May 15, 2013
Where Does In-Store Data Help Retailers Most?
While in-store data is continually hyped for its potential to improve marketing approaches, many stores are viewing such data as vital to enhancing store operations.
The findings come from a survey of the nearly 100 retail executives that attended the RetailNext Executive Forum held in Santa Cruz from May 1 to 3. When asked who were the most important stakeholders for in-store data, ranked in order of importance, store operations came in first at 81 percent followed closely behind by marketing at 76 percent.
Coming in third was business intelligence (BI), or customer insights, at 43 percent, while IT rated 10 percent and finance didn’t receive any votes.
In a blog entry, Tim Callan, chief marketing officer of RetailNext, noted that while IT or BI may have traditionally been viewed as the best providers of technology or data to internal users, store operations and marketing were chosen because they stand the best chance of improving the day-to-day effectiveness of the store’s critical programs.
"That’s significant because it highlights that Big Data and in-store analytics initiatives are largely driven by business pragmatists who value the bottom line improvements they enable," wrote Mr. Callan.
Beyond understanding the effectiveness of current campaigns and messaging, in-store information is being used to improve staff scheduling, optimize layouts, and improve the effectiveness of fixtures and furniture, according to RetailNext. The company reads in-store shopper engagement by collecting data from video cameras, point-of-sale systems, on-shelf sensors, Wi-Fi devices, time and attendance applications and other sources.
When asked which measurements were most important to their company, survey respondents answered:
- Conversion, 89 percent;
- Shopper yield, 53 percent;
- Average transaction value (ATV), 26 percent;
- Entrance traffic, 26 percent;
- Sales per square foot, 5 percent.
Overall, 60 percent agreed that in-store data collection and analysis had increased "significantly" compared to two years ago. Twenty-three percent believed it had "somewhat increased" while 17 percent saw it as "about the same."
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Discussion Questions
Which retail discipline or department do you think has the potential to benefit most from in-store data collection? What less-obvious benefits do you suspect retailers will glean by better capturing and analyzing data around shopper engagement?
Poll
Local, real time, and increasingly precise data and analytics will drive new benchmarks for performance in the store—in both operations and customer interactions.
There are several point solutions that are solving for specific use cases (recall Kroger’s line reduction solution). However, the challenge and the greatest potential will be in creating a flexible platform of sensors and analytics that will generate the insights that are not yet seen nor imagined.
First, this data ought to be used to measure the effectiveness of in-store promotional activity as this is an area that traditionally falls short after all the time and money have been spent on getting their shopper segmentation right. Has the promotion, be it an end-aisle or pallet display etc., been put up correctly and in the right place? Are the right products placed in the display. How many times have we seen the wrong products mixed in with a display?
Second, we could learn more about what products or categories should be adjacent to another and be able to test shopper behavior for more convenient shopping. There are many other areas to mine with this data, including shopability, highest rated department, most frequent product/category purchased, conversion vs. other competitors in the channel, and other channels/formats, etc.
How curious. The #1 client for in-store data is in-store operations. Who’da thunk it? My gast is appropriately flabbered.
My local Safeway is undergoing an internal remodel. It’s not a pretty site/sight. Customers are yelling at store employees. Seriously. There’s a structural steel post smack dab in the middle of one of the HBA aisles—carts don’t fit through. Also in HBA, the new shelf tag program obscures items on the new, more densely-packed shelving—especially for someone 6’2″. I have to kneel to see some of the stuff.
In-store data collection could have been employed here, but clearly was not.
Any area requiring an understanding of consumer purchases/behavior/attitudes needs the data (or parts of the data). Any interpretations based upon data analysis need to be available for everyone else using the data. Each business question requires a particular set of data be used for analysis, but the interpretations resulting from the analysis may be relevant and helpful to others who are working to understand consumers. Better understanding of consumers is relevant for many decisions for many departments. Capturing the analyses in any one silo is not productive.
It makes sense that store operations is first in line with regards to the value of in-store data. If you have OOS it’s tough to convert lookers into buyers.
Operations is the must have. Marketing is about additional lift and growth opportunity. I love sharing the story of a client that used in-store data and combined it with data related to state tax increases on cigarettes. What he found was when there was a state increase on taxes, his product (solution to help people stop smoking) went through the roof. The problem was, he was always OOS when that happened. By knowing in advance, he was able to work with his retail clients to ship in extra product (operations) and displays (marketing) when the taxes went up in a state. Sales tripled and everyone was happy, including the shopper.
Lesson: Silos are great for storing grain. Not so good when it comes to using data.
In-store, active sensing is a brave new world for retail. Much of the emphasis of late has been on shopper tracking apps, but we can define a larger framework:
DIMES – Demand, Items, Messaging, Employees and Shoppers.
To truly evaluate at-retail performance of any merchandising, marketing or store operations decision, a retailer must have a grasp of these five dimensions and apply scorecards and benchmarks to their evaluation.
The In-Store Implementation Network introduced DIMES as part of a 2011 workshop at the LEAD Marketing Conference:
http://www.slideshare.net/jtenser/sense-perform-isi-20110909
To RetailNext’s credit, it has been one of very few vendors to recognize the importance of sensing along multiple in-store dimensions. It may not offer a comprehensive DIMES solution set, but reliable partial data is much better than none at all.
This is indeed a “big data” proposition, and I am convinced it must intersect with SoMoLoMe data flows. Let’s not overlook the impact this will have on basic merchandising, targeted promotion and inventory forecasting.
Retailers in general have very little focus on shoppers, per se. For example, more shoppers buy only one item rather than any other number, in virtually any store in the world. On advising how to manage this FACT for increasing sales and profits, one senior retail executive asked, why would we do this if no one is PAYING us to?
Retailers are mostly supplier focused, NOT shopper focused.
I think this is a poorly worded survey. Of course in-store operations are most important. However, it’s business intelligence combined with the actual staff the provide information on how to optimize layouts, and improve effectiveness. The collection of data from video cameras, point-of-sale systems, on-shelf sensors, and Wi-Fi devices, and compiling that into usable information IS an IT function delivered through business intelligence. I do not think they should be separate choices on the survey because they are dependent on each other.
I think we are remiss if we begin to rank data’s importance to one function higher than another. With the data available today, I cannot think of a business discipline that couldn’t benefit greatly from the analytics tools in the marketplace. Take as much of the “gut feeling” as possible out of the decision making process.
Marketing, store ops, distribution/logistics, CPG supplier relations, etc., etc.
Although the questions aren’t exactly worded this way, the survey seems to look forward in terms of the collection of data but is stagnant—and maybe even looking in the rear view mirror—on the ways the data can be used. That is, given more data in the future, which of the existing and traditional operations will make use of it?
More interesting is a forward-looking perspective on how the in-store experience is changing opens up new perspectives. With so many people using their smartphones to assist with their in-store shopping, it creates the opportunity to both collect a LOT of data and also use the smartphone as a way to communicate directly with each shopper and provide product suggestions, personalized offers, etc. That drives toward an increased use by marketing/shopper engagement/etc., as well as the other groups.
I think of this type of data collection as “instrumenting the store,” analogous to how you would instrument a website to track and measure shopper behavior, and I think it’s a crucial step for brick and mortar retailers who want to stay relevant and keep growing.
Echoing some panelists’ earlier comments, I object to the silos that are implied by the survey. The real power in this type of data is when it is used cross-functionally to effect change to the customer experience in ways both large and small. On that note, I’d add store design to the list of stakeholders who should be in the room when the implications of in-store data are discussed.
Marrying all of the sources of in-store data is critical. While most retailers know a fair amount about operations metrics, far fewer know what their shoppers THINK about the experience in the store. So it is important to gather and look at all sources of information and how they paint the collective picture.
I am a big fan of collecting data—not just for the sake of doing it, but to seek and/or validate hypotheses on correlations and insights. As such, I think that just about every department (both in-store and back-end operations) could benefit from greater data collection.
I think what’s more relevant is “what do you do with the data.” While in-store data will definitely benefit store operations, making right use of it will benefit all other functions as well. Data can not be looked at in isolation. As to who will benefit the most will depend on how you use the data.
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Local, real time, and increasingly precise data and analytics will drive new benchmarks for performance in the store—in both operations and customer interactions.
There are several point solutions that are solving for specific use cases (recall Kroger’s line reduction solution). However, the challenge and the greatest potential will be in creating a flexible platform of sensors and analytics that will generate the insights that are not yet seen nor imagined.
First, this data ought to be used to measure the effectiveness of in-store promotional activity as this is an area that traditionally falls short after all the time and money have been spent on getting their shopper segmentation right. Has the promotion, be it an end-aisle or pallet display etc., been put up correctly and in the right place? Are the right products placed in the display. How many times have we seen the wrong products mixed in with a display?
Second, we could learn more about what products or categories should be adjacent to another and be able to test shopper behavior for more convenient shopping. There are many other areas to mine with this data, including shopability, highest rated department, most frequent product/category purchased, conversion vs. other competitors in the channel, and other channels/formats, etc.
How curious. The #1 client for in-store data is in-store operations. Who’da thunk it? My gast is appropriately flabbered.
My local Safeway is undergoing an internal remodel. It’s not a pretty site/sight. Customers are yelling at store employees. Seriously. There’s a structural steel post smack dab in the middle of one of the HBA aisles—carts don’t fit through. Also in HBA, the new shelf tag program obscures items on the new, more densely-packed shelving—especially for someone 6’2″. I have to kneel to see some of the stuff.
In-store data collection could have been employed here, but clearly was not.
Any area requiring an understanding of consumer purchases/behavior/attitudes needs the data (or parts of the data). Any interpretations based upon data analysis need to be available for everyone else using the data. Each business question requires a particular set of data be used for analysis, but the interpretations resulting from the analysis may be relevant and helpful to others who are working to understand consumers. Better understanding of consumers is relevant for many decisions for many departments. Capturing the analyses in any one silo is not productive.
It makes sense that store operations is first in line with regards to the value of in-store data. If you have OOS it’s tough to convert lookers into buyers.
Operations is the must have. Marketing is about additional lift and growth opportunity. I love sharing the story of a client that used in-store data and combined it with data related to state tax increases on cigarettes. What he found was when there was a state increase on taxes, his product (solution to help people stop smoking) went through the roof. The problem was, he was always OOS when that happened. By knowing in advance, he was able to work with his retail clients to ship in extra product (operations) and displays (marketing) when the taxes went up in a state. Sales tripled and everyone was happy, including the shopper.
Lesson: Silos are great for storing grain. Not so good when it comes to using data.
In-store, active sensing is a brave new world for retail. Much of the emphasis of late has been on shopper tracking apps, but we can define a larger framework:
DIMES – Demand, Items, Messaging, Employees and Shoppers.
To truly evaluate at-retail performance of any merchandising, marketing or store operations decision, a retailer must have a grasp of these five dimensions and apply scorecards and benchmarks to their evaluation.
The In-Store Implementation Network introduced DIMES as part of a 2011 workshop at the LEAD Marketing Conference:
http://www.slideshare.net/jtenser/sense-perform-isi-20110909
To RetailNext’s credit, it has been one of very few vendors to recognize the importance of sensing along multiple in-store dimensions. It may not offer a comprehensive DIMES solution set, but reliable partial data is much better than none at all.
This is indeed a “big data” proposition, and I am convinced it must intersect with SoMoLoMe data flows. Let’s not overlook the impact this will have on basic merchandising, targeted promotion and inventory forecasting.
Retailers in general have very little focus on shoppers, per se. For example, more shoppers buy only one item rather than any other number, in virtually any store in the world. On advising how to manage this FACT for increasing sales and profits, one senior retail executive asked, why would we do this if no one is PAYING us to?
Retailers are mostly supplier focused, NOT shopper focused.
I think this is a poorly worded survey. Of course in-store operations are most important. However, it’s business intelligence combined with the actual staff the provide information on how to optimize layouts, and improve effectiveness. The collection of data from video cameras, point-of-sale systems, on-shelf sensors, and Wi-Fi devices, and compiling that into usable information IS an IT function delivered through business intelligence. I do not think they should be separate choices on the survey because they are dependent on each other.
I think we are remiss if we begin to rank data’s importance to one function higher than another. With the data available today, I cannot think of a business discipline that couldn’t benefit greatly from the analytics tools in the marketplace. Take as much of the “gut feeling” as possible out of the decision making process.
Marketing, store ops, distribution/logistics, CPG supplier relations, etc., etc.
Although the questions aren’t exactly worded this way, the survey seems to look forward in terms of the collection of data but is stagnant—and maybe even looking in the rear view mirror—on the ways the data can be used. That is, given more data in the future, which of the existing and traditional operations will make use of it?
More interesting is a forward-looking perspective on how the in-store experience is changing opens up new perspectives. With so many people using their smartphones to assist with their in-store shopping, it creates the opportunity to both collect a LOT of data and also use the smartphone as a way to communicate directly with each shopper and provide product suggestions, personalized offers, etc. That drives toward an increased use by marketing/shopper engagement/etc., as well as the other groups.
I think of this type of data collection as “instrumenting the store,” analogous to how you would instrument a website to track and measure shopper behavior, and I think it’s a crucial step for brick and mortar retailers who want to stay relevant and keep growing.
Echoing some panelists’ earlier comments, I object to the silos that are implied by the survey. The real power in this type of data is when it is used cross-functionally to effect change to the customer experience in ways both large and small. On that note, I’d add store design to the list of stakeholders who should be in the room when the implications of in-store data are discussed.
Marrying all of the sources of in-store data is critical. While most retailers know a fair amount about operations metrics, far fewer know what their shoppers THINK about the experience in the store. So it is important to gather and look at all sources of information and how they paint the collective picture.
I am a big fan of collecting data—not just for the sake of doing it, but to seek and/or validate hypotheses on correlations and insights. As such, I think that just about every department (both in-store and back-end operations) could benefit from greater data collection.
I think what’s more relevant is “what do you do with the data.” While in-store data will definitely benefit store operations, making right use of it will benefit all other functions as well. Data can not be looked at in isolation. As to who will benefit the most will depend on how you use the data.