How Volument works
Special analytics for conversion optimization
Take any marketing book or growth publication, and they show you this process of converting first-time visitors into something more valuable:
Volument is a special breed of analytics that focuses solely on this conversion funnel: how the different groups of people pass it, where they drop, and how to maximize its throughput.
Everything from data collection, data modeling, and data visualization is designed explicitly for solving this one task.
However, being such a specific analytical tool, Volument is entirely useless for anything else than conversion optimization. For example, Volument cannot be used to measure your system uptime, while general event trackers such as MixPanel, Heap, and Amplitude are perfect for that.
Volument is custom-built for one purpose, and this specificity is the main difference between generic analytics solutions and Volument.
Measurement is fabulous. Unless you're busy measuring what's easy to measure as opposed to what's important
Volument attempts to predict how visitors behave in the future. This is important for two reasons:
- We can compare newly acquired market segments with the more mature ones.
- We can see the long-term impact of our experiments early on.
The ability to reliably compare the different segments is the basis for optimizations. For example, we can see in advance how a newly acquired marketing segment behave compared to all the current visitors (the baseline), or how much traction a new product launch will bring compared to what the situation was before the launch.
How predictions works
When predicting someone's future behavior, Volument looks for other visitors with similar behavior, and study how they are behaving later on.
This behavior 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 the dates continue to go well, you are more likely to continue with the relationship, and more likely take some form of action 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 behavior and see how they returned and converted later on.
We collect samples using the following rules:
- Visitors with more similar history are preferred over less similar history
- Recent visitors are preferred over the historical ones
- 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.
Volument predicts the future in sequence by first proceeding to day 1, and then continue predicting to day 2 on top of the existing predictions, then moving on to days 7, 14, 30, 60 and 90. 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.