The Future is History: Using Historical Data to Foresee Your Campaign's Results Before its Launch

1) Predicting Customer Behavior

Data can figure out how customers are most likely to act better than any other method, and particularly how they’ll act online. Matching behavioral data with customer journeys allows you to predict engagement points for where and when customers are most likely to convert. It also helps to pinpoint where along a customer journey you’re most likely to experience churn. These patterns give you numerous insights into how your campaign might operate so that you can achieve the outcomes you’re after by actually predicting when and where churn might occur. This in turn leads to greater levels of customer retention. Once you know who, when and how a customer is likely to churn, you can stop them at the source and ensure that they remain your loyal customer for longer. 

Building customer models is another feature of predictive analytics. The Aberdeen Group performed a study finding that companies that used predictive analytics were twice as likely to be able to target high-value customers and market their product to them appropriately. 

The major benefit of predictive analytics over traditional methods is that often findings can be completely counter intuitive. For instance, as Christmas approaches, you might think that marketing common Christmas gifts over the Christmas period is a simple matter of keeping up with trends, but analytics has shown that even that’s not so simple:

It turns out that different kinds of Christmas gifts sell at different times, so even the most simple of situations can change when you look at things analytically.

2) Refining Segmentation

Defining a target audience is part of every campaign, but predictive analytics can take it to the next level. One size doesn’t always fit all, of course, and oftentimes the most efficient approach is one that nurtures and segments customers based on highly specific behavioral and demographic data to help define content and generate sales. 

These mean so much more than deciding that, say, uneducated white males over the age of 50 are more likely to legal weaponry and guns. Predictive analytics gives a full overview of a campaign’s projected costs, revenue, and profit compared with its size, so campaigns are actually sized perfectly in tune with marketing and business objectives.

In the graph below from SPSS, targeting a greater number of customers would actually lead to LESS profit, which again is not necessarily an intuitive outcome.

3) Anomaly – or Outlier – Detection

Anomaly detection, or outlier detection, or outlier analysis, is a way to use data to spot unusual differences in behavior right away. These can be data points, events, or observations that suggest mildly or wildly abnormal behavior. Once you have spotted this, you can begin working out what’s really going on. This means you can catch technical issues earlier, but more importantly it means you can see a business opportunity just as it’s cropping up, for instance a change in consumer behavior that allows you to benefit before anyone else has noticed it. Essentially platforms like Dataroid allow you to make better and faster decisions using Anomaly Detection.