Predictive analytics is the process of using statistical algorithms and machine learning techniques to predict the likelihood of future events. Simply put, it’s the machine’s way of looking at past data to predict a future outcome.
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It’s difficult to imagine that you know what a customer is about to do even before he does but such cases are more common than you think. Ever been surprised by how Youtube knows exactly what you want to watch next or Amazon knows other items that would go really well with your purchase? Well, that’s the power of predictive analytics.
Below are some of the most common business applications for implementing predictive analytics in your business:
#1 – Customer Segmentation
You already know that all your customers are neither the same nor equally valuable but usually, our sales & marketing strategies don’t take that into account.
Segmentation allows you to split your customers into different groups such as ‘loyal customers’, ‘one-time customers’, ‘low-value purchasers’. These customers can then be sent highly relevant recommendations and targeted marketing messages, which would increase customer loyalty and lifetime value.
#2 – Churn Prevention
If you’re running a subscription model business, churn models can help you boost your most important KPI: customer lifetime value (LTV, CLV). The model helps you understand why customers are leaving and what you can do to extend their stay.
For example, if customers who subscribe to the monthly plan tend to cancel in their 4th month, you can send emails to keep them engaged. You can also create an annual upgrade offer they can avail at the end of 3 months.
#3 – Sales Forecasting
Another common business application of predictive analytics is sales forecasting. The idea is simple: we look at the past data and understand what factors impact our sales the most. Then, we predict what our sales would look like in the coming months.
The best part with a forecasting model is that after every implementation, the model gets significant feedback and keeps improving. In a few months, you’ll know accurately how much revenue and sales to expect and you’ll be able to take corrective action for slow months.
How to implement predictive analytics to a custom problem
These aren’t the only problems you can apply predictive analytics to. In fact, you can solve practically any problem using machine learning techniques.
Below is the strategic process used by most companies offering predictive analytics as a service to solve their customer’s problems:
Step #1: Define Your Problem
The #1 challenge to implementing predictive analytics solutions is that it is a resource-intensive process.
Therefore, the process should begin with business application and impact. The first step is to identify the most pressing problem and determine if using predictive analytics would be a good return on investment.
For instance, maybe you’re an on-demand video streaming company (like Netflix or Hulu) and your challenge is that a significant chunk of customers cancel their subscriptions. If the loss in revenue is significant, you should definitely try implementing predictive analytics.
Step #2: Gather Data
Once you’ve defined the problem, you’ll know where you need to get the relevant data from. In the previous example of the video streaming company, they can get started using their CRM data.
Following are some data sources you can consider:
· Your analytics solution like Google Analytics or even your web server logs
· Your CRM
· Your web traffic sources (AdWords, Google Search Console)
· Social Media (Facebook, LinkedIn, Twitter)
· 3rd Party Services (finding the right ones takes digging but here’s an exhaustive list)
Step #3: Use the right statistical model/machine learning technique
Once you have the problem defined and the data ready, you need to apply the right predictive models.
In the above example where we’re trying to predict if a streaming subscription gets cancelled, we’ll be using classification algorithms. This means that a person who has made a booking will be classified as either “likely to cancel” or “unlikely to cancel”.
Once the model is built, we pass our real-world data through it and see if the predictions are accurate. If it is, we know that the model is working well and ready for deployment.
Each of these steps requires ample technical expertise and a deep understanding of analytics and data science. Therefore, we recommend you either build a team of data scientists or try working with a company that offers predictive analytics as a service.
