Predictive Analytics is one of the biggest and fastest growing trends in the marketing landscape today. A quick search on the topic returns a multitude of results ranging from how to use it in planning marketing activities to mathematical theory and techniques. However, the common theme is simply this: utilising Predictive Analytics techniques is proven to increase revenue.
Predictive marketers are 2.9 times more likely to report revenue growth at rates higher than the industry average and 2.1 times more likely to occupy a commanding leadership position in the product/service markets they serve, as reported by Forbes.
There may be elements of prediction that you are already using across your business, like product recommendation or revenue forecasting. Aligning these techniques with your customer’s lifecycle and wider business goals can help you to build a more strategic and long term predictive plan.
The goal of Predictive Analytics as applied by B2C marketers is to understand customer intentions. By understanding intentions marketing can be more relevant, improving the customer experience and increasing conversion.
When developing any analytics project, predictive or other, the action is undoubtedly the business case at hand. The question you are trying to find an answer to needs to be well defined. It should be aligned to your longer term business goals to achieve maximum potential.
The next consideration is the data that you will be using for the analysis. Ideally, you want to have all your data in one place which is easily accessible. However, this needn’t veto your Predictive Analytics plans. Any good quality data can be used for analytics. Insufficient data will give you an indicative response and you can make changes while gathering more data.
The final consideration is implementation. Any Predictive Analytics models should result in outputs that are actionable. A platform that gives you the ability to act on insights immediately is the best possible partner in implementing Predictive Analytics.
Let us consider a concrete example to tie in all the points mentioned above. Suppose you are looking to improve repeat purchase rates across all of your customers – you would not only need to consider each individuals’ lifecycle stage but also aspects like product and channel preferences. The chart below describes how you would incorporate Predictive Analytics in achieving a higher repeat purchase rate. The red boxes represent business planning. The yellow shows how reporting plays an important part in the predictive process. The blue box is initial descriptive analytics to set the scene. The green boxes represent various different predictive techniques which when worked on together, help identify the right person, the right time, the right channel and the right content, to help achieve the objective of improving repeat purchase rates.
If in doubt, always remember this:
Any analytics, predictive or otherwise, should provide you with an appropriate and actionable solution to an understood business problem
Any activity or indeed lack of activity from your customers should be considered
The model should have the ability to take in more data or amend itself to any new business issues that may arise