• Predictive Analitics

    TengerData Services 2017
  • Machine Learning

    TengerData Services 2017
  • Pattern Recognition

    TengerData Services 2017
  • Integrated AI Systems

    TengerData Services 2017
  • Deep Learning

    TengerData Services 2017

The Offline Advertisment Problem

Machine Learning in Offline Adverts Effectiveness Updated: 2017-8-20

Tracking online ad campaigns

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 ads?

On the other hand, 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.


Offline advertisement spending

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 shows that most advertisers are still planning to spend a lot of money on offline campaigns in the coming years.


Tracking offline advertisement

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?


A million dollar question

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.


Are you wasting money?

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.


The ROI problem

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

Tenger Data Technologies 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.


Math Magic or The Holy Grail?

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 “math magic” Machine Learning model focuses on association, rather than causation.


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.


We keep it simple

We wrapped this “Holy Grail” 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.

Free Consultation

  • Predictive Analitics

    TengerData Services 2017
  • Machine Learning

    TengerData Services 2017
  • Pattern Recognition

    TengerData Services 2017
  • Integrated AI Systems

    TengerData Services 2017
  • Deep Learning

    TengerData Services 2017