Case Study

Optimizing marketing ROI with data science

How we used data science and Key Leading Indicators of Sales to better understand marketing campaign effectiveness and improve sales and marketing ROIs

2%

Increase in conversion rates online

Improved marketing ROI

Through enhancing valuable marketing activities

Customer profiles cleaned

Through accurate CRM data management

The Business Challenge

In today’s digital landscape, marketing campaigns have become incredibly sophisticated – and measuring their success takes an equally intelligent approach. However, some businesses are still relying on lagging indicators such as market share to determine if their goals have been met.

Our client was struggling to assess the success of particular marketing campaigns in regards to sales performance, as well as how the individual marketing components were interacting with each other and attributing to the results. As a result, a significant part of the marketing spend was not being optimized, as there weren’t enough valuable insights to learn from after campaign completion.

Optimizing marketing ROI with data science 1
Optimizing marketing ROI with data science 2

The Solution

To effectively undertake marketing optimization, there needs to be a granular understanding of the activities and variables that drive sales performance, beyond the amount spent on media.

This understanding is achieved by using a Key Leading Indicators of Sales (KLIS) approach, which supports performance-focused marketing optimization through the use of data science and machine learning. By exploring individual relationships between marketing and sales activities, as well as their systematic interconnectedness, KLIS can also answer business questions via a series of hypothesis testing that is done in parallel with the model building.

Additionally, with the KLIS system in place, the dependencies across online and offline activities were identified and managed for maximum impact. This was achieved by the use of advanced statistical analysis such as Bayesian belief networks and elastic nets – which helped to answer questions around the various factors that drive sales and marketing performance.

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KLIS MODEL: INTERLINKED WITH ALL DEPENDENCIES (PRODUCT SERIES)

Business impact

By using KLIS as a marketing spend optimization approach, we were able to create a statistical model that showed how each marketing activity affects these outcomes and how they affect each other. The findings helped our client reevaluate marketing campaigns’ value and focus on the campaigns with the highest ROI.

Crucially, the model also showed that some marketing campaigns and practices were not impacting the outcome variables at all. For our client, that meant saving costs on inefficient campaigns and improving conversion rates online.

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This project has indirectly received funding from the European Union’s Horizon 2020 research and innovation programme under project REACH Incubator (Grant Agreement no. 951981).

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