Kleenex – Uses existing data to maximise ROI during Flu Season…

hollerlab —  May 14, 2013 — Leave a comment

In the UK during flu season this year, Kleenex has combined existing data as well as Google’s Ad planning tool to create an adaptive planning platform to increase the effectiveness of their media buy. The tool has helped the business to anticipate, phenomenally accurately, the cities and regions that were suffering from or about to suffer from a flu outbreak.

They built the model by bidding against flu related keywords in the lead up to flu season to generate some real time search data about where people were searching flu related terms. They were then able to map out the relative, real time search volume and establish what sorts of thresholds then resulted into full breakouts by cross referencing this data against subsequent GP Visits and NHS Direct Calls. Through this they were able to build in triggers in an adaptive planning model that allowed them to create a responsive media plan which increased the relevance of their search, digital and above the line advertising.

Vicks also implemented a similar campaign based on historical data using the Google Flu trends platform. Essentially Google for the last six years has correlated flu and symptom related search terms via their engine against ‘traditional flu surveillance systems’ to get a real time as well as historical archive that studies the effects and spread of disease. In 2012, Vicks in the US used this data to target mobile media in proximity of pharmacists that stocked its new behind ear thermometer with a CTA citing the high risk of flu in the area.

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http://www.google.org/flutrends/au

http://www.nytimes.com/2011/12/23/business/media/using-googles-data-to-sell-thermometers-to-mothers.html?_r=0

Although the two campaigns are similar, the Kleenex campaign is the first instance we have seen in which someone has taken Google data and mapped it against local health system intelligence to create a real time, adaptive model. In the case of Kleenex, they were able to target areas at a 96% accuracy by region and city, that were in the infancy of a flu epidemic. This resulted in a Year on Year increase in sales of tissues by 40%.

It is great to see some creative, intelligent uses of data, to increase the relevancy of messaging. Rather than a shot-gun approach over a given time frame across a broad geographic region.

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