Case Study: Avansas TV Attribution

Medialyzer transforms TV ads and makes them measurable, optimizable, and manageable just like digital advertising. Thus, advertisers now have the chance to increase their return on TV investments with data-driven decisions.

One of our clients, Avansas, uses Medialyzer TV Attribution in order to realize efficient media planning while reaching a narrow target audience who need office products in a wide medium such as television with its TV campaign in April 2021.

It is not possible to manually measure the online returns of a large number of planned TV spots on an offline channel. However, TV Attribution, using machine learning, presents it as a dashboard for decision-making managers to enable the strategic planning of the next move according to alternative economic data, with an artificial intelligence algorithm that reports the online effects of each spot such as website visits and sales.

In the first period of the TV campaign, Avansas was able to clearly observe the effects of variables such as TV channel, TV program, day of the week, time of day, prime time/off prime-time on the return on investment through the dashboard.

During the campaign, changes were made to the channel selection. The budget was shifted from high-cost programs in measured channels to programs that captured the target audience on thematic channels.

It was observed that niche channels such as DMAX, NTV, and Habertürk were more efficient than measured channels for Avansas. The budget allocated to these channels was increased and in the second phase, channels such as 360 TV, Ülke TV, Beyaz TV were added to the media plan. The results from the newly added channels were also quite satisfactory.

Again, which programs were more efficient in this channel selection were presented to the managers. By shifting the budget to mid-day news, economy, politics, and weather programs on these channels, there was a significant increase in efficiency.

As a result of the analysis, it was seen that the off-prime time period was more efficient than the prime time for Avansas. Although 4.82% of the total budget was allocated to the daytime period between 09:00 and 16:00, 11.86% of the results came from this time period. On the other hand, the evening time period seemed relatively unproductive. In this direction, an increase in productivity was achieved by shifting the budget from the evening time period into the daytime. When analyzed as PT-OPT, the OPT budget was increased to 30% of the total budget and 57.37% of the results were obtained from this daypart.

Another action taken with the data provided by the algorithm was related to the ad position. On the basis of position, the spots in the middle of the commercials were more inefficient than the first and last spots. Then, the budget for the first and last spots was increased, thereby increasing efficiency.


Benefit analyzes of many spots on the air were carried out with an artificial intelligence algorithm, and actionable insights were presented to decision-makers with a user-friendly interface, with simple graphs and tables.

As a result of these changes made in the media plan, there was a 2.07-fold increase in productivity with an 8% lower TV budget and 91% more results.