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Pricing Research in the Retailer Category


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M/A/R/C Research discusses the best methodologies when pricing merchandise in the retailer industry.

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Pricing Research in the Retailer Category

  1. 1. Pricing Research in the Retailer Category Properly setting price is the most direct way to ensure optimal profitability. Improper pricing, either pricing too low (wherein per-unit revenue decreases faster than demand increases) or pricing too high (wherein demand decreases faster than per-unit revenue increases), leaves money on the table. The key to optimizing price is accurately measuring price-demand relationships, the functions that quantify how much demand will be garnered at each possible price. With accurate price-demand curves, basic economics 1 can be used to identify pricing that maximizes revenue or profit. A simple price-demand curve and its associated price-revenue curve are shown below. Price-Demand Price-Revenue 75% $93.0 65% $92.5 Revenue (million $) $92.0 55%Demand $91.5 $11.60 = 45% the price that $91.0 will maximize 35% revenue $90.5 25% $90.0 $10.00 $10.50 $11.00 $11.50 $12.00 $12.50 $13.00 $13.50 $14.00 $14.50 $15.00 $10.00 $10.50 $11.00 $11.50 $12.00 $12.50 $13.00 $13.50 $14.00 $14.50 $15.00 Price Price M/A/R/C believes the most appropriate methodology for pricing analysis—for accurately identifying price-demand relationships—in the retailer category is alternative-specific discrete choice analysis (DCA). In DCA, all relevant retailers are studied as separate alternatives in a competitive context that mimics the real-life options that shoppers face. DCA is highly customizable with the ability to study many types of effects and/or numerous prices simultaneously. As such, a final DCA model can be used to set multiple prices (e.g. grocery prices, household products prices, electronics prices, etc.) simultaneously such that overall store profit is maximized. And so the above example charts for the retailer optimizing numerous prices simultaneously would be multidimensional and not merely two- dimensional. For example, below are three-dimensional charts involving the simultaneous optimization of two prices. 1 Revenue = Price * Demand; Profit = Revenue – Costs.
  2. 2. Price-Demand Function (2 prices) Price-Revenue Function (2 prices) 50% 45% 40% 35% 30% 25%% Demand 20% Revenue 15% 10% 5% 0% $2 $6 $2 $10 $6 $14 $10 $18 $14 $22 $18 $26 $22 $30 $10 $26 Price B $34 $20 $30 $10 $38 $34 $30 Price B $42 $20 $38 $40 $46 $30 $42 $50 $50 Price A $40 $46 $50 $50 Price A A DCA model culminates in a simulator wherein prices can be set at any price along the entirecontinuum of price ranges studied and not simply at the discrete price points studied as test stimuli.Further, M/A/R/C overlays years of experience in calibrating DCA-model simulators to adjust for anysurvey biases, so that predicted demand estimates are as close to true market share as possible. Thisprecise calibration is accomplished by using DCA in concert with M/A/R/C’s Assessor® productforecasting methodology.M/A/R/C’s Assessor, a proprietary forecasting methodology originally developed at MIT and provensuccessful in thousands of practical applications, is one of only a few widely recognized, validated,research-based forecasting models in existence.Below are some highlights of the DCA methodology in the retailer category.» The Discrete Choice Exercise for Respondents (Shoppers) 1. Expose survey respondents to a variety of scenarios that include retailer brands that well represent the category and vary the average prices for relevant categories (e.g. grocery, clothing, electronics, etc.). 2. Optionally, also vary other attributes of interest besides prices, such as availability of offerings, product features (sizes, package configuration), store features, etc. 3. Ask survey respondents to allocate purchases across the retailers shown.» Benefits 1. Experimental design is utilized to select realistic combinations of levels of attributes – but also ensuring attributes are studied with statistical rigor (balanced and orthogonal) and then unique blocks of scenarios are shown to each respondent. 2. Respondents are allowed to allocate purchases in categories where alternative options are or will be available in-market.
  3. 3. 3. The test provides a realistic competitive context (multiple retailer brands in each choice scenario; price and other attributes customized to the brand) and influences the respondent to provide realistic responses to price. 4. Gaming is not likely, due to the even-handed treatment of alternative brands. 5. Any “over focus” on Price can be diffused by presence of other attributes.» Potential issues 1. DCA is more costly than other forms of pricing analysis.In the retailer space, in addition to DCA, it can be helpful to conduct as a supplemental component tothe overall pricing assessment what is known as van Westendorp pricing analysis. For a retailer itwould be conducted for each of the category prices – again, such as grocery, clothing, electronics, etc.– as the average of typical item’s or typical basket of goods price. The van Westendorp technique iseasy to implement, involving four questions wherein respondents directly state their expectationsregarding price levels. As such, being based on simple, direct “stated” measures, the results of vanWestendorp have high face validity; respondents are merely stating where they expect pricing to be.While some researchers extend the basic van Westendorp method to include the identification ofwhat they believe to be an optimal price point, we at M/A/R/C do not believe the simple “stated”data of the van Westendorp method can be used for such and do not use van Westendorp as asurrogate for rigorous price-demand estimation. Instead we use the van Westendorp method only toform a range – a window – of prices within which shoppers expect pricing to fall. For identifyingoptimal pricing we rely on DCA-based price-demand relationships. However, the results from DCA areenhanced by comparing them to the information from van Westendorp, where we can answer suchthings as: n Does the optimal price as identified from the DCA fall within the van Westendorp acceptable range? n How wide is the range of expected pricing? n How sharply does acceptance fall off if price goes beyond the acceptable range?Below is an example chart that represents the final deliverable of a van Westendorp pricing analysis(e.g. for a given category within the retailer brand) – the green highlight area represents the range ofacceptable pricing, where here “Acceptable” does not have anything to do with maximizing revenueor profit but is simply referring to being acceptable in the eyes of shoppers.
  4. 4. 100% Range of Too Cheap 90% Acceptable Pricing NOT Bargain ["acceptable" = in NOT Premium 80% minds of purchasers, Too Expensive not necessarily from 70% a rev or profit Cumulative % standpoint] 60% 50% 40% 30% 20% 10% 0% $6 $7 $8 $9 $10 $11 $12 $13 $14 $15 $16 Price Low=$10.50 High=$12.25Beyond the basic technical aspects of estimating a good price-demand relationship, pricing analysisalways also involves knowing and properly modeling all the category-specific, extenuating, and/orcomplicating nuances of the market within which the product or service being priced will be sold.Current competitor prices and positionings should be a strong consideration in setting price.Knowledge about government regulations regarding pricing in the category or otherwise how pricesare negotiated is important.Given the high importance of pricing and the many complicating facets that must be considered,M/A/R/C Research has devoted extensive effort in building deep expertise in pricing analysis. Withmany years of experience spanning all industry categories, we’re able to leverage our well-knownAssessor® methodology to ensure an accurate estimation of the price-demand relationship, offer awide array of modeling and simulation capabilities to custom fit most any set of market dynamics,and to deliver a truly optimized pricing strategy for the retailer category.M/A/R/C® Research ( is a custom marketing research firm dedicated to helping clients create, evaluate andstrengthen their brands. M/A/R/C’s teams design and execute qualitative and quantitative, traditional and online solutions whileadhering to a client-service ethic built on being easy to work with and delivering what is promised. Our core competency is measuringattitudes and behaviors to accurately explain and predict market share, revenue and bottom-line impact of a client’s actions. M/A/R/Chas been successfully designing, executing and analyzing studies to help clients across a range of industries since 1965. We deliverresearch answers with a business perspective in the language of decision-makers.For more information about M/A/R/C Research contact your Account Manager, or send inquires to Strong brands start with smart research. 800.884.6272