Customer lifetime value, or CLV, is a marketing metric that predicts the total worth of a customer to a business throughout their entire relationship.
In other words, how much money you or I might spend on ASOS or Amazon throughout our lives? It’s not something you necessarily want to think about, I know. But for marketers, it is a highly important metric and one that can help to improve business performance and drive customer loyalty.
To increase customer lifetime value you need to understand your customer
While a growing number of marketers are recognising the importance of measuring CLV, less than half we interviewed said they can do so.
The main reason for this appears to be teams lacking the ability to understand customer behaviour. Respondents cited this as a key challenge in building CLV. As stated in the report: “This is often due to companies not having a single view of the customer and key insights being missed.”
The more that companies can unite data sources, the better they can understand how a customer engages across marketing and sales channels. From there, companies will be in a better position to optimise strategy and build value.
Focus on the customer experience
There’s endless research to suggest that a good customer experience results in increased loyalty (which is a contributing factor for CLV). According to a consumer study by Temkin Group, 86% of people who received a great customer experience were likely to repurchase from the same company, compared to just 13% of people who received a poor customer experience.
How to improve CX is a large and lofty topic in itself, so perhaps then, this point should merely reiterate the importance of having a CX-focused strategy, whereby companies have a clear vision and set of principles relating to CX, which is integrated across teams and departments.
Deliver hyper-personalised communication
Hyper-personalisation is the main tactic used by companies looking to increase CLV. It’s understandable why it is such a big priority, as personalised interactions (where companies tailor communication to an individual’s needs and preferences) can increase satisfaction and overall loyalty.
There also tends to be a more direct and immediate benefit of personalisation – i.e. triggering customers to purchase at the moment.
A Salesforce study found a direct link between product recommendations and higher revenue. While recommendations resulted in just 7% of visits to an ecommerce site, these visits drove 24% of orders and 26% of revenue. Even better, 37% of shoppers who clicked a recommendation during their first visit returned, compared to 19% of shoppers who didn’t come from a recommendation. This proves that personalisation can deliver immediate results as well as longer term.
Aligning with customer beliefs and morals
Customers today are looking for more from brands than a product or service. According to Harvard Business Review, 64% of survey respondents cite shared values as the primary reason they have a relationship with a brand, indicating the importance of corporate responsibility and an outward focus on social good.
TOM’s is a great example of a brand that attracts and retains customers based on its philanthropy. Its ‘One for One’ promise means, from every purchase made, it gives shoes, sight, water, and safer birth services to people in need.
These shared values also do more than spur an initial interest in brands, with 94% of participants saying that they are likely to be loyal to a brand that offers complete transparency.
On the back of this knowledge, it’s worth considering how companies might be able to reinforce the values they share with their customers and to offer them ways to get involved with related initiatives or show support for a particular cause.
Offer additional value
Alongside factors like social good, it’s wise for companies to think about how they can offer additional value to customers. Again, this could be through personalisation, which enables the customer to seamlessly re-enter the purchase funnel (rather than start again from their own volition), or other factors such as community management and support.
Loyalty schemes can also offer additional value – giving customers perks and rewards for their purchases – as well as helping to enhance brand advocacy. There’s no guarantee they work, however, and it all depends on the sector.
For example, there tends to be a lot of churn for mobile and broadband companies, with customers signing up to receive the best deals (and then potentially leaving for another). As a result, longevity-based schemes – i.e. rewarding how long a customer has been with a company rather than money spent – can be more effective. Within fashion and lifestyle-based retail, data-driven schemes that offer personalisation also tend to offer greater value, instilling a sense of importance in customers.
Ultimately, finding ways to exceed customer expectations – whether through free returns, exclusive online content, or even personalised messages in packaging – can have a huge impact on whether a customer repeats a purchase in future.
Hyper-personalisation solutions identify consumers’ future behaviour ranking every SKU by the greatest likelihood of that individual consumer to purchase, and present it to that individual at exactly the right time, thereby maximising that individual’s customer lifetime value CLV potential. (i.e. Likelihood to Purchase, Discount Affinity, Likelihood to Churn).
Consider a subscription model
While broader tactics like improving CX to increase loyalty are an important part of CLV, other more conversion-focused strategies can also play a part. To increase revenue (both in the short and long term), for instance, a subscription model can be highly effective. It’s not right for all sectors, of course. FMCG brands are usually the most applicable, largely due to the customer’s ongoing need for products.
The benefit for customers of a subscription is often a discount (when compared to isolated purchases made multiple times) as well as ease and convenience. As a result, this alludes to a longer-term relationship with brands, which in turn increases the lifetime value overall.
On top of this, subscription models can instil more loyalty in customers, with the sense that they’re less likely to abandon or cut ties with a company than someone making one-off purchases.
If a subscription model is not viable, upselling or cross-selling opportunities can also be effective, e.g. offering customers a ‘first look’ or exclusive access to new products or deals.
Customer Lifetime Value: the current operational framework
Customer Lifetime Value can now be considered through SwiftERM’s autonomous individualisation. Product relevance is recalculated for each identifiable customer from that customer’s own behavioural and transactional evidence, rather than being assigned by a fixed segment, campaign or predetermined journey. This distinction matters because an individual’s interests can change between visits, purchases and periods of inactivity. A current decision therefore needs current evidence.
SwiftERM evaluates the products that are becoming most relevant to each person and applies that decision autonomously. The operating model does not depend on a marketer continually creating segments, selecting products or scheduling another campaign. It is designed to use first-party behavioural and transactional signals already associated with an identifiable customer, so the decision remains specific to that customer rather than a broad cohort.
For Customer Lifetime Value, the practical test is not whether a retailer can create more rules. It is whether the system can recognise a change in an individual’s buying intention, reassess the available products and act without waiting for a manual campaign cycle. The output must remain traceable to the customer evidence and the ecommerce catalogue used to make the decision.
Implementation also needs disciplined data handling. Product identifiers, availability and customer events must remain consistent enough for relevance to be recalculated. Existing ecommerce operations should retain control of catalogue truth, order truth and customer permissions. Autonomous individualisation then operates within those established boundaries instead of inventing a parallel customer record or an ungoverned product feed.
The commercial value of Customer Lifetime Value should be assessed through observed customer response, orders and turnover, not through the volume of campaigns produced. That keeps attention on whether product selection becomes more useful to the individual and whether the retailer captures demand that would otherwise remain unrecognised. It also separates autonomous decision-making from conventional automation that simply executes a previously written rule faster.
This current framework preserves the original article’s subject while bringing its explanation into line with SwiftERM’s present capability: one identifiable customer, individually recalculated product relevance, and an autonomous decision made from that person’s own changing evidence. The same principle applies as catalogue choice, customer behaviour and commercial conditions continue to change over time.
In operational terms, the retailer retains the commercial context while the software performs the individual calculation. That division is important: the catalogue and customer record remain governed business assets, while product selection responds continuously to evidence rather than to a static assumption made when a campaign was first configured.
Customer Lifetime Value: current technical perspective
SwiftERM applies this capability without fixed customer segments or prearranged campaign journeys. The governing objective is a current, individually recalculated product decision whose commercial effect can be measured against orders and turnover.
