Application of Advanced Analytics in Merchandising

Posted By: administrator
Posted On: 29 Jan, 2020
Last Updated On: 13 Jul, 2020

E-commerce market comprises of various functional elements. Merchandising is one of the most critical functions in the e-commerce industry, specifically in the fashion retail sector. The merchandising team is primarily responsible for managing the profitability and selling through rate balance for the firm. The main challenge for them is getting engulfed with complex data and pressurized to make smart, data-driven merchandising decisions to gain a competitive edge. Below are the four vital areas where we observe a large scale adoption of advanced analytics by the merchandising department.  

Merchandise Planning:

Merchandise planning is all about striking balance between the right product and the appropriate quantity. It is primarily all about understanding customer preferences and introducing products which they are likely to sell. These can be done seamlessly by analysing historical data. Below are some analytical questions that can help in planning:

  • What are the different customer segments present in the customer base based on their product affinities based on the geography, devices they use etc.
  • How does the sell-through rates & profitability vary across category, subcategory & products across these segments & how has the trend been recently
  • Which attributes contribute to an increase in Sell-through rates for a particular category or subcategory across these segments

Product Placement:

Product placement and website listings are the biggest drivers of sales. An effective analysis comes into play by ensuring that the listings appear to customers where the demand can be met or the listings come with an out of stock badge to ensure the brand integrity in front of the user.  So merchandisers spend most of their execution time on getting the placements right on the website. The key areas of focus on this activity are:

  • Product ranking on the top listing pages pages
  • Product ranking in lists like recommended products, best sellers, custom curation widgets etc
  • Banner Management & Optimization

Each of the above workflows enables smoother product discovery for customers. The key metric that is used to measure it is total clicks per session and CTR rates on the pages. Analytics helps in maximizing the clicks on products. We see about a 5-15% increment in conversions when improving the above activities based on data.

Pricing and Product Amendments based on Segmentation:

Segmentation always helps in serving the customers effectively. Identifying the categories of products and segregating them will also enable the merchants to plan properly. The Pareto principle of sales can be applied with product mix as well where we often see that 20-30% of the products contribute to 80% of the sales. This means that e-commerce firm is left with a lot of products which are slow moving and they find it tough to sell them. Below are the areas where analytics can be used to improve this situation:

  • Identify slow-moving products early in their lifecycle and forecast their sales pattern based on historical data
  • Make product changes like image changes, description changes to drive sales
  • Identify customers who are likely to purchase them in the existing customer base and run targeted marketing campaigns
  • Set up a pricing strategy to improve the sell-through rates
  • Identify fast-moving products and minimize discounts offered to increase profitability on them 

Inventory Management

Inventory data is an essential aspect of any e-commerce business for forecasting demand and planning supply. This greatly influences fund allocation and marketing budgets thus is an integral part of E-commerce analytics. Products going out of stock lead to an average 10% reduction in sales. In many sectors like fast fashion, products or its variants cannot be replenished due to the nature of the industry. Below is the analytical exercise that can help you with minimizing sales loss. The key in this exercise is to forecast the future sales of the product and project the expected date where they will go out of stock.

  • What is my current inventory level and What are my adds to the cart and sales trend for the last 3 weeks?
  • Am I planning to introduce discounts in the coming weeks and what kind of impact did we see for them historically?
  • Based on the above parameters forecast weekly sales and project the expected date of out of stock for the product

We will cover each topic above comprehensively with analytical techniques used in our future articles in this series.

Advanced Analytics in Merchandising

Retailers nowadays, compete to gain a comprehensive overview of their merchandising operations. Hence, Saras has come up with an intuitive solution HALO which is designed for merchandisers to help them devise new strategies to target specific objectives. The data-driven approach benefits the dynamic merchandising landscape. If you are always burdened with the above questions, the right way is to consult the experts. Talk to our experts for an optimal merchandising experience.

Click Here to sign up for Halo now.

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