CASE STUDY

Discount and Price Optimization Engagement for a Leading Retail Client Helps Increase Profitability by 10%

Dec 14, 2017

Today’s retail is all about taking advantage of the advances in machine learning and artificial intelligence to offer more targeted and personalized shopping experience.

According to a recent survey, it is estimated that the retail space accounts for 31% of the world’s GDP. The retail sector comprises establishments selling commodities for household consumption. With the recent shift to a more customer-driven economy, the customers in the retail space are craving for authenticity, convenience, and creativity in the products being offered. Amid the changing consumers’ preferences and the increasing consumer spending habits, the growing concern for healthier lifestyles is promoting the growth of the sector. In the United States alone, the hypermarkets and supermarkets accounted for 35% of the direct retail sales. However, the future of the retail industry depends on the key factors that will influence the growth prospects in the coming years:

  • Disposable Income: With the recent economic recession, the disposable income of the customers is fluctuating – and this, in turn, has affected the consumers’ purchasing habits. With the gradual recovery of the global economy, consumers’ spending power is expected to increase manifold in the coming years.

  • Online Retailing: With the relentless growth of technology, consumers have started looking for convenience while purchasing products. The transformation to a more agile and seamless experience is expected to address the purchasing disparities among the customers in the retail industry.

Speak with our analytics experts today to know more about pricing analytics and how it helps organizations implement an analytics-driven approach to price and discount optimization for improving sales and gaining market leadership.

To address these factors and devise new strategies to spur growth, leading companies in the retail industry are leveraging price optimization solutions. Price optimization not only helps deliver improved sales but also provides businesses with an opportunity to improve customer satisfaction. For most organizations, the benefits are gained through improved pricing efficiency across customer segments and the ability to cater to the dynamic economic and competitive environments.

The Business Challenge

A leading retailer with more than 3000 stores across Europe wanted to leverage the use of macro-level predictions and customer purchase history to determine the prices of the products and further decide markdown percentages. One of the major challenges faced by the client was the fact that the discounts and markdowns were based on the judgment and experience of store managers and salespersons, which were mainly driven by excess inventory levels. However, due to increasing pressure from competitors, on pricing, in different geographies, and the effect of seasonality on purchase patterns of customers, their predictions on pricing, discounting, and inventory levels were highly skewed.

The retail sector client was looking to optimize their pricing and discounting strategies across stores, customer segments, and geographies by leveraging their internal data to identify the major drivers of sales of individual products and determine the best pricing strategies to maximize revenues in the retail industry.

Our Approach

Data used, models applied, and the dashboard themes that we developed.

To address the concerns of the client, the industry experts built a data dictionary comprising information from the customer, sales, product, and pricing data elements. The assumptions and exclusion criteria applied for data cleansing comprised of treating missing values, outliers, and excluding rows with zero sales values.

Integrated multiple data sets at Order ID and Product ID level to create a single analyzable data set.

With the aim to target the most promising and profitable segments, the price optimization experts:

  • Applied clustering methods to defined customer segments based on customer demographics, purchasing, and product pricing data
  • Carried out a univariate analysis to understand the individual impact of price, discounts, and other variables on sales
  • Multivariate linear regression model to understand the impact of multiple factors, in conjunction with pricing, on sales
  • Devised a simulator for designing pricing and discounting strategies dynamically at the individual product and customer segment level
  • Created a real-time pricing dashboard integrated with the pricing models, which included real-time alerts and allowed managers or sales agents to react to sales volume lifts based on pricing and discounting

Request for more information to know more about how our cutting-edge analytics advanced solutions help the retail industry.

Business Benefits and Solution Highlights

With an aim to retain the most profitable customers and improve business performance in the retail industry, the price optimization experts at Quantzig:

  • Offered real-time visibility to the category and pricing managers on product prices, sales trends, customer demographics, and segment information
  • Provided the client and the sales team with an effective decision-making tool for setting prices and offering discounts on a real-time basis
  • Helped increase the productivity of the sales agents by enabling a pricing dashboard to simulate results of pricing changes

Additional Benefits and Insights

  • Devised a pricing and discount strategy based on geography, customer segment, historical sales, and seasonal indicators
  • Profiled the top three products by profit, geographies, and categories
  • Simulated sales volume based on customer segments, product category, price, and discount applied, etc.
  • Maximized the probability of closing a sale based on pricing and discounting strategies
  • Sales forecast based on price setting at the product level for the next ten weeks
  • Helped reduce excess inventory by setting prices that allow products to sell better

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