Improve Retail Demand Forecasting With Sentiment Analysis

Sentiment analysis is a powerful tool that retail and ecommerce companies can use to improve their demand forecasting. It involves using natural language processing and machine learning techniques to analyze customer sentiment and understand how it might impact demand for a particular product or service. Here’s a closer look at how sentiment analysis can be used to improve demand forecasting, along with some key considerations for retail and ecommerce companies:

What is sentiment analysis?

Sentiment analysis is the process of using natural language processing and machine learning techniques to identify and extract subjective information from text data. It can be used to determine the sentiment of a piece of text as positive, negative, or neutral, and can be a powerful tool for understanding how customers feel about a product or service.

Data sources for sentiment analysis

There are a number of data sources that retail and e-commerce companies can use to extract text data for sentiment analysis. Some examples include:

Customer reviews and ratings: These can be found on a company’s website or on third-party review platforms such as Yelp or Amazon.

Social media posts: Companies can use social media platforms such as Twitter and Facebook to extract customer sentiment data.

Customer service inquiries: Companies can use data from customer service inquiries, such as emails or chat transcripts, to understand how customers feel about their products or services.

News articles: Companies can use news articles to understand how their products or services are being perceived by the media and the public.

How to use sentiment analysis for demand forecasting

Once a company has extracted text data from one or more of these sources, it can use sentiment analysis to understand how customer sentiment might impact demand for its products or services. For example, if a company is seeing a high percentage of positive sentiment in customer reviews, it might expect to see an increase in demand for its products. On the other hand, if it is seeing a high percentage of negative sentiment, it might expect to see a decrease in demand.

An easy way to get started with these powerful demand forecasting techniques is to ditch your spreadsheets and leverage the power of machine learning. There are now specialized software solutions available that let retailers and e-commerce companies easily analyze data from their own systems as well as the internet to forecast demand across different products, locations, time horizons and more.

By focusing on demand planning, retailers and e-commerce companies can boost their top line and bottom line by accurately forecasting demand, reducing inventory turns, and improving customer service levels.