7 key ways in which Data Analytics is transforming Retail and E-Commerce
Context
Just like every other sector, data science and AI is playing a phenomenal role in transforming the retail industry. It has touched almost every segment of the retail industry and almost all enterprises have been realising that they need to utilise their data to stay in the competition.
Big data analytics expenditure in retail market was estimated at USD 4.2 billion in 2019. This market is expected to reach USD 13.3 billion by the end of 2025, registering a CAGR of 21.2% during the forecast period (2020-2025).
Now, the e-commerce industry has grown enormously in the last few years and the customer data getting produced in abundance has created an incredibly fertile space for data driven innovation.
So, in today’s date running an e-commerce retail business without implementing data science and AI is not exactly a smart move, if you want to stay competitive.
So, here are some key areas the team from Eshia Analytics has partnered with clients to innovate and transform their e-commerce retail business with data science and AI:
1) Demand forecasting and inventory management
One of the key areas of dissatisfaction among the customers is when the item of their choice goes out of stock. However, at the same time if an item gets stored in excess in the inventory then that leads to unnecessary expenses for the company.
So, demand forecasting is a scope that no retail businesses can afford to ignore.
All this while, guess work was the primary method for demand forecasting among the analysts. But with data science and sophisticated machine learning algorithms at our disposal the customer data can be used to predict demand and sales and hence optimize inventory utilization.
2) Dynamic pricing and price optimization
Historically, the retailers used to assign the prices to their commodities based on parameters like profit margin, cost of goods sold and manufacturer’s suggestions. But today more attributes like seasonality, demand, customer location and frequency of purchase can be taken into account for setting the prices and those have shown to increase profit margins.
Another key factor that drives the prices are the prices set by the competitors. Customers can check out in the internet at what price the other e-commerce retailers are selling a particular commodity and hence may choose not to buy it from a given retailer.
In that context big data and data science can help the businesses set the prices correctly in real time by taking into account the competitors’ prices and other features like popularity of an item and the average number of sales.
According to Deloitte, pricing solutions can get retail businesses an immediate margin performance of 2-4% and a sales growth of 1-2%.
3) Fraud detection
Over the years the cases of identity theft, phishing, account theft and frauds related to shipping and billing addresses have increased exponentially. The competency of the hackers and scammers have rendered the traditional fraud detection algorithms futile.
Today this stands out as one of the major problems for the e-commerce industry. But fortunately, modern approaches involving data science and machine learning are being adopted which are showing phenomenal results.
Clustering algorithms and deep neural networks help the e-commerce businesses make use of the huge amount of online data and save money, customers and their reputation from being lost through fraudulent activities.
4) Understanding customers’ sentiments
Understanding the customers’ tastes and sentiments has always been very crucial to the retail businesses. Data science and machine learning have not only simplified and automated the traditional processes adopted for these jobs but have also shown capability of providing very accurate results.
Social media serves as the most abundant repository for a data scientist to perform sentiment analysis. Natural language processing techniques are used to capture the sentiments and other attributes relevant for the study which can help businesses improve their product and services and meet consumer needs.
5) Understanding customers’ life time value
Customer lifetime value is the total value of a customer’s profit to the company over the entire customer-business relationship. It is very important for a retail business to know the life time value of a customer.
The customers’ histories of purchases, expenditure, repeated items, purchasing patterns and other vital attributes are taken into account. The data is cleaned, processed and consolidated into a format that makes it easier for the data scientists to apply statistical techniques and machine learning algorithms on. Knowing LTV of different customer segments helps companies decide their support strategy and maximum permissible customer acquisition cost for that segment.
6) Suggestive selling through recommender systems
The importance of recommender systems in e-commerce portals is incredible. Customers are no longer needed to surf through the entire website to find the products of their choices. The customer’s history of purchases, buying patterns and tastes can be analysed and combined with predictive analytics to build a list of suggested items.
Also, the customers get exposed to a variety of other items that are there in store for them and that can trigger the urge to buy more. According to McKinsey, almost 35% of the purchases on Amazon come from their product recommendations.
7) Market Basket Analysis
Market basket analysis is a technique that is in vogue for quite a long time now. E-commerce companies have been using it in the best way possible.
Given a particular customer is accessing the portal, market basket analysis will help determining whether the customer will buy anything and what items s/he might buy. Along with the customers’ historical data and the potential buying impulses, some of the attention is also given to how the products have been marketed.
The usage of advanced predictive analytics can help businesses implement this functionality and gain a lot out of it.
What Discite Analytics can offer
So, it is no longer a luxury but a need to have data science functionalities being implemented in your retail business.
While businesses may have the enthusiasm to explore the world of data to grow their businesses but they may not have a clue where to start or how to start? They may not have an idea about how to build a recommender system for their online portal or how to set their pricing correctly in real time to get an edge over their competitors.
They may not know how to upgrade their traditional redundant algorithms to keep a check on fraudulent transactions. Even the idea of hiring a team of AI engineers to deploy an intelligent chatbot to respond to consumers’ queries may seem very exorbitantly costly.
Just in case you see your enterprise falling in the same category of businesses then Eshia Analytics is here to partner with you!
Here at Eshia Analytics we have a team of extremely talented and experienced data scientists + engineers who have dealt with a variety of the problems stated above and together we can ensure that your company gains an advantage over your competitors, and maintains that edge.