We all know how simple it is to track the effectiveness of online advertisement. It is simple because we can count the clicks. Analyzing which online ad caused the sale or conversion is a fairly straight-forward process.
But what about offline advertising?
Tracking offline advertisement results is a major challenge. For example, analyzing the effectiveness of TV, Radio or Printed Media ad campaigns is a hard problem.
When you make a decision about your advertising budget, you should be concerned about your ROI. But, how can you tell which offline media spending is associated with your sales?
The problem is, if your advertising is not online, not programmatic advertising and not a direct response ad, you may have no way of knowing which ad campaign is causing the a conversions or sales.
The ROI Problem
According to eMarketer, in 2017, TV ad spending will total $72.01 billion, or 35.8% of total media ad spending in the US. This chart below shows that most advertisers are still planning to spend a lot of money on offline campaigns in the coming years.
Not knowing which revenues are generated by which offline media ad campaign, you may be wasting your money on media that does not actually produce the conversions. It’s understandable that you would like to see some detailed evidence of potential ROI before allocating your advertising budget. The problem is that when it comes to offline advertising, this evidence is simply not available.
Decision Science – The Solution
DTVS designed a highly complex algorithm to tackle this problem. Applying the algorithm, we built a Machine Learning model to look for hidden patterns in your ad spending and sales data. It’s not possible to determine causation because we don’t have access to every possible external factor that might have influenced sales. Thus, our Machine Learning model focuses on association, rather than causation.
Tracking offline advertisement effectiveness - How it works
Our Machine Learning model allows you to input the ad spending data i.e. TV, Radio & Newspaper spending and the Sales data for a single product in a given market. Based on this data, the algorithm will show which media spending is likely to be associated with the increase in sales. The core of our philosophy is: smaller - faster - cheaper.
We keep it simple
In accordance with this philosophy, we wrapped this model in a self-contained software. The software comes with “batteries included”; it will work on your PC. No network connection or external dependencies are required. The app will process your data and it will output the predictions. You don't have to upload your sensitive data to any third party server, all the magic happens on your own computer.