Today’s marketing is fragmented, many-sided and complex reflecting the consumer journey that touches diverse channels and mediums. Thus, marketers have to manage a wider media mix with increasing kind and number of data. Measuring ROI under these conditions is difficult.
Medialyzer’s AI-based algorithm measures the impact of each ad airing on online activity. Our cutting edge video recognition technology provides advertisers with fresh data in real time.
Analyzing your media investments’ impact on online consumer behavior is the key to boost your ROI. Master your consumer journey with our integrated marketing platform.
Request a demo
Who we are?
We are a tech and media enthusiast team trying to find new ways to measure advertisement’s impact on consumer behavior. Our journey started when we realized Advertisers have to rely on outdated techniques which that focus on quantity rather than quality that is needed to understand offline media’s role in driving marketing ROI.
We provide insights that allow advertisers to have a full picture of where revenue is being driven.
We won three awards at the MARTECH event held in 2020.
• Best Measurement Technology
• Best Martech Startup
• Best Data usage with our in TV attribution solution together with GroupM and Garanti BBVA.
GROUPM / SHOP & FLY / TVLYZER
Since Shop & Fly is the first credit card issued by Garanti BBVA after many years, we wanted to make a tremendous impact by planning a large-scale TV campaign. On the other hand, the product had a narrow target group of people taking both domestic and international flights, with a monthly credit card spending over a certain amount. Because the audience that we want to reach out of the medium with 55 million viewers does not exceed several million, we decided to look around for a different solution.
We wanted to transparently see the effects of each of the 3000 TV spots we planned on the goals such as website visits, forms and selling cards. However, there were some difficulties in detecting the effects of an offline channel on online returns. The filtering of the effects of TV-excluded media platforms and the separation of the effects of successive spots in intensive media planning could not be managed by a manual process. For this reason, we made use of the Tvlyzer TV Attribution product, which is developed in accordance with our needs and functioning with machine learning technology.
At the end of the first period of the campaign, which ended in January 2019, we could clearly observe the effects of the variables such as TV channel, schedule, day of the week, time of the day, prime time, on our return on investment. In the second phase of our campaign taking place in May 2019, we performed tests and changes in our media plan through these results. By this means, we obtained 26% more credit card applications than the relevant online channels with a 35% lower TV budget, thus increasing our productivity by 93%.
We have achieved significant increases in business results by reaching the right audience with the use of data from both online and offline channels together.