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The Art, Science, and Software behind (Optimal) Retail Pricing - Part...
The Art, Science, and Software behind (Optimal) Retail Pricing - Part 2
Part 1 of this blog post series
expanded on some of
’s earlier articles about companies’ need for better pricing management and optimization practices. This series, which focuses on the complexity of
, was inspired by
’s recent “edu-nouncement” on leading retailers' consumer-centric pricing and promotion strategies
’ recent (and still ongoing) educational series of Web-seminars
To recap Part 1
: due to the phenomenon of the
“cross-elasticity” of demand
, retailers may want to consider whether promoting an item would result in increased sales volume and, if so, whether that increase would represent incremental revenue or merely cannibalize sales of other items. Retailers have to be able to compare items on promotion against the entire department, product category, and subcategory.
These pricing and marketing decisions must also strike a balance between the retailer’s financial goals and its desired price and
image in order to enhance consumer loyalty and maximize sustainable, lifetime value from its targeted consumer segments.
and revenue reports have to account for
, affinity, vendor funds, and pantry loading. Pricing reports should also be able to compare promotional ad events (or versions of ads with similar themes) in terms of these retailer-specific metrics.
Some Retailers’ Vernacular Explanations
At this time, let me try to explain some terminology here. For one,
is when the sale of one product has a detrimental impact on another. For example, if a retailer is promoting
in one week’s ad, the sales of its
private label (store brand
) ketchup are likely to be negatively affected.
The soft drink product packaging example from Part 1
demonstrates another cannibalistic relationship in which each packing size is potentially a substitute for the others.
is when the sale of one product has a positive impact on another. This is sometimes also called the
. For example, a retailer lowers the price on hot dogs and it ends up generating better than usual sales on buns, ketchup, mustard, and relish, all at their normal prices.
In other words, complementary relationships must be considered when pricing an item. Again, the demand for hot dog buns may be impacted as much by the demand for hot dogs as it is by the price of the hot dog buns themselves. Thus, pricing hot dogs correctly is critical to both hot dog and hot dog bun sales.
To effectively set promotional prices, retailers need to have a thorough understanding of the uplift of demand. As an apparel example, non-promoted accessories such as bracelets, earrings, bags, or belts could also see a lift in sales if paired and displayed wisely with a promoted dress. Understanding cross-product relationships and knowing what related item sales will be driven by a promotion can help retailers translate better visibility into increased consumer demand. Needless to say, sales of complementary items can help recoup margins lost by a promotional discount.
is an effect that usually occurs when an item is on promotion. It essentially means that the consumer bought more of a product than they usually buy or need to buy because the offer was so good. Consequently they won’t buy that item again soon because they have stocked up. For example, if a consumer usually buys an 8 pack of toilet paper every week, but because of a great sale offer buys a 24 pack, which will last them 3 weeks, that consumer will not buy toilet paper again for 3 weeks.
Pantry loading relates to
, which is also an important factor when establishing a promotional strategy. Some products, such as toothpaste or soft drinks, tend to have relatively high brand loyalty. So running a sale on a particular brand of toothpaste will only attract the particular segment of people who already buy that item and not drive increased traffic but rather may result in pantry loading at a lower margin to the retailer.
(or conflict management) is an advanced business logic touted by Revionics that allows the retailer to avoid making needless (and counteractive) price changes if that price change is adjacent to an event. For example, “don’t raise the everyday price on a given item since we know it is going to be on promo next week!”
Be Also Careful With Vendors’ Promotions!
Situations often arise where
consumer packaged goods (CPG)
manufacturers offer a discount to retailers on promoted items.
are situations where the vendor has offered the retailer a deal, allowance, bill-back, etc., to either buy more or sell more goods. Typically, these offers have start dates and end dates, and in most cases, some portion of the vendor money is passed onto the consumer. The retailer almost always earns the vendor funds based upon some performance criteria established by the vendor.
While at first glance this may seem like an ideal way for retailers to drive sales at a discounted cost, these trading partner-based promotions don't always lead to increased sales. For example, promoting soft drinks and grilling meat over the fourth of July, when consumers would buy these for
regardless, is not ideal unless the sale of these items can drive sales of related items such as hot dogs, chips, and salsa (and related condiments).
The same is true for promoting salsa for the
weekend, unless it is done to spark sales of related items and perhaps liquidation of slow-moving items. Without these ancillary sales, the promotion would actually reduce profit margins, despite the retailer having gotten a lower cost from the manufacturer.
The Building Blocks of Pricing Optimization
In its abovementioned respective PR and Web-seminar, JDA and Revionics recommend three price optimization strategies that retailers must first adopt in order to form the foundation for high-performing lifecycle pricing. To plan and execute lifecycle pricing efficiently requires advanced technology solutions that integrate and optimize the following three key areas:
Initial (everyday or base) prices
Establishing the Optimal Initial (Everyday or Base) Price
Setting the “right” initial, base, or everyday product price is one of the most important and complicated steps retailers can take to ensure solid profit margins and top-line revenue. However, determining the right everyday price for a product is no simple task. In fact, the idea of uniform corporate-wide or chain-level pricing has become antiquated, as a product's price needs to vary across locations.
Furthermore, simply maximizing unit sales, margins, or revenue are not the only considerations. Cross-product relationships, competitive factors, brand statements, and multiple selling channels are just some of the factors that contribute to an individual product's price.
Consumers vary greatly from market to market, so determining preferences based on local demographics, as well as geographic location and economic conditions, is critical when setting initial product pricing. Astute everyday price optimization software would likely generate different optimized prices for the same item carried by competing retailers in stores located in the same geographic location. This is the case because consumer behavior varies between competitors and each retailer has its own vendor costs and strategic pricing objectives.
Beyond basic product assortment indicators--such as differentiation in client preference based on geographic store location--it is also critical for retailers to have an understanding of nuanced customer preferences. Advanced technologies can detect these nuances, such as the demand for ski attire in Phoenix, Arizona due to a large population of consumers that travel north to nearby Colorado during ski season.
To that end,
everyday price optimization
everyday price management
solutions enable retailers to establish base prices for their products, using optimization techniques based on a scientific understanding of consumer behavior as well as rules-based pricing methods. Software can enable retailers to more accurately tailor assortments and determine the most appropriate initial price.
Avant-garde retailers use everyday price optimization to also optimize and set retail prices in their stores based on their unique cost structure and strategic goals. Using such software, retailers can create “what if” scenarios and models in which they define strategic objectives such as increased revenues, profitability, or sales volume, and optimize prices to best achieve these objectives. Retailers should also be able to compare base pricing strategies over time.
A typical strategic objective might be to maximize net margins, while not sacrificing more than a certain defined percentage of sales volume. Key features of everyday price optimization solutions should include the following:
optimization and forecasting — full category price optimization, with store/item level forecasting of revenue, profitability and sales volume at various price points;
image item analysis — to determine effective price image items for key consumer segments;
benefits reporting — the ability to validate the effects of price optimization on actual sales of a particular category and to understand the degree to which factors such as promotions, seasonality, and macroeconomic shifts contributed to sales. Some examples would be “before and after” impact summaries and system recommendations and ranks of ideal prices for every item in every store.
Rules Can Still Be Helpful
Moreover, by using
everyday price management
software, retailers can define pricing rules and apply those rules-based prices to merchandise categories that are not modeled and optimized using everyday price optimization. In addition, retailers should maintain both optimized and rules-based prices as vendor costs and competitor prices change. Key features of everyday price management solutions should include:
rules management and rules-based pricing — a set of retail pricing rules with a rules editor and rules relaxation capabilities to handle conflicts;
advanced price maintenance — operational price management features to handle frequent vendor cost changes and competitive price changes; and
price delivery — regularly scheduled delivery of recommended base prices to customers.
Both everyday price optimization and management products typically utilize a library of configurable
that act as constraints on the optimization by limiting the set of possible outcomes. For example, a retailer can ensure that larger size items (e.g., 8 oz) always cost more than smaller size (4 oz) items but offer a better
, or that an optimized price is within a given percentage of a competitor’s price. Other typical pricing constraints would come from the ability to set prioritized quantity limits, control the frequency of price changes, and establishing the min/max size governor per item and per store.
In addition to increased sales volumes and profit margins, potential benefits from everyday price optimization could come from automating manual tasks included in pricing processes and finding profit opportunities in items not evaluated regularly. “Nice to have” benefits could also come from having appropriate tools to execute pricing strategy shifts and eliminating risks via building new scenarios.
Part 3 of this blog post series will analyze the other two building blocks of pricing optimization: promotions and markdowns. Then, the series will go into the next generation of pricing optimization. JDA refers to this next evolutionary step as
Consumer Demand Management (CDM)
, and Revionics refers to it as
Your views, comments, opinions, etc., about any above-mentioned pricing solution and about the software category
are welcome in the meantime. We would also be interested in hearing about your experiences with this nascent software category (if you are an existing user) or your general interest in evaluating these solutions as prospective customers.
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