Predictive Customer Lifetime Value as a key tool for optimising marketing and customer loyalty

Customer Lifetime Value (cLTV) is one of the most strategic metrics in modern marketing, as it allows companies to assess the overall value that a customer brings throughout their entire relationship with the company.

cLTV can be calculated through three approaches: descriptive, predictive and operational.

The descriptive model calculates cLTV using historical consumer data and identifies behavioral patterns of customer groups primarily through simple manual analysis.

The predictive model uses patterns within historical data to determine future cLTV. In this case, both the individual consumer’s profile and their remaining time as a customer are considered in the equation. This model requires advanced analytical capabilities, such as customer identification across multiple channels. It is important to have complete historical customer data and regular updates of sales and cost data.

The operational model automatically predicts cLTV using machine learning. Predictive accuracy and decision-making improve with each update.

For all three models, continuous updating of data and calculations is indispensable. cLTV must be updated after every customer purchase.

In this article, we will focus on predictive cLTV, which in Bytek can be calculated in two ways: using a probabilistic approach combined with clustering algorithms or using machine learning algorithms.

In today’s data-driven marketing context, predictive cLTV (pcLTV) has gained significant relevance. The ability to predict and manage customer interactions is crucial for the success of business strategies.

Integrating pcLTV into your martech ecosystem

The effectiveness of predictive Customer Lifetime Value (pcLTV) is closely tied to its integration within the entire corporate marketing and CRM ecosystem. A fragmented approach to pcLTV risks undermining the quality of business strategies: it is crucial that pcLTV does not operate in isolation but is tightly connected to all touchpoints and platforms that govern customer acquisition and management.

Integrating pcLTV with acquisition campaigns, such as those managed through Google Ads, is particularly useful. A traditional approach, which does not consider the lifetime value of the customer, risks treating all conversions the same way, assigning a static and potentially underestimated value to customer transactions. For example, an initial purchase of €150 might not reflect the true potential of a customer, who could have a pcLTV of €2,000 due to the high likelihood of future purchases. Without a dynamic assessment of predictive cLTV, marketing campaigns might waste resources on low-potential customers while undervaluing high-value ones.

The integration of pcLTV into marketing systems not only optimizes bidding strategies in advertising campaigns but also personalizes user experiences. A customer with a high pcLTV can receive preferential treatment as they share more information and preferences, such as a personalized UX on the site, more advanced chat support, or targeted offers, thus increasing the likelihood of retention and further enhancing the value generated. In fact, a 5% increase in customer retention rates can boost profits by at least 25%.

Imagine a purchase funnel that starts with a Google search and develops through various digital touchpoints; using pcLTV allows for orchestrating a sequence of actions that maximize return on investment.

An advanced cLTV prediction system, even from the first purchase, can provide valuable data to refine remarketing campaigns, directing resources toward those customers who, based on the analysis, show much higher spending potential than the average.

Another significant benefit of this approach is the ability to improve operational efficiency. Early identification of high-lifetime-value customers allows companies to reduce costs associated with managing less profitable customers. Adopting a strategy based on pcLTV, therefore, not only optimizes marketing performance but also contributes to a more sustainable management of corporate resources.

In summary, Predictive Customer Lifetime Value is not merely a static metric to be calculated but must be a dynamic element integrated into all business strategies.

Enriched Bidding Strategy: Maximising ROI

Enriched bidding, or Value Bidding if we use the terminology of platforms like Google and Meta, represents an advanced strategy for optimizing advertising bids, where the value attributed to a conversion is replaced or supplemented with a more meaningful parameter for the company, such as Predictive Customer Lifetime Value (pcLTV). This approach, which we call “enriched” due to its structured and complex nature, is based on multiplying the conversion value by a factor that takes into account variables such as pcLTV and membership in certain customer clusters based on RFM (Recency, Frequency, Monetary) models.

Implementing this strategy allows for the configuration of specific rules that direct bids toward customers with high potential long-term value. For instance, a customer belonging to a cluster at risk of churn but with a medium-range pcLTV could be targeted with an increased bid, raising an original bid from €10 to €15.

This methodology proves particularly effective in high-value campaigns like Google’s Performance Max or Meta’s Advantage Plus, which greatly benefit from optimized bidding. The use of advanced strategies like enriched bidding can improve returns on advertising investment by making customer acquisition not only more efficient but also more focused on customer segments with the highest long-term value.

Enhancing the User Experience with pcLTV

Predictive Customer Lifetime Value is a powerful element for enhancing user experience on websites and corporate platforms, enabling the personalization of content and the offering of premium products, leading to increased engagement and higher conversion possibilities.

Netflix has successfully adopted a user experience personalization approach for years. However, the ability to implement such an advanced level of personalization is no longer exclusive to large companies. The evolution of cloud computing, the availability of artificial intelligence models, and technological advancements have made these strategies accessible even to small and medium-sized enterprises. This not only enhances user engagement but also provides a competitive advantage over competitors who have not yet adopted such technologies. Companies using dynamic pCLTV to personalize customer interactions can experience an increase in cross-selling and an improvement in customer satisfaction.

The Importance of Quality Analysis

The quality of analysis plays a crucial role, just as much as strategy. Focusing exclusively on observed revenues can lead to overlooking fundamental aspects such as customer retention. The cLTV and pcLTV offer in-depth insights into a company’s ability to retain its customers. For instance, an increase in the average cLTV, from 1000 to 1200, with the same total annual revenue, indicates not only a reduction in the churn rate but also an increase in customer loyalty.

Segmenting marketing channels based on pcLTV can provide valuable insights into their performance and their ability to attract valuable customers. If a marketing channel generates customers with a high predictive cLTV, this might indicate that the message and channel are particularly effective for that target audience.

Working on cLTV and pcLTV as metrics thus becomes extremely strategic for evaluating the effectiveness of business initiatives.

Combining pcLTV with other types of analysis

RFM (Recency, Frequency, Monetary) analysis is one of the most established and widely used methodologies for segmenting customers based on three fundamental dimensions: the recency of the last purchase, the frequency of purchases, and the monetary value generated. Despite the common perception that RFM is an outdated analysis, it remains crucial for defining customer clusters and, consequently, for personalizing marketing strategies.

By combining this analysis with predictive Customer Lifetime Value, companies can identify high-value customers and take targeted actions to maintain or enhance their loyalty. For example, in the case of a ‘Disaffected Client’ with a medium predictive lifetime value, the company might decide to launch specific marketing campaigns to reactivate them. Conversely, for a ‘Top Client’ with a low predicted lifetime value, the company could adopt preventive measures, offering exclusive benefits to prevent their loss in the long term.