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Leveraging Big Data: Enhancing Forecast Accuracy with Sentiment Analysis

Fri, Sep 26, 2014


How close are businesses to turning social analytics into the gold mine it has the potential to be? The answer is not close enough. 

Big Data and Social Media Analytics: A Catalyst for Better Customer Insight

In today’s customer-driven industry, what separates the winners and losers is the ability to integrate data from different sources to develop deeper customer insights. This in turn can be leveraged to amplify sales potential by creating tailored shopping experiences based on an understanding of what a customer wants. 

While many retailers are still working on better utilizing the customer data they gather, they now need to raise the stakes by realizing the benefits of Sentiment Analysis. Until a few years ago, this dimension of Social Analytics was left unexplored, but today, it has become a powerful tool for understanding consumer preferences.

Using Sentiment Analysis, which defines the practice of analyzing words used in social media conversations, fashion retailers are at a unique position to form much-needed actionable insights and greatly improve forecasting accuracy, brand loyalty, and customer service. 

Analytics collected via social media conversations will present a more comprehensive view of a customer, helping businesses to meet perpetually shifting consumer demands more effectively as they express their likes and dislikes through these open, dynamic channels. Preferences around a particular product can be easily matched to social data to answer questions like "what do females in the 20-30 age group think of product x, and how did the response change after the launch of our new video campaign?”

This provides valuable insight for fashion retailers, from what products might perform best to what will most likely sell, and in which store locations. It will also gauge the success rate of product placements and help optimize retail experiences. 

How much value does it add to Fashion Forecasting Processes?

For fashion retailers, the value lies in its ability to capture and aggregate consumer interests and preferences – based on geography, demographics, seasonality, color, price groups and other characteristics. By measuring the positive and negative opinions expressed through social media, a retailer is able to apply predictive analytics to identify new opportunities, as well as forecast trends that would eventually impact their business. What’s more, fashion businesses will be empowered to predict what a customer wants by segmenting their customers more precisely to capture their attention and engage with them until such interactions leads to purchases. 

Winning On and Off the Runway

The ability to do this without the need to directly engage with customers is perhaps its greatest advantage, as there is no intrusion imposed on any customer. You have perceptions of your brand right in front of you as shoppers publicly announce their likes and dislikes on social media, giving you the ability to identify the right direction to head in, where to push harder and how to excel in satisfying consumers. In many ways this is big data in its purest form - the merging of multiple data streams in pursuit of real or near-real time insights.

Brands that can mine valuable data and react to it fastest will win - both on and off the runway. With improved fashion forecast accuracy, every aspect of the business will change, from what color will be in next season to producing products that fit different body types better. For fashion retailers aspiring to convert the data they gather into actionable insights, the opportunities are endless. 

Download our white paper to learn about the many ways customer sentiment can help improve demand forecasting.

Customer Sentiment, Social Analytic, Big Data