No more speculating about the effects of online advertisements in the physical world – Locarta measured restaurant visits for Subway Germany after its “Fit Five” campaign
- The Subway campaign caused more than 140,000 additional visits to restaurants during the campaign (May - August 2017)
- Locarta’s attribution solution measures the offline effect of online ads - without hardware
- Advertisers can finally calculate a reliable ROAS for their online to offline campaigns
Berlin, 9.2.2018. For years companies have been wondering, how many people entered their stores due to advertisement. Thanks to Locarta, Subway has the answer now. The Berlin-based market research firm analysed that Subway’s drive to store campaign lead to 141,385 additional restaurant visits during the campaign period between May and August 2017. “There is noone in the market, who helps us like Locarta to bridge the gap between online advertisement and offline effect”, says Judith Steinmetz of Essence, the responsible media agency.
Exposed users (in-app, mobile or web) were 19.5% more likely to visit a Subway restaurant than users who have not seen any of the ads. The results are based on Locarta’s smartphone panel. It was used to measure how many exposed users visited one of the 637 Subway restaurants in Germany. When comparing this number with a control group, that has not been exposed to the “Fit Five” campaign, it is possible to isolate the effect of the campaign on restaurant visits.
Measuring the offline customer journey with mobile data
“Approximately €3.58 Bn will be spent in 2018 on mobile advertisement in Germany. And the upward trend won’t stop soon” explains Locarta’s CEO Jan Rettel. ”What was lacking until now were appropriate analytics tools to measure the effect of online ads in the real world”. This strongly affects retail, where 91% of revenue is still made in brick and mortar stores, but also other industries with a lot of sites, like gastronomy.
Locarta helps companies not only to quantify the impact of their advertisements, but also analyses which creative was the most effective, what consumers react to ads and when exactly an ad leads to a change in behaviour. The insights can be used to for the make-up of the next campaign, but can also improve targeting of a running campaign.
Locarta’s visit detection is industry-leading
Since its start Locarta has build a panel of currently 1.8m smartphone users - one of the biggest in Germany. With its proprietary technology Locarta is able to detect store visits with high accuracy - even in dense surroundings like malls. “This is anything but trivial”, states Rettel, “since GPS is often not sufficient to detect whether somebody visited Subway or the shoe store next door”. GPS does not work inside of buildings and is also inaccurate in high density areas. Therefore Locarta uses the individual wifi fingerprints of stores in addition.
In urban areas every store is surrounded by wifi networks. The detectable networks in a store and their signal strengths create the individual “fingerprint” of that store. When this fingerprint is detected by a user’s smartphone, one knows that the respective user must be inside the store. In case of her standing in front of the store or in the one next door, signals would differ due to different distances and obstacles (like walls or ceilings) and with it the wifi fingerprint.
About Locarta GmbH
Locarta is a market research agency that provides a new class of analytics based on location, with the aim of making the offline world as measurable as the online one. To do this, the company has built a panel of 1.8m German smartphone users that have agreed to anonymously share their location. Locarta's products include high-fidelity measurement of the store visits generated by ad campaigns, mapping of the catchment areas of physical stores and detailed analyses of how people move around cities.
Locarta was founded in 2015 in Berlin by Jan Rettel (former founder of a New York hedge fund), Vlad Vlaskin (former CTO of the ad platform AdMoment) and David Roberts (former McKinsey manager and founder of the Y-Combinator startup Tuxebo).
You can download the case study here.