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Case Study: Global Entertainment Firm Improves Email RoI with Data Science

Implementation of advanced techniques and automated processes to reduce marketing costs and maximise email campaign RoI.

Marketing Return on Investment

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Our customer is a mass media entertainment brand with a worldwide presence. It relies on analytical software to process its business-critical customer data, to allow key departments to share essential marketing communications with its customers. Analytics-informed decisioning is vital to ensure ongoing brand awareness amongst our customer’s existing customer base.

Business issue

The customer was struggling to coordinate targeted email campaigns across departments. This led to communications being shared with their own customer base that did not reflect individual preferences or purchase history.

Why was this required?

The customer’s difficulties in accurately targeting its emails stemmed from an unclear interdepartmental strategy as to how departments should prioritise communicating with certain individuals on the database. This led to many emails not being effectively targeted. Meanwhile, little email customisation meant content didn’t always fully resonate with recipients, lowering the perceived value for the individual and, therefore, reducing the propensity to respond to calls to action or make a purchase. In turn, this reduced email campaign effectiveness.

The customer had in-house data scientists, however, they were too busy with other work to commit to this project. Therefore, the customer turned to Amadeus Software for support.


The customer wanted a solution that would allow them to optimise their email marketing campaigns and target customers according to their propensity to purchase offerings from a particular business department. This would enable them to generate greater sales and new business opportunities, leading to maximised RoI from much more effective campaigns.

Amadeus’application of data science techniques enabled the customer to generate a statistically significant uplift in ticket sales for an individual arm of the business.

Amadeus solution delivered

In response to the customer’s challenges, Amadeus used the existing analytics platform to reengineer all email marketing and implement the following new approaches:

  • Bespoke propensity modelling of historic data across multiple lines of the business to provide accurate predictions of future customer behaviour to certain messages. Modelling utilised various statistical techniques which were new to these business departments, such as logistic regression, variable clustering and dimension reduction.
  • The modelling process was automated to produce detailed monthly reports, scoring customers’ propensity to buy within certain parameters, based on previous purchase behaviour and interests. This reduced the manual intervention required to generate this insight
  • ROC curves were created to monitor any degradation of these models. Should degradation occur, an automated system alerts users that model maintenance or refinement is required.


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Following the implementation of these techniques, our customer obtained several key benefits:

  • Precise email targeting ensured future email messages were only received by those to whom the content was most relevant. In doing so, this reduced the cost of sharing such campaigns.
  • One particular branch of the business identified a statistically significant uplift in ticket sales following the implementation of these data science initiatives. This enabled profit maximisation for that particular branch of the business by increasing customer spend whilst reducing the cost of its email marketing. This equated to the marketing team generating greater RoI from their campaigns.
  • Increased potential value for each individual contact per campaign thanks to an increase in the likelihood of them making a purchase.
  • Improvements in customer experience – any emails received by customers now directly target their interests, increasing the perceived value of the campaigns for the individuals.