Prioritizing Amid a Sea of Data: Victories & Roadblocks Behind Today’s Analytic Investments
Data is the new oil and analytics is the refinery — this timeworn phrase continues to hold water, but it’s next-gen analytics that are taking the driver’s seat in today’s retail and consumer goods industries.
In the face of an uncertain future, rife with unprecedented demand spikes and fluctuating consumer sentiment, the ability to turn raw data into actionable insight is more than just a lifeline for brands and sellers — it’s the competitive advantage for triumphing during the next phase of recovery.
In “Investing in the Analytic Future,” a webinar hosted by RIS News and CGT and presented with Amperity, we dive into the Retail and Consumer Goods Analytics Study 2020 to put even more context behind the numbers.
This supplementary discussion unlocks even more insight from our report and is a must-read for anyone in retail or consumer goods.
The full transcript and presentation slides from the conversation are below.
Tim Denman: Welcome everyone to the investing in the analytic future webinar, which is hosted by RIS News and Consumer Goods Technology and presented in partnership with Amperity. I am Tim Denman, editor in chief of RIS and CGT.
With me today to explore the current state of the retail and consumer goods analytics landscape is Amperity's VP advanced analytics, Chris Chapo. Chris joined Amperity earlier this year and brought with him more than two decades of retail analytics experience, most recently as senior VP of data and analytics at Gap, where he led the team responsible for data-driven, evidenced-based strategies across the enterprise.
Prior to Gap, he led data teams for a wide variety of companies including Apple, Intuit and JC Penny. Chris, thanks for taking some time to join us today.
Chris Chapo: Thanks for having me, Tim. I'm looking forward to the conversation.
Denman: Many of the data points and trends we will be discussing today were recently covered in-depth in the RIS and CTG annual Retail and Consumer Goods Analytic Study, “Meeting Adversity with Data,” which was sponsored by Amperity.
The report was published in late June and the data was collected from mid-April to the first week of June. This was the first benchmark research on either brand that was conducted entirely during the current health crisis. The fact that this vital data was collected during the heart of the pandemic provides a unique look at the current state of analytics and how it will likely evolve in the months and years to come.
In any major research project, it's important to pinpoint the respondents' demographic profile and I'm not going to go into any data point here on this blog, but as you can see at just a glance, our survey takers represent the upper crust of the retail and consumer goods industries. The respondent pool comes from companies big and small and the vast majority call the headquarters their home.
Eighty-one percent of consumer goods respondents hold director level titles or higher, while 76% of their retail counterparts can claim the same. This elite group of high ranking consumer goods and retail analytic executives hold positions throughout the enterprise. On the retail side, store operations, IT and technology and marketing represent the largest portion of survey takers.
For CG-ers, it's supply chain, IT and technology again, and trade marketing and category manager were the most prevalent titles. As the retail and consumer goods industries continue to pour energy and focus into the ongoing COVID-19 recovery, they are emerging from the pandemic with a renewed focus on analytic investment. While both retailers and CG's have clearly allocated a large portion of the IT spend to analytic pursuits, those numbers are only going to rise in the coming years.
Today, about 60% of CG-ers allocate less than 10% of their total IT spend on analytics, but by 2023, 52% predict that more than 10% of their budgets will be spent on analytics. For retailers, analytics represents a smaller portion of their current IT spend, with about nine out of 10 reporting less than 10% of their budget is allocated to analytics but just like their CG counterparts, retailers expect their monetary commitment to analytics to rise, just on a slightly smaller scale.
By 2023, 76% of retailers expect analytic budgets to be less than 10% of their total IT spend but 10% are expecting to see budgets rise in the 10 to 15% level.
Now, where is that increased investment headed? Well, it's being allocated to meet some foundational challenges. In addition to building clear analytic strategy, retailers point to a limited tool set and the lack of knowledgeable staff to lead their analytic programs. Consumer goods execs are facing the same roadblocks and also point to their inability to successfully integrate data from multiple sources.
Both sides of the house are hyper-focused on increasing their demand forecasting ability, consumer insights and inventory planning capability and tool sets. As you point out on the last slide, the bulk of retailers and CG execs point to a lack of in-the-know analytic professionals as a major roadblock to their data-fueled success.
A deeper look into this phenomenon finds that CG-ers on average have three times the number of analytics-focused employees compared to retailers. But that gap looks like it's going to shrink as twice as many retailers are looking to hire analytics personnel compared to CG-ers. As you can see in the graphic on the bottom right on this slide, analytic responsibilities are distributed throughout the organizations.
From both CGs and retailers, the most popular approach is to have analytics managed by individual departments. This approach allows the departments to slice and dice the data as they see fit. So, that is just one of the many different approaches that we're seeing. A fair amount of responses reported a centralized department owned the data and insights and the doling it out throughout the rest of the organization.
One has to give a high level self-evaluation of their analytic prowess. About a third of CG-ers rate their analytic capabilities better than their direct competition in the key categories of strategy, tools and personnel, which is slightly more than a third of retailers that reported the same.
Interesting enough, when it comes to ranking themselves against industry leaders and for this, known industry leaders we point to PG, Walmart, Amazon, the big boys. Both industries placed themselves significantly behind.
We've been conducting this survey in one form or another for years and since its inception, the report has always focused on those parts within data sharing. Although the results have gotten more favorable over the years, the fact still remains that CG-ers want more data from retailers. And retailers are reluctant or unable to share it.
In terms of sharing data internally, a third of retailers report they are making progress toward a shared data model and an equal number report there is still a strong way to go. And the say stands true, plus or minus a few percentage points for the CG-ers.
As I talked about at the top of this webinar, the data collection of this report occurred during the pandemic, allowing RIS and CGT to add some COVID-focused feedback. We asked CG-ers and retailers the impact COVID has had on their analytics efforts.
Fifty-two percent of CG-ers report that their analytic resources have shifted or changed and the remaining around 48% report there was either no change or that change had yet to be determined.
Interestingly for retailers, just 23% reported change in resources, but 43% report no changes at all. And another 18% report the reliance on new data sources and more frequent reporting, 18% each.
Anyone interested in the full analysis of the findings is encouraged you to check out the full report following the webinar. Right now, I'm going to hand the mic over to Chris who's going to expand on these themes and explain how Amperity can help.
Chapo: Thank you so much Tim, I'm excited to share a little bit about the company that I'm a part of called Amperity and our perspective on how do we actually support customers to address the concerns that Tim just mentioned relative to data-driven, customer-driven transformation.
One of the core beliefs that we start with is that in order to be able to actually bring an analytics practice forward, we actually have to bring all the data together that a company has into a single location because by doing that, we will actually be able to help that company serve their customer needs better. And in order to do it, you see through some of our bullet points here, is we believe that one of the core challenges, once you've got the data together, is actually stitching together the identity that a customer may have across all the different touchpoints.
You get a very different point of view of the performance of your business and your opportunities if your online data and your view of customers is different than what your in-store data looks like, which is different than if you actually leveraged call center data. Again, another one of the big challenges that we often see is that bringing data together from multiple sources can take a lot of time. So being able to bring data in, in its raw format and not have to deal with ETL allows you too much more rapidly move forward.
Then leveraging a lot of these cloud-based technologies like AWS brings to life — allows us to map that, be able to handle massive amounts of data in a cloud native sort of way and scale properly.
Now, once you've got it together, what do you do with it? And this is, I'd say, one of the core beliefs I think is really important as we begin to talk about how to solve the problems Tim mentioned. We boil it down into three core concepts. I think the first is you have to know who your customers are to be able to solve them. So bringing it together at scale, creating what we call Customer 360.
Once you know they're customers you have to figure out what am I going to do with this? What are the hypotheses I have of things that are working or not and how can I use evidence to help me do that? Being able to democratize data, predict outcomes, create segments.
Then I'd say the last step is then once you've figured out what you want to do, is to be able to serve individuals and integrate them into things like campaigns, personalization, or as we begin to talk about some of the challenges that Tim mentioned earlier, into other business processes like demand forecasting and inventory allocation. The platform that we bring to life runs on top of AWS to be able to solve these sorts of challenges.
The last thing I'll say before we jump into our Q&A is these are just a selection of a few customers that we feel honored to be able to help along this journey and we've learned a lot along the way, both what works as well as what are some of the challenges.
I'd say as we move into some of the Q&A questions, hopefully that will set the stage then, to help you understand some of the breadth that we're going to bring to life here.
With that, Tim, I've love to turn it back over to you to dig into our questions.
Denman: Thanks, Chris. When asked how the use of analytics changed during COVID-19, the participants in the survey responded that had had an overwhelming sense of urgency. As an analyst professional with more than 20 years of experience at grands like Gap and Apple, what would you advise brands to focus their most urgent efforts on?
Chapo: That's a really great question, Tim. And I'm going to answer it with two different perspectives. And a lot of this is what we've learned in talking to customers. I think the first and the most urgent one, obviously, is to figure out how do we actually serve our customers safely?
And I'd say one of the things I personally believe in is that our frontline employees are the ones who we're actually putting out there to help serve our brands and our customers. And we need to figure out how to serve them safely. So in order to do that, I've seen a lot of companies use data that they're gathering, insights they're gathering from public sources, to understand things like infection rates, what's working and what's not. So I'd say that's a starting point. And where they needed a sense of urgency, because you've got one chance to do this well.
The second, and this is actually the most important to set you up for success for the future, is using analytics to plan the business, and historically having been in a lot of these companies, the way you plan a business is you look to what happened last year, and you say, "This is what's going to happen this year."
You build on top of that with things like, "We've launched a new capability," which is like a buy-online-pickup-in-store that's driving a little bit of uptick in online sales. And so we baked that into a plan. But given where we are now, all bets are off. I've heard several companies beginning to use near-term demand forecasting methods, signals they're getting from others in the industry to being to build a bottom's up plan and say, "Here's what we think demand's really going to look like. Here's what we believe is when things are going to come in."
And not doing it at the traditional, I would say, high-level view, which is: “You have this many orders last year; this is how many you can expect this year.” Doing it at a micro or geography level because you can imagine, and we all are feeling this, is each market, each county, each city is going through ups and downs with regards to how they're responding to it.
So those are a couple of examples of things I would say urgently, I personally would recommend and we've helped several of our customers with, relative to the sense of urgency in COVID-19.
Denman: Just as a follow-up, are there any areas that brands or retailers should really think about prioritizing first? There's a wealth of things they could do in their dataset. What are the things brands should be considering when determining these priorities, like which one to act on quickest?
Chapo: I'd say particulars like moves to the customer-facing side of the house is figuring out how to remove as much friction as you can from that person's experience. And part of the reason for this, as we've talked with customers, is the expectation many customers are having coming in, particularly if it's a physical retail store, is that they want to come in and buy a product and get out, versus historically what we try to do is figure out how to engineer an experience where people can try new things, try new products, learn more about them.
And honestly, right now, most what we're from customers is, "How do you actually remove the friction from the experience?" So relative to the analytics supporting of that would be, "How do you actually measure that you're doing that properly? What are instrumental to your retail store experience? What are the typical bottlenecks? How can you potentially bring new capabilities to life like buy-online-pickup-in-store? Or schedule appointment-based activities?"
Those are some of the ones that we've heard customers starting to bubble up when you're talking about how you actually serve the customer. And I'd say the second piece for that is we're all probably experiencing, both personally and looking at our businesses, we're seeing — it’s kind of an obvious statement — but more people buying online than they've ever done before.
People may not have had that history, if you will. So for that segment, if you think about e-commerce personalization as an example, how do you actually remove friction for that group of folks? Whether it be adding new chat capabilities — which could be powered through automated chat experiences — because they don't quite know how to navigate your website to even certain things like messaging, "Here's what you should expect to see when you're expecting to get your product."
But putting that lens on is you're thinking about those even e-commerce experiences that you're starting to bring to life.
Denman: Right, exactly. I know retailers and brands have been investing in analytics all along — at least the ones that are succeeding are. But the ones that are continuing to invest, even during the tough times like now, what kind of benefit are they seeing for continuing to invest in their infrastructure, despite the challenges they're facing?
Chapo: I've got three thoughts to it, the first being just those who've invested, either historically are getting up to speed now, is figuring out how do I drive scale in my analytics process. How do we get faster in discovering insights? How do we actually bring in new data sources, things we haven't historical had to look at before? I've talked about some of it as signals relative to what's happening on a geographic level for COVID. How do we integrate that?
Those who've actually built the muscle can do that quickly. I'd say the other part with regards to scale is, this is unfortunately going to be the realities as we look forward over the next few months, is many companies, particularly traditional retailers are, at having been shut down for a period of time are starting to look at their cost structures and how are they actually going to be able to keep the same amount of people and their headquarters teams employed as they have in the past? Which is the sad reality of what's happening.
So those who've been able to invest in analytic infrastructure can figure out, how do I do get better and faster with potentially fewer people in place? And so I'd say that's one of the big benefits, is really driving scale in this period of time.
A second one is, I would say, it's being able to accelerate. Those who've invested in this, accelerating getting to table stakes, probably what they should have been doing before. I was on a call with a couple of folks last week where one of the themes that came up was, we had these aspects or these projects in our roadmaps for six to 12 months. Some of them were customer-facing ones. Some were more self-service analytics capabilities I just mentioned.
We've had these on the books for months but given the pressure we're facing, we can't just debate things anymore. We actually have to get this stuff done. So accelerating getting these table stakes done and removing any of the organizational barriers is another one of those benefits we're starting to see.
Then I'd say the third piece and this is one that remains to be seen, is how all of this will translate into what the new experience and the new reality for people are going to look like. I would say one of the open questions I personally have and it's heard from others, as I mentioned in the last comment, people are trying online for the first time. Is that going to continue post-COVID?
And what are the analytical things we're going to need to put into place to help understand how those behaviors are different than customers from before? Then as I mentioned in the first question, how do I actually build that into my business planning?
One example that I've heard from many retailers is typically units per transactions may be lower on an online purchase than in a store. If you think about the experience and people walk around the store, they pick up products. Well, if it's lower online and they have a higher return rate and it costs more to ship, you need to figure out how to either increase your sales online so you're driving frequency, or potentially create a better experience to have a higher basket online. While, at the same time, figuring out how do I do that profitably and ship it profitably?
Underneath that there's a ton of analytics that need to be created and so I'd say the jury's still out in terms of how all that's going to play out as we begin to emerge from this crisis that we're currently still in.
Denman: That makes complete sense. As we talked about during my formal presentation, in the study we asked retailers and CGs to tell us about their IT budget, how much they're spending today and what they expect it to be three years from now in terms of how much that budget's allocated to analytics. Not surprisingly, there's going to be growth in spending and analytics.
But the question is, should this be an IT spend? Because, other departments like marketing obviously are greatly benefiting from this data fueled enterprise. Do you have any advice on how internal budgets could best be aligned?
Chapo: That's a great question and I agree with you. When I read this, I wasn't surprised at all, because but what I think the challenge or I'd like to say it differently, the concern that I'd have for many companies is they're not quite sure, even if it is more budget, they're not quite sure what to do with it except to say, "Let's bring in this magical analytics thing which solves all our problems and let's go. IT team, go build us the best database out there, or the best analytics platform, or get the best in breed customer data platform in place."
The observation that I'd have is, while that will be important and there will be a certain amount of investment that will need got be made in infrastructure and basic technology which is likely going to sit within the IT world, one of the things that I've seen a few companies lean into is beginning to put budget and money into developing and training their team and their talent, particularly in the marketing team or the e-commerce teams or even merchandising teams to understand how to use this technology.
You can have a great car, but if you don't know how to drive it, it's kind of pointless. So one of the things that I personally have seen and would recommend is teams investing, both in the infrastructure of an IT perspective, but actually investing more in people development to drive experimentation on how to drive these business outcomes or the improvements you're seeing.
So part of that has to do with budgets to run experiments in the space. There's one customer we were talking to a couple of weeks ago and they were saying, "Well, I think this is a great idea to try to do this thing but I actually don't have the money to create. I've got the analytics, but I don't have money to create the content to personalize the website, because I don't have a team to do it."
Another would be relative to things like shipping speeds. One hypothesis many people have is if you can improve shipping speeds for your prioritized shipping for some of your best customers or maybe some of your newer customers, that that might be a good investment to make because it will pay off in customer lifetime value. And it takes budget to do that.
So I think that's one of the things, putting aside budgets to do that. And then the training, hiring and change management to scale. That's not typically thought about probably when people are putting down on paper what they need to invest to bring analytical capabilities to life. But it's the old adage of it's not just the tools, or the tech, it's you've got to have the people and the process there to get value out of it.
That's how I would see things, but I'm excited to see and learn from others how that continues to evolve as well.
Denman: According to our data, brands that are diverse in the areas they are choosing to invest their analytic dollars, visualization, security, in-store analytics. In your experience, where should the brand and CG folks spend their time and resources to fulfill these initiatives?
Chapo: Yeah, I think all of those are great use cases and I loved seeing your data where people investing in and thinking. All of that all makes sense. To some degree, some of these go back to the table stakes I mentioned before. We're accelerating the things we probably should have been doing before. But one thing I would encourage folks to focus some time and resource on is spending a bit of time to looking about all aspects of your value chain, for which you could apply analytics and investing in those that have the biggest leverage.
This sounds basic to say, but oftentimes people will prioritize it based on what's the easiest to bring to life. Something like a customer marketing use case might be the easiest one because we can actually test it, it's easy to stand something up, it might be "seen as easy." Even digitalization might be seen as easy because it's taking information that's already there.
But if you were to think about the customer point of view of how do you generate value from your business, if you don't have a product that someone wants and it's not in the right location, all that other stuff, I don't want to say it's wasted effort; it's just not optimized well.
So I would encourage people to really look hard and pick one aspect of your business that if you can begin to reinvent a bit, where you're leveraging analytics to power that's core to your success, that would be a great place to spend a bit of time and resource. So if you're an apparel retailer, for example, maybe it's around what are the products? How do you take insights information and test your products before you actually put them in the real world and actually develop an entire supply chain to produce, manufacture and ship to DC's and get into your stores.
That could be a huge leverage point and there are many of those types of things I would encourage people to look at. I would say if you could pick one that you could double down on, which may not necessarily be the most obvious one, that's the one where I'd spend a big of time on.
Denman: There's a million different things that you could use analytics for. But you can't get any of that value out if you don't have the data. And as we discussed earlier, RIS and CTG have been researching this for the better part of a decade and always asking at least this question, "How much data are retailers sharing with CG-ers?" And it always comes back to CG-ers want more data, and retailers either are unwilling or unable to provide them with that data and most of that data is POS data, I imagine.
Why do you think this is the case? What is holding retailers back from sharing everything? And is there anything CG companies can maybe offer their retail partners to get access to this critical data?
Chapo: That's a very interesting question. When you posed it to me beforehand, I started to reflect on it and actually, it goes back, at least my perspective, to how you started this conversation with data being the new oil, if you will. And the analytics is the refinery to generate value from it.
Maybe said differently: For many people data equals insights, which actually equals 10th in thought of the equal power. And I don't profess that I've done a ton of firsthand research on this, but one aspect of ways to address that could be things like, "Well, how do you actually create a win/win situation for both a CG company as well as a retailer around this concept of data?"
So an example of some of the things that I've seen happen before is where people actually, by sharing data, and doing it in a way where it's privacy compliant, it's not like I'm going to get competitive things out there that we don't want to share. But doing it in the right way, you can do things like product code development. How do I take a product that maybe as a CG-er, generic product that I have and actually tailor it for the customer base that this retailer has.
In order to do that, I might need some more insights about that customer, like how they're different from others. And so ways that companies can solve these types of things and hopefully in a compliant way is, and I've actually seen this, is leveraging things like white-room activities where data gets put into places together. A lot of times you actually have to share some bit of customer information to do that and doing it in a way where it's protected is super important.
That's why we've developed things like sandboxes to enable those types of capabilities, but actually doing it in a way where, again, you actually focus on a problem like a product code development sort of space. So that is again, a win/win for both.
The other piece that I've seen a few folks do, if there still gets to be tension and challenge for that, is for a CG company to find a way to incent customers to connect with them directly. It's part of the journey. So I'll give you one example that I personally thought was very exciting. It's not directly CG, but it's adjacent.
Starbucks, as everyone probably knows, has their own stores, their own brands. They sell directly to consumers in that sort of fashion. They have their own loyalty program, the Starbucks Awards Program, which incidentally I'm addicted to, but that's probably a topic for another day.
But one of the things that I personally have done, is I actually will buy Starbucks from my local grocery store, the beans. And one thing they've done to incent me to [do] because it's easy, it's frictionless, I'm at the store buying other things, to incent me to do that and still participate in their loyalty program, is by having the ability to take that purchase and actually get points for that.
I have to actually share my receipt back with them so they can validate that I made that purchase. As a customer, I've gotten a benefit, but also the CG company gets a little more information, maybe in a way they wouldn't naturally have gotten if they had some of this tension.
So those are just a couple of examples, one being figuring out a way to make it a win/win. Something like product code development. Second is figuring out a way to create a direct connection. Could be a couple of ways for folks to move forward. But it is a tension point and I think it will take some time in order to bridge the gap on that one.
Denman: Certainly it's a challenge, and it's going to require a lot of change management to make it happen. Here, change management is always a difficult thing in any vertical, with any brand. What are some of the things you emphasize through your retail brands concerning customer data initiatives as far as their larger analytic strategy?
Chapo: This is an old adage that someone shared with me, and it actually sticks with me every day and I share it often. I'd say it's pretty important to establish a big vision of where you want to go towards. But pick a few small bites of places to get started. So big vision, small bites.
Part of the reason I say that is the big vision actually helps people get excited and understanding what the opportunity could look like in the future. Example could be, imagine a world if you're a retail brand where you're planning your next season around what customers want and need. You're leveraging signals from social data sources to help you better plan and manage your inventories to get into season and then, when you're selling to that consumer, you're actually giving them the product, as I mentioned before, where we've removed the friction.
So personalized offers, personalized product recommendations. You can imagine a world in that space. But it's super hard to bring all that to life. So what's the first small bite you're going to take in order to make progress against that vision? And in terms of determining that, there's a couple of dimensions that I would recommend people to look at.
One is what I call the ease dimension. It's the obvious one. It's how easy is it to get going? How easy is it to stand up? The second, and this is the obvious one, too, is the impact that this thing could have.
Typically, when people do that, you'll create a matrix and you'll pick a couple of things and usually it's something digital, maybe because it's a great place to start. So I would recommend leveraging that, but maybe one more thing to put on top of it is figuring out where there's white space where no one's really tackling anything?
Giving a team some resources and some time and budget and permission and all the things you need in order to begin to show what a new way of working could look like, because oftentimes there are many companies who have a much more product-centric view of how they operate or channel view.
Thinking about adding customer analytics into that view can be really, I'd say challenging, because we've had 20, 15, 10 years of ways of working to create a space where they can, in a safe way, begin to experiment and create the pull. But always tie it back to that big vision that I just talked about.
And as you mentioned, change management is hard. And I would also encourage people to invest in change leaders in your organization to drive this journey. This is going to sound a little odd coming from someone from a technology company, but I think this is going to be important. It's really not about the tech installation, well that is important. It really is around changing the business process and quite frankly, helping the people in place learn what it is to leverage customer within their decision processes. So to bring in someone who can actually help you do that.
Denman: You talked a lot about big vision, change management — all of that kind of ties into this idea that brick and mortar is slowing down and online is booming, and obviously this has only brought it to the forefront in the recent pandemic. But can you talk a little bit on what does this transition away from brick and mortar mean for the brands, for the first parting data for brands, specifically?
Chapo: Yeah, I would say as I mentioned a couple of moments ago about this concept of urgency, I think that what would be super important is to figure out is how do I leverage my first party data to identify and remove friction points in the customer experience? And create it in a way that, and use this digital world that we're now in to mimic what people really love about physical brick and mortar worlds.
When you think about great experiences you've had in retail, brick and mortar retail, it's about a person. It's about what they were able to help you do, how they made you feel, how they made it super easy for you to get what you needed, but still treated you with empathy and were friendly and helpful.
Those are the phrases, when people talk about great experiences in retail, you think about that. Of course, you have to have a great product, but that's the essence of the experience. So the question would be, how do you take your first party data that you have about a customer and create that same sort of sense of experience digitally and in this new online space for many people?
Yes, you have to do the basic things of make your website easy, make sure it doesn't crash, make sure that the order fulfillment process works. That's kind of the table stakes. What I would say is how do you actually leverage it to create an experience that the person feels like it's tailored for them? So instead of having to search through thousands of products, because I know you, these are the products I think you might want to check out.
And it doesn't have to be super fancy. If you know your customer base, for instance, I'll just give one example. It's like there might be people who buy men's products in apparel retail and people who buy women's products. And some people buy kids’. When they come to the website, why don't you show them what they typically buy? It doesn't have to be this fancy thing.
You begin to create an experience that's tailored for them. And all of that gets powered through things like first party data and experimentation.
Denman: We do have a couple of questions from the audience to get to, but before we do, I am going to lean on you one more time to analyze our data. We asked survey respondents what the impact of COVID would be on their analytics approach and more than half the CG-ers reported that their resources have already shifted, but less than a quarter of retailers reporting the same. Why do you think there's such a difference between the two sides of the house, and where do you think this money's being allocated during the lack of resources in the face of the crisis?
Chapo: It's interesting. I saw that and I reflected on it a bit myself and chatted with a few folks that I know were in the trenches. My hypothesis, based on those conversations, is that a lot of retailers honestly are focused right now on the operational realities of what's happening. The front line issues of the shutdown.
I'll give you one example. You've got inventory coming in that you've already bought and you need to get it to stores — well, you may not need to have all those stores open. How do I rebalance it? Where do I put inventory that's happening? That sort of question you would need, even when you're running. It's just you're focused on, in the new reality, how you operationalize that.
Things like driving to new experiences, how you rapidly work in personalization. You need to do that now. It's just much more important now. So part of it is that, as I say, is that it feels a lot, at least in this moment, people are so focused on this operational reality that they're in. So people are doubling down on things they've had to do.
Now, the question will be, "Can those teams carve off some time and resource to tackle the next generations of things? And what are the folks in the CG world focusing on now that they weren't before?"
There are things we can learn from that the retailers can absorb. That's going to be, I'd say, it will be a really interesting to learn and explore on.
Denman: Right, 100% agree. If you look at just the numbers and the historical numbers of how much of it, the IT budget gets spent on analytics, CGs are always spending a little bit more than retailers. I think a part of it is, to your point, is that a huge portion of the IT budget for retailers is just tied into the store. Whether it's your POS, all the stuff that's in the store that CG-ers don't have to deal with.
So I think that's the probably the reason why you don't see the huge difference, why you see the traditional huge difference, and you haven't seen retailers being able to shift funds so quickly to analytics in this crisis, because the funds maybe just aren't there.
We have a question specifically about Amperity, and does Amperity have experience with customer/use cases in the B2B space? We talked a lot about B2C, but what about B2B?
Chapo: It's a good question and I'd say, yes we do. And what we've learned and what we've traditionally focused on B2C companies, what we've also learned is that many of the challenges with regards to bringing data together, making it useful, stitching identities together translates I would say across those boundaries.
I'll give you one example. We'll have to keep it a little generic. But imagine a B2B world where a customer needs to understand leads from various different sources. So I'll get leads from say, a webinar, I'll get leads from a conference. I'll get leads from, I would say, just other sources that we buy data from, bring it together in a location where I can actually identify the duplication against those, create a lead 360. It feels like you might just create a customer 360, is a process that is very analogous to a B2C world.
Then figure out how do I activate that? Now, there may be different activation points. You're not going to send it necessarily to a large email service provider, but getting that list to the right sales teams, helping them maybe create a score to prioritize the people to call first versus bottom. That, to me, is one example. So that's an example of how we work with B2C companies, leveraging the same approach that we take for B2C.
Denman: I think we have time for one more question. It's a pretty interesting one. What is your view of CPG enterprise data management and are CPG-ers as far behind as they think they are?
Chapo: That is a really interesting question. I'll admit I'm not as into the weeds around that specific question, but I would say one of the challenges that everyone faces with data management and I would say, CPGs know the difference is around things like definitions of product, of customer. Having hierarchies of what this actually means, where is it sold, what does it look like?
My sense is that that sort of challenge, as I've talked to some CPG companies, is actually easier to solve than if they're farther ahead than in the retail sort of a world. Because retail has many channels, many service interactions; it's a different sort of model, if you will. So that's one where I feel like folks are ahead.
I think one of the challenges that people are probably feeling is coming from the fact that there's just, as you mentioned before, a lack of first party data. So I might be able to get a great view of how I think of my product and how things can sell, but getting the insight of how do I actually sell more of it, how do I connect with that consumer, how do I understand other purchase patterns?
To me, that's probably where people feel like they're further behind, but that's probably because they don't have the raw data to do it. That's just a hypothesis and I would love to learn more from others as well.
Denman: So would I. It's a very interesting question. Chris, we are out of time. So I thank you for joining us today, and I thank everyone for listening. Stay safe out there.