Most shoppers are only sensitive to the prices of certain items. Learn what those are and compete on those prices, while maximizing margin on others.

Retail has never been for the faint of heart, but today’s omnichannel retailers face unprecedented challenges.  Retail formats are evolving as off-price discounters like TJ Maxx proliferate, and Amazon continues to assert online dominance. Millenials wield formidable buying power – some $200B annually, according to Kelton Research – and set the trends, from fashion to food. And 51% of millennials say consumer opinions found on a company’s website have more impact on their purchase decisions than recommendations from family and friends.

In addition to being trained to wait for discounts, perhaps the most rapid shift has been toward customers expecting consistent prices and assortment with flexibility in fulfillment – in near real-time.  Retailers who fail to adjust their pricing, merchandising and inventory strategies accordingly risk lost sales, excessive inventory costs, and a hit to the top and bottom line.  More importantly, they risk losing the customer and all their future sales. 

How can omnichannel retailers meet these lofty and growing expectations?  With the right strategies, technologies and processes to deliver an end-to-end merchandising experience cross-channel and 24/7. According to a Cassandra Report, 72% of millennials begin shopping before setting foot in the store, getting product insights and price-shopping by looking online. Similarly, 55% of shoppers prefer to use both stores and on-line resources throughout the buying cycle.

With shoppers accessing information across all channels, demanding free shipping or click-and-collect fulfillment, how can retailers survive against unrelenting cost and price pressures?

Customer-Centric Omnichannel Pricing through Science-Based Optimization

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The reality is that shoppers are only price-sensitive in a fraction of the assortments. It’s here that shoppers also derive their price image of a retailer.  It’s imperative for retailers to know exactly which items attract vigilant shopper price comparisons and strive to deliver cross-channel price consistency on those key items. This leaves flexibility to recover margins on other items in the assortment. Right pricing should consider costs, shopper price sensivity and competitive price responses, while supporting specific category roles and strategies, such as increasing basket size, driving traffic, margin enhancement, and incremental revenue generation, to name a few. Retailers must accommodate different pricing strategies within categories and across channels, yet structure them so that strategies can cross channel boundaries when necessary as well as be unique within them.

Fortunately today’s big data analytics and machine learning algorithms can give retailers optimal price recommendations that span across channels to bring that retailer’s price strategy to life on the shelf and online.  Science yields granular insights into shopper price sensitivities and machine learning algorithms can determine optimal pricing across channels – while evolving to keep up with changes in demand due to evolving market trends, price sensitivity fluxuations and competitive market conditions.

Increasing Competitive Complexity 

Rather than just keeping an eye on the local competitor down the street, retailers must now be cognizant of competitor on all channels across the world.  In this brave new world, not only are shoppers no longer loyal to a single retailer, they are not even loyal to a single country.  A recent PwC study found that 56% of consumers said they would shop an out-of-country retailer if better prices were available.

Add with digital giants like Amazon and Walmart/Jet.com promoting speedy and free shipping policies, you have a retail environment where many retailers struggle to remain relevant.  The temptation to implement knee-jerk competitive matching policies and stepped-up promotional frequency has proven to result in a race to the bottom with unsustainable margin erosion.

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Yet shoppers continue to cite price as a top priority in the shopping experience – along with premium customer experience. Foresee Experience Index reports that 89% of consumers report price is their top priority for web, 67% for mobile, and 72% for in-store.  And they feel betrayed when retailers get the price/experience balance wrong.  EKN Research found that 86% of consumers will pay up to 25% more for a better customer experience, while American Express found that 78% of consumers have bailed on a transaction or not made an intended purchase because of poor customer experience.

Meaningful competitive pricing doesn’t react everytime a competitor changes their price nor does it mean implementing price matching.  Retailers that have taken this approach get sucked into a deadly downward vortex with disastrous impact on their margins.  Even Walmart, which earlier publicly touted their in-store price-matching policy, recently announced that they have halted local competitor advertisement price matching in 500 stores and are instead lowering prices on those items shoppers have proven more sensitive to.

Effective competitive pricing means first knowing who your true competitors are – those that shoppers compare you against and leave you for.  Next you must know how those competitors have historically reacted to your pricing and be able to predict how they will repond to your future pricing.  This requires two essential components:  visibility into historical pricing trends and real-time visibility into competitive pricing and how it benchmarks against a retailer’s price changes.  Retailers can leverage science that absorbs competitive data, and identifies competitive elasticity to predict future responses and factors that along with shoppers’ price sensivities. This lets retailers determine and change prices in a deliberate, focused way at whatever frequency is needed – whether every few minutes or once a week.

Promotion Optimization:  Meet the Customers Where They Shop with the Offers that Matter

The methods retailers utilize to reach shoppers have exploded far beyond mailing print circulars, offering in-store promotions, and promotional  endcaps and displays. Retailers have to use a myriad of promotional methods to reach shoppers and these vehicles must be personalized and provide an exceptional experience, using the right promotional vehicles with the right offers on the right products as just the right time…no exceptions.  Retailers who get this wrong will lose not only the sale, but the shopper altogether.

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Identifying the right promotional offer for shoppers and knowing which promotional vehicles shoppers will respond to will have a major effect on shopper demand. The challenge is choosing the right ones and keeping them consistent across both the digital and brick-and-mortar worlds.   

Retailers must also comply with restrictions associated with supplier-provided trade funds, which may include meeting performance thresholds and giving suppliers’ promotions specific locations within specific promotional vehicles such as a premium location in the store, a major ad on the website home page or mobile app, or the front page on their print circular. Yet, both retailers and their suppliers agree that promotional compliance and execution continue to be major issues.  Many promotions prove ineffective, failing to meet performance expectations, and lack the necessary compliance when it comes to those promised locations in the retailer’s promotional vehicles.  This jeopardizes future trade funds and results in lost shopper loyalty.  

The good news is that retailers can ensure effective and compliant promotions by taking advantage of data science and optimization solutions with self-learning intelligence. Retailers should focus less on promotional frequency and instead take a strategically driven approach that focuses on shopper-centric promotions that increase shopper loyalty, drive revenues and protect margins.

Sophisticated promotion optimization can recommend products, offers and promotional vehicles to support strategic and financial objectives.  This self-learning science can learn down to a very granular level such as ad size, placement location, supporting imagery or copy and then incorporate this into future recommendations.

Promotion optimization can identify the products and promotional offers shoppers want while also identifying the optimal combination of cross-channel promotional vehicles to reach customers when and where it matters. Today’s promotion optimization science identifies shopper price elasticity for each item in the assortment to recommend products, offers and promotional vehicles while simultaneously maximizing trade funds of the supplier partners and factoring in inventory constraints.

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Revionics provides price-optimization software for retailers.

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