Why e-Commerce industry necessitates a healthy mix of predictive and prescriptive analytics models?

With a better chance at supply chain management, marketing strategy, customer retention your business will be well on your way to a massive success.

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analytics in ecommerce

At the beginning of the big-data age, retailers and ecommerce companies hired data scientists who used descriptive analytics to comprehend the reasons of historical successes and failures and take manual decisions to improve pricing, promotions and assortment. Fast forward 2 decades, data scientists at many big retailers use predictive analytics to get directionally accurate forecasts for a handful of scenarios. But since these analyses focus on past results and don’t provide recommendations for future course of action, they fall short in helping retailers make the quick changes required in today’s omnichannel world. Here, Prescriptive analytics models are the need of the hour.


Before we dwell deeper in Prescriptive, let's rewind a bit and try to answer a few questions like, What is Predictive Analytics? Why is it important in the e-commerce industry? How does it benefit the retailer or the customer? Well, these are expected questions when anything new looms over the tech horizon. Breaking it down to one question at a time we’ll see how simple it is and how effective it can be.

Let’s begin with what exactly is Predictive Analytics? Predictive Analysis is a set of technology powered approaches that can help businesses predict and understand customers’ purchase behaviour, trends, future activity via data modelling, ML and AI, data mining, deep learning algorithms, and mathematics.

How will it help the e-commerce business? Well, unlike retail business, direct customer interaction is minimal in e-commerce business. Thus, understanding customer needs and requirements can be determined by Predictive Analysis. This will help you in managing your supply chain and provide the customer with exactly what he expects to find on your site, tailor-made to suit his needs.


When you meet a customer in person, your body language, your attitude and your product leave a lasting impression and your personal touch is what determines the success of your business. Most customers go back to a shop based on their experience at the store and then the product quality, this gets you retainer clients. In the E-commerce world, this personal touch is what Predictive Analysis makes up for. It helps you provide the customer with exactly what they might need; thus helping businesses upgrade and stay on top of the e-commerce market.

Customer Predictive Analytics will break down a customer’s online behaviour for you, helping you to understand how your marketing strategy should look. It gives you a detailed breakdown of how your e-commerce portal is drawing customers and what they look at and what they look away from. A perfect website with the best content and material also can fail if you have failed to place the product appropriately. Predictive Analytics will do this for you,

  • Number of daily pageviews
  • Number of people clicking on your offers
  • Percentage of people engaging in your clickbait
  • Average time spent on your website

Once you get the details of this, you will understand if your marketing strategy is actually working, did the customer engage in the clickbait and if they moved on, then at what point did your strategy fail. Predictive Analytics is like a Six Sigma project which will help you eliminate misses as much as possible.

Powered with predictive analytics, essentially a retail or an ecommerce company will be able to get answers to how their customers are likely to behave in certain scenarios. This is very insightful information, but it is not enough. What if the company wants the customers to behave in a different way? Instead of just knowing how customers will respond in their usual patterns, what if the company wants to influence the customer to respond in a particular way, which is more favorable for the business. For example, make the customer purchase at a greater frequency, upgrade to higher end products, visit the company website more often, etc. This is where Prescriptive Analytics fits into the retail picture along with Predictive Analytics.

With the help of Predictive Analytics retailers and ecommerce companies can tap into in-house and market data to identify the specific SKUs which have the biggest impacts on basket size and profit, and then by applying Prescriptive Analytics give right recommendations to category managers to adjust pricing, promotions and assortment in the brick-and-mortar store and online store to boost revenues, profits and customer loyalty. This takes guesswork out of the picture, and allows category managers to take decisions with greater confidence. In fact, it is estimated that prescriptive analytics raises same-store sales by 7-12%.


Every business is a successful mix of loyal customers and new acquisitions (with single purchase). The newly acquired customers could be floating or window-shopping frequently. With a combination of Predictive and Prescriptive analytics models, an ecommerce company can help the shopper easily discover the right product, something that meets the functional requirements as well as fits within the budget. Such scenarios have the highest chances of resulting in a sale. When this cycle repeats the company bags a loyal customer.

Improving your marketing strategy, Predictive Analytics will also help you understand which marketing strategy gives you how much customer traffic. So, suppose channel A is not resulting in any clicks; well, that channel is not your clickbait option then. Prescriptive Analytics will tell you how much of the Channel A budget should be moved to Channel B vs Channel C for best outcomes.

Thus when working in conjunction, Predictive and Prescriptive Analytics can provide your E-commerce business with an edge over the others. With a better chance at supply chain management, marketing strategy, customer retention your business will be well on your way to a massive success.


The article is authored by Dr. Anshu Jalora, Founder and Managing Director, Sciative