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Case study
Advertising, B2C

Adidas E-commerce Google Ads Case | 13% revenue growth

Adidas asked us to test whether automated Google Text Ads could perform as well as (or better than) manually built campaigns.

Adidas case

+13%

revenue

+40%

time saved

+18%

roas

ABOUT

Adidas

Adidas is one of the largest sports brands in the world and sells products in hundreds of markets worldwide. With dozens of advertising accounts in Europe and thousands of campaigns, scalability is essential to their digital strategy.

THE

CHALLENGE

adidas's European marketing team managed more than 40 Google Ads accounts in different languages. Campaigns, ad groups and keywords were largely built by hand, while product stock was constantly changing. The challenge: automate without sacrificing performance.

OUR

RESEARCH & GOALS

We chose a test approach: a clear A/B split between manual campaigns and feed-driven Google Text Ads. For this, we used DataFeedWatch to dynamically generate ads based on the live product feed. What made this approach unique was the use of regression analysis via SA360 to genuinely isolate the effect on ROAS and revenue.

A/B test: regions split into test and control group with difference in revenue
  1. we randomly assign each region to a test and control group
  2. Google Ads is used to implement bidding tool A in test regions and bidding tool B in control regions.
  3. We can measure the difference in behaviour between the test and control group:
  • The effect of the campaigns on the KPIs that matter
  • The efficiency of the campaigns in terms of, for example, incremental CPC, CPA or ROAS

Methodology:

Map of Germany divided into 66 GMA sub-regions
  • We divided Germany into 66 sub-regions, also known as GMAs
  • GMAs are drawn so that their borders run through the most sparsely populated areas possible and not through known commuter areas.
  • This minimises contamination, where a user could travel from a control GMA to a test GMA and be exposed to advertising.
Chart with observed and predicted revenue, daily difference and cumulative effect
  • The top panel shows the actual revenue of the test group and the estimated counterfactual revenue
  • The middle panel shows the daily difference between the actual revenue of the test group and the counterfactual revenue. In other words, the estimated daily increase/decrease in revenue as a result of the treatment.
  • The bottom panel shows the sum of these differences. In other words, the estimated total increase in revenue as a result of the treatment
  • The shaded area around the estimated counterfactual revenue in each panel represents the counterfactual 95% confidence interval.

“

DGTLbase helped us combine scalable automation with performance marketing – without sacrificing creativity or control.

– Team adidas

THE

SOLUTION & IMPLEMENTATION

We connected the product feed directly to the Google Ads account with smart rules for ad copy and targeting. Thanks to this automation, we were able to:

  • generate ad copy dynamically
  • launch campaigns faster when stock changed
  • scale far more efficiently across multiple markets

We also carried out statistical analyses (95% confidence) to measure exactly what the new campaigns contributed.

Flow: product feed via DataFeedWatch to Google Ads

THE

RESULTS

+13% extra revenue during the test period
€18% extra ROAS compared with manual campaigns
Less time spent on campaign management
Greater scalability across multiple countries
  – No out-of-stock products
  – Unique keywords and text ads with their own ad groups

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Adidas E-commerce Google Ads Case | 13% revenue growth