At Wiser Solutions, we understand that increasing your share of shelf is a top priority for any business. It’s about more than just having your products available; it’s about making them the go-to choice for consumers.
That’s where suggestive selling comes in.
This powerful strategy can be the difference between a product that merely exists on a shelf and one that flies off it.
In this webinar held on August 22, 2023, you can hear Kelsey McCarthy, Dan Ray, and Bruce Nagle of Wiser Solutions, delve into the art of suggestive selling and explore how it can significantly boost your shelf share.
So, whether you’re an established business looking to enhance your retail tactics or a budding entrepreneur ready to make a mark, we can equip you with the insights you need.
Matt Ellsworth: All right, great. Let’s get going. Welcome everybody. Thank you so much for joining. My name is Matt. I am the senior director of marketing at Wiser Solutions. I won’t be participating too much today, but I’ll be available in the background if there are any questions, make sure to drop them into the Q&A and we will definitely do our best to answer them.
Just for everybody’s knowledge this webinar is recorded. And we will send the recording to all attendees after the event is over.
So again, thank you so much. I just wanted to give a quick intro to Kelsey McCarthy, who will be your host today. She is our in-store product marketing manager extraordinaire, and she’ll do some more formal intros and take over.
So, thank you all so much. Kelsey, the floor is yours.
Kelsey McCarthy: Thank you, Matt. I appreciate it. And like Matt said, my name is Kelsey. I work in product enablement here at Wiser and I will be hosting today’s session for you. I want to take the time to introduce two other speakers joining me today.
Dan Ray, Director of Customer Success at Wiser and one of the early founders of ShelfSpace, a retail execution platform that Wiser acquired in 2020.
And also, there’s Bruce Nagle, VP of Market Development and former founder of RW3, a second retail execution platform acquired by Wiser in 2021.
Today, we are going to spend a little bit of time first exploring the challenges brands face when it comes to measuring and increasing share of shelf. And after that, we’re going to walk through two specific case studies, examining how Wiser specifically partners with brands to provide access to real-time shelf data and suggestive selling insights through the power of image recognition.
As Matt said, just reiterating, there will be a Q&A period at the conclusion, so please utilize the chat box as much as you need, and we’ll go over those at the end.
All right. So, to help ground the conversation a little bit, I want to talk, at a high level, just about the concept of share of shelf.
So, we’re going to discuss share of shelf as a means for assessing a brand’s market position and further mechanisms to improve it. Unlike traditional market share measurements, share of shelf focuses, you know, primarily on physical space allocation. That being said, there is a strong correlation as we generally see that the brand shelf share is a physical predictor of their performance and a leading indicator of dollar share growth or contraction.
Optimizing share of shelf is critical for brands seeking to grow market share and drive new sales. And by continuously analyzing shelf allocation, brands can gain deep knowledge about their own positioning and then opportunities for expansion.
So, first I’m going to have Bruce speak a little bit more about the main challenges that the CPG industry faces when it comes to the retail execution cycle today.
Challenges in Shelf Execution
Bruce Nagle: Morning everybody. Thanks, Kelsey. Kelsey did a really nice job of talking about the value associated with measuring physical share and how it can impact dollar shares. And I’m going to talk through the challenges in shelf execution and what we see and hear from our customers as the core elements associated with accomplishing this in your organizations. We’ve grouped these into the four key areas as it applies to retail sales and execution cycle.
So, number one, shelf strategy, development and execution strategy aligned with your business goals. And see winning customers having a well-developed, thought-out shelf strategy. They know the main factors that drive sales, availability, promotional compliance, share shelf, visibility that are critical for revenue growth.
No new news there. However, when working with emerging companies, we’ve heard that there can be a lack of a holistic plan, containing straightforward, digestible, actionable data to inform the process.
Resources, data, systems can be limited. For a data driven execution strategy, these brands velocity, however, may outpace shelf allocations, so they might invest in off shelf points of interruption until they can earn a retailer’s confidence for additional space in line. Larger, more established brands have resources, but need to develop an ongoing holistic business strategy.
Once you have the strategy outlined, including the right measures and metrics warranting quantification, the next step is to pull together all the data in a way that is timely, accurate, and actionable. Dan will talk more in depth on this later this morning and dive into how we apply image recognition technology to it as well.
Moving on to execution visibility, or what I call activation. Succeeding in selling more and growing in the brand comes down to execution, harnessing the insights in real-time to impact store conditions on or off the shelf. Emerging companies must develop a trusting relationship with the retailer at all levels.
Having a compelling story with proven data to support the push for growth is a big step in gaining legitimacy as a brand and a partner. Creating that story can be challenging with limited resources and visibility and anything further than just sales numbers. Tapping into and activating down and upstream insights into a proactive selling story can be a game changer for sure.
For larger companies having the challenge of visibility for quantified insights to continue to enhance the sales story is important for long term consistent share and sales results. Applying the same strategies to new product lines becomes easier over time, securing similar results.
Moving on to measurement, making sure your strategy supports the right measures and importing infrastructure to drive sales in store is critical. The next step in managing the challenge of getting products to the shelf according to the plan that’s put in place. This is where the rubber meets the road, positive revenue impact is secure.
Strictly utilizing demand data, distribution, POS, syndicated, etc. can certainly provide directional insights. But, to achieve a true competitive understanding of your actual product sitting on the shelf, it’s important to verify what’s called the real brand, what’s really happening in the store.
Finally, share growth, where the win happens. Assuming you have the first three elements directionally correct, results should follow.
The reality is in store retailer conditions always are changing. So regardless of the results, you need to take them to inform the shelf strategy going forward, which becomes an ongoing continuum to be refined and updated as you move forward plan, execute measure, and then update the plan for the next cycle quarter, whatever you decide are the right intervals for your business.
As a result of putting these efforts in place, the National Association of Retail Marketing Services attributes 8. 1 percent lift in profits to planogram compliance, another noted 3 percent increase in brands on-shelf availability, resulting in 1 percent increase in sales.
Before I turn it back over to Kelsey, the one thing I’m, for those that aren’t familiar with Wiser that are on the call, or us individually, just want to let you know that we’ve worked with many best-in-class CPG companies of all sizes in regard to retail execution over the past several decades.
Additionally, Wiser has over 600 customers globally, many of those in the CPG and FMCG space.
With that, thanks so much. And back over to Kelsey.
Successful Brand Are Aggressively Focused on the Shelf
Kelsey McCarthy: Thank you, Bruce. Sorry, getting myself off mute there.
As Bruce was mentioning some of the Wiser customers. At Wiser, we are lucky to be able to work with both many fortune 500 brands, but also high growth and up-and-coming brands in the CPG space.
Throughout our work with companies of any size, we want to share four primary areas where brands are realizing success today in retail sales and execution.
So firstly, their incorporation of category insights into the brand’s shelf strategy early on in the planning process.
Understanding your own products, availability, share compliance, and so on is definitely critical for prioritizing and creating focus around things. Things like resource allocation, pointing out revenue gaps from poor execution, informing sales strategy, etc.
Today we see brands leveraging a combination of both sales metrics and in-store checks to prioritize visits to high potential stores for the highest return. And then we also see them relying on this data to understand how their category is represented across retailers or channels in order to make informed changes to assortment and trade spend going forward.
The second area where we’re seeing focus from brands, especially those with heavy emphasis on field team sales, is the incorporation of data into the reps’ daily process in-store.
So beyond corrective actioning and in-store surveying, brands are utilizing more prescriptive performance insights to direct reps to stores with then the highest sales potential. By surfacing how much a store should or could potentially be selling.
And then where it’s falling short, field reps can much more easily prioritize opportunities in their region specifically. And by empowering the reps with a way to strategically sell into stores, with real time shelf insights that highlight how they can improve the sales for their category overall, reps, you know, establish greater credibility. They play more of a consultative role in store, instead of the traditional sales and support role.
The third area of focus here has been the use of benchmarking, score carding, whatever you want to call it, store compliance in order to fix and address more systemic problems and also highlight new opportunities.
Many of you in the audience today are probably pretty familiar with the concept that many forward-thinking brands are using, sometimes titled something around the perfect store. Speaking to the general trend that brands are seeking to build out and track more clearly defined metrics around in store compliance.
This could look like a variety of things. It could look anything like an emerging brand using a scorecard to track two to three in store metrics to a category captain capturing metrics across their total presence in store to help maintain the positioning they have against top competitors.
We will dive into one of the more exciting use cases, to me, later on where Dan will discuss image recognition used in this context to expand the breadth, quality, and speed of the data delivered.
And then finally the fourth. At a more strategic level, understanding how share shelf correlates and then to what extent with market share can serve as a huge advantage to increasing the latter mentioned. Successful brands use this to their advantage in both improving their own shelf strategy and trade spend in-store and then also attacking competition.
For instance, you see through analysis that your close competitors, you know, low sales and high share of shelf that makes for somewhat low hanging fruit when it comes to a conversation regarding improving your own shelf space.
So, I just talked a little bit more generally about the techniques that brands are using to improve their presence and grow on the shelf.
I’m going to now hand it over to Dan to start diving in and taking us through two different customer use cases.
Dan Ray: Awesome. Thank you, Kelsey and Bruce.
So yeah, let’s now talk through some real-life examples of how our customers are using Wiser specifically to tackle these challenges. When we work with customers on the strategies and one of the common high value uses of share of shelf is the classic space to sales comparison, right?
It’s comparing the share of space at the shelf to the share of sales for that given category. So, let’s dive into that use case and showcase how our customers are up leveling that concept through Wiser.
What Does It Solve?
So, what does, you know, what ultimately are our customers trying to solve here? You know, in a perfect world, one might imagine that the share of shelf always mirrors share of sales bridge category. But, as we know, many times, it’s not the case.
And sometimes it shouldn’t be the case, which means too often brands find themselves under spaced relative to the share of sales and the category.
Take, for example, a well-established brand, you know, you’ve proven your sales and yet too often getting squeezed on both sides due to the category allocating too much space to products that just aren’t selling. Or, say as an emerging brand, you’re not getting the same level of attention yet, right, as the traditional brands. And therefore, your space allocated to you lags behind your sales.
On the flip side, sometimes you have a product that has potential sales, or sales potential but can’t get there until given the appropriate space. Maybe you’re an established brand launching a new innovation SKU, or you’re a high growth emerging brand entering a new market or channel. So, either way, there are powerful ways to use share of shelf to protect the existing space that you’ve rightfully earned. As well as again on the flip side, expand additional space needed to support current sales and expected growth.
Another challenge is having to rely too much on salesmanship and relationships to grow space at the shelf. You know, obviously relationships we know are important, especially in this industry. But having a data driven conversation on top of your salesmanship and relationships just solidifies your sales groups as a trusted advisor in a more consistent, scalable way, right? Even a mediocre salesperson can excel when positioned as a trusted advisor with fact-based selling.
And then what I’d say is the biggest challenge for this use case is the amount of time and overhead it takes to capture and identify face-to-sales opportunities.
Too often, sales teams are just simply not using the face-to-sales data because of the overhead that it’s required to properly analyze and utilize, you know, solid argument with that data. So, it’s about making that information as brain-dead simple and readily available for everyone from, you know, field sales reps walking into the store to key count managers during category reviews.
How Does It Work?
So, let’s talk about how this works. This is where our suggestive solution, or excuse me, a suggestive selling solution comes into play to overcome these challenges by utilizing the latest in image recognition technology.
So, it starts with just two things.
First, capturing photos using the Wiser app. And second, integrating your category sales data.
Now, some of you may be thinking, “Well, I don’t have my own field team,” or “My merchandising team doesn’t use Wiser.”
Well, first of all, if your merchandiser, your brokers aren’t using Wiser yet, obviously that’s the first place to start to help making that happen.
But even if that’s not an option, Wiser has a built-in crowdsource offering of hundreds of thousands of secret shoppers that can capture the photos for you. So don’t let the size or limitations of your current field teams stop you from leveraging this technology, this solution.
Okay. This next part is where the magic starts to happen, right?
The moment the photo is captured, it gets sent to our image recognition engine that identifies each product on the shelf among other metrics that can be derived from image recognition. So, whether the product is half hidden, turned sideways, flipped upside down. It’s actually surprising, how accurate, even in real world scenarios, the technology has gotten now.
This thing gets sent to our backend data cruncher that compares the Share of shelf to the category sales and summarizes the findings. So, it even calls out the most impactful and relevant gaps, to recommend to the retail buyer or the store manager. This then gets sent immediately to the mobile app, if you’re a field team using Wiser mobile, the Wiser retail execution management app. As well as to your dashboard reporting suite to be used in aggregate for further action and analysis at the HQ level.
So, there’s a lot of AI and machine learning, all the buzzword drag going on in the background to make this happen. But, in reality, the experience in the sales team is pretty simple. You snap a photo, out comes recommendations on what products are under shelved, versus what competitor products are over shelved.
Where Does It Apply?
So where does this apply?
As you probably picked up on those two, you know, obvious immediate applications for this information versus at that field level. Reps can use this as a way to prioritize their stores, right? They pull up stores nearest them, review the latest share of shelf metrics and calendar in the stores with the easiest space to sales comparisons for their, or excuse me, space and sales discrepancies for their brand.
And then in the store, they snap the photo of the current shelf and within less than a minute have all of that analysis for suggestive selling right there to show the store manager as they make their case for more shelf presence. This right here is what establishes themselves as a trusted advisor, right?
They can make recommendations on ways to optimize the shelf for their own products, but also give recommendations on the entire category. Including which competitor products make the most sense to remove, and which products have their own SKUs to replace that with.
Now, obviously, there’s going to be accounts where no matter how good the data shows, right, you won’t get that authorization to make changes to the home shelf.
But one of the things we’ve seen is the ability to secure secondary placements due to the data that the reps show in-store.
You know, for example, Mr. Store Manager, I know we can’t change the shelf here, but you can clearly see how we’re being underrepresented in your store, therefore losing out on sales for both of us. What do you say we set up a display over there to help close the gap on where we should be at, right?
And that’s what gets the incremental increase in revenue beyond just the home shelf. Then from an aggregate view, you’ve got all that to roll up to the, all that roll up of the store information that can be used by key account managers and their conversations with retail buyers.
Being able to quickly pull this up without having to search through, you know, planograms and recalibrate reports, etc., but rather, having a central place that already combines it all to quickly identify, you know, any opportunities to strengthen their recommendations on which products to add and which competitor products to remove. And what can be powerful is, you know, the fact that this is based on actual shelf photos. So, it’s what we’ll call the “realogram” right instead of the planogram.
Additionally, we do based on category cells we can forecast or estimate the increase in overall sales so actual dollar amount that you can use as you recommend the changes to the retail buyer.
So, here’s just a quick screenshot, for example, where the data is calling out, you know, here on the bottom left here, what stores have the greatest opportunities, you know, AKA discrepancies and space to sales analysis.
Then it calls out what competitor products to the right there are most grossly over shelved. And then which of your own products are grossly under shelved.
It then estimates, you’ll see it a little bit above that, the potential impact to the category by making the recommended change. So, all of that is right there in front of the rep without having to do any prep work or any diving into emails and Excel spreadsheets. It’s just right there in the dashboard.
So, we wanted to show an example, an actual example of what this looks like using snapshots of a real-life store visit by one of our customers that Kelsey will run now through.
Kelsey McCarthy: Thank you, Dan. So, like Dan said, I’m going to go through a really specific example just to demonstrate how this might work in store.
Representative of obviously many others, but this just kind of breaks it down to be able to walk through this experience while they’re applying it in store. So, in this case too, I know I noted up there that this is at a Publix. I know Dan touched on this earlier, but in this case, they were still able to gain an incremental placement in store, even though they wouldn’t have normally been able to influence planogram necessarily on this visit.
So to start, the rep would capture the shelves’ current state in a photo. Merchandise the home space to planogram and then they would receive analysis from the image recognition engine in minutes where they’re while they’re still standing there at the shelf, showing a breakdown of the category sales performance in their current shelf state, and then suggestions for improvement for their own brand, like Dan said, competing brands as well.
And as you can see here, or not see, the font is very small, but in the recommendations that Dan spoke about previously, the sales metrics demonstrated that the brand was under shelved relative to their sales in the category in this case, and then recommended the most obvious area of impact for an increase in sales for them to then action on. So, in this example, it was increasing a certain number of facings for a specific product.
And then the rep would then use that as ammo with the store manager, showing them the potential impact of implementing their sales suggestions. And then in this case, it allowed them to place a product and an additional cooler in the front of store.
This is a before and after a snapshot here of that shelf specifically. Not only did they gain three additional facings, but also positively impacted their products visibility in the store and then reordered additional products while they were there.
So, zooming out to a macro view. How does this impact the team?
This team specifically was able to recognize a win in about one in five store visits, such as, you know, for them, it was gaining additional placements, increasing facings, new order, etc. A win may vary slightly in definition, obviously based on your team’s priorities.
This brand did have an emphasis on gaining secondary placements in-store. And over half of their wins specifically aligned with this goal here. Some of the wins I’ve demonstrated here, some of the wins recognized immediate value. Some would recognize value in the near future. That was something they were interested in quantifying.
They were also interested in understanding the actionability of the data from the rep’s perspective, including some feedback that I’ll provide here. Generally, reps were excited to be getting these wins in the store with the data provided, and their commentary mostly validated our expectations for the use case of their data in the store.
My personal favorite is the second one up, where the product was actually pending deactivation in the specific location that they had visited, and then the grocery manager reactivated after seeing a potential sales impact discussed with the rep there with the data provided.
Perfect Store Strategy
Dan Ray: Yeah, I just love how, I mean, this is the big question that people have right is, “Is this worth investing in and actually producing results?” You know, for their team immediate results.
And it was exciting to see, you know, we’ve with our early adopters, these metrics are actually pretty consistent. You can see with these are quotes directly from the reps about the specific visit, like the one we went over earlier. Where it led to a direct impact on that same store visit or on the next visit as you’re triggering, you know, some kind of order or additional incremental sales.
So, it’s just exciting to see that it’s the reality happening. And these impacts are being had directly related to the information. Like when you see, you know, the general manager really liked the data. So, they come back on the next visit to free up a spot for our brand. Manager saw the data on Wiser and a shipper.
Just like a few of them were like, you know, I was able to identify an opportunity I wouldn’t have otherwise identified if it weren’t for the data on this, you know, from the image recognition. So, it’s just exciting to see it come together, that it really is happening in store.
So, to talk about the second use case here. This one applies, you know, not just to your CPG or FMCG, but especially for those that I’m looking at more of a global strategy. So, this one, as Kelsey referred to earlier, sometimes called a perfect store strategy or my perfect store or store score.
But it’s especially popular for mature brands that are looking to set a strategy and goal post nationally or even globally.
What Does It Solve?
So, in general, you know, what is this trying to solve? Creating a perfect store program is challenging simply due to all the variations across markets, channels, accounts, and even stores within accounts, right?
So, how do you establish a North star per se, if everyone, you know, that to shoot forth, if everyone’s living in different planograms, set sizes, etc.?
So, the goal of establishing that national global strategy beyond just the standard compliance measurement, measuring that standard on a national global scale, tends to be inaccurate and inconsistent.
So, how do we maintain that consistent method of measurement that fuels and tracks execution to that strategy we’ve set?
If our method is different, and our metrics are different, how can we expect to learn, adapt, and improve the brand as a whole? And then again, the biggest challenge to make the above happen is being able to do so in a way that doesn’t add too much overhead to your sales teams.
You know, the last thing you want to do is take away quality selling time with administrative burdens. So, this again is where image rec. comes into play, leveraging IR to minimize that admin time, while also directing them to highest priority opportunities.
How Does It Work?
So, how does this work? The key difference from the previous face to cells use case is establishing the metrics that define the goalposts, right? The shared shelf is just one possible metric. But using image recognition and our retail execution management app, you can combine both IR driven metrics, as well as survey driven metrics within the exact same interface.
So other metrics we see here are, does the shelf have our core products? Where on the shelf is it located? You know, top, middle, bottom, eye level, missing tags, appropriate stock level, etc.
And it’s exciting to see how much more and more metrics that image recognition is able to capture versus survey. And it’s just going to continue to grow and expand. The rest of the process is pretty similar to the first use case. So, I won’t belabor the slide here, but reps capture the photos and data that gets sent to our image recognition engine routes to our data analysis tools and spits out the perfect score reporting.
For example, in a point system so that each store ends up with the store score. This thing can be rolled up by retail banner, channel, market, team, etc. And then measured over time.
Where Does It Apply?
So, where does this apply? Again, you’ll see some of the same themes here. Field reps get instant feedback in store about how this score compares to the scorecard that we’re scoring against.
Field managers better direct their teams to highest priority opportunities. Account managers have a clear picture and story, you know, of which each account needs to get to. And then beyond just the sales team, the scorecard results get leveraged cross functionally as part of the strategy across the different organizations.
So, what are the results we’re seeing from this? You know, leveraging the share of shelf and image recognition in this perfect store type strategy, we’re seeing a lot more cross organizational strategy and collaboration. For example, one of our international customers they’re using this strategy and originally implemented across all their various regions, but each region is, you know, doing it differently. And now on Wiser it’s connecting the regions to collaborate together on a method that works globally.
Secondly, you know, clear execution and performance targets. Actionable result delivered immediately to the rep during the store visit.
You can go through the rest, Kelsey.
Learn and evolve faster on what the picture of success should be on a global scale, and then managing down to the ground level with by store and by rep view.
So remember, because this is shelf level information rolled up, it makes everything actionable all the way down, back down to the ground floor.
All right, so, with everything said and done, you know, if you remember nothing else, there’s two takeaways I’d want you to have from this webinar.
First, it’s that practical use of image recognition is now a reality. You know, IR has been around for years. I remember the first time really looking into it, you know, delving into it five to six years ago and it was cool. It was exciting, right? But it took 24 hours to get the results back and cost a dollar a photo.
Fast forward to now, it’s immediate. There isn’t a per photo cost. Customers right now are getting the value only dreamed of just a few years ago.
The second is that it’s not something you can just adopt overnight. Our big focus is how to start now to bake it into our customers’ short- and long-term strategy. So that as technology and use cases continue to grow, and it’s growing fast, all the different ways and metrics that IR, can pull. As that grows, our customers continue to have that competitive advantage in the marketplace.
Bruce, anything else you would add to that?
Bruce Nagle: You know, I think you covered a lot of ground, Dan, and pretty impactful.
I just welcome all the attendees to continue the conversation with us so we can address any specific questions or ideas they’re thinking about as to how this relates to their business, and how we can tailor it to them.
With that, I’ll turn it over to Kelsey for questions and answers.
Kelsey McCarthy: Thank you, Bruce, I appreciate it and I do see some coming in here. So, I will start going down in order of receiving them.
So, first one, and Dan and Bruce open to either of you addressing here. But first you talked about several different metrics that can be used for perfect store strategy.
Which ones can image rec identify versus which ones have to be answered by the team or by survey questions?
Dan Ray: Yeah, yeah, I can, it’s a good one. Right now, what image recognition does surprisingly well is identify products on the shelf, and where they are on the shelf, and the number of facings.
So, things like, you know, what is your distribution? Obviously, we talked about share of shelf. Is your core products there? So, when you’re doing a my perfect store scorecard, for example, it’s identifying the products you know should be everywhere, regardless of whether it’s authorized. And so identifying is the core there or not globally and being able to roll that up.
Where is it positioned? You know, is it top, middle, bottom? Is it at eye level? Some people talk about the diamonds. What products are adjacent to it? So, there’s a lot of different metrics that are, are being pulled from image recognition.
And then the other ones, you know, things like, does each product have a shelf tag?
That’s sometimes better answered the survey question. But again, it’s getting better and better. So, we’re going to see that over time, you know, pricing, things like that start to get incorporated image recognition.
But, not to oversell it, you know, what it does really well is identifying every single product, regardless of position. Where it’s at the shelf, what other products are in the category, how many facings, etc.
So, any metrics around that is key for image recognition, right?
Kelsey McCarthy: Alright, thank you. And then this one relates heavily. So, I’m going to pivot to this next.
Are categories and image recognition pre-trained, and how long might it take to train a new category?
Dan Ray: Yeah, really good question. This is where it’s just going to get better and faster over time.
We have categories right now that are already pre-trained. So really easy to just jump in the bandwagon and leverage the work that has been done by, you know, years and years of training over time with many companies being part of it. Every company and customer that uses it, it’s going to get a little bit better.
So, we actually, you know, you can send us the product. We’ll stage the photo, but we’ve gotten to where it could really turn key. So even if we don’t do that, a majority, you know, a few of our categories that we’ve already trained, are ready to go up and running. So, there is a bit, though, that we’ll do to just ensure that accuracy is at the highest is possible, but it’s a really straightforward process, and those categories are going to, you know, continue to expand.
So, some of the ones that we, you know, have a lot of training around are your beverage, alcohol. We’re getting into ready to drink as well as, you know, dairy. There’s a couple others I know the team has been starting to–
Bruce Nagle: I think yogurt’s a drink. I think yogurt’s drink.
Dan Ray: Yeah, yogurt. All dairy. So yeah, it’s growing fast.
Kelsey McCarthy: Cool. Thank you. And then the other two kind of pertained, I think more to the, and maybe the suggestions that reps would be getting on their mobile, or through headquarters, but this one is specifically for reps.
Do the reps in the field get information right away, or how fast might it take to process?
Dan Ray: Yeah. Yeah, like I said, there’s a lot that goes on in the background, but it does happen surprisingly fast. So, assuming you’re on a good connection, it can, we’ve seen it happen as fast as five to 10 seconds. But knowing that sometimes reps or something, you know, in the field a little bit, you know, that the connections a little weak, what we tell customers is up to a minute.
And it’s very consistently under a minute. Minute is what I would say on the long side. So it’s, you know, for the workflow for the rep, it’s almost not even a factor where you take a picture, you do your work, and then before they’re even thinking about it, it’s ready for them.
Kelsey McCarthy: Yep, that makes sense.
And then this one I think is both at the, you know, applying to the field team, but also at, you know, the headquarters kind of perception.
When it estimates added sales, I’m assuming when it estimates, I’m saying, you know, the analysis that’s been out there. When it estimates the added sales, does it factor in the products that you are taking out as well?
Dan Ray: Yeah, it’s an important one to keep in mind is when it is giving that recommendation, we wanted to make sure that it’s as relevant as possible.
And so, it’s keeping in mind this idea, this assumption that the store manager and or the retail buyer is not wanting to increase the space. That if you’re going to add in a product, you got to take one out.
And so what it does, it looks at the sales of both of those products, and it quickly identifies, okay, here are the ones that are obviously needing to be added in, and here are the ones that if you need to replace one that you would take out, and then it does combine the two. So, it’s a real estimate of the combined sales between the removal and the addition, to be as, you know, as accurate as you can for a forecasting.
Kelsey McCarthy: Gotcha, okay. All right, that was all of the questions asked live here today.
So, thank you, especially Dan for the thorough answers there. I appreciate it, and with that, like Bruce said, we hopefully look forward to talking to both our current customers, but also future customers on anything that we discussed today.
So, contact information here. You can also always reach out to email@example.com.
And with that, I would like to thank everyone for attending, and have a great rest of your week. We appreciate it.
Bruce Nagle: Thank you.
Dan Ray: Thanks y’all.