Why is everyone obsessed with CLV?

Why is everyone obsessed with CLV? There’s a better way to measure customer value

On the surface, customer lifetime value seems like a great way to measure marketing success and inform strategic business decisions. But that’s just the issue – it’s all on the surface.

Businesses that base customer-related decisions on monetary value alone are doing themselves a disservice.

TAP CXM principal consultant Darron Gregory has developed a better measure of customer value that looks deeper and broader, to understand an individual’s true value and access untapped potential across the entire customer base.

Why customer lifetime value isn’t the answer

Profit is good for business. We’ll give you that. The logic follows that customers who spend more over time are better customers. 

This is the basis of customer lifetime value (CLV or CLTV), a metric representing the total revenue a company expects to generate from a customer throughout the relationship.

As far as marketing KPIs go, customer lifetime value makes sense. Increasing CLV indicates improving customer retention and declining churn, both of which are important for growing a business.

But go a step further, and it’s immediately apparent that CLV is only useful as a high-level metric. That’s because CLV is based on a few assumptions that limit a business’s ability to assess value properly.

Assumption 1: Direct purchases are the primary or sole indicator of value

Direct purchases are only one way a customer contributes to revenue – and ‘customer’ here also means potential buyers. Indirect contributions like brand loyalty, word-of-mouth advocacy and regular website engagement can make a customer with low purchasing power just as valuable as one with a high CLV.

Assumption 2: Past behaviour indicates future intentions

Customer lifetime value looks at an individual’s purchase history to gauge future revenue expectations. But even businesses that should be able to get this right, like subscription streaming services, are realising that customer loyalty is harder to win nowadays.

Assumption 3: Customer acquisition is a one-off expense

The basic CLV formula takes the value and frequency of purchases, multiplies it over the length of the relationship, and subtracts acquisition costs. More sophisticated models factor in customer servicing costs. Still, these models fall apart in complex multi-brand environments. The solution is often to make a new customer record for each brand instead of combining data to segment, target and personalise marketing. 

You know what they say about assumptions…

When a business allows CLV to drive strategic decisions – which seems to be happening more and more – it ignores a huge amount of potential value within the customer base.

“The best-case scenario is one where the business makes strategic decisions that benefit existing customers, like developing new products, adding services, or improving customer experiences if there’s a potential to squeeze a little extra from the relationship,” says Darron.

“While all those things are good, they ignore avenues that could grow the business across more than one dimension, like advocacy, cross-selling or non-core business opportunities.”

How we solve the CLV problem

There’s an algorithm for everything nowadays. So why not customer value?

Darron and his CXM consultant cronies use a proprietary algorithm to calculate a holistic customer value score by combining attributes from four groupings. 

The outcome is a single score that can be measured and tracked over time. It factors in transaction history. But unlike pure CLV, our customer value score is a composite of monetary and non-monetary attributes that are weighted based on the business’s goals. 

Unfortunately, we’re not at liberty to share the algorithm here. That kind of IP is dangerous in the wrong hands.

(That’s a joke – but we do like to keep some aces up our sleeves).

Customer value attributes 

Looking for the algorithm?

You'll have to ask Maria

Going beyond CLV to measure success more accurately

The aim is twofold: firstly, we want to understand individual customers on a deeper level, and secondly, we want to highlight the potential for diversified revenue streams that exist across the landscape of current and potential customers.

Deeper customer understanding

“If you’re not thinking holistically about the customer, you’re not recognising the potential of your customer base,” says Darron.

Combining numerical and categorical data, then weighting it based on potential value, creates a more complete picture of an individual’s potential value. Essentially, it creates a segment of one.

“Weighting is incredibly important,” says Darron. “It’s very rare to get it totally right on the first try, but we can revise and refine as the data comes in”.

The composite score can then be reverse-engineered to understand which levers a brand needs to focus on. This can happen at the individual level as well as the segment, market or brand level.

Side note: Every organisation will use different attributes. They’ll also change based on data availability. Best practice is to implement a customer data platform (CDP) or data warehouse configured for shared access, but it’s ok to start with any combined data source like a CRM.

Excel at omnichannel

“The behaviour of customers and potential customers has changed massively,” says Darron. “If you don’t think progressively about all the different ways customers can interact, then you’re not thinking fairly about customer value.”

By definition, CLV is limited to revenue-earning channels. This directly conflicts with many of the core tenets of good CXM, in particular creating experiences that are cohesive, coherent and consistent. 

In contrast, customer engagement or value scores assign a measurable metric to non-earning channels based on their role in the customer journey. 

“You need to be able to look at all four pillars and calculate the score,” says Darron. “This can happen anywhere you can identify the data. It’s not platform specific.” 

Granted, getting this right isn’t easy. It requires visibility over the entire customer journey, which means building information pipelines to collect and collate that data.

For this reason, our CXM consultants tend to work on overhauling customer value measurement at the same time as data management capabilities. The two are interdependent; you can’t have a complete customer view without centralising data, and centralising data is pointless unless you do something with the insights.

Clarity for everyone

Our customer value score is a single high-level number that is easily understood at the board level. If the number goes up, things are going well. If it goes down, we have work to do.

CLV does the same job.

Where a holistic customer value score becomes more functional is within the different operational teams. For example, community managers and email marketers are more concerned with engagement and loyalty than transactional data, while the UX team cares more about website and app engagement than a customer’s loyalty status.

All these metrics matter. How much depends on the organisation. What’s important is the ability to unwind the high-level number to analyse, address and prioritise value-adding strategies.

If email CTR is weighted heavier than social media engagement, improving emails takes priority over social content. Similarly, if app users are more valuable – in terms of revenue, cross-sell potential or subscription drives – then the UX team knows which experience to fix first.

Customer value score in action

Let’s say you work in the marketing division of a major football team (soccer, for our US pals and Australian mates). 

CLV has been a useful metric so far, but you know there’s something missing from the picture. Revenue remains steady, but the team’s social media channels are stagnating, and you’re seeing other teams gain a following among audiences you’d like to reach.

Step 1: Centralise customer data

Working with a CXM consultant, you have broken down the data siloes between teams and centralised customer information. For this example, let’s say you went with the gold standard* and linked all incoming customer data streams to a shared warehouse overlayed with a robust query system.

*Another side note: Velocity is the biggest advantage of using a CDP over a data warehouse. The system can recalculate customer engagement scores in real-time according to incoming data signals. However, linking to a data warehouse makes it easier to understand why the score has changed at the characteristic level; that is, if the nature of the characteristic has changed. CDPs might not have the same historical granularity. The solution here is periodic data snapshots to maintain a cohesive record.

Step 3: Define the limitations of CLV

Until now, the organisation has tracked CLV and provided perks for high-value customers, like merch discounts or priority ticket sales.

But after combining data from the ticketing, digital marketing, social media, merchandise, membership and corporate partnership divisions, you’ve got access to a load of new insights. 

The first insight is that a not-insignificant segment of your database lives in another country. They’ll buy almost anything with their favourite player’s name on it, but priority ticket sales don’t mean a thing.

Another segment – platinum ticket holders who buy a corporate box every season – are more interested in private meet-and-greets than merch. 

Step 4: Assign value to characteristics

Platinum members are traditionally considered more valuable because their net revenue contribution is higher. But those merch-crazy overseas fans are crucial from a retail perspective. 

Then you dig up a third segment. These are people who live close to the stadium and attended games in the past, but you haven’t seen them for a while. Despite having the hallmarks of loyal fans, their CLV dropped, so they stopped qualifying for ticket release campaigns.

Step 5: Assign a customer value score

Each attribute – corporate box revenue, merch sales, home game attendance, social media following, proximity to the club, word-of-mouth influence – is weighted according to the club’s overall strategy.

Apply our algorithm, et voilà, we have a customer value score.

Step 6: Prioritise, strategise, personalise

After analysing the individual and combined scores in each segment, you identify the levers that should generate value in line with the team’s growth targets.

For example, you might:

• Release exclusive merchandise for popular players in targeted markets

Get the whole family kitted out to simultaneously grow brand awareness, increase merchandise revenue and claim a greater market share among overseas fans.

• Run a re-engagement campaign to bring local fans back into the fold

During the ticketing process, you collect enough data to match a sub-segment of occasional fans with profiles that are highly engaged on the team’s social media channels. Now you know which lever to lean on for brand advocacy.

• Offer out-of-season hire packages for corporate boxes

Keep loyalty topped up among high-earning corporate members while also opening a new revenue stream. 

Evolve your customer value approach

Every business exists to make money. But how will it keep making money, and how will the amount of money grow sustainably in the coming months, years and decades?

These are questions that customer lifetime value (CLV) can’t answer. 

A customer’s value to an organisation is both deeper and broader than monetary metrics convey. Our CXM consultants can help you broaden your horizons and access value-adding insights with a solution tailored to your organisational setup and growth goals.

Get in touch to talk customer value with TAP CXM.