E-commerce players use data analytics to predict what you are going to buy?

Today morning I was searching for a particular brand of cat food online. And then I went back to work. While I was working, I was distracted 4-5 times by various suggestions for cat food that popped up every now and then.

Has this ever happened to you?

Did you think someone is spying on you?

Worst case scenario- aliens maybe?

Relax, it’s called data analytics.

Data analytics is the process of inspecting, cleansing, transforming, and modelling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. With every click on the internet, there is an increase in the amount of data. A data science technique, data analytics helps us make sense of this mounting data.

With a drastic increase in e-commerce players, there is a need to keep track of every click you make on their portals. Depending on our search patterns, suggestions are made by the website for purchase. This is called predictive analysis. The website understands what you are most likely to buy from them. Companies like Amazon, eBay, Groupon etc. use this method for targeting customers.

These websites integrate their computing infrastructure with various analytics platforms like Sisense, Microsoft R Open, Oracle Crystal Ball, IBM SPSS Predictive Analytics Enterprise, SAS Advanced Analytics etc. These programs are designed to analyze the following:

  •     Customer’s past purchases
  •     Items customers have rated and liked
  •     Items in virtual shopping carts
  •     Purchases similar to the ones by competitors

Data Analytics allows e-commerce companies to use the collected data to predict region wise demand ensuring that their inventory matches the requirements of customers. It also aids in faster dispatch with accuracy, improving customer expectations. There are a lot of advantages to using data analytics.

Customer’s preferred choice

An e-commerce retailer can predict what the customers are looking for when they land on the website.  Predictive analysis search determines their past click-through behaviour, preferences and history in real time allowing a more personal relationship with the site.

Topmost selling price

Data analytics allows e-commerce companies to determine the highest rate a customer is willing to pay for a particular product or service and they can change and alter accordingly.

Target recommendations and promotions

By correlating data from multiple sources, e-commerce companies can suggest personalised recommendations which will work for a particular customer. Similarly, predictive analytics identifies the promotions which strike the best chord with customers improving sales.

Improved price management

The pricing trends can be adjusted with the sales information in order to determine the right price at the right time. This helps to maximise revenue and profit for the e-commerce company.

Reduced fraud

Usually, all data analytics solutions come with pre-built fraud models which can be deployed easily. It identifies potential fraud even before the customer has completed the transaction, reducing chargebacks and administration time.

Advanced supply chain management

Since data analytics allows us to understand customer demand, it becomes easier to maintain a steady supply chain process for the e-commerce website. This includes planning, forecasting, sourcing, fulfilment, delivery and returns.

 Enhanced business intelligence

A deep understanding of consumer behaviour leads to better service overall, thus offering them the products they want, at a price they want and with excellent after-sales service.

 Maximized sales

Predictive analytics builds a model to support real-time pricing that uses input from various sources such as historical product pricing, customer activity,  preferences and order history, competitor pricing, desired margins on the product and available stock to optimise pricing and maximise profits.

Nowadays, the availability of cheap computing power, the growing usage and popularity of cloud and automation tools allow the small players as to compete with the more prominent names in league with the help of these data analytics platforms. The availability of big data tools has democratized the landscape massively leading to a rise in startups and multiple e-stores.