Pricing Strategies and Promotion Best Practices

By Jim Sills, Chief Technology Officer, Revionics Inc.

Category Management is undergoing a quiet revolution. Gone are the days when a category manager could trust in intuition and experience alone. The new generation is embracing Retail Science to make better price, promotion, merchandise and assortment decisions. Retail Science applies sophisticated data analysis to help better understand what customers want. Data sources include Point-of-Sale (POS), Transaction Log (TLOG), competitive pricing, panel, syndicated, weather, demographic, and location attributes. Data cleansing, quality assurance tests and outlier analysis are essential for measuring causal relationships. The result is a demand model that accounts for price elasticity, promotional lift, merchandising, seasonality, cannibalization, affinity, space, and assortment. Category managers use this demand model to evaluate and compare scenarios. For example, a supplier may offer a incentive to promote Cheerios. The category manager can evaluate the category profit accounting for vendor incentives, cannibalization, and affinity.  This analysis shows how cannibalization of private label erodes category margin. Even the impact on loyalty customers can be evaluated in terms of basket size and trip frequency by customer segment.

Other examples where Category Managers are leveraging Retail Science include:

Store-Zone Clustering.  Stores in proximity to competitors, population density, household income, median age, and other factors influence customer behavior and sensitivity to price. Store zone clustering identifies the optimal store zoning and can improve profit by 1% of sales for some retailers (higher profits have been realized and this benefit is above and beyond that from price optimization alone). This analysis is based on category-store price elasticity and takes into account demographic data, competitor data, and store attributes. The principal components driving a store into one cluster versus another are evident from this analysis.  For example, zone one may be characterized by highly price sensitive, middle-income, densely populated, customers with a given ethnicity ratio and strong competition from Walmart within 2.5 miles. The strength of each of these factors in driving a store into a given zone is evident from the analysis.

KVI Items. Key Value Items (KVI) have the greatest influence on customer price perception and represent an important segment of a retailer’s business. Frequently, just 10% of a retailer’s items account for 90% or more of customer price perception and have the greatest influence on traffic. These items can come from many different categories and there can be multiple groupings. Examples include highly sensitive items, competitive items, traffic drivers, and basket builders. Retail Science can be applied to identify the top KVI items combining item profit and sales with price elasticity, market-basket analytics, and syndicated data. Understanding which items are the “true KVIs” and positioning them aggressively yields the most return while allowing the freedom to price non-KVI items in line with margin targets.

Pricing. Price elasticity relates the change in units to the change in price as indicated in the table below.

Price Elasticity      Price Change      Unit Change

1.0                          -10%                     +10%

2.0                          -10%                     +20%

0.5                         +10%                        -5%

Retailers can realize more profit and sales by increasing the price on items with low price elasticity and decreasing the price on items with high elasticity. The first step is to identify the category role and strategy. For example, some categories are identified as Convenience, Traffic Drivers, Margin Enhancer, and Turf Protector in this source from AC Nielson:

Consumer-Centric Category Management.  Hoboken: John Wiley & Sons, Inc., 2006

Willard Bishop is especially strong in working with retailers to identify how best to define category roles and map those roles into strategies that can leverage Retail Science. These strategies and the science account for Private Label to National Brand Gaps, Good-Better-Best relationships, Ending Numbers, Price Chance Frequency, Minimum/Maximum Price Change rules, Price-Per-Unit relationships, Margin Targets and Competitive Price Index. Competitive prices can be collected or purchased from Rival Watch.

Promotion. Promoting the wrong product or the wrong offer erodes category profitability. As mentioned earlier, Retail Science can be used to evaluate supplier incentives. It can also be used to recommend the best items to promote and at what offers. During the planning stage a Category Manager can use the Demand Model to evaluate “what if” scenarios. For instance, which is the best item to promote on the front page in a major feature? What is the impact of merchandising the item in an end cap or a display? Is BOGO better than 10 for $10?  In all of these comparisons the Retail Science accounts for supplier funds, cannibalization, and affinity.

Category Managers are now using Retail Science to segment loyalty customers and identify the best one-to-one offers that will drive basket profit and trip frequency. Market Basket Analysis is applied to identify item-level affinity to understand how much a promotion on meat will drive sales in produce.

Retail Science benefits Category Managers best when it is imbedded in tools that support Supplier Collaboration and Ad Planning including pre-press layout with integration to publication tools such as Adobe InDesign or Quark.

Markdown. Simple clearance strategies such as 25%, 50%, and 75% markdown spread across three months leave money on the table. Too often items are marked down when demand is sufficient to clear inventory. Similarly, there are items with large inventory that require deeper or earlier markdown to maximize profit. Retail Science identifies the best markdown amounts and dates. Category managers can specify strategy objectives such as clear inventory or maximize profit. Coherence rules can be applied to simplify signage and shelf tags.

Assortment & Space.  Space and price are inseparable. Suppose that demand is high for a particular item, so high that the retailer often has a whole in the shelf. Is it better to increase the price, or to add another facing?  Retail Science can be used to jointly optimize space and price. In some cases the recommended number of facing is zero—meaning that it is recommended that the item be removed from the assortment. Retail science can also leverage syndicated data from IRI to recommend new items to add to the assortment. At a macro level, categories that need more or less linear space are identified.

Price, Promotion, Markdown, Assortment, and Space impact category profit and sales. A single Product Lifecycle application that integrates all of these applications is extremely useful to Category Managers. For one, a single integrated platform can identify and resolve pricing conflicts. Such as, a 10 for $10 promotion on August 1 would be in conflict with an Everyday price change from $1.19 to $0.89 on July 25. An integrated platform can also provide a single unified forecast which can be compared with the financial plan and actual profit and sales.

At Revionics we specialize in a SaaS-based Product Lifecycle platform that is putting the power of Retail Science into the hands of the next generation of Category Managers.

By Jim Sills, Chief Technology Officer, Revionics Inc.

Are you satisfied with your gross margin and sales per square foot? If not, consider putting the customer first by adopting consumer-centric technologies for pricing.  In “Putting the customer first“, Susan Boyme emphasizes how important it is to “evaluate price elasticity and tailor pricing across specific regions and individual stores.” Revionics is working with Insight-out-of-Chaos to taken customer centricity to the next level by identifying the best items to promote by customer segment. Loyalty data was analyzed in terms of basket profit and trip frequency. While the revenue and profit per basket of loyalty shoppers were found to twice that of non-loyalty shoppers, it was surprising to learn that loyalty shoppers as a whole vary widely in shopping frequency and basket profitability. It was evident from the analysis that there is a large opportunity to increase increase basket profitability and shopper frequency by targeting incentives to specific customer segments. At the same time retailers can build customer loyalty in their VIP shoppers through customer centric offers.

Our research found that basket profitability and trip frequency are largely independent, which fall in contrast to recently reported results from Mark Aguiar and Erik Hurst at NBER. Their research using AC Nielson Home Scan data suggest a “doubling of shopping frequency lowers prices paid for a given good by 7 to 10 percent. Using this elasticity and observed shopping intensity, we can impute the shopper’s opportunity cost of time. Our imputed measure tracks the life-cycle profile of wages rather closely, particularly after middle age.” Their research is presented in “Home Production, Consumption, and Labor supply” at http://www.nber.org/reporter/2009number4/2009no4.pdf.

The authors report finding “elasticity of substitution between time and market goods in home production of roughly 1.8. Food expenditures fall dramatically after the age of 45 while our estimates of actual food intakes increase slightly after middle age. We find that roughly 10 percent of the decline in food expenditures after middle age is attributable to lower prices paid because of an increase in shopping time.

Revionics results were from a high-end retailer, which may explain the discrepancy.

Market basket data was analyzed to identify affinity relationships. The best items by customer segment were identified to drive profitability and trip frequency. In this case, meat and seafood were strong drivers of both basket profit and frequency. Cheese, coffer, and tea were good candidates for basket builders and prepared foods helped drive trip frequency.

The analysis requires an understanding of cannibalization as well as price elasticity and affinity. When these relationships are understood, retailers can make better decisions about what item to promote at what price to specific customer segments. For more information, please email Revionics at info@revionics.com.

By: Todd P. Michaud, President and CEO,  Revionics, Inc.

As we look at the retail market, we see most retailers plagued with either inadequate Retail Demand Intelligence (RDI) and Forecasting tools or on the contrary, some retailers have too many disparate systems that contradict each other.   This is even true for those retailers who have selected comprehensive solution portfolios from the largest of software vendors since so many software vendors have merely intellectual property that they have acquired through poorly architected interfaces.

The market is ready for an open, integrated forecasting solution provider that has the expertise, willingness and I/P to synthesize these disparate systems.     As you probably are aware, today Revionics offers Full Life Cycle Price Optimization and we are in the process of readying our the release our Inventory Replenishment Optimization solution prior to the end of the year.  All of these capabilities will be based on our fully integrated forecasting and RDI platform.

Despite the breadth and depth that we expect of our own solution portfolio, we know that all retailers will have other solutions already installed that we will need to integrate with.   In essence, our forecasting platform needs to become the glue that unifies the disparate solutions together.    We have architected our Integrated Forecasting capabilities in this fashion.    In this environment, there are many demand influencing factors that we must account for such as…

•             The influence of Price, Promotion and Markdown
•             The influence of Introductions and Discontinuation
•             The influence of Supply or Demand Shock (Product Availability)
•             The influence of Seasonality and Holidays
•             The influence of Cross Effects (Affinity, Cannibalization, & Pantry Loading)
•             The influence of Space
•             The influence of Weather

Candidly, many of the solution providers in the retail segment simply can’t effectively do these things.   They can’t integrate with upstream or downstream solutions by other software providers.   Consequently, we have decided to differentiate Revionics on the basis of open scientific and analytical capabilities.    We do not believe that retailers will want to settle for large, monolithic software portfolios if it is at the expense of best of breed and competitive advantage functionality.