Predictions

Know in advance how your marketing experiments perform.

Predicted A/B testing / How a new blog entry likely generates traction

Why?

While Volument is already order of magnitute faster than traditional A/B testing this feature makes thing even faster. You'll see the outcome of your marketing experiments in hours, instead of days. The predictions are applied to following:

  1. Marketing campaigns
  2. Global site-wide optimizations
  3. Landing page optimizations

How?

When predicting someone's future behavior, Volument looks for other visitors with similar behavior, and study how they behave later on.

For example, let's take someone with a highly engaging first visit and one return visit the next day. To predict future behavior, we collect all the other visitors with close-to-similar initial behavior and see how they returned and converted later on.

This is not much different from real human interaction: you are more likely to go on a second date if the first date went exceptionally well. And if things continue to go well, you are more likely to continue with the relationship, and more likely take some form of action later on.

We collect samples with the following rules:

  1. Visitors with more similar history are preferred over less similar history
  2. Recent visitors are preferred over the old ones
  3. Visitors on the same marketing segment are preferred over other segments

When calculating the score, the best matches are consumed first, followed with the next best samples and so on. So the longer you use Volument, and the more visitors you have, the more accurate the predictions will come.

Predicted traction

Prediction accuracy

Volument makes predictions in sequence by first proceeding to day one, and then continues predicting to day two on top of the existing predictions, then moving on to the next days depending on the average visitor lifetime. On each iteration, we use the best available samples available.

The prediction accuracy or “confidence” depends on the amount and quality of the samples on each iteration.

  • Extensive benchmarking for choosing the best machine learning algorithm
  • Applying the selected algorithm to Volument data model
  • Calculation of the prediction accuracy
  • Option to choose between predicted and non-predicted results
  • Displaying predictions in a user-friendly way

Comments

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