Convenience Store Decisions: Boosting Your Data IQ

From analyzing customers to assessing their inventory, more convenience stores are using operational data to their advantage.

By Howard Riell, Associate Editor, Convenience Store Decisions

Today, collecting data is clearly essential for convenience store retailers. But capturing and making use of the most pertinent information—from customer analytics to competitive intelligence—can spell the difference between operating in the black or not.

Data solutions aren’t always black and white, however. Often, it requires operators to assess category gross margins and related variables—especially in the form of inconsistencies or problems—and figure out how to address them.

Within a c-store today, being able to track customer tendencies, the workforce, fuel and product sales and inventory is vastly important to a retailer’s competitiveness.

“Typically, they are not properly accounting for variations in inventory,” said Steve Montgomery, president of b2b Solutions LLC , a Lake Forest, Ill.-based consulting firm, referring to typical scenarios. “The second reason is that something is amiss at the sites. This is true if the margin is higher or lower than is normally achieved.  In either case it should be investigated. Are the changes following the same pattern as their other locations as far as sales, margins, etc.? If not, why not?”

For many retailers, knowing how to gather and assess information from fuel inventory and customer sales is an important aspect of any store operation that sells fuel.

“Variations can be reported in both spread sheet and graphical format. Specific items can vary by chain. However, everyone should be monitoring sales (dollars, units and gallons), margins, customer counts, transaction size and expenses,” said Montgomery.

C-stores must also track fuel pricing processes and procedures.

“Obviously being in a c-store, fuel pricing is a huge deal and so you want to make sure that you are keeping up with your competition on that. It’s an easy question but a hard answer,” said Karla Grimes, director of operations for the Kent Cos., which runs 40 Kent Kwik Convenience Stores.

Being in the fueling industry, Grimes continued, many operators should ensure that they are staying up with that crucial parts of their business, as well as the competition.

“Of course, as far as consumer analysis goes, you’ve got to get into your competition and look at their stores and see what they are doing; how they take care of their customers,” Grimes said. “That’s a big part of it.”

“We do a lot of our data in-house,” added Alex Garoutte, marketing director of Kent Cos., based in Midland, Texas. “There are a lot of (data analysis) companies out there that are valuable. You just have to determine if it’s worth spending the money on.”

Inside sales, of course, deserve just as much focus.

“What are you doing, and what differentiates you from your competition?” Grimes said again.

A good strategy for a chain of stores, according to Montgomery, is to look at the same type of data gathered from all locations at one point in time, and the same locations over time to determine patterns of activity. “The first allows the retailer to compare the results of his stores. The second will show whether changes are following the same pattern as their other locations,” Montgomery said.

Deciphering the data and then putting the results to use are the next steps. Disseminating the necessary operational information to key store personnel such as managers is vital and too often overlooked, Grimes added.

“You’ve got to decide what data you want to use and then figure out how to push that out. It could be a group of store managers, it could be employee leaders,” Grimes said. However you are going to do it, I think you have to go about getting that pushed out to every level of the company. And what you need is to have key players who are going to do that for you.”

First, a retailer must determine what data to share.

“Our experience tells us that the most important data that convenience store operators use is their own sales data,” said Brian Nelson, co-founder and chief operating officer of NewsBreak Media Networks Inc., a Knoxville, Tenn.-based programmatic merchandising platform for the convenience store industry that converts fuel-only customers to multi-product purchasers.

“The average c-store completes around 1,100 transactions (or more) per day. That is 1,100 unique customer surveys on what products customers want, and what products they will likely purchase together,” Nelson said. “A c-store’s proprietary sales data is a constantly updating data set that can be used to map and predict market trends, purchase patterns and inventory expectations. Ask any researchers what they would give for the ability to consistently collect 1,100 surveys daily.”

Data analysis and predictive analytics are areas that are experiencing rapid growth and innovation. If operators don’t seek help from experts, Nelson said, their category managers, marketing directors and executives are going to need to become data experts, which takes those individuals away from their proper roles and responsibilities.

“Seeking support from experts can help operators get to actionable information faster,” Nelson said. “Experts often have the infrastructure in place to parse, analyze and model historical and dynamic data quickly, instead of building it from scratch.”

Each time a customer completes a transaction, Nelson explained, he or she is giving the c-store operator direct feedback.

“Taking that feedback across multiple customers and multiple locations can give operators great insight into how they promote their products and even what products they carry,” Nelson said.

Market-basket data can be analyzed to help operators decide what products to promote together and what time of day they should be promoting those combination deals. For example, by promoting a discounted salad with a high-end water product, a c-store could increase unit sales for the promotional product while simultaneously driving up the total market basket.

C-stores can utilize data analytics to drive overall marketing initiatives, promotions and even inventory management and product selection. Analysis can include integrating key consumer buying variable data and demographic data into a store’s existing sales data in order to build location specific, programmatic merchandising campaigns.

Nelson encouraged operators to innovate and try new product offerings and marketing strategies, but warned that caution is called for. “Use data analytics as a tool, but don’t get stuck in the data.”

While a c-store operator will likely never get sales data from his competitor, operators can get a sense of how they stack up against the market, state or region from their vendor representatives. The companies that represent the brands that are sold through the c-store industry, Nelson said, can be a great resource to operators and category managers for target demographic information and customer analytics.

Craig Alberino, the CEO of Grey Jean Technologies in New York City, said his firm generally works with behavior and identity data. “That’s things that you buy, places you go, even the weather. It could be various influencers.”

Knowing why consumers buy what they buy helps drive engagements and forge deeper relationships, resulting in more predictable revenue.

“It’s our view that all retailers, convenience stores included, could be serving customers better by knowing them more intimately,” he added.

Taking transaction and loyalty information inventory numbers, promotional calendars and more, and enriching it with elements like census statistics, weather and social media can provide a sharp focus on buying patterns for small geographic area around the store.

“Even if there is somebody in my dad’s age category, which is 70-plus, he’s still carrying an iPhone,” Alberino said. “He’s still looking for deals, and he is hypersensitive to gas prices. He knows if there is two cents less per gallon down the road, and he’s going to go to that other pump. Even though he might be spending more, it’s the trigger and it’s kind of a win, that he beat the system. And so, you are almost ‘game-ifying’ it a little.”

Retailers naturally want to drive up customer visits and average sales per transaction. More convenienc stores are evaluating customer sales over time to gauge future buying tendencies.

“This data is far more informative that just total sales,” said Montgomery. “It can tell why sales are up or down. Is it because fewer customer are coming to the store, or because they are buying less each time they come or both?”

Another tool is the correlation between gallons sold and store sales changed, he added. “Are they still buying your fuel but shopping more or less frequently inside you store? The data will not solve your problems, but should tell you where they are occurring.”

One of the easier analytics that can be checked is the correlation between fuel sales and in-store sales, Montgomery said. “Does an increase in gallons result in increased store sales? If so, does it impact certain categories more than others? These types of insights can help retailers determine what role they want a category to play in their overall marketing strategy.”

Montgomery suggested retailers benchmark their data with sources such as the National Association of Convenience Stores (NACS) because it might show opportunities to better sales and margins or control expenses.

“However, every store (or) market is different. The most important benchmarking retailers can do is against their own data,” Montgomery said. “That the industry averages ‘X’ is not as meaningful as whether you are making steady improvements in sales, margins and expense control.”

In retail, data and data gathering comes in many forms and the benefits are just as varied.

“In any retail setting, convenience stores included, we generally work with behavior and identity data,” said Alberino. “Identity data would be things that you buy, places you go, even the weather. It could be various influencers. So generally when we are working with a retailer we will take transaction information and any loyalty information they might have, inventory, promotional calendars, and we will enrich it with census, weather, social media, etc. What is happening in the vicinity of your physical presence, within a mile of that convenience store?”

Among the data he suggests is scheduling of local events.

“There could be a rock concert happening, for instance, and in fact one of the convenience chains we’ve spoken to was in charge of sponsorship for a very large concert venue,” Alberino said. “We were talking to them about how they could increase traffic. Because many people were making long journeys across the West from California to the venue, how do you get them to stop, not only at the pump along the way, but also to stop in and get other things?”


Originally published on Convenience Store Decisions

Boosting Your Data IQ

How artificial intelligence is transforming retail personalization

ai-jpg__640x360_q85_crop_subsampling-2Retail personalization is certainly not a new concept, but there are many new advances in personalization helping retailers create deeper relationships with shoppers through meaningful, relevant and contextual experiences.

Personalization through the years

Ten years ago, Amazon and Netflix were the poster children for personalization. Amazon was commended for its ability to show different home pages for different customers based on their past clickstream paths or previous purchase behaviors. Other retailers’ personalization efforts simply greeted returning customers by name or enabled them to save website preferences. Then there were those that took a one-to-many approach, such as versioning their site for entire segments of visitors.

These rudimentary approaches are now considered table stakes for today’s retail marketers. Consumers are far savvier than they were ten years ago. They not only want personalization, they expect it!

Today’s retailers are rising to the occasion. Retail personalization is having a major resurgence, thanks to the proliferation of big data, as well as the implementation of machine learning across distributed platforms.


Stitch Fix’s AI success

Some retail organizations have built entire businesses around AI. Take Stitch Fix, for example. The styling subscription service uses AI to tailor clothing and accessories to busy women’s personal tastes, budgets and lifestyles. Stylists work with a team of more than 60 data scientists to choose tailored items for each shipment. By applying machine learning to the process, the computers become smarter as they handle more and more data.

This strategy of marrying humans and machines has been wildly successful for Stitch Fix. Over 80 percent of clients return within 90 days for a second order, and a third of clients spend 50 percent of their clothing budget with the subscription service.

While we’ll continue to see businesses in and out of the retail space launch with AI at the core, this approach certainly won’t make sense for all retailers. Instead, established retailers can apply AI to different business units. Recently, Macy’s announced its customer service unit is testing a “mobile companion” tool using AI. The tool enables shoppers to get answers based on the store that they are physically shopping in rather than having to find a sales associate. While this is a great way to leverage AI to engage with current shoppers, what about using the technology to acquire new ones?


AI’s role in marketing

Applying AI to marketing not only helps retailers acquire new customers, but also encourages repeat business. As recommendations and offers become more tailored, shoppers’ loyalty will continue to deepen as they associate the brand with personalized and relevant experiences. For retail marketers that want to stand out from the hundreds of advertising and marketing messages shoppers see on any given day, AI-driven marketing is a must.

AI enables marketers to harness powerful algorithms to find patterns in internal and third-party data, and then look for repetitions in these patterns. One of the core underpinnings of AI that is transforming retail personalization is machine learning. Stated very simply, machine learning is about solving problems using probability and statistics. Used in the context of personalization, machine learning can continually adjust the data sets until the right marketing message for each individual shopper is presented at the moment, and through the channel that matters most.


This can all sound very daunting for marketers. For those just getting started in AI-powered personalization, keep the following in mind:

1. Don’t be overwhelmed by data. Many retail organizations have data everywhere and have no idea how to consolidate it, let alone make sense of it all. If you don’t have internal resources to organize disparate data sets, simply outsource this important task to a personalization technology partner.

2. Pull in as much third-party data as possible. From POS to loyalty data, retailers have a lot of information about customers. But to get a true 360-degree picture, it’s important to pull in as much third-party data as possible. I’m not talking simple demographics here (though that’s helpful as well). Your data combined with factors such as location, time, social media activity and price sensitivities can really make the difference in understanding how, why and when customers shop, as well as the right purchase triggers so retailers can present the most relevant marketing messages.

3. Determine the best output channel. Many retailers have seen the power of email personalization for driving traffic and sales to their stores, both online and off. But there are many other channels where personalization works well — in particular mobile. As we saw in the Macy’s examples, mobile can be used to augment in-store service, but it’s also a great vehicle for timely promotions and offers for shoppers on the go. In addition, the emergence of AI-powered chatbots is something to keep an eye on, as retailers look to follow their audience onto the appropriate channels, including social.

You don’t have to be a personalized subscription service like Stitch Fix to provide personalized service to prospects and customers. A huge impact can be made by applying AI to one area of your business at a time — from marketing to customer service to merchandising. A once over-hyped technology, personalization has arrived and is the future of retail thanks to the power of artificial intelligence.


Originally published on Retail Customer Experience


Taking Amazon-esque Personalization to the Brick-and-Mortar Store

amazon-brick-mortar-trans-300x300Amazon is now arguably today’s most popular and most shopped online retailer in the world, and is often looked upon as a personalization leader. This is in large part due to Amazon’s ability early on to show different home pages for different customers based on their past clickstream paths or previous purchase behaviors.


Today’s shopper now has the expectation that all retailers will know them personally as a consumer and serve up personalized experiences the same way Amazon does. When a retailer communicates with them – whether online or in a physical store – they expect to receive messages, deals and offers that are relevant to them, at the exact time when the offer is most relevant, and in a manner they expect to be communicated with based on their personal preferences. In short, they want one-to-one personalization.


While physical store associates can provide great customer service, online retail has an advantage when it comes to personalization because the retailer can tailor its homepage and customer recommendations based on easily trackable clickstream paths and browsing data on recently viewed or purchased items. So how can retailers apply this level of Amazon-esque personalization in-store?


Enhancing Beacon Technology

Perhaps the easiest way to leverage personalization in-store is through beacon technology, which has grown in popularity over the last few years. Beacons can detect when a customer approaches or leaves a specific location, enabling retailers to push timely messages to shoppers that promote certain products or offer other useful information. While many retailers use beacons to entice customers to make a purchase with store-wide deals, this may not always result in a sale because the offer isn’t always relevant to all shoppers.


However, when beacon technology is coupled with data about consumers’ past behaviors, preferences or other valuable data about that specific customer and layered with predictive analytics, retailers can identify the messages that will resonate with each shopper and provide them with a truly personalized deal or offer.


Let’s say a customer is walking by a department store and receives a mobile notification on a 20 percent off deal on sneakers. The customer enters and roams the store, but ultimately decides not to buy anything. Perhaps they had just bought a new pair of sneakers, or the sneakers that were on sale didn’t include their favorite brand.


This is where predictive intelligence steps in. Now imagine the department store has all of this data on this same customer – data that reveals things such as:

  • Transaction History: The customer always buys a new pair of shoes during the first week of each month, but never on days in-between;
  • Likes: She particularly likes comfort over style, often buying sneakers, flats or low-heel boots over stilettos;
  • Lifestyle: She is a working nurse, and spends 12-hour shifts 4 days a week at the hospital running around from room to room checking in on patients;
  • Cause: She often contributes to charitable organizations that provide resources or aid to impoverished countries.


Knowing these data points – which provide context for when and why a consumer makes a purchase, and work in conjunction with the customer’s location – the retailer can now send a mobile offer that is truly tailored to the customer and is much more effective in enticing her to make a purchase.


Case in point: Perhaps the customer is in the store on Labor Day weekend. You now know she prefers to buy shoes at the beginning of the month, prefers comfort over style, is on her feet all day most days and has a soft spot for philanthropic endeavors. You know then that offering her a deal for 20 percent off a pair of TOMS would more likely result in a purchase than if you were to offer her 20 percent a pair of Sam Edelman heels. To top it off, the deal was sent to her at the optimal time she would buy.


Using this type of technology in conjunction with predictive intelligence allows retailers to personalize in-store offers to each individual shopper by speaking to their individual tastes – offering more compelling deals that have a greater chance of resulting in a purchase.


Delivering Deals Anywhere, Anytime, in Any Weather

Perhaps the greatest thing about predictive intelligence is that it’s not restricted to beacons or any one location. Predictive intelligence enables retailers to know the data points that are relevant in any purchase decision and send targeted offers based on this data, no matter where their customers are located. This not only encourages them to make a purchase, but can actually encourage them to come into the store if the deal is in-sync with when, where and why a customer prefers to make a purchase.


For example, a local coffee shop learns the following about one of its customers:

  • Location: The customer works at an office two blocks away;
  • Time: He usually makes a coffee run at 2 p.m., when he hits his afternoon work slump;
  • Weather: He opts for a hot latte every time rain is in the forecast.


The next time it rains, the coffee shop can deliver a deal for this particular customer that is specifically tailored to his preferences and behavior – perhaps offering one dollar off a medium latte sent to his mobile just before two o’clock. They know they can get his attention due to the time, his proximity to the shop and his preference for hot drinks on rainy days, and they’ll be more effective in enticing him into their store because they were the only nearby coffee shop to offer a deal for what he wanted at a time when he wanted it.


Retailers can deliver this level of personalization anywhere their customers are located, whether they’re in an office, at home or even lounging on a beach. No matter where a customer is, what time of day it is or what the weather is like, predictive intelligence learns how to utilize the right combination of data points to drive a purchase. The possibilities are endless and the variables limitless; retailers just need to be armed with the right intelligence about their consumers’ unique preferences and behaviors.


Originally published on Multichannel Merchant


CB Insights: 45 Artificial Intelligence Startups Targeting Retail In One Infographic

AI isn’t all self-driving cars and chess-playing computers. There’s an emerging market for AI use in e-commerce.

Investors poured a record high $1.05B into artificial intelligence startups in Q2’16, and AI is already affecting more areas of our lives than many people realize. Even retail and e-commerce companies are increasingly integrating the technology.

Recently there’s been a rush of AI announcements and acquisitions by major retailers: Just this week, Etsy acquired Blackbird to enhance its search functionality through AI, followed the very next day by Amazon acquiring (formerly GoButler), another AI-powered searching tool. And earlier this month, e-commerce unicorn Houzz (see our full unicorn tracker here) announced a deep learning initiative to help users find and buy products by clicking on images.

Using CB Insights data, we dove into the wide array of AI startups focused on retailers and e-commerce businesses, including AI-powered personal shopping apps, natural language processing and image recognition tools for shopping websites, predictive inventory allocation tools, and more.

The area is emerging, and most companies focused on retail AI remain in the very early stages. However, we have seen several larger deals in recent months. ViSenze, which lets users search e-commerce sites by image or find visually similar items, raised a $10.5M Series B in September, while Trax Image Recognition, which visually tracks the performance of goods on grocery shelves, raised a $40M Series C in June. Several startups are backed by top investors from our smart money list, such as search optimization tool Zettata, backed by Accel Partners, and predictive customer targeting platform AgilOne, backed by Sequoia Capital.

While there are numerous other AI startups focused broadly on personalized marketing and ad targeting, we limited this market map to startups whose core focus is retail and e-commerce. The startups in this graphic have raised roughly $650M in total disclosed equity financing. Scroll down to view the graphic, category explanations, and a full company list with select investors.


Retail AI market map

See the market map below. This market map is not meant to be exhaustive of startups in the space. Graphic includes private, independent companies only.


Category breakdown

We divided our market map into the 12 categories listed below:

Real-time product targeting – Machine learning to present online shoppers with personalized product recommendations. These companies typically update e-commerce websites in real time to present product selections best suited to the individual shopper.

Real-time pricing & incentives – Machine learning to adjust pricing, sale options, rewards, and coupons in real time to try to push hesitant shoppers toward conversion.

Natural language search – Algorithms that use natural language processing to improve search functionality in e-commerce websites.

Visual search – Image recognition platforms that help e-commerce websites let visitors search by image, instead of text, and match relevant products to specific images.

In-store visual monitoring – AI-powered software that analyzes photo and visual content of store shelves to help brands track how their products are stocked and promoted in real time.

Conversational commerce – Chat software and chatbots focused on helping shoppers make purchases in a conversational text format using natural language processing.

Predictive merchandising – Big data analysis to optimize purchasing, allocation, and product assortment across stores and e-commerce. The aim is to better predict demand in different geographies to avoid waste and prevent inventory from going out of stock.

Sizing & styling – AI-powered software to help retailers integrate improved product sizing and outfit-building tools into their websites.

Multichannel marketing – Startups using AI to create targeted marketing campaigns across desktop, mobile, email, and other digital channels. Inclusion limited to startups focused specifically on e-commerce.

Integrated online & in-store analytics – Startups that combine both digital and physical store analytics to help retailers better understand their customers.

Location-based marketing & analytics – Startups that combine digital and physical store analytics, while also integrating beacon technology to track shoppers’ locations.



Originally appearing on CB Insights “Don’t You Look Smart: 45 Artificial Intelligence Startups Targeting Retail In One Infographic”: