Category: Retailer

05 Dec 2018
Agent-based Shopping

The Rise of Agent-based Shopping

 

Retail is at a tipping point:

This was a main theme at GroceryShop’s inaugural event last month in Las Vegas. The event was attended by over 2,200 retail and CPG executives and was billed as the industry’s leading event for innovation.

Opinions about the tipping point were punctuated by Nielsen’s prediction: in 5-7 years, as many as 70% of U.S. consumers will regularly purchase consumer packaged goods online (“Digitally Engaged Food Shopper”). By 2022, they speculated consumers could spend $100 billion per year for online groceries (equal to $850/year/household).

For shoppers, digitization lowers barriers, making it easy to source product, compare buying options, find offers, transact, and take possession. For retailers, this means further downward pressure on price as competition evolves across more market segments than ever before: store, product, time, customer, channel, pickup and delivery options, cross-sell alliances, marketplaces and shopping apps.

 

Direct to consumer

Another big topic at the show was brands going Direct To Consumer (DTC) via Instacart, Amazon, and other platforms. For shoppers, it’s only getting easier as computers and AI get better at sourcing, comparing, and finding buying options that best meet a shopper’s current need. In this digital future, relevant offers will find a shopper based on her context (location, preferences, urgency, etc.). Margins will follow a retailer’s ability to make its assortment, offers, and delivery hyper-relevant to a shopper’s context.

 

Artificial Intelligence

Innovative retailers are leveraging AI to defend margins by segmenting markets better and by personalizing services and offers. In her talk, Google’s Laura Antonolli demonstrated Google’s AI-driven conversational assistant, in a machine-to-human interaction. Traditionally, a sales assistant’s role is to connect, understand, personalize and serve. In Laura’s demonstration, Google’s AI assistant searched for a hair salon, called to coordinate calendars for an appointment, and selected a haircut from an array of services.

 

Agent-based shopping

We are witnessing the rise of a new paradigm in retail – agent-based shopping. Laura said shopping agents and voice-activated search are a new battleground, stating that 22% of Google’s mobile search is voice. Forrester Research Analyst George Lawrie reported that “digital is no longer just a marketing channel, it’s now a sales channel.”

In one retail use-case, George shared that Alexa users spent, on average, £8 (eight British pounds) more per basket than at Morrisons. As of today, use cases for digital agents and household consumables are limited. That said, the promise ahead is for agents to create hyper-relevant offers and close sales based on an individual shopper’s context, values, and preferences.

 

Demand-side attributes

In large part, limitations come from the need to understand how shoppers compare substitutable product offerings and how those comparisons change under different pricing and offer scenarios. This applies to both within and beyond a retailer’s four walls. As an industry, we’re really good at supply-side product attribute data (i.e. weight, size & ordering info.). But we have yet to understand product attributes on the demand-side. Whether online or in-store, the old adage “price drives sales like no other factor” still holds true. From that perspective alone, comparability of pricing and offers within a category and across the marketplace are significant dimensions of demand-side attributes.

Comparability lies at the heart of understanding shopper values and preferences. As such, this represents a central value driver in any automated shopper feedback system. Without comparability information, offer personalization is largely blind to a shopper’s context, i.e., blind offers are not relevant.

 

Data takes a new turn

This brings me to a second take-away from the event. We are witnessing a fast acceleration in the variety of demand-side data elements offered by vendors in the space. At GroceryShop, these companies included: Engage3 (my company), Nielsen, Gladson, 1010data, EnterWorks, Label Insights and many more. Each of these companies offer unique data elements relating to different and new demand-side attributes.

As an example, Engage3 provides store-specific comparable pricing data. Nielsen provides measurement data aligned with many causal data elements. Label Insights provides detailed on-package attribute, ingredient and claim data. Data sharing and data feed integration between data vendors is also accelerating. These sharing and integration relationships open new paths to support the full promise of agent-based retailing.

Given the importance of product comparability, expect comparability to emerge as a primary focal point for integration. At Engage3, these sharing and integration relationships are beginning to yield new benefits, including:

  • More trustable competitive pricing and product comparability data
  • More precise methods for measuring price image
  • A broader and more frequent census of the competitive market
  • Deeper insights into strategic and tactical pricing dynamics
  • Improved automation and control over strategic pricing

All of this helps retailers automate and increase ROI from their pricing, offer generation, segmentation and personalization efforts.

In his talk, Earth Fare’s CEO, Frank Scorpiniti, spoke about AI as an “invisible advantage” that “removes repetition.” He reported that price promo and promo cadence all together yielded ROI of 300 basis points (3% of sales).” Adding “automation requires data quality” and that Engage3’s data has “near 100% accuracy.”

Getting data trustable is one step. Preparing integrated data for automation & advanced analytics is a step beyond. Supporting a client often means integrating a variety of data feeds from across a client organization with that of multiple data vendors. And building those feeds into a model is crucial so decisions can be automated in a controlled way.

Adding to the momentum, research dollars are beginning to flow as universities define research priorities in this space. One university we spoke with plans to create a center for excellence in food through the integration of personalized health, nutrition, and sustainability. Their effort would align both industry executives and academics from the business, engineering, supply chain, medicine & nutrition schools.

Agent-based shopping is set to emerge as a new battleground. Retailers that are positioned to make use of these new data feeds will climb the evolutionary path faster. Expect the industry to evolve rapidly in support of agent-based shopping.  It’s an amazing time to be in retail!

 

 

30 Nov 2018
Tariffs

Pricing in a Post-Tariff Market

Pricing in a Post-Tariff Market

As the markets closed on September 17th, the United States announced another round of tariffs against Chinese products. The tariffs, this time consisting of $200 billion worth of goods, were implemented on the 24th and will increase from 10% to 25% over the coming months. In return, China fired back with a list of 5,207 U.S. imports to be taxed, totaling $60 billion. National retailers like Walmart have responded by voicing concerns to US Trade Representative Robert Lighthizer and addressing potential costs to American consumers (CNN).

What started as a way of bolstering American business has become an all-out trade war between the two countries, with retailers in the crossfire. In such a situation, it can be difficult for affected retailers to implement new strategies quickly and effectively. Thankfully, with some insights into their competition and the national price leader, the most prepared retailers can come out on top.

 

Effects on Price Image

The prime question is this: who is going to pass on the cost to consumers first? Every retailer in the nation is waiting with bated breath for the answer. Private letters from Walmart and Target, among many others, have hinted at increased costs on the horizon, but there is no certainty of the first retailers to implement them (CNBC). The issue is that whoever passes the cost first hurts their price image the most. Items that were not hit by tariffs may still drop in sales because of the price adjustment, making the threat far greater than anticipated.

Still, this is only the beginning. The first retailer experiences the largest effects of the tariffs, but the next retailers to pass on the cost to consumers are also affected. This is where real-time competitive data can make a difference. Imagine that competing Store A raises the cost of a certain tinfoil to $8 in a market, and you are able to monitor that increase. From there, you can raise the price on that same product at your store to a lower price point than Store A. Though both products are affected by the tariff, your price image for tinfoil is maximized because 1) you were not the first in the market to raise the cost and 2) you are selling that product at a lower price than Store A.

 

Sliding Scale Tariffs

What makes these observations more crucial is the nature of the tariffs themselves. The increased cost for the latest affected products will go from 10% to 25% by January 1st, 2019, meaning that these small-scale retail battles will be happening on a weekly or even daily basis. Having up-to-date information on your competitors—both on a local and national scale—will translate to more victories.

The reality is that most retailers will face losses in the coming months because of tariffs, and stores hit especially hard by the increases have already taken steps to address them (USA Today). Accurate and timely competitive data can help to mitigate losses, especially when monitoring the national price leader. This is where store-level pricing is most important, because some price zones will be more heavily affected than others. Competitive data can inform a retailer when their competition is responding to tariff costs and how they can respond effectively. Implementing enterprise-level decisions in stores and going down to the local level can translate to a significant competitive edge in a post-tariff market.

 

A Watchful Eye

With the tariffs increasing to 25% by the end of the year, the market is racing to recover losses—the earlier a retailer can adopt a competitive strategy, the better. Because most retailers operate on low profit margins per item, an increase of even 10% on a product adds up quickly. In the case of a KVI, the increased cost to the retailer could negate any profit on that item, or even come at a loss. Figuring out a competitor’s pricing strategy and how often they update prices makes for valuable insights for decision-making.

At any point in the timeline, a strong price image is necessary to drive traffic to your stores. As we get further into the year, keeping an eye on the local effects of tariffs will be as important as pricing on a national level, and accurate competitive data can make all the difference. With the right insights, the tariffs present a unique opportunity for retailers in the coming months. Click here to learn more about Engage3’s automated price monitoring and register to receive information.

25 Oct 2018
KVI and Price Image

Known Value Items – Drivers of Price Image

A Shopper’s Store-switching Decision

A KVI is a known value item. It’s an item that disproportionally drives the price value perception. So, in a grocery store it would include eggs and an automotive store might include motor oil, and a convenience store it might include cigarettes.

The reason that KVIs are important is because they drive a shopper’s store switching decisions. If the retailer’s prices are out of alignment with the prices that shoppers remembered, then the shopper can reevaluate their decision to shop with that retailer.

A question you can ask a shopper is, what items do you stock up on? And at what price points do you stock up? And you’ll begin to understand what a KVI is with the answers you get to that question.

A Tiny Number of Items

Another element that’s really important with the known value items is that it’s a very tiny number of items that drive a retailer’s perception in the marketplace. Typically, about a third of the price perception comes from only two-and-a-half percent of the products. It’s a very concentrated number of items, and this holds true across grocery, drug, mass,convenience, pet, auto—virtually all retail sectors. So, getting it right is critical.

Dynamic KVIs

Traditionally, retailers will evaluate their KVIs once a year. Over time it’s gotten to a more periodic basis where they’re doing it more often, but the market’s changing faster today than it’s ever changed before. Things are getting localized, things are getting personalized, and with that the shopper’s price perceptions are being set more dynamically.

All of these things mean that calculating KVIs based at the enterprise level is the wrong way to do it. The analysis needs to come down to the store level, down to the shopper level, down to the daily level, and have items coming in and out of the KVI list at those lower levels.

Increased Complexity

The challenge is that all this results in a lot more complexity that needs to be managed. The comp shop programs that were easy for one person to manage before now explodes the amount of competitive data that’s needed and the amount of management time that’s required.

A Platform to Manage Margins and Price Image

The retail marketplace is only going to get more competitive, and retailers need a platform to support themselves in this new environment. At Engage3, we’re on a journey to build that platform to enable the retailer – the early adopters – to outpace their competition so they can outperform them in terms of Margin and Price Image.

20 Oct 2018
grocery

A History of the Grocery Cart

“The wonderful thing about food is that everyone uses it, and they only use it once.” – Sylvan Goldman

The grocery cart, now a retail standard, originally looked nothing like it does today. In 1936, Sylvan Goldman and a young mechanic by the name of Fred Young invented the first commercial grocery cart. It was humble at first, but the pair’s invention went on to change the retail world forever.


The First Cart

original grocery cart
The original design was two metal folding chairs stacked on top of one another with wheels at the base of the legs to roll the cart around a supermarket.

In 1934, Goldman bought the grocery chains Piggly Wiggly and Humpty-Dumpty, both based in Oklahoma City. Around this time shoppers were buying new, heavier kinds of products but still using hand baskets to carry them. The increase in canned goods and refrigerated items inspired Goldman to make shopping easier for his customers. He grabbed his handyman Fred Young and a few supplies, and the two spent a night coming up with a prototype of a rolling grocery basket.

At first, Goldman’s plan didn’t succeed. Women compared the cart to a baby stroller and refused to push around the cart while they shopped. “I’ve pushed my last baby buggy,” they told him. Men were offended at the idea that they could not carry all their groceries around the store, and worried that the carts made them seem weak. Still, Goldman persevered.

He hired young women to model the carts and push them around his supermarkets, demonstrating their utility. This strategy immediately converted a few people. He then recruited male and female actors of all ages to advertise his grocery carts, and suddenly his stores were filled with happy shoppers unburdened by their groceries. Goldman began selling his carts to competitors, and quickly turned his former folding chairs into a booming business.


Trouble on the Horizon

Watson's telescoping cart design
Watson’s telescoping cart design

The grocery industry, however, would soon be introduced to a landmark invention: telescoping carts. In Missouri, business owner and machinist Orla Watson came up with a design for a grocery cart that improved upon Goldman’s basket-carriers. The cart allowed for space-saving convenience in supermarkets and parking lots by nesting multiple carts together instead of disassembling them. Watson filed for a patent in 1946, but had his invention contested by Goldman. In the meantime, Goldman produced replicas of the nesting carts to compete against the new challenger. Goldman sold his new carts for three dollars less than Watson’s, using his manufacturing resources to effectively drive his competitor out of the market. Finally, after an extended legal battle, Watson was granted the patent in 1949. Goldman was required to pay him royalties for each nesting cart produced.

The design of the grocery cart would remain the same for decades, but minor additions helped to shape the cart into what it is today. Most notably, carts were outfitted with seats for children beginning in the mid-1950s. These seats cemented the grocery cart as a supermarket necessity.

The shopping cart can be found today in any website with a product to sell, but its history is rooted in a late-night idea and some tinkering in an Oklahoma supermarket. In the next installment of this history of the shopping cart, we’ll be looking at some of the modern additions to grocery cart design, ranging from security devices to complete redesigns and the jump to online shopping. We’ll also look at where cart-less retailers stand in the market today. Click here to subscribe to our newsletter, and stay up-to-date on future videos and publications.

13 Sep 2018
Ken Ouimet Efficient Frontier Theory

Price Optimization 2.0: The Efficient Frontier in Strategic Pricing

When managing a financial portfolio, you’ll need to determine how much to invest in each asset. If you have a thousand assets, it becomes a very complicated decision on how much to invest into each one. Harry Markowitz’s Nobel Prize Winning portfolio management theory, called the Efficient Frontier,  allows investors to boil this problem down into a strategic decision of how much risk to take for a given return.

In retail, you’re managing a portfolio of products. Instead of an investment decision, however, it is a pricing decision of how much you will be investing into each product to lower the price. The Efficient Frontier allows us to boil that pricing decision down into 2 dimensions – Price Image and Profit Goals. We use this tradeoff in retail pricing – where you have to manage thousands of pricing decisions to achieve your desired return. The question is: How do you roll that up into a strategic decision?

Because changes in retail pricing are becoming faster, more localized, and being split across different channels, we knew that we needed a tool to pull all that together and manage a consistent strategy across different price types. It turns out that the Efficient Frontier theory is the most powerful tool to solve this complex problem.

If we apply the Efficient Frontier theory to retail product pricing, then the dimensions we’re considering are the Price Image along the X-axis and the Profit Goals along the Y-axis. The highest point of the curve is where you make the most profits. Those are short-term profits. We then get the point-of-sale data that’s recording short-term decisions from customers, and that’s what the demand models are modeling.

There is a risk associated with pricing at the top of the curve because while that is where you make the most short-term profits, it’s also where you’re burning your Price Image. At this price point,the customers aren’t going to come back. The strategic decision to make, therefore, is how far you have to go down and how much you have to invest into having a lower Price Image. The decision is unique to each retailer and each strategy. It’s even unique to each category, fulfillment type, or each channel that you’re pricing.

There is a lot of information and power that goes into creating one of these Efficient Frontier curves. To illustrate: imagine that you have just two products, and each product has ten possible price points. If you look at all the possible ways to price those two products, you’re going to make a grid of prices, and it’s going to be a 10×10 grid. That’s a hundred different price points you’ll need to evaluate for just two products. If we add a third product and it has another ten price points, it’s now a cube and there are a thousand elements in that cube. The growth is exponential–you add a fourth product and there are ten thousand elements. What happens when you have a category of a thousand items? You will now have just one store, one category, with a thousand items. The number of pricing scenarios that need to be evaluated is now ten to the thousandth power. That’s more atoms than there are in the universe!

These are huge computational problems, and this is where algorithms become important—algorithm scalability is extremely important in these optimizations. To manage a hundred million prices in a dynamic market is a really complex undertaking. How do you boil that down to something you understand and make a strategy around? The Efficient Frontier is a tool that can collapse all those decisions into strategic decisions where someone, like an executive, can get their head around. It enables that executive to integrate the strategy all the way to the tactical execution, while automating those decisions.

This is what is exciting about price optimization problems. The same models that were created for investment portfolios are applicable to retail situations, and they allow you to make pricing decisions confidently–even in the face of 10 to the thousandth power price scenarios.

25 Jul 2018
Retail Predictions

Ken Ouimet’s 5 Big Predictions for the Retail Industry

Engage3 is working to create the ultimate platform for retailers to monitor and develop pricing strategies. Even when focused on products at the store and item level, changes in the market influence these strategies significantly. I met with our CEO, Ken Ouimet, in front of the beautiful Mondavi Center in Davis, California. With big changes at Amazon and Walmart this past year, I asked him to describe the future that he sees for the retail industry.

Gartner identified Ken as one of the pioneers in the retail pricing optimization space. In this video, he shares his insights and enthusiasm for what’s ahead. You can also watch the video here.

 

 

17 May 2018
retailer

C&S Wholesale Grocers Partner with Engage3

On April 18 to 19, retailer C&S Wholesale Grocers held their 10thannual Tech West Expo at Thunder Valley Resort Casino in Lincoln, CA. Over 100 independent grocery retailers from the West Coast, including Hawaii and Texas, converged at the town Northeast of Sacramento.

Listed by Forbes as the tenth-largest privately held company in the United States, C&S Wholesale Grocers is a wholesale distributor of food and grocery store items with headquarters in Keene, NH.

C&S recently released their strategic retail pricing system and has chosen Engage3 as their partner for competitive retail pricing. “Engage3 IS the premiere partner in retail price information,” said Frank Puleo, VP of Retail Services at C&S.

Engage3 is a leading provider of solutions that help retailers and brands improve their pricing performance and compete more profitably through data science & analytics.

“We have worked with Engage3 for seven to eight years to deliver the best competitive pricing platform in one of our regions. Over the years, we’ve extended that partnership and now we have it on a national level,” noted Corey Quiring, Sr. Director of Corporate Retail Services at C&S.

15 May 2018
COO

Engage3 COO Edris Bemanian Talks Pricing Strategy, Pressures, and the Market

Last month, Robert Schaulis of Andnowuknow interviewed Engage3 COO Edris Bemanian on his observations of pricing pressures from the likes of Amazon and Lidl. “The biggest trend is that pricing and assortments are becoming more dynamic and localized,” Edris says.  He notes that e-commerce is now becoming a fundamental part of retailers’ strategies versus just a “me too” approach. Read the full article in Andnowuknow.com.

26 Mar 2018
MissionControl_Competitive Pricing Platform

What to Look for in a Competitive Pricing Platform

What to Look for in a Competitive Pricing Platform

Precise and Accurate Data

First and foremost, a competitive pricing platform must have the ability to collect precise and accurate pricing data. This allows retailers to target competitive shops, optimize frequency, and specify which items to focus on within regions or individual stores.

Rather than casting a wide net to see what useful data gets brought in, retailers must be able to get a global look at the actions of their competitors while also drilling down to store-specific opportunities. When they have both views, they can see clearly where they are winning and losing. Essentially, such a system puts both a telescope and a microscope into the hands of merchants and their pricing analysts, enabling them to comprehensively study their competitor’s universe. It allows them to reverse-engineer their competitor’s approach to pricing and to develop a targeted response, especially if they see a weakness.

Quality Assurance Workflow

Real-time_QA_Competive Data collection
A strong quality assurance workflow ensures that data being collected is accurate.

A competitive pricing platform must also have a strong quality assurance workflow. With today’s mobile app-enabled technology, automated processes can greatly reduce manual errors and ensure that only quality data is being captured at shelf edge. Additionally, such apps can compare shelf data against historical records, flagging any SKU pricing that seems historically unreasonable. Advanced analytics can assure that the data being captured is accurate in terms of price, brand, sizing, and product attributes. This technology can eliminate much of the human error that has plagued competitive shop programs.

Product Attributes

With the rise of private labels, competitive pricing platforms must be able to compare product attributes. In traditional competitive shop programs, as many as 40% of items go unaccounted for because there is no UPC match. To solve this problem, competitive pricing platforms must be able to utilize visual data capture technology and advanced character recognition to compare product attributes. This allows product linking to occur not just by UPC, but also by key attributes and statement of ingredient similarities, i.e. gluten-free and organic. This creates a more accurate picture of a competitor’s private label pricing strategy and their total value proposition.

Customized KVI Lists Based on Statistical Analysis

MissionControl_Competitive Pricing Platform
Dynamic KVI list support can help you customize by store.

Historically, cost and timeliness have made it difficult to acquire quality competitive data. Given the dynamic nature of the retail environment, static KVI lists are not responsive enough to the realities of where to focus competitive pricing efforts across various geographies and store-specific categories. The retailer needs a pricing platform that allows them to shift from static KVI lists to ones that are easily customized by banner or even by specific store. Rather than taking a blanket approach, the critical decisions of where, what and when to comp shop should be based on strategic statistical analysis.

Drill-Down Capabilities

Merchants need the ability to drill down and understand the decisions competitors are making within specific regions, designated market areas (DMAs), cities and individual stores across their overall pricing strategy or within specific merchandise categories. This would enable merchants to lead their competition by being right on pricing with the right items that are important to customers at a localized level. Such flexibility in designing and executing a more targeted approach to competitor pricing would allow for significant savings in budgeted dollars for competitive shops. A retailer could go after the data they actually need when they need it, rather than spending dollars on costly full book programs.  

Correlating Online and In-store Pricing

In today’s world of e-commerce, more and more retailers are taking an omni-channel approach to selling. A technology-enabled competitive pricing platform needs to take advantage of advanced web crawling algorithms to acquire this competitive data and correlate it against the data captured by auditors in physical store checks. This would enable a more efficient and cost-effective approach to acquiring competitive pricing data.

Aligning Objectives

As advanced analytics enable faster and more accurate decision-making, organizations will need to change to more cross-functionally aligned metrics that strategically drive the financial success of a company. When considering today’s retail organizational structure, is what drives a merchant’s decisions the same as what motivates the employees in a pricing department? Having the data to make decisions regarding competitive pricing at the speed of retail requires a major step forward in enabling accurate pricing decisions to be made with a sense of urgency and strategic intent. However, to fully unlock its true impact to P&L, the retailer will benefit from progressive thinking around how to align objectives and an incentive structure that motivates and drives collaboration. This will enable different departments with complementary skill sets to pull the rope in the same direction and drive a total value proposition focused on the customer.

 

23 Jan 2018
NRF 2018 VR

NRF 2018: Retail’s Biggest Show Did Not Disappoint

I had the pleasure of attending NRF 2018 in New York City, and I must say that the self-proclaimed “Retail’s Biggest Show” did not disappoint. AI, big data, voice, and augmented reality companies were out in full force.

After attending a good number of the tracks, three main ideas about how to win in the next year seemed to rise above the fray: focus on pricing, deliver value to your customers, and enhance your user experience.

It was also clear that some of the big retailers are starting to position themselves as technology companies.

Kroger showcased their product called Kroger Edge, a shelf-based digital display that not only shows dynamic product prices, but also rotates to show advertisements and products’ nutritional information. Think rotating digital billboard ads like those you see at your local gas pump. The plan is to have this new technology installed in 200 Kroger stores nationwide by the end of 2018. You’ll see them first at the end-caps, and then at the regular aisles.

Below is a short video I recorded:

I spoke to Kevin Fessenden, Manager of Research and Development for Kroger Technology, who said that they plan on selling their solutions to non-Kroger retailers. This capability would allow stores to seamlessly adjust their prices and even offer personalized experiences to shoppers right where they make their purchase decision.

Doug McMillion, CEO of Walmart
Doug McMillion
CEO of Walmart

Walmart CEO Doug McMillan called his company a technology company after being named The Visionary at the NRF Foundation Gala on Sunday. Walmart’s $3B acquisition of Jet.com followed by other acquisitions of ShoeBuy, Moosejaw, and Bonobos  signals its serious commitment to eCommerce and enabling technologies. But wait, there’s more. Walmart also started its own technology startup incubator, Store No. 8, in Silicon Valley.

From robots scanning your store shelves, software systems that use lights and mirrors to provide shelf location of items, to store-traffic counting software, NRF 2018 was dripping in mind-blowing retail tech.

With more than 35,000 attendees and 3,400 retail companies represented, getting through the show could be overwhelming. At the “Brick and Mortar Store Strikes Back” discussion panel, Jason Breazeale, Senior Manager of Innovation at Ahold Delhaize, had a tip for attendees. “I usually visit the vendors in the perimeter of the expo halls, because those are the true innovators. I don’t spend much time in the center-hall. Those small companies will have the latest technology that just might get you your competitive advantage,” he said.