#customer lifetime value model

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Comprehensive AI-powered marketing cloud solution that can automate upsell, cross-sell and retention campaigns across digital channels

Comprehensive AI-powered marketing cloud solution that can automate upsell, cross-sell and retention campaigns across digital channels

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Customer Lifetime Value (CLV).

When you manage customer lifetime value There are four types of processors you need to keep track of. The first system measures you need to measure sales margin, but there's one additional factor. Potential sales.

The potential sales is the expectation of what an individual customer will achieve over their lifetime, aggregated up into segment codes or any other kind of measure you wish to have. That is important because you want to make sure you hit a journey of that customer and achieve that customer lifetime value and not lose them halfway through. When you plot your information, which is on a time on the X-axis and customer lifetime value on the Y axis, you have a percent contribution by the customer, a couple of groups that you plot.

When you plot that you will get something called inflection points. Those inflection points describe or indicate a difference in behavior.

That behavior can happen due to a number of reasons and those reasons explored by investigating data so that's the second thing is a measurement and looking for variations. That variation can be due to gender. It could be due to a city, a state or purchase pattern, or a product or purchase that they happen to acquire from you. One of the important variables to measure is also the year of acquisition. The year of acquisition is because the rate at which they progress towards the customer lifetime value is going to depend on the age that the customer has been with you.

So the longer the number of years, the contribution towards the lifetime value is going to either diminish or accelerate based on the behavior you're tending to see. The third aspect you're trying to measure is recency, frequency, and monetary. Now you can make this complex, so recency is the recent number of days of this shop with you, the frequency, the number of times the shop of your year, and the monetary amount they spend with you over a year. Now you can make that into a three by three by three matrix.

That means you divide your customer base into 27 cells.

Each of those cells is going to have a CV characteristic that TV characteristic and potential sales can be achieved with various marketing strategies or tactics. You can also get more complex. You can add complexity by going out a five by five by five made. We have customers that want to do that. That's 125 cells.

So please manage the number of sales. You want to manage the number of clusters you want to manage of customers with some things that you can actually execute. From a marketing point of view, there's no point in managing clusters and groups of customers if you can't market to them and achieve a significant result. Now, when you add to this complexity, things like segmentation, you're going to have to add new processes like an artificial intelligence program. Because Artif Intelligent is the only one that can take your recency frequency monitory CLV and all the customer segmentation to throw at it to figure out what material differences and where to inflection points come that define through data which customers are unique.

The last item to take care of manages attrition. So when you lose a customer, it's important to understand why you lost them, but it's also important to understand how much once you lost sales. Now your lost sales are equal to the potential sales for that customer minus what the CLV was at the point it turned out, that is a truly lost sales. When you have truly lost sales and you match it over the segmentation, you can figure out where the difference comes from and what the real lost sales are.

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