Reducing churn through cross-channel customer journey insights

dataThe cost of keeping an existing customer is in general around 10% of the cost of acquiring a new one. Reducing churn to increase client retention therefore is a major reason to optimise the customer journey. And to do so, you need a proper cross-channel view of your customer journeys.

The company in this case operates in a very competitive market, where retention is a key success factor. Therefore, they wanted to optimise the cross-channel customer journeys to make sure their customers get the experience and the service they are looking for. A special cross-channel team was in charge of establishing and maintaining this process.

Combining data sources

To optimise customer journeys you need relevant insights on all your touch points first. The company communicated with customers via many channels, such as the website, e-mail, the call center and social media. Besides that there were various other sources with client data, stored in separate data silos. The company wanted to combine all these data sources to create one rich customer view, to map the entire cross-channel customer journey and to fulfil the business needs the company had. This all had to happen very fast, because the project had to prove that integrating separate sources of touch point data is indeed possible within a relatively short amount of time.

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Connecting instead of replacing

A key factor in the success was a data science language called DimML. This allowed the implementation team to make excellent use of the existing systems, without having to redesign the infrastructure that was already in place. Thanks to DimML, the amount of technical changes was brought down to a minimum. After all, this project was not about implementing a new technology that would be another addition to the work load of the IT department. It was about implementing a solid and stable solution to deliver the truth about the company’s customer journeys.

Proof of concept (POC)

As a start-up to the total project, often a proof of concept (POC) is advised. In this particular case, it embodied the delivery of a working environment, based on integrated data from several data sources.

1) Collection and storage of data from the following sources:

  • SiteCatalyst web data
  • Call center data
  • Incoming email data
  • Outbound data (mail and e-mail)
  • Generic client insights
  • Data from an external website

2) Determining what data will be analysed (examples):

  • Changing dates of address mutations per client
  • The former and new postal codes and house numbers of these clients
  • Information from inbound calls and emails and outbound mail and email about these mutations

3) Plotting this data on a time line
The data determined in step 2 was plotted on a time line. Furthermore the client contact history was available for        a detailed view of the customer journey over time.

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4) Deriving management information

In this stage actionable management information is derived from the plotted customer journeys. At first count the      exactly similar customer journeys but also the journeys that were more or less the same.

5) Data visualisation

Several visualisations were created, such as the top 10 customer journeys in general, the top 5 of drop-out points.

Implementation total solution

For the implementation of this customer journey solution, the following activities (among a few others) are


  1. System design
  2. Initial setup
  3. Data collection
  4. Data modelling
  5. Customer journey reporting
  6. Training
  7. Go-live


The cross-channel customer journey reporting environment enabled the company to really explore the various customer journeys their prospects and customers had. This cross-channel perspective changed their view on the performance of several individual departments. Even though these units were meeting their targets like response speed and performance, customers still dropped out later on in the customer journey. Before the project, this Customer Journey was never really noticed. Now it is, and actions to reduce churn are taken on regular basis. The result is a substantial reduction in the churn rate!

Ronald van Loon

Helping Data Driven Companies Generating Business Value. Ronald has been recognized as one of the TOP 10 GLOBAL BIG DATA, DATA SCIENCE, BUSINESS INTELLIGENCE INFLUENCERS BY ONALYTICA, DATA SCIENCE CENTRAL, KLOUT, MAPTIVE AND PREDICTIVE ANALYTICS TOP 10 INFLUENCERS BY DATACONOMY, is author for leading Big Data sites like DATAFLOQ, DATA SCIENCE CENTRAL and public speaker at leading Big Data, Data Science and Internet of Things events. Recent keynotes: -Happy Customer event, Belgium -Big Data Week, London -Exchange Summit, Barcelona -Big Data Expo, Utrecht -B2B Goes Social, Nyenrode University, Netherlands -Several in company inspiration key notes Do You Have a Compelling Data Science, Big Data (Analytics) or IoT Message and You Want to Co-Author with me or want me to share your articles? ➨➨Just drop me a note via LinkedIn Want to stay up to date with latest Awesome Big Data, Data Science & Business Intelligence case stories, insights & tips? ➨➨FEEL FREE TO CONNECT ON LINKEDIN (you can use my email if required) ➨➨JOIN LINKEDIN GROUP “Awesome Ways Big Data Is Used To Improve Our World” ➨➨FOLLOW PUBLICATIONS BY RSS: Examples how we help companies ✓ Improve quantity & quality of B2B leads by a data driven Social Media Thought Leadership approach ✓ Improve Customer Experience: provide quantifiable insights & actions in the online & offline Customer Journey ✓ Decrease IT cost & centralize web data​: stream web data to your Data Warehouse ✓ Reduce your churn & increase upsell: predict your customers next action ✓ Manage Data Governance Interested in one of our 100 success stories from top European retail, telco, finance, travel, media & entertainment, manufacturing, energy or service companies? Please feel free to connect with me on LinkedIn (LinkedIn Open Networker/LION) ✉ Twitter @Ronald_vanLoon ☏ +31 (0) 20 7600 700

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