The Current Retail Context
To create sustainable advantage over competition, retailers are trying to enhance their product offerings, service levels and pricing models. To prevent value erosion and to protect margins, retailers are trying to reduce their cost-to-serve per customer and thereby ensure that the total cost of ownership of a customer over time is reduced. Managing promotional spends is another critical area for retailers to focus and target customers more effectively and efficiently.
Paradigm Shift for Retail Focus
Over the last decade or so, retailers world over have focused their resources to create efficiencies in their sourcing, supply chains and store operations with the purpose of reducing the operating costs and maintaining a hold on their thin margins.
Figure 1: Customer Satisfaction
However, today these process improvement efforts have become par for the course. There is an urgent need for retailers to revisit the core aspect of their business, which has been out of the center of their attention: customers. The task ahead is to reorient the organization to shift the paradigm from customer relationship management (CRM) to customer relationship building.
Customer satisfaction is no more a logical conclusion of management of the selling process; it requires in-depth understanding of customers and products (see Figure 1). Development of strategies with thorough knowledge of customer needs, behaviors and motivations along with product trends and associations clubbed with execution is the only approach that will lead to service levels significantly superior to competition. Technology acts as an enabler to provide insights into customer behavior, product interrelationships, and successful promotion types for different channels.
The wealth of data available to retailers presents the opportunity for competitive advantage, but the gains only accrue to those who exploit it the best. Though CRM, data warehouse (DW) and business intelligence (BI) tools implemented by retailers are useful to a great extent, retailers are still seeking decision-enabling insights. The objective of data analysis has evolved from knowing a customer to predicting his behavior.
The objective of this article is to look at the applicability and benefits of analytics in retail, with special emphasis on predictive analytics. Some of the key challenges in implementing analytics program are also examined, and suggestions to overcome these trials have been outlined.
Introducing Analytics
Without utilizing the powers of analytics, the ROIon the large amounts of data collected will always be suboptimal. Analytics goes beyond the preformatted logic in BI tools by analyzing the problem in a given context and then using as large an element of domain knowledge as the statistical techniques.
Analytic techniques, such as design of experiments, cross tabulations, statistical analysis and data mining, help in uncovering patterns and trends within large databases. When used for creating forward-looking suggestions, they provide the edge to decision-making.
While descriptive analyses help to identify issues and examine causes, predictive analytics enhances the accuracy and effectiveness of decisions that directly impact your bottom line.
Descriptive Analytics: Developing Insights from Data
As the term suggests, descriptive or causal analytics enable the uncovering the reasons behind any event or trend and brings critical marketing issues to the forefront. The techniques used answer queries of what is happening, where and amongst whom. Some analyses applicable for retail are:
- Out-of-stock analysis,
- Category and brand dynamics,
- Promotion-effectiveness analysis, and
- Benchmarking analysis.
Predictive Analytics: Going Beyond "What and Why"
Data warehousing tools feed data into descriptive analytic techniques to answer the questions of "who/what/where/when/how." However, all these answers help in interpretation of past events. The critical question of how a customer will respond or how an event will unfold in the future remains unanswered by descriptive techniques or BI reports. Predictive analytics play a decisive role in a similar way as diagnostic checks do in a medical treatment scenario as opposed to symptom-based treatments. It allows the retail organization to enhance its decision-making powers by looking at the future with analytical rigor.
Predictive analytics enables:
- Targeting customers more effectively for campaigns,
- Improving response time to market changes,
- Increasing employee productivity, and
- Improving customer service at stores.
Power of Analytics in the Retail Domain
A customer-focused industry with several channels of engagement with the customer, the retail industry has great potential for application of analytics, which can help it to serve its customers better.
As data from the point-of-sales, terminals and credit cards/store cards are synchronized and mapped along with data flowing in from other sources, analysis of this integrated data has the power to impact the retailers' bottom line positively (see Figure 2).
Predictive analytics hold the key to taking advantage of these opportunities such that retailers can increase their ability to forecast their customers' behaviors and plan accordingly.
Figure 2: Power of Analytics
The Analytics Complexity Model
An organization trying to implement analytics should plan for the natural evolution of the analytics process. For an entrant into analytics space, the need is to establish credibility for data analysis using simple techniques rather than advanced models (see Figure 3). Similarly organizations with a mature DW/BI foundation and a culture of data analysis should progress to the advanced stages and create analytical models, which enable improved decision-making. Thus, in the beginning, the level of analytics is dependent on the current familiarity and sophistication of the data analysis process in the organization.
