We’ll explore how the Extreme Apps for Retail initiative places new knowledge in the hands of on-site sellers — to the customized benefit of shoppers at the very point of sales and in real time.
By leveraging power of SAP HANA big-data software infrastructure, Hewlett Packard Enterprise (HPE) hardware, and Capgemini targeted analysis and intelligence, these Extreme Apps are designed to make the physical retail experience king by leveraging the best of online assets – all brought to enhance the user experience at the mobile edge.
To learn more about how individual buyer information and group buying behavior inferences combine to customize the buying experience anywhere and anytime, please welcome Frank Wammes, Chief Technology Officer, for Capgemini Continental Europe. The discussion is moderated by me, Dana Gardner, Principal Analyst at Interarbor Solutions.
Here are some excerpts:
Gardner: We’ve had so much change over the past five years in retail. It’s a vertical industry that’s under lots of pressure with a need for innovation. What, in your mind, are the top trends driving this desire to use big data to better enhance users’ ability — at the retail site — to get customized buyer experiences and customized deals based on their individual needs and wants?
Wammes: Retail indeed is one of the industries which is most impacted by the outflow of financial crisis in 2007-2008, where a lot of companies struggled. They ask: “Okay, how are we going to revive our business?” It’s been an industry where you could see the winners and the losers very clearly. But there are a few things that everybody in the retail industry is now thinking about and need to answer.
First of all, the big opportunity that retailers have is leveraging the whole big-data movement. There is so much data that retailers have about their customers and the consumers, structured and unstructured data, that they can benefit from. The only question is how they’ll do that and they’re going to make sure that all the data that they processed comes to action in order to create better experiences in the store or on the website.
The second big trend is how to gain the loyalty of your buyers? We see that it’s very easy for consumers to switch between different brands, between different retailers, between different stores, and loyalty is something that came in the past but it’s something that will not come automatically now or in the future.
But if you give a client a real custom experience, and they know that every time they come to you they’ll get the same experience and they’ll get the benefits because of their loyalty, they’ll adapt their needs in real time and will keep their loyalty towards your brand. So the second question is how do you increase their loyalty.
And third, it’s really the combination of the online and physical retail experience, the only general experience that people have. How do you make sure that during the buying journey of a customer, they continuously have the same experience?
Wowing the buyer
We always joke that if you go to a retail outlet in your specific country, how many retailers, when you have bought something online and you want to cancel it and you want to buy something in the store, can you go to that store, cancel that order, and make sure that you can take a physical good out of the store? In 95 percent of the cases, that will not be the case. It’s very easy to surprise your customer if you can do it. So how can you wow your buyer and give them the real experience?
Those are the three big things: leveraging all the data to increase the loyalty in both online and offline worlds.
Gardner: It’s interesting that we’re using big-data and intelligence to, in effect, combine what happens online with what happens in real-time and real space. Until fairly recently, people expected their online shopping experience to be the one where analysis was being derived from their actions, from their history, their clickstream, and so forth.
It’s fascinating that we’re able to now bring analysis to the physical site, and it seems that shopping is one of those things where so much more can be done when you’re actually in touch with the goods, to be able to feel them, see them, try them on.
Why have we had a problem getting to this point where we can combine the best of online analysis capabilities and data gathering with the physical world? What have been some of the problems that needed to be solved in order to get to this point?
Wammes: Once you went online, people could capture where you came in from through your IP address. So if you consistently came through that IP address you didn’t even have to have a loyalty card. We knew that you were a returning client.
We probably knew that you bought something. That was the reason why, from an online perspective, we’ve been able to give you much more personalized offers or a better experience towards your needs using the intelligence and the big data.
The issue was that in the physical store, once you entered, we didn’t know who you were. Probably at the counter, at the moment that you already made your purchase, you drew your loyalty card. That was the moment that we could do something for you, but that was already at the end of the purchase.
A lot of the technology has changed. One of the things is that you can have your sales agents in the stores, or your sales representatives in the stores, and have them use tablets.
So once people are shopping in the physical store, I can create a contact moment and I can probably ask them for their loyalty card or if they’ve bought something, yes or no.
Even more important, one of the other things that you can do now is with beacon technology. Once you come in with your phone and you already have a connection to the company because you’re in some kind of a loyalty program, you already downloaded an app from that specific store, at the moment that you enter, we know that you entered.
We can upload a picture on the sales rep’s mobile device, so that he can proactively approach you and say, “It’s so good that you came back again. How was the coat that you bought last time?”
The moments that we can have in these interactions with our customers within the retail store gives us the possibility to the leverage from the insights and the big-data capabilities. That’s something that we didn’t have in the past.
That is the thing that helps. Now, we have the capabilities and the technologies to crunch all that data in real time. It’s good that I can recognize my customer, but more importantly, I now have the technology to instantly, in real time, crunch all the data so I can give him this personal experience.
Gardner: I can see why you are calling it “Extreme Apps.” It really is powerful and interesting that you can do all this now –- to have someone greet me and recognize my last purchase and follow up on that. Clearly, with my opt-in at a store, I’m giving them information, but I’m getting a lot back in return. It really is groundbreaking.
Is this something that we’re seeing only in retail or are there other vertical industries, not to go too far a field from our discussion, but is this something that’s applicable beyond retail and is that something you’ve considered?
Wammes: There is a little bit of retail in a lot of different industries. We initially focused on hardcore retail. The reason we did it is because we looked the industries where so much transactional data is coming in that we can crunch the data and use the power of in-memory analytics. That was the starting point.
Then you can look at utilities, because with the utilities there are so many streams of information and so many transactions that you can crunch the data and get a personal experience, particularly now with the deregulation of the utility industries. This is something that we’re also actively looking into, making sure that the retention of the consumer will be increased.
The banking industry, the insurance industry, all have this kind of retail perspective. On the other hand, we’re also are in talks with some oil companies that have their retail outlets, sometimes directly or sometimes indirectly.
We had some discussions with a very large beverage producer. We said they could perhaps offer analytics as a service towards retailers, so that the retailer themselves don’t have to buy the analytical capabilities. The companies could offer this as a service so that they have more influence and insight on what’s happening with their product. Perhaps they could put that into the hands of an independent party so that the retailer doesn’t see all this insight.
We see the retailer as the starting point because of this experience, the customer experience, that you directly can enhance. But there is a lot of retail kind of experience in the banking and insurance industries. So the opportunities are more diverse, and it all leads to how can I optimize the personal experience the individual buyer has with our company.
Gardner: It’s fascinating. We’re really combining the physical world, the mobile tier data, across existing industries in really new ways.
Let’s explore how that “Extreme” experience benefit on the front-end is made possible by some extreme technology on the back-end, so to speak. We have several different players involved here: SAP, HPE, and Capgemini. Explain to me how these partners in this ecosystem have come together, and what each contributes to the ability to deliver these capabilities.
Wammes: Definitely, because it is extreme, but it also required some extreme engineering of new technologies in order to create it.
What we have done is a combination of some very strong partners of ours, where we try to leverage the new technology. First of all, it started with SAP HANA. It was also the question that SAP posed to us. We have HANA as the in-memory engine, but we have it just as an engine. Can you, with your creativity and your industry knowledge, create some solutions on top of it? That’s where this whole Extreme Apps started.
SAP delivers the HANA technology, and what we also offer, but this is optional. We also say that if you don’t have a proper back end to provide you all this data and to capture all the data and all the transactional data, Capgemini has built a retail template on top of the SAP Business Suite. So we have a preconfigured retail solution for those companies that don’t have a proper or a state-of-the-art enterprise-resource planning (ERP) system yet Capgemini’s One-Path solution.
First of all, SAP comes in with their traditional Business Suite, but the Extreme Apps are explicitly built into the SAP HANA platform. HPE delivers the hardware and the services, the hybrid cloud expertise. Together with SAP and HPE we look at the architecture, because you always want to have all your data in memory mode. We also took in technologies like Hadoop to make a distinction between the hot data and the cold data that we work on in our analytics space.
What Capgemini added on top of it basically was the algorithm. We leveraged on the algorithms that we had to put into this Extreme environment. It runs now on the Capgemini data centers, on the HPE systems , but we can leverage this in multiple ways.
You can host it in the Capgemini data center, you can have it installed on-premise, we can have it in the cloud, and we can also deliver it as the normal traditional license and transaction price. But we also engineered it together with SAP and HPE so that you can also have it in a price-per-month scenario.
Gardner: So in addition to this flexible-deployment capability — where you can bring these Extreme Apps anywhere, anytime, anyplace — you also have a set of APIs available so that this can be customized and adapted to a mobile, web, point of sale, and so forth client. Tell me about the role of the API and the ability to customize apps and delivery.
Two big scenarios
Wammes: If you look at the Extreme Apps that we have right now, we have two big scenarios. We’ve already built other analytics around it, but there are two big scenarios. One is the Market Basket Analysis and the other is the Next Best Action.
The Market Basket Analysis is the tool for the merchandise agents. They want to know whether if they make a promotion, does it really add to the margin of the company, or if they look at a certain promotion, why is it performing better in location A compared to location B?
You want to leverage a lot of the analytics and the visuals that are already on the web, but that’s through your normal web browser. It’s the professional user using it. We deliver the standard analytics and the standard visuals. There isn’t a lot of tailoring around it.
The Next Best Action is a completely different ballgame, Dana. You want to provide the capability to offer something while the user is making his purchasing decision, and that can be through all different kind of things. It can be when a client is shopping on your web site and then you want to have this engine immediately promoting something that is relevant for the user.
It could also be that he walks by your company, and this is an example that we had with a lingerie store. They said when they have the telephone number of a client and they know through our beacon technology she is walking somewhere around our store, we give her a promotion. So we give her a 10 percent discount if she comes into the store and buys within an hour.
We already said it’s good that you give the 10 percent promotion, but wouldn’t it be much better if you give a very explicit promotion based on her buying pattern and based on the buying patterns of others who bought similar kind of products? Then, the promotion really becomes valuable. You want to have the promotion on your mobile. For your sales reps, you want to have it in a specific function, which gives them the opportunity to have a good conversation with the client while they are in the store.
We have developed some standard screens already, whether it’s for mobile, tablet, the web. More importantly, all these companies already have their mobile apps, or already have a sales representative outlet. We need to create an API so they can embrace it and incorporate it within their own existing environment, so that they really can start quickly and don’t have to do a complete rebuild of their environment.
This is the way the API works. Through the API they can get the promotion data and can incorporate it in their existing applications.
Gardner: Frank, where are we on the roll-out or milestones for this Extreme Apps for Retail initiative? Tell us a little bit about that: when it started, where we are now, and when we should expect to see more of these apps in actual use.
Wammes: It began about two years ago when we had the discussion with SAP, where they first started to build applications on top of HANA. How can you add value from an industry perspective towards this technology platform? That’s where we started. We built it.
We also crafted it together with a clothing retailer. It was not just created within the buildings of Capgemini and with the help of HPE and SAP with no client. We built it together with a client. We immediately knew the issues that they had, and not only leveraging our own industry expertise. So that was basically the first client.
And then we went into a do-it-yourself retail chain, where we implemented it. We saw that when the users, and particularly the professional users of the application, saw what the potential is, what you can do with real-time, in-memory capabilities, immediately additional questions emerged.
We started with these two applications, but then the question was, “If you have my point- of-sale-data, can I also create a report so that I can show my CFO what the daily sales are, but in a very advanced graphical way?” By the way, we leverage all the standard visualizations that you get with HTML5. So you can set up many libraries.
So quickly, we had four or five additional reports that were built, because we already worked on the past data. This is where we are right now. We have the two main scenarios. If a client installs one of the main scenarios, we already provide them with the reports that we have and that we build from the other clients.
We have some additional algorithms and test environments where we continuously are in discussions with our clients. Which algorithm is most valuable to your business? We said, “If you’re an SAP dominant client, and you’re already leveraging the power of the Extreme Apps, we’ll make sure that we extend the scenarios that we have with that algorithm that you have.”
We can anticipate that, in the coming year, we’ll build more based on the proof of concepts (POCs) that we will do with our clients, where first we’ll test the algorithm and then we’ll build it into the HANA platform, thereby enhancing the portfolio of the different Extreme Apps scenarios.
Gardner: Given that these services, these apps, are available and are proven in the field, if an interested organization wanted to start leveraging these capabilities, how long does it typically take, and what’s involved in getting this actually in implementation?
Wammes: That’s the cool thing. There are a lot of aspects, which you also already have recognized, that required Extreme Apps for Retail. Perhaps the most Extreme is the implementation time.
We leveraged the environment that we have within the organization, the combined Capgemini-HPE environment. If you deliver your data in the data structure that we ask, then we can store your data into Extreme Apps, and within two weeks, you can start experimenting to see whether the technology works for your company.
Give us your data, and we’ll load it into the Extreme Apps environment. For your Market Basket Analysis, you can already do the first analysis, where you can see where you can improve your promotions, whether you are making the wrong decisions and putting items in promotions which negatively affect each other.
We can already provide you environment where you can do your proof of value to showcase that, within a very short time, you can have a return on investment (ROI).
Because we have this API, we can also immediately integrate it within your existing environment, whether it’s your app, your web browser, or your Internet page. You can already start experimenting by giving a little bit more advanced, more analytical capabilities.
So it’s not only that you recommend this product because other people who bought product A also bought product B. Rather, because you bought these series of products, I compared it to people who also bought these series of products, but they then bought product B. So the advancement of the analytics is much bigger than the traditional, “If you buy A, then buy B, because others also bought B.”
This is something that we can have installed very quickly, and once you want to go in production, it depends on whether you want to go on-premise, or whether you want to go hybrid. In the meantime, you can leverage the environment that both HPE and Capgemini set up in our own data centers.
Gardner: So you can integrate to a retail organization’s website capability, their online marketing or marketplace and selling, and any buying capability — and also reach out to their point-of-sale retail outlets in as little as two weeks?
Wammes: Exactly. I think the combination is now the cool thing that we see, and that’s also Dana, some things that we learned. We started off with the traditional model and we built the scenario. If we went to a client, they needed to buy the HANA license, they needed to buy the hardware, and they needed to buy a scenario from us. Then, we built it in a offering where you do it on a monthly basis. What we’re now seeing is that together with other solutions, we can have it integrated in some engine.
So for instance, Capgemini has another piece of intellectual property (IP), which is called RM3. RM3 is a middleware solution where you can optimize your promotion, so you can mix and match the promotions to tailor it as much as possible to the individual need.
But now, we can put the Extreme Apps in it and make the promotion more advanced. We’re in talks now with some other clients who have their own engines, where they give promotion capabilities through mobile apps, but they don’t have a powerful analytics module behind it to make it personal. Now, they can have this Extreme Apps as the engine.
It has also been a journey for us learning that it is not only the capability of doing the analytics, but it has changed the way that you can do your business models. This applies both to the retailer, as well to the conglomerate that we are working with.
Gardner: Of course we know from the benefits of data and analysis that the more data and the longer period of time, the better the analysis. So, you’re able to give your individual retailers more insight into individual behavior. They’re able to see their own processes, promotions, and enticements work better, but stepping back, you’re also, at the Capgemini level, getting a lot of insight into an even larger set of data across multiple retailers, multiple types of shopping environments, and multiple types of buyers.
Does that mean that you’re going to get better algorithms and better insights from this larger historical set of data that can then be applied back into this set of Extreme Apps?
Wammes: That is a very good suggestion for an additional business model, Dana, to be quite honest.
Now, we separate the different environments. So, at this time, it’s the environment that we set up and the algorithm work for the specific individual client.
What we now do, which goes a little bit to your point, is that we learn from how the different clients that use our Extreme Apps leverage the Extreme Apps to optimize their promotions and their interactions with the client. That’s the first step.
At this time, we’re not at the point that we say we can leverage the knowledge that we take from the multiple client sets. However, what you refer to is something that we’ve thought of already, but it comes back to the example that I gave on the beverage producer.
If you, as a consumer goods company, can provide an engine to a retailer or to a multiple set of retailers, where you say, “We can help you in optimizing your promotions so that, in the end, you will sell more, and if you sell more, we will also sell more.”
That’s where the learning on the multiple clients and multiple different retail stores will kick in. We’ve thought of that concept, but not so much offered the users of our Extreme Apps solutions. It’s more in the context of whether consumer product companies can offer this as some kind of analytical capability towards different retailers?
Gardner: Perhaps, Frank, in a year or two we will have another conversation where we will talk about how synergistic shopping works. When you buy one type of product, it might mean you will be buying another soon, and some coordination and intelligence can be brought to that.
Wammes: Yeah, definitely.
Gardner: In the meantime, do you have any examples of either named or unnamed organizations that have put the Extreme Apps for Retail to use? What business benefits they get from it? Any measurements of success, such as, we were able to increase share of wallet, we were able to increase larger sets of purchases by certain buyers? Anything along the lines of proof points for how well this works?
Wammes: Yeah. Well, I can mention some industries. We can’t disclose specific types of retailers. When we looked at the business case that we got for the do-it-yourself company, their main business case was on the Next Best Action.
They saw the potential to do an increase of about 25 percent, because they could better target the promotions that they gave. It was also because we started to introduce the Next Best Action on the apps and on the website, which is a very growing business of course in that specific industry. So making sure that the up-sell and the cross-sell emerged was really the business case on the do-it-yourself side.
With the food retailer, it’s much more about the merchandized planning. What we saw particularly was the promotion. That was a business case where it was something about four percentage points of improvement that could be achieved easily. So there wasn’t much action.
The benefit really was that, through the analysis, we could see which products had affinity with each other, but also what the potential financial benefit between those two products were if you would not put them into promotions again, or if you put them explicitly into promotions?
As an example, and I think it’s the most easiest example, but everybody understands it, if you sell crisps in your promotion, don’t put your beer into the promotion, because there is such a high correlation between people who buy beer and will automatically buy crisps as well. So don’t do that.
Through these kinds of correlations and affinities, we could have a four percentage point improvement on the revenue, making sure that people would not do the promotions again.
We were able to reach a couple of percentage points because we could sell products which were very slow selling and where you could have issues on your expiry date. We could identify what kind of products they would sell. So if I have this low turnover of goods, but I put a promotion on high turnover of goods, with a high probability that the low turnover of goods would sell as well, then I would get rid of the inventory.
We also saw that the three percentage point potential was really about don’t put something on promotion where a product with a high affinity is out of stock.
These are the real examples that we have in the different industries.
Gardner: Now, these are benefits that are clearly significant to the seller. We’re talking about retail, where it’s very competitive, and there are large revenue numbers involved. So a couple of percent is a lot.
But what about the buyer? Have you done any surveys or questionnaires, found out why the buyers are benefiting from this, and what it makes in terms of loyalty develop with them?
Wammes: The research was really on the business case and the elements that I gave to you. So, I can’t give you the exact numbers on the loyalty.
However, in our interaction with our clients was that they said that it’s the opportunity to give this personalized experience in a relatively inexpensive way. We always use some clients as the real best practice on how can you integrate your online and offline shopping experience.
So for instance, for us, Burberry is one of the stars in having a very integrated omni channel experience. What we see beside the business case effects that we just discussed is that they said the fact that they can really have a personalized conversation when somebody enters the store, with data already up front, gives high value. However, we didn’t measure it.
I have a very good case in The Netherlands. There was a very large retailer that went bankrupt on December 31. They had 10,000 employees, and on the verge of the new year they got to hear that they are unemployed. They were one of the oldest big retailers in The Netherlands, big department store.
I’ve visited that department store a lot. The issue always was that when I came to the floor, there were no people that came to help me. They didn’t come to advise me. They didn’t come to assist me. When I finally grabbed a product to buy, I had to stand in a big line, because there were only a few cash registers on this very big floor.
It’s a very bold statement, but I think their future would have been much brighter if somebody would have approached me and already knew that I bought something because they have a loyalty card and they knew what I bought in the past. They knew what my interests were. And they could have greeted me and said, “Mr. Wammes, it’s so good that you’re here again. Can I show you around because I noticed that you marked it as interest on our online store and let me show you?”
I could buy it from that person as well, because they have this integrated credit card mechanism attached to their tablet. That would really be a complete transformation of doing business in that department store. If so, 10,000 employees wouldn’t have had that bad message at the end of the year.
Gardner: You’re basically saying that the personal touch in the retail environment is empowered now and can come back. We’ve all noticed in the past years, even decades, that the amount of personalization, personal touch, and human interaction in sales has gone down; it’s very much self-service. If it remains self-service, what’s the difference between online and bricks and mortar? Not very much. So you really with this capability, this Extreme set of Apps bringing the relevant nature of person-to-person sales and service interactions back into vogue — and making it very economically powerful.
Wammes: Exactly. You’ve hit the nail, as we say in The Netherlands. One of my colleague said it’s all about relevant personal experience, and it should be relevant personal experience. Technology is not threatening that. If you apply it in the right way, you strengthen it. And I think that’s really where we can have a great omni-channel, relevant personal experience delivered towards the consumer.
Gardner: We’re just about out of time. I just want now for a brief moment look to the future. Now that we’ve taken this significant step into Extreme Apps for Retail, what comes next? What might we consider the next chapters in being able to leverage these capabilities around real-time, vast data being brought to bear, fast APIs for implementation and delivery of the visualization and other data, and then this newfound empowerment of that salesperson, that personal advisory service, at the retail outlet? What might we expect in the next months and years?
More artificial intelligence
Wammes: Well, let me start far in the future and then bring it back a little bit. If I go far to the future, bringing in even more artificial intelligence (AI) will not only even enhance the creation of strong algorithms that increase this relevant personal experience, but also AI, in contrast, will give robotics a chance to interact with us.
There are already some examples, for instance, robots driving around in airports to help people along the journey. But the sales agent will be supported by AI to give more relevant personal experience towards our client. AI is definitely something that will kick in in the coming years.
The roll-out of beacon technology, so that we really can recognize the individual consumer, is something that will be more broadly explored in the coming years in the industry.
We’ve seen a lot of companies talk about big data, but a lot of retailers are still struggling a little bit with how to really apply it? What we’ve seen is with the clients who implemented the Extreme Apps for Retail is that because people were exposed to the enormous power of what in-memory, big data solutions can bring, all of a sudden the imagination is awakened.
In some of the examples that I gave earlier people said, “If you have this data anyway, can you then give us some very nice visual analytics to use that?” The most powerful part of the solution is that we put a toolset into the hands of people who, in the past, were always limited by the IT department, because it was difficult to build, it costs lot of money, and was very difficult to maintain.
With the new technologies, it’s very easy to create stuff that is very visual and powerful. Therefore, the imagination becomes the limit of what we can do. That’s perhaps the most surprising part, and that’s the thing I can’t answer, because I don’t know yet what kind of things people will come up with. But we’re entering an area where imagination becomes a driving force of the things that we can do.
Gardner: For those who are reading or listening to our conversation today, if they want more information about how to learn about this to start the journey towards understanding how it might benefit their organization, where would you point them?
Wammes: First of all, they always can contact me at firstname.lastname@example.org or go to my Twitter account, @fwammes. If you go to the Capgemini site, there’s a section called Ready2Series, and Ready2Series is the solutions where Capgemini owns their own IP. Under the Ready2Series, you’ll find more information about the Extreme Apps, and you can learn more from the solutions that we have there (https://www.capgemini.com/sap/sap-hana/extreme-applications-for-retail.
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