One size fits all? Think again.

With the evolution of retail industry, the number and complexity of factors affecting a Retailer / CPGer have grown exponentially. As retailers grapple with ever-shrinking product lifecycles, changing customer behaviour and pressure on margins, analytics driven business intelligence is playing a pivotal role in shaping their decision-making. Retail Analytics has at its core the process of taking data from multiple sources of the retailer’s business cosmos and churning business intelligence that enable performance improvement. The key result areas of a retailer evolve with the maturity of the retailer as well as the industry. Consequently, the focus areas where retail analytics can make a tangible impact also evolve during the lifecycle of the retailer. Retail analytics cannot be viewed as a black box that should be taken or left alone. The retailer should identify its analytical requirements to ensure fast, efficient and cost-effective decision-making, based on the stage of evolution that the retailer is at. In essence, analytics is only an enabler that the retailer can leverage to meet its objectives – make more customers buy more and make them more comfortable with the retailer. Over time, as the retailer matures, it progresses along several stages. We can analyze the lifecycle in four phases:

  • Start-up
  • Expansion
  • Maturity
  • Decline / overdrive

Each stage requires a corresponding jump in organizational complexity – disciplines become departmentalised, motivations of people become different, contrarian points of view are dulled and decisions bog down. Hence the role of a function that helps make sense of it all becomes more conspicuous. That role is played by retail analytics.

Retailer’s Business Landscape

The key decision variables for a retailer are based around five major factors: suppliers, customers, competitive landscape, external factors (economy) and internal operations. Success of a retailer is dependent on how it is able to analyze its dependency on these factors at various stages of growth and focus on preparing itself for the change. Hence appreciating the varying growth drivers and focused analytics to best-enable decision making around these drivers will result in maximising ROI on analytics. The key focus areas and how specific retail analytics can address these during the various stages is elucidated below

Startup phase

During the start-up phase a retailer needs to analyze competitive landscape including the range, pricing and positioning in customers minds.

Based on the analysis the retailer needs to identify and validate the positioning that it needs to establish in the consumer’s mind. Consumer analytics (purchase behaviour, segmentation & positioning) is the key to success at this stage.

Retailer focus Key drivers and decisions Analytics needs
Capital optimization Brand visibility Capture consumer mind share
Consumer mindshare segmentation and positioning Panel data analytics
Customer identification and market acceptance Identifying potential customers/ customer segments
Location planning demographics and catchment Consumer analytics demographics
Format planning Consumer analytics buying behaviour
Merchandise planning Department, class, line level of planning
Competition Tracking Market profiling Competition Intelligence: Range, Pricing

Expansion Phase

Once the retailer has successfully built a market position with its first few stores, it aims for an accelerated growth in the segment. The focus is on building top-line by expanding to multiple geographies and maintaining operational profitability. The retailer needs to fine-tune its positioning in the market-place by analyzing the location specific consumer behaviour. Demand forecasting, location specific consumer purchase behaviour analytics and ensuring product mix are the key analysis that the retailer should focus on at this stage of its growth.

Retailer focus Key drivers and decisions Analytics needs
Top line growth Profitability Brand stablishment Location planning Consumer analytics demographics Cannibalisation effect
Consumer loyalty Customer loyalty program modelling
Merchandise mix Basket analysis Assortment Optimization SKU Rationalisation
Demand forecasting Simple time trend analysis Seasonality

Maturity

Once the retailer has grown to a considerable size and has significant market presence, externalities play a pivotal role in its growth. The focus at this stage is turf protection and maintaining profitability. Significant investments are required to ensure operational efficiencies. The analytics needs are highest at this stage, as the retailer needs to take a macro (plan across channels and geographies) and micro (local market customization) perspective at the same time.

Retailer focus Key drivers and decisions Analytics needs
Optimization Bottomline focus Competition Watch Brand Consolidation Merchandise planning Basket analysis association analysis Integrated forecasting Category Scorecarding Cross-channel optimization Local market assortment optimization
Supply chain efficiencies Costing Fast growing item stock levels
Demand forecasting Internal factors promotions External factors seasonality, economy
Pricing & promotions Pricing and price sensitivity analysis Promotion analysis
Operational efficiencies Store Sales Mix Sales analysis by store size, footfalls Labour Cost Analysis Asset Turnover, ROA Sales & Margin Inventory Turnover Loss prevention
Space planning Planogramming
Customer loyalty Attrition modelling

Decline/Overdrive

With size the retailers tend to lose their nimbleness and agility to respond to market dynamics  mainly changing demographics, emergence of new channels and stiffer unforeseen competition. At this stage the retailer runs the probability of declining market share. The retailer needs to reinvent itself at this stage by rethinking its fundamentals, primarily positioning and choice of format & channel mix.

Retailer focus Key drivers and decisions Analytics needs
Repositioning Market Positioning Affect of changing consumer demographics
Format rethink – competitive landscape Channel mix Consumer analytics – Channel choice preferences, usage analysis Emergence of new formats

Conclusion

Understanding the retailer’s lifecycle superimposed on its business landscape will help define and prioritise the evolving business intelligence needs of the retailer. Once this is understood, the retailer can then best utilise key driver-specific analytics to achieve performance optimisation and thereby the business objectives. The Indian retail scenario is now witnessing a phase of unprecedented growth, predominantly in organized retail sector. As the retail scene gets more and more competitive with new players, new formats and new strategies, retailers will be faced with more challenges sooner than they thought possible. Having the right tools technology and processes for an analytic environment today can help retailers keep up and stay ahead of competition tomorrow. For any retailer, business intelligence is one of the most important strategic levers they will use to steer their business going forward.