Predictive Analytics: Why it Matters
Predicting your customers’ activity would sound almost magical, but it is possible using predictive analytics.
In the recent years, big data has become a significant focus point for businesses. Gathering massive amounts of data is now routine for most of us. It’s not a question of whether or not it is important to collect information on your customers. The key points that should come to mind are the type of data to collect and analyse, and from there, the process to predict consumer behaviour based on patterns and trends derived from the data. Predictive analytics is on the rise, and now everyone can benefit from it.
What is predictive analytics?
Predictive analytics comes in when collected customer behaviour and information is analysed to predict how users react to certain marketing activities. From there, marketers can automate their processes with predictive planning. A business running with predictive analytics is based on algorithms, machine learning and analytics via identifying the future outcomes based on historical data.
As easy-to-use software and mass marketing tools have become available for smaller companies as well, predictive analytics has taken over spaces in marketing. With predictive analytics, businesses can determine future responses of their customers, their purchases, their activities on-site and promote cross-sell offers. It helps attract, retain and add value to the most profitable customers.
Where can you use it?
Predictive analytics can be beneficial for every company that deals with an online space. It can help you profile your customers better. Remember that the business which knows more about their customers has the bigger market advantage.
Predictive analytics shines when you need to profile your best, most active and most loyal customers. Profiling can reflect on what user journey they take, what products they prefer, how often they come to your business and so on.
Once you know enough about your overall and also the most loyal customers, you can quickly profile your prospects which can help you determine and acquire high quality leads to your sales. By focusing your sales team on high-quality leads, you will have a greater impact on your sales. Predictive analytics can create highly effective marketing campaigns as well and can help you to cross-sell to your loyal customers. And as a bonus, there is no better tool than predictive analytics to profile and value a loyal customer – it can get the most out from your loyalty program.
Primary examples of usage
The very basic predictive analytics technique is the A/B testing. For example, A/B testing on an email marketing campaign involves two email templates. An equal amount of traffic will be sent to these two versions, and the responses will be compared through this test. You can enhance this process with multivariate testing, where you compare not just two outcomes but multiple variables.
You can use predictive analytics technique with regression modelling. With the regression model, you compare multiple variables (predictor) to one variable (response). In an email marketing campaign, for example, you want to research how your users react to several variables, like send time, content, subject line and much more. All of these, you may want to research to a single variable, which is important for you, like click-through rate (CTR). In the end, your outcome should be a prediction, where you can predict how users react to different content, subject headline, etc., concerning CTR.
Predictive analytics is the brain of your marketing
Normally, you have tools and techniques you use, like direct mails, social media content, targeted ads and much more. You operate them separately most of the time, or you might use marketing automation which helps you save time and target more valued customers. Predictive analytics sits on the top of your marketing automation where you analyse all the data combined in your marketing and with systematic predictive models you can easily make your marketing automation more efficient.