Predictive analytics is a form of advanced analytics that helps to get information from existing data sets in order to dictate patterns and forecast future outcomes and trends. Marketing, insurance companies and financial services have also been users of predictive analytics, as have large search engine and online services providers.
When ML drives predictive analytics in e-commerce, it can reverse-engineer customer behavior to drive enhanced experiences by analyzing data generated from multiple sources (including websites, mobile apps, social media, and the Internet of Things) in real time.
How does this work? Let’s take a look.
a. Predictive Analytics Enables Personalized Customer Journeys
The insights gained from customer data (past behavior, expectations, and desires) will help you tailor online shopping experiences to perfectly fit the profile of each customer. Delivering Personalization at such a granular level can boost brand loyalty and improve customer retention rates.
For example, almost 80% of what’s watched on Netflix is based on recommendations. This approach has helped the company save as much as a billion dollars in value thanks to customer retention.
Online retail giant Amazon has been effectively leveraging its comprehensive collaborative-filtering engine for years in order to make accurate recommendations. This approach has helped the company up-sell and cross-sell successfully.
If we take Amazon’s category of DVDs, for example, recommendations of similar movies purchased by other customers have helped generate as much as 35% of the sales annually.
Now the company wants to take this to the next level by leveraging predictive analytics to ship products to customers even before they buy anything. While anticipatory shipping sounds like something out of a science fiction movie, it has the potential to enhance the brand’s free one-day Prime delivery service.
In this scenario, Amazon will push its one-day shipping offer with popular items and categories like beach towels, beauty products, cleaning supplies, and similar.
b. Predictive Analytics Helps Improve Customer Lifetime Value
When you have the insights allowing you to make highly accurate predictions, you can have a better understanding of the Customer Lifetime Value, or CLV. For example, if a customer spends $20 a year on your products for ten years, then their CLV will be $200.
You can calculate this based on past purchasing behaviour and the products they are forecasted to buy in the future.
With the help of big data and smart algorithms, you can extend the CLV by doing the following:
- Enhancing customer experiences
- Funnelling traffic from social media platforms
- Recommending products that complement past purchases
- Segmenting your email and SMS subscription lists
When you’re alerted to the early signs of customer dissatisfaction, you can set your customer retention protocols in motion and reduce the churn rate.
c. Predictive Analytics Allows Brands to Augment and Refine Products
When you have an in-depth understanding of your customers’ expectations, your business will be well-placed to adapt your offering to meet the demands of your target market. Essentially, this will mean centralizing your resources on the most highly-demanded SKUs while balancing your investments in market research and new product development.
You can apply predictive analytics in all the areas mentioned above, so that your efforts complement each other for maximum effect. But none of these benefits can be possible without oceans of data and highly sophisticated intelligent algorithms.
The good news is that you don’t have to be a multinational corporation like Amazon to afford predictive analytics technologies. Even small and medium-sized enterprises can access smart insights to get ready for Black Friday and beyond.