Analytics in Retail Industry
The most competitive retailers today are increasing response rates and revenues by using predictive models and other analytics to make relevant, personalized, precisely timed offers to customers. Analytics provide a concrete means of realizing the long-standing exhortation to “Know your customer.” Retailers who know their customers analytically are making smarter strategic decisions about online/ brick-and-mortar store design, merchandising and other investments. Analytics enable us to implement these organizational-level strategies in individual-level offers.
What values does Analytics bring to Retail?
For retailers, one of the greatest values of analytics is to provide decision points for determining how to treat customers differently. Analytics provide a reliable means, based on statistically valid data analysis instead of hunches or observational judgments, of deciding what actions to take with your customers
- Will it be profitable to offer free deliver
- Which offers will have the biggest impact on a customer’s shopping behaviour?
The Opportunity and the Challenge The opportunity to achieve competitive advantage from “analytical retail” is enormous. With the help of KUDWI analytics, retailers can:
- Develop close relationships with customers based on a deep understanding of their behaviours and needs
- Deliver the targeted advertising, promotions and product offers to customers that will motivate them to buy.
Widely-Adopted Analytical Trends in Retail
“Assortment Optimization and Shelf Space Allocation” Using analytics to determine what products to offer in what quantities
Rapidly maturing with regard to micro-level analysis and chain-wide assortment planning; more emergent at the macro level and for cluster-specific or store-specific optimization. Assortment and shelf space optimization has historically been a periodic (e.g., seasonal) activity, but it is evolving toward a continuous process. Space optimization was often done by manufacturers as category captains in grocery retailing, but is evolving toward a joint process between retailers and key suppliers.
“Customer-Driven Marketing “ Use of customer data to segment, target, and personalize offerings
Mature but evolving; new channels (e.g., mobile devices) are always emerging, as are new restrictions on customer data acquisition and management (which vary by country). Customers are most likely to respond to offers and promotions that are relevant to their needs and consistent with past behaviors. More recently, sophisticated retailers have largely focused on actual customer behavior as a predictor of future buying behaviors, and have used internal behavioral data to target offers.
“Integrated Forecasting” The use of statistical forecasting to support multiple functions
Forecasting is very mature, but rapidly evolving due to ever more sophisticated automated statistical forecasting technologies. Benefits of better forecasting include effective and efficient allocation of resources, reduced inventory stockouts and excess goods, faster and more accurate management decision-making, and enhanced coordination between functional groups, headquarters and stores, and with external suppliers.
“Pricing Optimization” Using analytics to determine the optimal pricing of products and services through their lifecycles
Pricing optimization has been shown time after time to increase sales and margins; it is one of the most direct routes between analytics and the bottom line. Accenture research suggests that one third of retailers have at least 10% of their merchandise left over at the end of a season, and for some the figure approaches 25%.
“Product Recommendation” Using analytical approaches to recommend product offerings for particular customers
With the proliferation of product offerings in retail has come a need for analytical approaches to recommend particular products. Many online retailers have discovered that they can ease this process for consumers by offering recommendation systems.
“Workforce Analytics “ Optimization of staffing with regard to cost, customer shopping patterns, and locations
Workforce management and optimization applications help to align labor schedules to store traffic, required fixed activities and other variables driving store activity. They can substantially improve schedule effectiveness and labor-to-sales productivity, as well as customer service levels. Analytical tools are also used for workforce acquisition, both in identifying the most effective employee attributes, and in scoring potential employees as hiring candidates.