Mix and Fix: Product Assortment (part 2)

Choice of assortment can break or make a particular brand. Consider a scenario where the product assortment is extensive. For example, if there are about 100 different varieties in a soft drink line, the customer would probably have a hard time picking a particular variety.

Mix and Fix: Product Assortment (Part 1)

Variety is the spice of life! A good variety and apt selection is the key to attracting customer to a retail store. This variety that a retailer offers is known as the product breadth. It is also known as product assortment width, merchandise breadth, and product line width.

Click and Collect

As technology takes over shopping experience, several new methods of purchasing have come into play. E-commerce portals and online stores have taken it as a challenge to create multiple delivery channels that cater to the needs of the customers.

Sales Prediction

Sales prediction or sales forecasting refers merely to the process of estimating future sales. One of the critical analysis of the companies, these reports help ascertain the performance of the company based on numerous factors.

Part 2 - Brand reputation

Brand reputation management is a two-part process with the first part being how the brand handles itself to its customers and the second part being how it handles social media backlash or reputation crisis.

Part 1 - Brand Reputation

Brand reputation is the knowledge, or feedback others have about the brand. The brand can be both individual or company. A startup has little or no brand recognition as they are new-born without much public knowledge, while an MNC with global operations has a more prominent brand reputation.

Identify abnormality in Product sales across stores

A sale of any product that does not represent a normal market sales transaction. for example, a store sells 100 units of product A  in a month, on the same time frame other stores are selling only 20 units, then the first store would be considered as an abnormal sale.

Isolation Forest

In Data Science data patterns that have different characteristics from normal data are called anomalies. By detecting anomalies, it provides critical and actionable information in various application domains such as eCommerce, retail , banking etc.

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