A major food company had, till recently, measured price sensitivity & distribution attribution at an overall brand level in their market mix, but not at a pack level. This led to inefficiencies and stock pile-ups, cutting into margins.
RainMan created a market mix models (MMM) with drill-downs on price elasticity. The result was the discovery of price elasticity by pack, allowing for differentiated pricing. This allowed the marketer to take up the prices for the less price sensitive variants while protecting those more likely to be hit by price increases. This led to more optimal pricing adding as much as 6% to the margin, making the company’s bottom-line a lot healthier.
Pricing is one of the key decision for a brand or a retailer. A number of factors need to be taken into account while setting the price. One key resource available to the marketer is past data of sales at a daily level (from own showrooms), or weekly data from Multi-Brand Outlets (MBOs). As it happens given competitive pressures and stock clearances - prices for the merchandise will be varying over time. This is a gold mine of information, and can be used to optimize pricing and improve margins.
A leading Men’s brand wanted to understand if price increase can be taken on some of its categories, such as Shirts, T Shirts, Pants of different types, Blazers, Shoes etc. After understanding the data across own showrooms in 3 states and modelling the data to understand the price elasticity by category, RainMan suggested an optimum pricing by category (either increase by a certain percent, keep the same or decrease by a certain percent) such that the overall value sales went up while keeping the volume constant or decline marginally.
The analysis clearly indicated the inefficiency of earlier pricing and suggested the need to use data available to improve the quality of price setting.
An Alco-Bev major wanted to understand the sizes of each of the categories of alcohol such as Whisky, Vodka and so on into the future for a plan strategy for a 5 year period.
The advantage of this methodology is that ROI of key digital channels can be compared and spends optimized in line with these insights, significantly improving overall efficiency.
The problem this CPG brand was faced with was a typical problem- Brand had presence across multiple connect points, paid media, owned media ( website, Facebook page etc) and earned media ( Twitter and other social mentions) but not knowing clearly what works across market clusters, and the interrelations between these connect points.