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How to maximize Customer Lifetime Value?

In general, Customer Lifetime Value (CLV) is the KPI that describes the revenue or profitability we can expect from each of our customers throughout their lifecycle. Historical CLV is defined as the actual earnings (gross margin minus direct costs) from customers over their lifetime so far, adjusted after subtracting the acquisition cost.

The best way to increase revenue is by maximizing the Customer Lifetime Value (CLV) of each and every one of our customers.

To optimize the CLV for all customers, we need to provide each one with the best possible experience throughout the entire cycle of interactions with our brand.

Customer Lifetime Value is the essential KPI for measuring the results of any Predictive Marketing strategy

Generally, Customer Lifetime Value (CLV) is the KPI that describes the revenue or profitability we can expect from each customer over their lifetime. Historical CLV is defined as the actual earnings (gross margin minus direct costs) from customers over their useful life to date, adjusted after subtracting the acquisition cost.

Thanks to Big Data and Artificial Intelligence, we can use CLV to identify customers or groups of customers whose value is trending positively or negatively. Identifying customers in a negative trend serves as a warning to trigger relevant actions to prevent the customer from disappearing completely.

Utiliza la Inteligencia de Pleasepoint para aumentar el ROI de tus acciones de CRM, marketing y publicidad digital.

Pleasepoint es la plataforma de machine learning que segmenta los clientes de tu CRM con predicciones de compra por cliente y personaliza tu marketing digital a escala.


Solicita una demo para ver las ventajas de trabajar la segmentación de clientes utilizando el Customer Lifetime Value, personalizando según el perfil de buyer-persona basado en datos y la recomendación de productos one-to-one.

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Many factors can influence the evolution of CLV: recent interactions with the customer, the size and discount of the first purchase, time between purchases, time spent navigating the website, interaction with email or social media campaigns, acquisition channel, geography, seasonality... Predicting CLV is not our destination since, as Predictive Marketing Ninjas, we can take many actions that change the course of history. The most important thing to learn now is that once you acquire a new customer, you should focus on communication to retain them and increase CLV.

As Predictive Marketing Ninjas, we must focus our efforts on communication and interactions with each customer with the goal of maximizing CLV individually, disregarding the acquisition cost.

We present the method for iterating over KPI analysis and multiplying sales

To improve results, it's easiest to apply this analysis cycle, starting with a few KPIs that provide basic information, and then adding data with the goal of gathering more information after each iteration.

  1. Use CLV as an indicator to measure project ROI.
  2. Visualize the evolution and calculate the progression of CLV.
  3. Segment/Cluster the customer database based on CLV.
  4. Create personalized campaigns and analyze the results.
  5. Identify useful KPIs to increase CLV and iterate.

Identify and leverage opportunities to increase Customer Lifetime Value individually for each customer

The customer lifecycle is the concept we use to describe the evolution through the different stages a customer goes through, including acquisition, the moment of purchase, and subsequent customer retention. This approach emphasizes the individual moments each customer goes through and encourages marketers to think about the best way to communicate based on the stage each customer is in.

The first stage is customer acquisition. When you get a customer's first purchase, you still cannot consider that a relationship has been established between the customer and the brand. The main objective after acquiring a new customer is to transform this new customer into a repeat buyer.

Big Data and Artificial Intelligence make it easier to detect growth opportunities or identify consumption patterns. To understand, analyze, and correctly use the data, you should:

  1. Identify the seasonality of products or services and the temporal spaces.
  2. Identify anomalies, outliers, and trend changes.
  3. Create funnels to understand the buying processes with all interactions.
  4. Personalize communications based on stage and consumption preferences.

Si has llegado hasta aquí esto te interesa.

Pleasepoint es la plataforma de machine learning que segmenta los clientes de tu CRM con predicciones de compra por cliente y personaliza tu marketing digital a escala.


Solicita una demo para ver las ventajas de trabajar la segmentación de clientes utilizando el Customer Lifetime Value, personalizando según el perfil de buyer-persona basado en datos y la recomendación de productos one-to-one.

Solicita una demo

Multiply your company’s value by increasing the CLV of all your customers

Customers are the primary asset of any business, so Customer Lifetime Value (CLV) is the most important KPI for Predictive Marketing Ninjas.

If you can maximize the CLV of each and every one of your customers, you will also be increasing the overall valuation of your company.

Predictive marketing allows you to communicate personalized messages with each customer at scale and helps you easily double the conversions of your digital marketing campaigns.

Increase Customer Lifetime Value for all your customers by personalizing content and promotions for each at scale

This is the time to apply all the knowledge acquired about Customer Lifetime Value and work on it jointly. As a company, you don't have just one customer. To increase the total value, you need to increase total sales and the sum total of CLV for all your customers.

A primary goal is to acquire, at a minimum, the same number of customers you will lose in a year. The number of customers to acquire depends on the churn rate and purchase frequency. If you have a high churn rate, you’ll need to acquire more customers to compensate for the loss. Predictive Marketing allows you to optimize the acquisition process by targeting higher profitability customers with retargeting campaigns.

When a customer stops buying from you, it's too late to get them back. If you are proactive in communicating with your customers and offering relevant content, you achieve a long-term relationship. Strategies to prevent customer churn and increase retention are crucial for growing CLV.

Still, when a customer has stopped buying from you, they are not 100% lost. On average, it's 10 times cheaper to reactivate an inactive customer than to acquire a new one. As a Predictive Marketing Ninja, you’ll be able to identify the early signs of customer inactivity to prevent their complete loss.

Real results from real companies

See how organizations across industries have transformed their operations with AI intelligence.

eCommerce PrestaShop Success Story: Real Sociedad.
#01 Success story

eCommerce PrestaShop Success Story: Real Sociedad.

The goal is to offer a unique experience to fans at every touchpoint.

Success Case: How Norauto personalizes their campaigns.
#02 Success story

Success Case: How Norauto personalizes their campaigns.

Norauto is clear: They need a data strategy for their campaigns.

Online supermarket success story: One-to-one for Condis.
#03 Success story

Online supermarket success story: One-to-one for Condis.

Condis’ challenge is achieving real-time one-to-one personalization in both its platform and CRM for each user.

Case Study: Flormar revolutionizes loyalty with AI
#04 Success story

Case Study: Flormar revolutionizes loyalty with AI

Flormar revolutionizes its loyalty with predictive AI and achieves a much higher average ticket.

Success Story: AI Agent for Atelier Libros.
#05 Success story

Success Story: AI Agent for Atelier Libros.

Success story of Atelier Libros: How to offer an efficient and personalized experience in its digital library.

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