Retaining customers has a cost too. Customer churn occurs when customers take their business elsewhere. The naïve reaction is to retain all existing customers as long as the retention costs are less than the CLV. Well, maybe. In the past a low churn rate was the goal. Today, that is changing and businesses are considering intangibles like on-going support for deprecated products and the expectation of on-going discounts in their decisions to “fire their customer”. Likewise, some customer segments will always be “one-time buyers”. If these customers generate razor-thin margins, wouldn’t it be better to allow them to churn if we can replace them with customers with higher CLVs?
Here’s the thing, there is no standard, generally-recognized calculation for CLV. It will be different for every industry and every business, based on the weightings that are important to them. Similarly, the calculation can change over time as you learn new things about your business model and customer mix.
To be fully transparent, most CRMs will provide you with “their” CLV calculation. Be skeptical. Their formula is usually opaque. They market it as “value-added AI” as an up-sell. CLV shouldn’t be a black box AI model. You need visibility to tweak it. Next, a good CLV will take a lot of (if available) behavioral data into consideration. Your CRM won’t have access to that data. Remember that the more data YOU can gather about your customers will aid in not just a better CLV metric, but a better understanding of your customers.
It’s always best to start out with a simple CLV calculation using the “historical sales” approach (using our existing data as our guide). No need to boil the ocean. It starts with revenue estimation. Here’s the process:
All of the above numbers we can gather from our existing sales databases and apply some simple math. Nothing controversial.
Now it starts to get a little qualitative.
Let’s look at an example: Let’s say you run a convenience store and we want to determine the CLV for our morning commuters. Feel free to adjust my assumptions:
That’s the basics.
Now we need to get more advanced in our calculations.
First, segment your customers. Instead of averaging all of your customer’s together to determine revenue per purchase, try segmenting your customers. I’ll cover customer segmentation in another article but let’s just say that segmenting your customers is often a qualitative endeavor. Each “cohort” is designed based on what you think is important. Does gender affect purchasing decisions? If so, that would be a cohort. Or you could segment by geography, avg revenue size, transaction date…basically, whatever you think makes up a good cohort. This is where we can begin to really customize CLV for our purposes.
Second, determine the profit margin. Should you be basing CLV on revenue (sales income BEFORE expenses…aka the top line) or profit (income after expenses…aka the bottom line)? Some companies want to see CLV based on revenue, some on profits. There are pros and cons to each. Revenue-based CLV isn’t affected by changes in costs. If you decide you want a profit-based CLV then just determine your profit margin and multiply the existing CLV by it.
In theory, without factoring in the profit margin we have really just calculated CLR (customer lifetime revenue). The “value” in “CLV” really should be factoring in costs at this point, but everyone has a different opinion on this.
How do you calculate the profit margin?
Remember, the profit margin may be different for each product you offer…you should start to see how the CLV calculation is getting more complex.
But, to keep it simple, here is our updated CLV formula:
Next, churn rate is a factor. If 20% of your customers “churn” during a given purchase cycle, well…we need to know that. You might be able to determine the churn rate by looking at your existing sales data if it is available for longer time periods.
The new formula:
That’s the basic CLV calculation using historical data. There are a few things we can do to make the calculation a bit more advanced:
A good data scientist can look at all of the input data mentioned above and build a model algorithm that determines CLV by fitting regression curves to the historical data.
But, we can really do that ourselves in Excel without ML. Where ML shines is we can add additional data points which would be difficult to model in a straight equation, allowing the ML algorithm to determine the weightings for each of these inputs based on historical data. Examples:
Here are some reasons why this calculation is so important:
It’s easier to describe with an example:
In this example the ROI is $11.50. Said differently, for every $1 spent on this campaign we generated $11.50 in profits over the lifetime of this customer.
Everyone talks about “Digital Transformation”. But what is that exactly? At the Microsoft Technology Center (MTC) we believe Digital Transformation is about monetizing your data…and that simply means we are using data in new ways to make money. Customer Lifetime Value is nothing new, but historically it’s been difficult to quantify that number. The data wasn’t always available, or it wasn’t available in a timely manner. That’s no longer the case in 2021. We can do amazing things with data and compress time-to-value. What took weeks or months to do in the past can now be done in hours if we utilize tech smartly.
Are you convinced you are leveraging CLV to drive your revenue goals? Can you measure your CAC and marketing campaign ROI? We can help you.
The MTC mandate is to be the Trusted Advisor for our customers. We do that by showing how data can add business value. The tech is easy, what’s hard is understanding the processes that work. We can help your people build CLV prototypes in just a few days. We’ll work with you to think through the inputs of a CLV calculation that will add value to your business in your industry, and then help you build it. In 2021 we should be taking more “small bets” and calculated risks with our data projects versus creating months and years-long capital projects with high fail rates. Building CLV models, quickly, by leveraging the Azure Cloud, is perfect to determine if it really is different this time.
Does that sound like a good investment of your time? Contact me or your account team today.
Are you convinced your data or cloud project will be a success?
Most companies aren’t. I have lots of experience with these projects. I speak at conferences, host hackathon events, and am a prolific open source contributor. I love helping companies with Data problems. If that sounds like someone you can trust, contact me.
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Dave Wentzel CONTENT
data science Digital Transformation data architecture