User Prediction is a growing field that analyzes current and historical facts to make predictions about future user behavior. It combines statistical techniques from predictive modeling, machine learning, and data mining.
Now how is that different from User Analytics?
Analytics is backward-looking manual proceduce. A person looks at data, tabulates statistics, and makes a call.
Prediction is automated. A computer looks at the data and makes a call.
For example, pretend there is a brown bear in the room. You could say "Brown bears tend to eat people." That's Analytics.
Or you could say "There's an 80% chance that this brown bear will eat me." That's Prediction.
Now why's that important?
Prediction uses techniques like Feature Selection and PCA to decide what's important, and what isn't. Does the size of the bear matter? Definitely. How about the color of his eyes? Maybe not so much. Traditionally one of the trickest parts of regression models, automating these decisions enables:
Instead of just 1 bear in the room, imagine there are 1,000 bears in the room, and that some are big, some are small, some are white and some look kind of hungry. Wouldn't you like to know which one is the most likely to be a threat? Of course you would! An algorithm can tell you in an fraction of a second where to look. The analyst on the other hand is going to take a bit longer. Analytics is manual. Prediction scales.
So you know what your users are going to do. What’s next? How do you take that information and turn it into a strategy that positively affects your bottom line? Here are some common techniques:
Rank prospects against a scale that represents the perceived value that each lead represents to the organization. Prioritize your finite resources to maximize your ROI.
Adapt your site to accommodate user’s needs in real time. Help your users move through your site the way you want it to be navigated, all while making their experience better.
Calculate your Average Order Value (AOV) to determine the blockages or hurdles your users experience when shopping on your site.
If your customer just purchased a tent, they don’t need to see ads for more tents. Push ads for related products such as sleeping bags, bug spray and hiking shoes to increase purchase frequency and drive sales.
Use previous purchases, coupon utilization, and economic factors to predict users’ price sensitivity and offer discounts and coupons when they matter most.
LTV or Life Time Value prediction is a tool that allows you to predict the value of a user over their lifetime. This tool can be used to help you allocate resources and organize your functional teams to support your most lucrative clients.
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