CASE STUDY

How did we enable a leading retailer to improve their customer experience and NPS ratings? 

Oct 6, 2022

Highlights of the Case Study: 

Particulars Description 
Client A leading CPG manufacturer in the US worked with Quantzig to enhance customer loyalty and NPS ratings and maintain long-term relationships with its customers. 
Business Challenge The client sought the assistance of Quantzig to improve its customer engagement process and better understand the demands of its customers. 
Impact Quantzig provided an end-to-end customer analytics solution that helped our client support its entire customer engagement protocol and improve NPS ratings.  

Game-Changing Solutions for the Retail Industry  

The rating systems that were used a century ago gave rise to the ones we use today. For instance, movie ratings first appeared in the 1920s, while hotel rating systems rose to prominence in the 1950s. These systems rely primarily on consumer reviews of a good or service. Although the star rating system and the net promotor score (NPS) each have their share of problems, these two systems are perhaps the most widely used rating techniques now in use. Both of them strive to offer a readily comprehensible, numerical customer satisfaction score that enables businesses to determine how satisfied customers are with their goods or services and track this score over time. 

Customers may efficiently assess product recommendations, gather customer comments and reviews, receive personalized advice and tips, product information, and push notifications and updates about new products with Quantzig’s consumer analytics solutions. 

The Challenges of the Client 

A leading CPG manufacturer in the US worked with Quantzig to enhance customer loyalty and NPS ratings and maintain long-term relationships with its customers. The client struggled to develop a thorough understanding of customer needs, which prevented it from engaging and retaining customers. It resulted in a high churn rate and a high customer acquisition cost. As a result, the client sought the assistance of Quantzig to improve its customer engagement process and better understand the demands of its customers. 

The client faced difficulty in designing a feedback collection strategy with market segmentation, audience segmentation, and customer maturity due to the lack of a formal or mature customer experience program. The client was struggling with the following issues.  

  • Inconsistent customer experience 
  • Customer churn 
  • Employee disengagement 
  • Low customer trust 
  • Siloed views of the customer experience 
  • Difficulty in quantifying customer loyalty 
  • Venturing new markets 

Quantzig’s Customer Analytics Solutions for the Retail Industry  

Quantzig provided an end-to-end customer analytics solution that helped our client support its entire customer engagement protocol. Additionally, Quantzig’s solution assisted the client in launching a cross-functional, omnichannel program to improve customer experience and align priorities across crucial divisions, including marketing, operations, and the contact center. Quantzig combined data from different sources such as product, channel, and account to build a single analyzable dataset. The client then implemented customer loyalty analytics, using multivariate regression-based models and clustering to generate insights that ultimately increased customer loyalty and NPS ratings. 

The Quantzig team created a churn probability simulator to calculate the probability that a customer will break off contact with the retail client based on transactional history, product selection, and relationship length. Advanced sentiment analysis was used by our analysts to evaluate customer sentiment across a variety of feedback channels, including social media and user experience surveys. This action aided in measuring the brand’s reputation and comprehending the particular requirements of its target market. 

Impact Analysis of Quantzig’s Customer Analytics Solutions in the Retail Industry  

Quantzig’s customer analytics solutions helped the client identify customer needs and aligned customer analytic solutions closely to encourage the customers to be loyal and even increase the satisfaction rate. The client implemented live chat to assist customers in resolving issues instantaneously and reduce the burden on the support team. The client also engaged with customers in real-time as a part of a well-planned customer engagement strategy. It enabled the support agents to use live engagement tools such as co-browsing and video chat for quick identification of the errors as a part of the action plan to improve NPS. 

Quantzig’s client adopted customer-centric strategies to improve NPS ratings. As a result, the client could deliver a better customer experience (CX) and great customer support. In addition, Quantzig’s customer analytics solutions helped customers with live engagement, worked on customer feedback, precisely segmented customers for better planning, and measured customer feedback regularly, which resulted in improved NPS ratings. In addition, the client could also provide self-service options. 

Key Outcomes 

Quantzig’s customer analytics solutions enabled the client to improve its customer engagement process and better understand the demands of its customers. The client also engaged with customers in real-time as a part of a well-planned customer engagement strategy. Using live engagement tools such as co-browsing and video chat for quick identification of errors improved the NPS scores. Moreover, the client now measured customer feedback regularly and improved the NPS ratings. Furthermore, the client also provided self-service options

Broad Perspective on Customer Analytics Solutions in the Retail Industry 

Retailers may extract valuable information from the voice of the customer analysis, independent of the format (picture, video, and text), using AI-powered consumer sentiment analysis, and utilize that knowledge to develop plans for better customer service and product innovation. An ML sentiment analysis platform uses several ML tasks to examine data and uncover insights. After using text analytics to extract entities and features from the data, it begins with granular aspect-based sentiment analysis to provide exact customer insights through a sentiment analysis dashboard. Some aspects of a sentiment analysis API include natural language processing (NLP), the ability to deal with open-ended questions, sentiment detection of ratings, and semantic clustering. 

Key Takeaways 

  • Focused on better customer experience (CX) 
  • Delivered great customer support 
  • Helped customers with live engagement  
  • Worked on customer feedback to improve NPS score 
  • Precisely segmented customers for better planning 
  • Measured customer feedback regularly and improved NPS ratings  
  • Provided self-service options 

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