Using customer lifetime value to overcome a growth plateau


David Howlett

Data Solutions Architect


Analytics and Data Science

As businesses continue to optimise towards transaction revenue, eventually they reach a plateau in performance. Targets become stagnant and it becomes increasingly difficult to justify increasing media spend to drive further business growth.

 

This is often the result of the digital marketing strategy not being aligned to the fundamental growth drivers of the business – profit, new customer acquisition and customer retention.

 

Lifetime value (LTV) is key to solving this performance plateau and enables businesses to pivot their digital marketing strategy to investing not only in new customers, but the most valuable and profitable customers to drive long-term business growth. This heightened, longer-term perspective is not visible when optimising towards transaction revenue alone.

 

Once LTV has been implemented into your digital strategy, you’re no longer restricted to investing based on a singular purchase and may find that some products are best suited to loss leaders or have unseen profitable growth value.

 

Moving away from transaction revenue highlights the importance of looking at individual product metrics. Every product has a very different value to the business that cannot be illustrated simply via transaction revenue and can easily become exploited when implemented into your wider strategy.

The lifetime value solution

LTV calculations enable businesses to calculate the expected value of a new customer over periods of six, 12, 18 months, or whatever is right for your business. Many businesses often calculate what an average new customer is worth, although struggle to inject this into their marketing strategy which forms a disconnection between the business growth objectives and optimising their digital strategy effectively.

 

Fundamentally, your digital marketing team can’t effectively optimise towards a LTV if there is no way for them to calculate it within their platform. For example, tracking within Google Analytics (GA) if a sale is via a new or returning customer or dynamically calculating LTV at the point of sale, and pushing that value back through into GA.

We recommend starting simple and working your way towards more complex and robust modelling solutions, whilst nurturing a greater understanding of your audience.

 

Calculating the average new customer value and what percentage of sales are new customers allows you to instantly input LTV into your digital marketing strategy. You’re then able to sense check if there is room to invest more and grow if you were to use LTV.

 

The next crucial step is to determine key factors that impact LTV and exploit them. Generally, we want to avoid sweeping averages because it removes the ability to find opportunities.

Getting the most out of LTV

KPIs are only valuable when they enable you to segment data and find opportunities. For example, revenue enables investment into lower-funnel activity but becomes less effective as a KPI as you move up the funnel.

 

The real power of LTV comes when you segment by product and specifically basket composition. For example, the average 12-month LTV of a customer may be £80, but for customers who bought expensive shirts, it is in fact £140. That same LTV calculation will likely change again if you consider combinations of products bought together. Customers whose average basket value is high may ultimately have a higher LTV.

 

Your customers are giving you all the data you need to effectively score them and estimate their future value simply by analysing what products they look at, what they purchase, the value of their basket and how frequently they purchase.

 

Determining LTV based on product and basket composition enables your digital marketing strategy to target new customer growth by product and enables targeted investment into your most valuable customers.

 

Ultimately, it is possible to calculate the LTV of every individual customer based on their individual user journey through the application of machine learning. This enables you to value your existing customer pipeline and future customers who have shown interest but are yet to purchase. We build these solutions for our most cutting-edge customers and believe this is the future of marketing and staying ahead of your competition.

Implementing these advanced solutions is a critical step to moving your business away from optimising to hit this week’s targets and instead optimising towards long-term profitable growth.

 

To enable you to truly calculate lifetime profit of a customer, not revenue, you then need to factor in returns, margins, delivery costs and any other factor. These areas are looked into in more detail in our retail webinar, ‘unlocking retail data to drive true business growth.


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