The role of predictive analytics in Indian FMCG
All eyes are on the Indian FMCG and retail sectors that “form the direct path to the India Growth Story - CII National Retail Summit and CII National FMCG Summit 24th June 2014 at Mumbai. Despite the economic slowdown of past few years, the FMCG was the last sector to slow down. It witnessed a double digit growth at a CAGR of 11%, spurring high investments.
As Indian marketers strive hard to keep up with ever-changing consumer preferences and pricing dynamics to keep ahead of competition, they are faced with a vast amount of organized and unorganized data from disparate systems. There is need to develop a suite of analytics capabilities that are capable of addressing the complex issues faced by the FMCG businesses. In particular, predictive analytics has become core to the decision-making process.
Thus, senior executives in the consumer packaged goods sector have shifted attention to predictive analytics, as they realize that reviewing past performance is no longer enough to be truly competitive.
What does predictive analytics do?
Predictive analytics makes use of information obtained from historical data or research to predict outcomes based on variables such as weather, pricing, promotional campaigns or co-branding initiatives to cite a few. It applies statistical modeling, forecasting and optimization algorithms together with business rules, to transform hindsight into insight, moving beyond the descriptive to the predictive, from the ‘what’ to the ‘why’ of businesses.
Benefits of Predictive Analytics
Ultimately, manufacturing a consumer product is all about selling, servicing and delivering. What product? How many units? Shipped by when? Which distribution channel? What promotional strategies? These are some questions addressed by predictive analytics in real-time.
How Predictive Analytics can transform Indian FMCG businesses
Predictive analytics leverages tactical measures that analyze marketing programs efficacy to build predictive models prioritizing sales and profits. Thrust areas are building of future market strategies for asset optimization, lead generation, service models, brand building and product pricing based on market response rate prediction. Based on historical data, predictive segmentation can be made for best market locations, purchasing behavior models, optimal promotion strategies and market share targets for an overall effective ROMI.
Market Mix Modeling
Predictive analytics is leveraged to create marketing mix models that evaluate the potential value of all marketing inputs, to discover the marketing investments most advantageous for revenue growth. Multiple regression techniques are applied to predict the optimal mix of marketing variables. Regression is based on independent variables like advertising mix, promotional campaigns, regional trade policies, industry trends or competitor data. Predictive models are built to determine how these relate to sales, revenues or profits. As market mix modeling sets forth the roadmap for a company’s growth, predictive analytics in a FMCG company has become core to its functioning.
In the high growth B2B sector in FMCG, predictive analytics consolidates the consumer experience in real-time to create and anticipate customer experiences to create profitable interactions and move them further i the buying cycle. Locational variables and browsing history are analyzed for geo-behavioral targeting and targeted advertising with contextual ads; while time-series are correlated with external factors to make predictions of sale volumes, fast-selling items and consumer trends.
Predictive analytics not only helps predict the outcome of price changes, but also facilitates optimal pricing based upon variable factors like festivals, competitor launches, Government policies, geographies, consumer preferences and the like. Consumer trends, rate of consumer churn and other variables are analyzed for optimal pricing solutions. A company also factors-in the price elasticity of demand for various products to predict impact of pricing on product sales. Predictions of PED typically help decide the pricing strategy, differential pricing policies based on demographics, where products are price inelastic.
Supply Chain Management
Supply chain management is central to the Indian FMCG industry, with typical problems of high inventory, high stock-out ratio, erratic logistics and transportation costs with lengthening supply and distribution lines. Using sophisticated algorithms, near real-time demand data is integrated with traditional projections, to increase supply chain flexibility and agility for a higher ROLA (Return On Logistics Assets).
From resource allocation, manufacturing and distribution to marketing performances and sales forecasting, predictive analytics can rationalize the entire business operations for improved decisions based on ‘what if’ scenarios. Operations can be tied-in to financials by predicting areas of cost trimming, optimal utilization of resources and revenue expenditure, fraud prevention and bad debts reduction, for an effective business ROI.
Areas of Predictive Analytics deployment in a FMCG company
The role of predictive analytics solutions in FMCG is all-important for delivery of actionable insights in real-time. Managers and key executives responsible for decisions that decide whether a business sinks, floats or emerges winner, can only rely on the power of predictive analytics for business success.
As the Indian FMCG industry shifts towards demand-driven consumer-centric supply-chain system and pricing, predictive analytics solutions are the only way to go.
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