Global media & entertainment provider

Global Media & Entertainment firm adopts more targeted and immediate marketing campaign measurement process, reducing resource-strain and enhancing agility.


reduction in time spent reviewing data by Marketing Department


reduction in KPIs tracked by Marketing Department


improvement in speed to decision for campaigns


Our client, a large global Media & Entertainment provider, had been the preeminent player in its category for years. Users and revenues went up consistently quarter after quarter. However, a new competitor app with a slightly different value proposition was taking significant market share and emerging as a real threat. The Marketing department was tasked with stopping user leakage by running several expensive and eye-catching campaigns. The campaigns were creatively ambitious and targeted a wide variety of segments and products, however it was not clear how effective each of the campaigns was until long after the campaign had ended.

There was a continual expansion of KPIs, and each campaign usually had a positive impact on one or two KPIs, making it difficult to evaluate whether a campaign was successful or not. There was almost no mid-campaign adjustment, even though the client had significant user data, and the campaigns ran for several weeks. Because of the ambiguity, each campaign ran longer and longer (and cost more) before a decision could be made about its effectiveness. Further, initial analysis showed that a significant percentage of the campaigns had limited or zero impact on the most important KPIs.

If the client could better align its campaigns to reach its KPI targets, it could adjust failing campaigns, turn up the volume on successful campaigns, and derive much higher marketing ROI at a critical competitive time.


Although the client had access to terabytes of user data, there was a cultural norm of adding KPIs and reshuffling priorities. Further, they had a significant analytics department, but several positions were unfilled and as the economy turned would not be filled for the foreseeable future. Any demand for new data collection or analysis would take months to be delivered.


The team reviewed existing data collection, reporting practices, and follow-up actions as well as performance of individual campaigns, and made the following recommendations:

  • KPI prioritization: our team reviewed dozens of marketing KPIs and re-set clear priorities.
  • Campaign performance analysis: we analyzed four years of campaign performance and assessed whether or not campaigns had an impact on meaningful KPIs. There were several expensive campaigns that had no discernable impact on major KPIs.
  • Translating behavior to preference: The client had two principal families of KPIs. Behavioral KPIs are easy to track – did the user click on a button or not? Results are available in real time so actions can be taken. Preference KPIs are more difficult, as they involve a change in a user’s perception of the brand. These scores were only reported 6-8 weeks after campaign completion. The team looked at how actions (behavior) had shaped preference in previous years and identified Behavioral KPIs that correlated positively with Preference, allowing a temporary measure of success.
  • Data collection practices: the team recommended a change in data collection practices to poll panels more quickly and frequently, making organic preference data available long before the campaigns ended, enabling the client to adapt the schedule, placement, and frequency of campaigns to performance.
  • Dashboarding: The Marketing team was looking at large collections of data in spreadsheets. We developed a dashboard that would allow the client to focus on a precious few KPIs–the baseline, the current value, and the expected value by the end of the quarter. This allowed fact-based conversations to happen faster and actions to be taken more decisively.


Taken together, the set of changes recommended by Artemis allowed the Marketing Team to eliminate ambiguity in evaluating campaign performance, reducing the need to track and review  huge data sets, thereby reducing resource demands on the team and accelerating and improving the quality of decision making in Marketing.

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