CASE STUDY

Customer Analytics Engagement Helps Decrease Customer Churn by 15% for a Leading Retailer

May 31, 2017

With most of the businesses in the retail sector going digital, the industry has become a data-rich environment and also one of the fastest-growing industries. Right from the store app to the contact center, data is being collected at every touchpoint. High customer churn and inventory pileups are significant issues in the retail industry. To meet the evolving needs of the customer, our analytics team developed the concept of the next best offer to next best action by including churn propensity, lifetime value, and social media conversations in the analysis for making context-based suggestions.

Quantzig’s customer analytics engagement team analyzes various POS, CRM, product, and other internal data to recommend products to the customer that best fit their profile based on their interests and purchase history. Our customer analytics engagement converts raw data into actionable insights to improve customer satisfaction and meet the ever-changing demands of various customer segments to improve sales. The solution from the engagement offers improvement in the product bundles and price modifications based on customer preferences.

The Business Challenge and Quantzig’s Approach

With an objective to improve sales and retain customers, the client – a leading retailer in the US, approached Quantzig to perform a customer analytics engagement. The scope of this predictive analysis engagement was to personalize the customer experience to expand their customer base, improve customer satisfaction, and meet the ever-changing demands of various customer segments. The client was facing significant issues due to high customer churn and inventory pileups. They wanted our analytics team to analyze the POS, CRM, product, and other internal data to recommend products to the customer that fit their profile based on their interests and purchase history. The team used collaborative filtering to identify relevant marketing strategies and product offers for new customers.

The primary objective of this customer analytics engagement was to develop the next best action and offer analytics solutions to improve sales and customer retention.

To cater to the needs of the client, our customer analytics team developed a next best action solution to gain an in-depth understanding of what products are most suitable for each customer. Moreover, our customer analytics experts also analyzed historical data such as sales data, customer data, transaction data, product data, pricing data, promotions, customer feedback, and social media metrics.


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Business Benefits and Insights

With a keen eye on helping a leading retailer improve sales and retain customers, Quantzig’s analytics team developed a next best action solution to gain an in-depth understanding of what products are most suitable for individual customers. The customer analytics team identified the right campaign/channel for each customer stage based on the association between marketing response and marketing efforts and provided metrics and analysis for customer footfall by weekend and weekday. The engagement increased the marketing response by 2x due to the implementation of a targeted marketing strategy. The study focused on the propensity of a customer to respond to unique marketing activities and the likelihood of a customer to purchase particular products.

The client was able to gain descriptive insights into customer demographics such as age segment, income group, marketing response index, and promotion usage percent and associated customer behavior such as average revenue generated. During the customer analytics engagement, the team analyzed purchase behavior at the segment and individual customer level to identify customer needs and interests appropriately. To improve targeting strategy, the team identified customer response to marketing campaigns and promotions. Customer analytics strategies were developed by classifying loyal and satisfied customers based on NPS, longevity, and purchase behavior. Due to the lack of information about new products, the majority of customers were dissatisfied, which led the team to analyze the social chatter to get a better understanding of the brand perception and product feedback.

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