Maximize customer lifetime value with data, analytics and machine learning


Maximize customer lifetime value with data, analytics and machine learning

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Customer experience is a strategic objective in virtually all companies. Yet, delivering the desired experience systematically across all customer touchpoints can impose challenges.

Since customer experience is an interdisciplinary field, it requires an interdisciplinary approach that combines strategy, design, data, analytics, and machine learning – depending on the customer’s industry and maturity.

At Siili, we approach customer experience with the following key criteria:

  • Maximized customer lifetime value should be the ultimate target
  • Optimize customer journeys rather than single touchpoints
  • Develop analytics and personalization capabilities based on set KPIs
  • Build a customer 360 view to enrich understanding and prediction of customer behavior

We will shortly describe these key criteria beneath, and why we see them as critical for success.

Maximized customer lifetime value should be the ultimate target

Ultimately, customer experience is about maximizing customer lifetime value. We define customer lifetime value as the combination of “increased revenues”, “improved customer loyalty” and “the ability to serve new customer needs or existing needs better”. Succeeding in all three subsegments of customer lifetime value creates a win-win situation – both the company, but most importantly, the customer is satisfied.

Optimize customer journeys rather than single touchpoints

Fragmentation and sub-optimization are key concerns for customer experience. If too much focus is put on developing single touchpoints, the overall customer journey might be compromised.

Especially in B2B industries, the customer is often not one, but several persons. They all affect the decision-making process, but in different phases of the customer journey.

Further, several seller-side functions, e.g. sales, marketing, product management, accounting or order handling, are a part of the customer experience.

We believe in a holistic approach. Ensuring a clear customer experience strategy, defining customer personas and tangible KPIs along the customer journey minimize the risk of fragmentation and sub-optimization.

Develop analytics and personalization capabilities based on set KPIs

Often touchpoints are developed in silos, bottom-up. We believe the most efficient way to achieve set KPIs is by developing analytics and personalization capabilities top-down. Start by defining what to achieve and only thereafter how to do it.

A top-down approach gives clarity; what personalization capabilities do we need in order to maximize the customer lifetime value? How do we increase the utilization of our existing technologies? A top-down approach minimizes internal silos while ensuring transparency – less overlap between technology, teams and functions, and more co-operation towards the same goal.

Build a customer 360 view to enrich understanding and prediction of customer behavior

Building a customer 360 view is critical for personalized content, offering, and the customer experience. Yet, trying to gather all available data of the customer behavior is not only extremely expensive, but also often impossible.

Ideally, companies should collect the minimum amount of data on customer behavior required for a decision. The aim is to create a seamless understanding of the customer behavior. To add to the confusion, also several platforms have overlapping capabilities. At Siili, we help our customers navigate between different technologies. We focus on the critical capabilities needed for maximized customer lifetime value.


This is the first part of a blog series. In this series we will introduce 7 key perspectives on how to design and deliver on a customer experience that maximizes the customer lifetime value.

The blog series focuses on critical capabilities needed in digital channels:

  • Crafting a customer experience strategy – Key points for maximized customer lifetime value
  • Recommender engines – The journey from rule-based systems to machine learning applications
  • Crafting digital experiences – Benefits of content management systems and data science
  • Increasing the customer lifetime value with web analytics – Use cases along the customer journey
  • Automating the processing of customer feedback – Use cases of machine learning
  • Building a customer 360 view – what critical capabilities to focus on in a sea of noise?
  • Ensuring tangible results with data driven design – The experimentation culture


If you are interested in Siili's approach to customer experience, click the button below to subscribe to the blog series. 

Interested to hear more about our solutions? Please click on the link below:

Written by:

Jarkko Malviniemi, VP, Offering & Technology

Jussi Ahola, Director, Data & AI Services

Charlotta Välimäki, Key Account Manager

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