Orange Doubles Revenue Thanks to Ad Impression Attribution

Orange Telcom and Digiline doubled revenue from brand campaigns, while retaining return on ad spending (ROAS), thanks to impression level attribution.

• All clicks and impressions from brand campaigns were used to construct individual customer journeys.
• Algorithmic attribution revealed enormous differences in ROAS of the same campaigns running across different placements.
• Budgets were cut from placements with lackluster returns, with the money instead invested into the notable performers.
• Sales revenue was increased by 100% while the same ROAS was maintained.

Orange (#1 Telco in Slovakia) and its media house, Digiline, had been running brand awareness campaigns in the form of pre-rolls, skin formats, and videos for several years.

The problem had always been knowing which placements were the most effective in bringing in sales and contracts. With the traditional measurement methods based on last click data or through a post-click window, neither the agency, nor the client, had been able to differentiate the worthwhile from the unworthy. This in turn, led to a conservative strategy, causing overspending on channels that were favored by last-click attribution.

The challenge became integrating the right technologies that could provide new insights about the true ROI of their advertising euros. With the right tools, there would be no more stumbling in the dark. Meaningful action would follow.

MEASUREMENT

Knowing that measuring clicks wouldn’t get us far, Roivenue and Digiline worked together closely on the implementation of impression level measurement. The key combination of tools were Google Analytics, Gemius and Roivenue’s data-driven attribution suite.

Five different goals with revenue values were configured in Google Analytics to include all conversions Orange considered valuable. Two in particular included: direct web store orders and uses of the store locator tool.

Next, Gemius Direct Effect and Gemius Prism were used to tag every delivered impression with a unique ad ID.

All this data was then passed to ROIVENUE™ where conversion paths consisting of clicks and impressions from all users were reconstructed. The suite then calculated algorithmic attribution results and true ROAS for each placement.

The main point to keep in mind is that, potential customers saw different placements across the internet and if customers who saw a certain placement ended up eventually converting, than the ROAS for that placement would increase.

The analysis showed that there really was a significant difference in the performance of different placements. In fact, they ranged between a return on marketing investment as low as 0.9, all the way up to a return of 14.0. For reference, the average value was 5.4.

Impression based attribution thus showed that there really was up to a 16x difference in the performance of different placements – something that couldn’t have been discovered using the previous measurement stack.

OPTIMIZATION

​The next step was optimization. The goal for the upcoming season was to maintain the ROAS at a higher level of investment.

But now of course, the game had changed. With a much clearer perspective of which placements were bringing the best returns, the budget could be spread in a much smarter way than simply doubling everything. The placements that preformed well received more than double of their previous allocation, while ones that were struggling were decreased.

ROIVENUE™ dashboards allowed Digiline Media house to shift budgets from worse performing placements to the ones with higher returns. They were even able to do so on the fly; thanks to fresh data every week.

With a smarter budget redistribution among different placements, Orange and Digline were able to double sales revenue while maintaining the same ROAS.

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