CASE STUDY

Social Listening Helps a Multi-Brand Retailer Develop a Robust Social Media Strategy

Dec 27, 2017

Client: A leading multi-brand retailer in the US with more than 500+ stores across the country

Typically, the retail industry comprises of organizations selling commodities for household consumption. Today, the customers across the retail industry space are craving for convenience, creativity, and authenticity in the products being offered owing to the recent shift to a more customer-driven economy. Additionally, the growing concern for healthier lifestyles is promoting the growth of the retail industry amid altering consumers’ preferences and the rising consumer spending habits.

However, the future of the retail sector depends on the key factors that are expected to influence the growth projections in the coming years.

  1. Online Retailing: Today, consumers have started looking for convenience while purchasing products owing to the relentless growth of technology. Moreover, a move toward a more agile and seamless digital experience is expected to address the purchasing discrepancies among the customers across the retail industry.
  2. Higher Disposable Income: The disposable income of the customers was subject to fluctuation due to the great This had affected the customer’s buying behavior. With the steady recovery of the global economy, customer spending power is expected to rise in the coming years.

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These factors are forcing organizations operating in the retail industry sector to leverage the use of social listening solutions. Social listening solutions enable firms to capture, analyze, and interpret the opinions, voices, and sentiments of stakeholders from diverse sources across the extended enterprise in a systematic way. Social listening also helps retail industry firms to align product positioning with customer expectations, develop features, products, and services for local markets. Companies can also improve customer satisfaction with personalized marketing and customer service across micro segment customers with the help of social listening.

The Business Challenge

The client, a leading multi-brand retailer in the US with 500+ stores across the country, wanted to leverage the data generated on the web and social media about the retail industry, their brand, products, offers and discounts, customer issues, and competitors. They wanted to improve decision making and enhance customer satisfaction owing to the increasing pervasion of digital technologies. Moreover, due to the widespread reach of the business, it was not possible to create a strategy or analyze the “voice of customers” across all segments of the shoppers that visited their retail stores. As a result, they wanted to go beyond tracking basic metrics on social media and develop an operational strategy for enhancing customer-centricity—yielding benefits in better product offerings, higher brand recognition, and stronger customer loyalty.

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Our Approach

Defined use cases in the following areas, before developing a dictionary for entity extraction

  • Sentiment Analysis (Brand, Category, and Product level)
  • Customer Issues
  • Competitor Analysis

The client had provided six months of historical data (from some of the social media sources) while Quantzig scrapped the rest of the data from online sources which included

  • Facebook
  • Twitter
  • Pinterest
  • Reddit etc.

Data scrapping was done using a multi-server set-up and parallel data crawling and extraction techniques.

  • Developed and automated scripts for data cleansing
  • Stemming and Lemmatization
  • Applied Natural Language Processing (NLP) techniques for entity extraction – names, locations, shopping experience, product, etc.

Topic Modelling to automatically classify comments, phrases into major categories and identity the main characteristics of each topic

E.g., Price comparisons for fashion accessories being sold by the same brand in clients and competitor stores, etc.

Key Findings and Business Benefits for the Client

Strategies that we recommended based on our analysis to analyze unstructured data from web and social media sources

By analyzing the data from sites like Pinterest, the client created a dedicated section for highlighting items that were frequently ‘pinned’ for each brand

  • Categories like leather accessories, handbags, men’s shoes were the most frequently pinned categories
  • We recommended that they stock inventory levels for each store based on the local interests of the customers

Identified aspects of individual brands within the store that influenced customer segments. E.g.

  • ~50% of the customer had negative sentiments about the assortment, fit, and sizes for a specific fashion label
  • Lack of availability of options was the top complaint
  • These findings helped the retailer to plan for appropriate size distribution and improving the assortment based on seasonal shopping patterns

Provided inputs to the marketing and sales function to align promotions and social media campaigns based on the current topics of interest for meeting customer demand.

To know more about how our social listening engagement helped a multi-brand retailer in the United States, request more info.

  • Updated the marketing teams regularly on the shift in polarity (Positive to Negative and vice-versa) for them to improvise marketing campaigns
  • Analyzed comments about price comparisons and discounts for the same products being sold by competitor brands
  • Customers were not satisfied with the discounts and prices of the product in the leather accessories and home retail sections, due to better deals being provided by competitors
  • Share of voice for top brands for the client as compared to their competitors
  • For sports merchandise and fashion apparels, the competitors share of voice was almost 3x
  • Identified which outlets were perceived as providing the best value – quality products at reasonable prices
  • ~ 60% of the comments classified the retailer as a ‘value for money’
  • For leather accessories and home retail sections most of the products were either cheap or expensive

 

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