The article is written by Chhaya Narula, originally for Tattva - annual Consult Club magazine of IIM Ahmedabad and is being published on this blog after her permission.
Pricing strategy has been a major pain point for companies, be it deciding on the prices while launching new products, reacting to price changes by competitors or even deciding on the amount of discount necessary to remain competitive in the market. Whether the objective is to maximize profits, gain market share or just to survive in the market, pricing and discount have always been a prime tool in the arsenal of marketers and strategists in the companies.
What is usually done
Not many years have passed by since we used to decide prices by adding a fixed markup on the cost of a product or matching (qualitatively) our competitors on their prices. And when we felt adventurous, we calculated the value of the product in eyes of the customer and did value based pricing. When psychologists started to notice pricing strategies, they come up with several theories to help marketers in fine tuning the prices like fractional pricing, anchoring on the left most digit of the price, relative pricing, etc.
With the advent of e-commerce and large-scale retail stores, prices of products change very frequently and so does the discounts given on them. In such cases, it becomes very difficult for companies to monitor and adjust their prices based on market conditions, competitor’s prices, the price of complementary goods, discounts given, etc. Thus in such cases it becomes imperative to be one step ahead of the competitor to keep your prices at the right level, not only to keep or gain market share but also to maximize your profits. Decision-making in companies has become more data driven with the advent of a large amount of good quality data, technology to process that data, and a commitment to use the available data and technology to make smarter decisions. Activities such as advertisement decisions, supply chain management, optimization, etc. are predominantly done using technologies to convert data into actionable insights. It was only the logical next step for technology to penetrate pricing strategies, and this is not a recent phenomenon but has been done since computers became able to process and create models on large data streams.
Pricing and promotion are two major levers that a company can use to optimize and control their sales. Both these levers affect not only the volume of the product but also profitability. There are several attributes related to product that directly affect the sales of the product. These factors include - the price of the product, discounts, sales channel, Won distribution, Competitors’ price, discount and competitors’ distribution in the market. Promotion, on the other hand, includes any activity by the company that influences the buying decision of the customer at the point of sale. This is another vital axle that affects the volume of the product. Promotion happens in different forms, from a small banner near the product aisle to free gifts and multi-packs.
Why is optimal pricing strategy important?
Since there are numerous factors that affect final sales of the product, it becomes difficult to judge the success of a strategy qualitatively, be it price change or promotion. Finding the actual reason behind increased or decreased sales of the product is important to repeat the performance or correct the mistakes. Wrong attribution of a reason to the increased sales can lead the manager to repeat the wrong strategy in next cycle with very negative results. For example, a client increased the price of a product and saw an increase in revenue by about 5% and was convinced to keep the prices at the same level in next quarter or maybe even increase it marginally. But when a detailed analysis was done, it was found that price increase actually resulted in lost revenue which was offset by other factors namely, increased distribution of a variant of client’s product and also delisting of a competitor variant. These factors are either controllable but non-repeatable or non-controllable. Hence it is absolutely critical that a negative aspect is not repeated. Because of this reason, next quarter sales might take a hit if price was increased further. In order to set the prices at the optimal level in next quarter, a new pricing strategy was required. Similar to the example above, before starting any analysis, few hypothesis are constructed in the business frame on the basis of common business sense or exploratory data analysis whose validity needs to be tested using appropriate models and simulations. These business questions are in the form of a claim, be it competitors product affecting the company’s performance or company’s strategy affecting own sales, for example, would a new product launch in a premium tier affect the sales of the same brand in lower tier given certain reactions are expected from the competitors.
How is it done & key considerations
While doing pricing and promotion analysis, the granularity of analysis can be controlled depending on the business context. Different SKU’s are often clubbed together in the forms of PPGs (similarly priced and promoted goods). Henceforth, using a log-linear modelling technique the volume of the PPG is regressed on its price, competitor’s price, discounts, distribution and promotions. The elasticities for price and discounts are calculated along with discount, promotion and distributions lifts. Here, elasticities help to understand the percentage change in the volume of the product given a percentage point change in its price (Own- Price elasticity) and percentage change in competitors’ price (Cross- price elasticities). These values are then used as inputs in simulators to test the business hypothesis.
The analytical framework used in pricing models to determine optimal pricing strategy cannot be applied across industries and geographies without understanding the business context. For example, developed economies have well-developed data collection systems that allow the use of granular weekly data, covering a large portion of the market along with reliable data on local promotions. Whereas in developing economies, one needs to extrapolate available data to cover the whole market in the absence of complete data. In certain countries, manufacturer controls the prices to a great extent whereas retailers have a larger control in other countries. Thus, these factors need to be accounted for while making the hypothesis or creating models.
To summarize, any strategic decision on prices or promotions which a manager or consultant used to make based on deep business understanding or accounting for precedence in the industry are now being tested using simulations before actually implementing them in the field. These models not only help in understanding the effect of changes in strategy on sales; but it also helps in classifying the factors affecting sales into controllable and non-controllable bins, thus giving managers the right levers to tweak.
Thus, with advancement in technology, increasingly, the role of the consultant is not just to provide a better analytical solution for pricing strategy based on all the seemingly related factors but also to bring down the gap of uncertainty between the analytical solution and real life business solution by taking into account the abundance of available data analytics techniques & machine learning which may bring out correlations which at the outset may look to be seemingly ludicrous.
About the autor
Chhaya Narula, Senior Analyst, Fractal Analytics
She has worked extensively on promotion and pricing strategies for largest global consumer good companies. She has also worked on driving loyalty programs for leading retailers across the globe and devising strategies for telecom companies to acquire new customers. Earlier she did her graduation from Delhi University and Masters in Economics from JNU, Delhi.
This article is her own interpretation of pricing strategy from an analytical point of view and not an official statement by the company.