTV, if someone has a good experience and need, they will come back on their own if you remind them you exist. For those that had an OK experience or poor experience, it’s usually a large sale that has the best chance of bringing them back. They mentally reduce the value of the product based on their experience, and you’re just looking to match price to experience for them to make another purchase. “The sandwich wasn’t worth the $10 I paid, but I’d pay $5.”

So, here’s some background on how hard it is to really nail down LTV outside of the above sandwich example. I worked for a smart lightbulb company. We had data from orders, we had data from requiring an account in our app, we had data in the cloud per account, per device, per usage, per functionality, per settings, everything. This product is something used every day at certain times of the day, so we can track usage patterns over time by account (email), by location, etc. Most marketers that hype up CAC:LTV could only dream of the data we had around usage, naming, locations, errors, etc. I also worked across customer support, so I talked to and communicated with a lot of customers, tens of thousands over 3+ years. Our products were WIFI, though they were all similar, people purchased our products for different reasons. I’d also hear about different issues with different setups all the time too, buildings, homes, routers, light versions, firmware, etc. We had millions of data points. Although we delivered the same product to everyone, the reasons they purchased it, the use cases they envisioned, and the experience of ownership were all going to be different and out of our hands. The same is true for all of your products. If you sell a swimsuit, it might be months before someone uses it. What’s great for lounging by the pool might be terrible for rough waves.

My point is, there are too many factors that determine the use of a product that are external to the actual product to accurately predict LTV.

Note: We sent Over the Air updates too, they just weren’t guaranteed to improve everyone’s experience as it was too nuanced with setups. But then I realized with all the information we had access to and all the analysis we could do coupled with actually talking to thousands of customers a year, we couldn’t find a pattern.

This is honestly why I work so hard on the customer journey and focus on building the best experiences on websites and through channels to allow the most people possible to feel good about making a purchase. Usage, experience, value, and need determine repeat purchase. #ecommerce #strategy

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