Meet George Jetson – Your New Chief Procurement Officer

Transcript of a discussion on how rapid advances in artificial intelligence and machine learning are poised to reshape procurement.

Listen to the podcast. Find it on iTunes. Get the mobile app. Download the transcript. Sponsor: SAP Ariba.

Dana Gardner: Hi, this is Dana Gardner, Principal Analyst at Interarbor Solutions, and you're listening to BriefingsDirect.

Gardner

Our next technology innovation thought leadership discussion explores how rapid advances in artificial intelligence (AI) and machine learning are poised to reshape procurement -- like a fast-forwarding to a once-fanciful vision of the future.

Whereas George Jetson of the 1960s cartoon portrayed a world of household robots, flying cars, and push-button corporate jobs -- the 2017 procurement landscape has its own impressive retinue of decision bots, automated processes, and data-driven insights.

We won’t need to wait long for this vision of futuristic business to arrive. As we enter 2017, applied intelligence derived from entirely new data analysis benefits has redefined productivity and provided business leaders with unprecedented tools for managing procurement, supply chains, and continuity risks.

To learn more about the future of predictive -- and even proactive procurement technologies -- please join me in welcoming back Chris Haydon, Chief Strategy Officer at SAP Ariba. Good to have you with us, Chris.

Chris Haydon: Great to be here again.

Gardner: It seems like only yesterday that we were content to gain a common view of the customer or develop an end-to-end bead on a single business process. These were our goals in refining business in general, but today we've leapfrogged to a future where we're using words like “predictive” and “proactive” to define what business function should do and be about. Chris, what's altered our reality to account for this rapid advancement from visibility into predictive -- and on to proactive?

Haydon: There are a couple of things. The acceleration of the smarts, the intelligence, or the artificial intelligence, whatever the terminology that you identify with, has really exploded. It’s a lot more real, and you see these use-cases on television all the time. The business world is just looking to go in and adopt that.

And then there’s this notion of the Lego block of being able to string multiple processes together via an API is really exciting -- that coupled with the ability to have insight. The last piece, the ability to make sense of big data, either from a visualization perspective or from a machine-learning perspective, has accelerated things.

These trends are starting to come together in the business-to-business (B2B) world, and today, we're seeing them manifest themselves in procurement.

Gardner: What is it about procurement as a function that’s especially ripe for taking advantage of these technologies?

Transaction intense

Haydon: Procurement is obviously very transaction-intense. Historically, what transaction intensity means is people, processing, exceptions. When we talk about these trends now, the ability to componentize services, the ability to look at big data or machine learning, and the input on top of this contextualizes intelligence. It's cognitive and predictive by its very nature, a bigger data set, and [improves] historically inefficient human-based processes. That’s why procurement is starting to be at the forefront.

Haydon

Gardner: Procurement itself has changed from the days of when we were highly vertically integrated as corporations. We had long lead times on product cycles and fulfillment. Nowadays, it’s all about agility and compressing the time across the board. So, procurement has elevated its position. Anything more to add?

Haydon: Everyone needs to be closer to the customer, and you need live business. So, procurement is live now. This change in dynamic -- speed and responsiveness -- is closer to your point. It’s also these other dimensions of the consumer experience that now has to be the business-to-business experience. All that means same-day shipping, real-time visibility, and changing dynamically. That's what we have to deliver.

Gardner: If we go back to our George Jetson reference, what is it about this coming year, 2017? Do you think it's an important inception point when it comes to factoring things like the rising role of procurement, the rising role of analytics, and the fact that the Internet of Things (IoT) is going to bring more relevant data to bear? Why now?

Haydon: There are a couple of things. The procurement function is becoming more mature. Procurement leaders have extracted those first and second levels of savings from sourcing and the like. And they have control of their processes.

With cloud-based technologies and more of control of their processes, they're looking now to how they're going to serve their internal customers by being value-generators and risk-reducers.

How do you forward the business, how do you de-risk, how do you get supply continuity, how do you protect your brand? You do that by having better insight, real-time insight into your supply base, and that’s what’s driving this investment.

Gardner: We've been talking about Ariba being a 20-year-old company. Congratulations on your anniversary of 20 years.

Haydon: Thank you.

AI and bots

Gardner: You're also, of course, part of SAP. Not only have you been focused on procurement for 20 years, but you've got a large global player with lots of other technologies and platform of benefits to avail yourselves of. So, that brings me to the point of AI and bots.

It seems to me that right at the time when procurement needs help, when procurement is more important than ever, that we're also in a position technically to start doing some innovative things that get us into those words "predictive" and more "intelligent."

Set the stage for how these things come together.

Haydon: You allude to being part of SAP, and that's really a great strength and advantage for a domain-focused procurement expertise company.

The machine-learning capabilities that are part of a native SAP HANA platform, which we naturally adopt and get access to, put us on the forefront of not having to invest in that part of the platform, but to focus on how we take that platform and put it into the context of procurement.

There are a couple of pretty obvious areas. There's no doubt that when you’ve got the largest B2B network, billions in spend, and hundreds and millions of transactions on invoicing, you apply some machine learning on that. We can start doing a lot smarter matching an exception management on that, pretty straightforward. That's at one end of the chain.

It's not about upstream and downstream, it's about end-to-end process, and re-imagining and reinventing that.

On the other end of the chain, we have bots. Some people get a little bit wired about the word “bot,” “robotics,” or whatever, maybe it's a digital assistant or it's a smart app. But, it's this notion of helping with decisions, helping with real-time decisions, whether it's identifying a new source of supply because there's a problem, and the problem is identified because you’ve got a live network. It's saying that you have a risk or you have a continuity problem, and not just that it's happening, but here's an alternative, here are other sources of a qualified supply.

Gardner: So, it strikes me that 2017 is such a pivotal year in business. This is the year where we're going to start to really define what machines do well, and what people do well, and not to confuse them. What is it about an end-to-end process in procurement that the machine can do better that we can then elevate the value in the decision-making process of the people?

Haydon: Machines can do better in just identifying patterns -- clusters, if you want to use a more technical word. They transform category management and enables procurement to be at the front of their internal customer set by looking not just at their traditional total cost of ownership (TCO), but total value and use. That's a part of that real dynamic change.

What we call end-to-end, or even what SAP Ariba defined in a very loose way when we talked about upstream, it was about outsourcing and contracting, and downstream was about procurement, purchasing, and invoicing. That's gone, Dana. It's not about upstream and downstream, it's about end-to-end process, and re-imagining and reinventing that.

The role of people

Gardner: When we give more power to a procurement professional by having highly elevated and intelligent tools, their role within the organization advances and the amount of improvement they can make financially advances. But I wonder where there's risk if we automate too much and whether companies might be thinking that they still want people in charge of these decisions. Where do we begin experimenting with how much automation to bring, now that we know how capable these machines have been, or is this going to be a period of exploration for the next few years?

Haydon: It will be a period of exploration, just because businesses have different risk tolerances and there are actually different parts of their life cycle. If you're in a hyper growth mode and you're pretty profitable, that's a little bit different than if you're under a very big margin pressure.

For example, maybe if you're in high tech in the Silicon Valley, and some big names that we could all talk about are, you're prepared to be able to go at it, and let it all come.

If you're in a natural-resource environment, every dollar is even more precious than it was a year ago.

That’s also the beauty, though, with technology. If you want to do it for this category, this supplier, this business unit, or this division you can do that a lot easier than ever before and so you go on a journey.

If you're in a hyper growth mode and you're pretty profitable, that's a little bit different than if you're under a very big margin pressure.

Gardner: That’s an important point that people might not appreciate, that there's a tolerance for your appetite for automation, intelligence, using machine learning, and AI. They might even change, given the context of the certain procurement activity you're doing within the same company. Maybe you could help people who are a little bit leery of this, thinking that they're losing control. It sounds to me like they're actually gaining more control.

Haydon: They gain more control, because they can do more and see more. To me, it’s layered. Does the first bot automatically requisition something -- yes or no? So, you put tolerances on it. I'm okay to do it if it is less than $50,000, $5,000, or whatever the limit is, and it's very simple. If the event is less than $5,000 and it’s within one percent of the last time I did it, go and do it. But tell me that you are going to do it or let’s have a cooling-off period.

If you don't tell me or if you don’t stop me, I'm going to do it, and that’s the little bit of this predictive as well. So you still control the gate, you just don’t have to be involved in all the sub-processes and all that stuff to get to the gate. That’s interesting.

Gardner: What’s interesting to me as well, Chris, is because the data is such a core element of how successful this is, it means that companies in a procurement intelligence drive will want more data, so they can make better decisions. Suppliers who want to be competitive in that environment will naturally be incentivized to provide more data, more quickly, with more openness. Tell us some of the implications for intelligence brought to procurement on the supplier? What we should expect suppliers to do differently as a result?

Notion of content

Haydon: There's no doubt that, at a couple of levels, suppliers will need to let the buyers know even more about themselves than they have ever known before.

That goes to the notion of content. It’s like there is unique content to be discovered, which is whom am I, what do I do well and demonstrate that I do well. That’s being discovered. Then, there is the notion of being able to transact. What do I need to be able to do to transact with you efficiently whether that's a payment, a bank account, or just the way in which I can consume this?

Then, there is also this last notion of the content. What content do I need to be able to provide to my customer, aka the end user, for them to be able to initiate the business with them?

These three dimensions of being discovered, how to be dynamically transacted with, and then actually providing the content of what you do even as a material of service to the end user via the channel. You have to have all of these dimensions right.

If you don't have the context of the business process between a buyer and a seller and what they are trying to affect through the network, how does it add value?

That’s why we fundamentally believe that a network-based approach, when it's end to end, meaning a supplier can do it once to all of the customers across the [Ariba] Discovery channel, across the transactional channel, across the content channel is really value adding. In a digital economy, that's the only way to do it.

Gardner: So this idea of the business network, which is a virtual repository for all of this information isn't just quantity, but it's really about the quality of the relationship. We hear about different business networks vying for attention. It seems to me that understanding that quality aspect is something you shouldn't lose track of.

Haydon: It’s the quality. It’s also the context of the business process. If you don't have the context of the business process between a buyer and a seller and what they are trying to affect through the network, how does it add value? The leading-practice networks, and we're a leading-practice network, are thinking about Discovery. We're thinking about content; we're thinking about transactions.

Gardner: Again, going back to the George Jetson view of the future, for organizations that want to see the return on their energy and devotion to these concepts around AI, bots, and intelligence. What sort of low-hanging fruit do we look for, for assuring them that they are on the right path? I'm going to answer my own question, but I want you to illustrate it a bit better, and that’s risk and compliance and being able to adjust to unforeseen circumstances seems to me an immediate payoff for doing this.

Severance of pleadings

Haydon: The United Kingdom is enacting a law before the end of the year for severance of pleadings. It’s the law, and you have to comply. The real question is how you comply.

You eye your brand, you eye your supply chain, and having the supply-chain profile information at hand right now is top of mind. If you're a Chief Procurement Officer (CPO) and you walk into the CEO’s office, the CEO could ask, "Can you tell me that I don’t have any forced labor, I don’t have any denied parties, and I'm Office of Foreign Assets Control (OFAC) compliant? Can you tell me that now?"

You might be able to do it for your top 50 suppliers or top 100 suppliers, and that’s great, but unfortunately, a small, $2,000 supplier who uses some forced labor in any part of the world is potentially a problem in this extended supply chain. We've seen brands boycotted very quickly. These things roll.

So yes, I think that’s just right at the forefront. Then, it's applying intelligence to that to give that risk threshold and to think about where those challenges are. It's being smart and saying, "Here is a high risk category. Look at this category first and all the suppliers in the category. We're not saying that the suppliers are bad, but you better have a double or triple look at that, because you're at high risk just because of the nature of the category."

Think larger than yourself in trying to solve that problem differently. Those cloud deployment models really help you.

Gardner: Technically, what should organizations be thinking about in terms of what they have in place in order for their systems and processes to take advantage of these business network intelligence values? If I'm intrigued by this concept, if I see the benefits in reducing risk and additional efficiency, what might I be thinking about in terms of my own architecture, my own technologies in order to be in the best position to take advantage of this?

Haydon: You have to question how much of that you think you can build yourself. If you think you're asking different questions than most of your competitors, you're probably not. I'm sure there are specific categories and specific areas on tight supplier relationships and co-innovation development, but when it comes to the core risk questions, more often, they're about an industry, a geography, or the intersection of both.

Our recommendation to corporations is never try and build it yourself. You might need to have some degree of privacy, but look to have it as more industry-based. Think larger than yourself in trying to solve that problem differently. Those cloud deployment models really help you.

Gardner: So it really is less of a technical preparatory thought process than process being a digital organization, availing yourself of cloud models, being ready to think about acting intelligently and finding that right demarcation between what the machines do best and what the people do best.

More visible

Haydon: By making things digital they are actually more visible. You have to be able to balance the pure nature of visibility to get at the product; that's the first step. That’s why people are on a digital journey.

Gardner: Machines can’t help you with a paper-based process, right?

Haydon: Not as much. You have to scan it and throw it in. Then, you are then digitizing it.

Gardner: We heard about Guided Buying last year from SAP Ariba. It sounds like we're going to be getting a sort of "Guided Buying-Plus" next year and we should keep an eye on that.

Haydon: We're very excited. We announced that earlier this year. We're trying to solve two problems quickly through Guided Buying.

Our Guided Buying has a beautiful consumer-based look and feel, but with embedded compliance. We hide the complexity. We just show the user what they need to know at the time, and the flow is very powerful.

One is the nature of the ad-hoc user. We're all ad-hoc users in the business today. I need to buy things, but I don’t want to read the policy, I don’t want to open the PDF on some corporate portal on some threshold limit that, quite honestly, I really need to know about once or twice a year.

So our Guided Buying has a beautiful consumer-based look and feel, but with embedded compliance. We hide the complexity. We just show the user what they need to know at the time, and the flow is very powerful.

Gardner: Well, it certainly sounds like an area where intelligence would have a very marked improvement, and we'll look for some interesting news there as well.

I'm afraid we'll have to leave it there. You've been listening to a BriefingsDirect thought leadership podcast discussion on how rapid advances in AI and machine learning are poised to reshape procurement.

We've heard how, as we enter 2017, applied intelligence, derived from entirely new data analysis, benefits redefines productivity. Lastly, we've been presented with SAP Ariba’s view on where we can take business intelligence aspects into more types of process and more refinement of the procurement function.

With that, please join me in thanking our guest, Chris Haydon, Chief Strategy Officer at SAP Ariba. Thank you, sir.

Haydon: Thank you.

Gardner: And a big thank you as well to our audience for joining this SAP Ariba-sponsored business innovation thought leadership discussion. I'm Dana Gardner, Principal Analyst at Interarbor Solutions, your host and moderator. Thanks again for listening, and do come back next time.

Listen to the podcast. Find it on iTunes. Get the mobile app. Download the transcript. Sponsor: SAP Ariba

Transcript of a discussion on how rapid advances in artificial intelligence and machine learning are poised to reshape procurement. Copyright Interarbor Solutions, LLC, 2005-2016. All rights reserved.

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How New Technology Trends Disrupt the Very Nature of Business

Transcript of a discussion on how major new trends and technology are translating into disruption, and for the innovative business -- opportunity.

Listen to the podcast. Find it on iTunes. Get the mobile app. Download the transcript. Sponsor: SAP Ariba.

Dana Gardner: Hi, this is Dana Gardner, Principal Analyst at Interarbor Solutions, and you’re listening to BriefingsDirect.

Gardner

Our next technology innovation thought leadership discussion focuses on how major new trends in technology are translating into disruption, and for the innovative business -- opportunity.

From invisible robots, to drones as data servers -- from virtual reality to driverless cars -- technology innovation is faster than ever, impacting us everywhere, broadening our knowledge, and newly augmenting processes and commerce. We'll now explore the ways that these technology innovations translate into business impacts, and how consumers and suppliers of services and goods can best prepare.

To learn more about the future of business innovation, we’re joined by two guests, Greg Williams, Deputy Editor of WIRED UK, and Alex Atzberger, President of SAP Ariba.

Greg, as we see a lot of trends happening, a lot of change in the industry, people talk about the pace picking up quicker than ever. What are some of the major disrupting trends that you see in technology, and then which of those do you think are going to be the most impactful for business people?

Greg Williams: You listed a whole bunch of things, which are all incredibly important in moving forward. They're near-term in ways that sometimes people don’t consider them to be near-term. Technology shifts tend to be almost things we don’t notice. They're not happening slowly any longer; they're happening quickly, and we're almost not seeing them.

Atzberger

We talk about something like robotics. Now, you can see all kinds of incredible things. You can see in Japan a robot caregiver that can lift elderly people out of their beds and can care for them in that way. You can see slightly sinister videos from Boston Dynamics of robot dogs running along that look pretty scary. But what most innovation looks like are things that we almost don’t notice are there.

For instance, and this is a boring example, an ATM is kind of a robot; a vacuum cleaner is; an elevator is. Those are things we don’t necessarily notice. They're not as dramatic as we think.

I should just caveat that and say that everything is moving very, very quickly right now. That's why it’s hard to make very clear predictions.

The other thing that’s important is this joining up of lots of different technologies. That’s the biggest trend that I see right now. We can talk about satellites and drones, which are effectively servers in the sky or we can talk about autonomous mobility and augmented reality, but it’s all about connecting the dots.

Technology players

One thing that's interesting now is the way that car manufacturers are all technology players. Every automotive manufacturer is figuring out that what they have is a computer on wheels. They have to figure out how, when people drive into a parking lot, they make an automatic payment via the vehicle. How can the vehicle know that people’s groceries are ready to be picked up at a certain point.

Williams

Although it’s nice to list robots and autonomous vehicles and other clear technological shifts, the thing that we're really seeing is the speeding up and this coming together, this joining and connecting of the dots. Basically, all are based on three things: ubiquitous computing, mobile technology, and the cloud. Those three things underpin pretty much everything that we're going to be talking about in the next 20 minutes.

Gardner: Alex, when I hear Greg, I'm thinking business networks, although people in the consumer space might not think of them as business networks. It’s the network effect, it’s intelligence shared, it’s linking things up and allowing the pace to increase and people to share knowledge and activities. What do you see as the crossover from the consumer space in the behaviors and culture of technology and then how does that translate to the business idea of a network?

Alex Atzberger: I was recently in Dubai, and they have a Museum of the Future that they're launching this year. In the Museum of the Future, you can see what it would be like to be going to a doctor to get a new body part to jump higher or move faster. You look at these types of ideas, and the business embraces the same sort of idea. How can I augment my business to actually run smarter and be better? What are things on which I can augment myself to use data better?

You can no longer be an island as a company. You need to share ideas and innovation with others.

You can no longer be an island as a company. You need to share ideas and innovation with others. You need to be connected, and when you're connected, you can transform your business, you can do new things, you can take on new capabilities, and you can augment your business.

Companies ask us, "Now that I'm connected to a network, how can I get data out of that network to improve my business processes and do things better?" That's what they basically call the augmented enterprise, to get augmented intelligence to that business.

Gardner: We're seeing different patterns, not only in adoption, but expectations. People are seeing a mobile device tied to a cloud that has deep learning capabilities, and feedback loops that are applying the data back and forth. People are becoming ready for the next move. They want the technology to guide them. And they also don't want to take the time to learn a process; it has to be intuitive to them.

So how do these human behavioral aspects of anticipating a proactive technological helping hand impact both us in our consumer space, as well as what we would expect in our business environment?

Simplicity is key

Williams: Simplicity is absolutely key to all technology. We have to think about the end user. The end user or the customer is always the most important thing in any kind of technology process.

Going back to what Alex was talking about in terms of artificial intelligence (AI), what it’s going to allow us to do is be a lot more predictive in terms of consumer behavior and customer behavior.

If you look at something like natural language processing now, some of the startups in that space who are working with automotive manufacturers, to go back to my previous example, they will look at trends on social media and elsewhere. They can look at import and export data maybe and they can look at those predictive trends and make predictions about General Motors, their sales in the next quarter.

From the sky, we can look at parking lots at malls like Target, Costco, and Walmart and we can make predictions about how the quarterly earnings report for Walmart or whatever is going to be pretty strong this quarter.

Simplicity is absolutely key to all technology. We have to think about the end user.

What we are looking at is this constant connecting of the dots, and to Alex’s point, this incredible accumulation of data. That’s the real tough thing for businesses right now. I don’t think there’s any business out there that doesn’t understand the value of data. This phrase "big data" is one that you'll hear at every single conference, but how can we possibly parse value out of that? How can we use that data in a predictive way, rather than as a lagging indicator?

Most businesses have used data as a historical indicator. So, it's looking at sales reports or whatever other data is important within your organization. How we can use all those external factors is going to become increasingly important for businesses. Can we see how our competitors are doing by looking at the job postings that they have maybe? How can we see what their next move is in terms of manufacturing by looking at their import/export data? Can we look at the amount of money they're spending on Google AdWords and see what keywords they're spending money on?

As I said previously, it’s about connecting of the dots and bringing this information together, and also figuring it out, having someone within your organization who's not going to get overwhelmed by this data, but is curating it, and knows what’s important and what’s not important to the enterprise, because a lot of it isn’t.

Gardner: User experience plays a huge role in how we can consume and make good on this technology, on this data, on this analysis. What Greg said about simplicity can be deceiving. It might seem simple to the end user, but an awful lot has to happen in order for that effect to take place.

So Alex, one of the interesting things I've seen with SAP Ariba recently is this notion of Guided Buying. I love that word "guided," because you're anticipating the user, heading them off on complexity, but what does it take behind-the-scenes to actually make that happen?

Guided Buying

Atzberger: There’s a whole lot that it takes to get this going. The idea of Guided Buying was always that simplicity that all customers are asking us for. It’s really about how I make the user feel empowered and give the power to the user, but at the same time, embed intelligence in the software.

In our cloud applications, we thought through every step of the process, starting with monitoring how users were behaving with the system. So it’s a design thinking approach, and it starts off with deep empathy with the user. That’s the first point.

The second point is understanding what the business actually wants to accomplish, because the business actually runs a business. They have rules, methodologies, things that they want to achieve.

I was with one CPO who told me, "Alex, I look at this beautiful software, but you're making it too easy to buy. I don’t want people to just go out and buy stuff." That’s absolutely a good point, but what we're doing is embedding the logic of the buying in the enterprise into Guided Buying. That’s the difference between B2C and B2B.

The idea of Guided Buying was always that simplicity that all customers are asking us for. It’s really about how I make the user feel empowered and give the power to the user, but at the same time, embed intelligence in the software.

In B2C you can have that beautiful experience. You just want to make the experience so seamless that you drive commerce. In B2B, you want to guide the commerce, to be more relevant and fit your company goals. That requires a slightly different approach to how you solve that problem. We're obviously deeply committed to solving that problem in the context of giving users as much freedom and choice as possible while enabling the business to achieve their goals.

Williams: Alex used a really great phrase and it’s one that we actually had a discussion about in the office, which is the importance of design thinking within organizations. When you think about software or any technology, the user experience is your brand. So, it’s the people experiencing it.

Pretty much in every organization now, the "design brief" is a really important part of the organization. Maybe designers need to be brought in, whether they're software designers or in the B2C space, UX designers. They need to have a seat at the top table these days, because they're such an integral part of defining any kind of brand.

Atzberger: We hire a lot of designers into SAP Ariba, but interestingly, a lot of the engineers come and say they need to think about design as well. So, it’s not like design is still a separate department. At one point, design becomes part of what we call a scrum team that basically builds the software, and an engineer should have a point of view as well in terms of what is good design.

You could argue that there are some sites that don’t necessarily look pretty, but they're really easy to use. So, it’s not just about the visualization and the fonts, etc.; it’s about also how many clicks and the logic behind it. That’s where product people want to be product people. They don’t want to just be engineers or just designers.

Important element

Gardner: I suppose another important element to this is not only that user experience where one-size-fits-all, but a user experience where customization is brought to bear, and because of the technology, because of the intelligence, access to a cloud infrastructure, we can do that. There are examples of customization at the individual worker level, where role-based and policy-based approaches can do that.

We're also seeing with the SAP Ariba cloud, you're bringing master data, vendor data, for example, into the cloud, cleansing it, making it usable, but still keeping it germane to that particular company, so that this isn’t just a business app for everyone. Let’s delve a little bit into this idea of customization specifically to a company and then even down to the individual user. How is that so important now in business applications, Alex?

Atzberger: The premise of the cloud was always speed. What you gave up for the speed was the ability to customize, especially in enterprise systems. What we're now saying is that you can have a level of individuality and things that are important to you, either through configuration or through extending the platform that you're on.

That’s the power of the technology that comes to bear when you look at platforms today. If you look at Amazon Web Services or what SAP is doing with the HANA Cloud Platform, it’s essential, because it gives the capabilities to companies to actually customize further.

At the same time, we have a concept of the private and the public persona, because at the end of the day, there is some data that’s private to a company and then there's data that's publicly shared. We need to be very sensitive of what data is relevant and in what context.

Gardner: Greg, one of the areas where business can get out in front of the technology curve is this idea of customization and anticipatory or predictive analytics’ benefits. It seems that we're only scratching the surface here. When I go on Netflix, they still can’t pick shows that I really want to watch. When I go to Amazon and they have My Box or My Stuff, it's really just things I already bought with a little bit of augmentation.

What we're now saying is that you can have a level of individuality and things that are important to you, either through configuration or through extending the platform that you're on.

If we can take this to the full potential of customization, and I think businesses can because they have access to the data and they can be policy-based and in probably a better way than a mass consumer environment could, what’s the potential here, when the machines can really start getting us customization, predictive analytics, and apply that to how we get productive in our business sense? It strikes me as something quite significant?

Williams: Yeah, it is. I was talking to someone in a California startup who is developing a sales tool. This person worked for many years in a very large enterprise that builds CRM software. His new business is very interesting because he's trying to do what you described. He's trying to do it almost being a search engine for the entire business Internet. I know this has to be verified, but their claim is that they are much more efficient than regular salespeople.

Say you're trying to sell your software product into a telco. You'll spend a lot of time learning about the person who purchases, those services. You'll go to conferences, read blogs, develop networks, and put a lot of effort into this process.

His startup suggests that they'll be able to not only identify the companies that you're able to sell into, but they'll be able to identify the actual individuals. It will become a lot more detailed in terms of this is what they're interested in and this is what they're not interested in. This is the conference that they've been to. Increasingly, we'll have more-and-more intelligence on people, their habits, their preferences, their interests, and their connections.

Creative business

Take your Netflix example. Netflix moves simply from being a content delivery service to being a creative business by looking at this kind of Venn diagram of its users interests. They saw that there was a sweet spot that overlapped with Kevin Spacey, David Fincher, and the original House of Cards from the UK. They saw that there’s this huge amount of people who love those three things. They said, "Great. Let's commission this series."

Every time that users interact with the service, it's helping to improve it. Netflix knows what you watch, when you watch it, where you stop, where you don’t finish, where you fast-forward, and where you rewind. So, they're collecting huge amounts of data that can be used not just to understand consumer behavior, but also to get insights that can be used for decisions around content.

Gardner: So, Alex, translate this to the business environment, the business network that your company is aligned with can be the determiner of how effective this new trend towards customization, anticipation and being more of a science than an art for sales for example that Greg mentioned is. This to me says the right network with the right information is a crucial decision for you. How does that work in terms of companies differentiating themselves based on who they work with in their ecosystem?

What we see a lot is that businesses are connecting to networks to conduct global business, to find new market opportunities, and become much better at actually mining and understanding that data to become more pointed in terms of what solutions they actually want to provide to the market.

Atzberger: First of all, any company that engages in a network and then captures the data to make better business decisions is already on that journey. If you look at the social networks today, if you like three things on Facebook, Facebook knows more about you than your best friend. If you like more than 10 things, Facebook knows more about you than your spouse. That’s the logic, and the same happens in business networks as well.

What we see a lot is that businesses are connecting to networks to conduct global business, to find new market opportunities, and become much better at actually mining and understanding that data to become more pointed in terms of what solutions they actually want to provide to the market.

But we're still at the very beginning of this trend. We're working with companies on enabling Data as a Service, where they leverage the data itself to create more insight into their business, pursue better business opportunities, change their product offering actually and innovate with their supplier base. If we do that, we're impacting real change, and that's absolutely feasible today, but we're still early on.

Gardner: Any examples, Alex, of companies that really get this and that are showing some demonstrable benefits, that are really tagging on innovation to what their businesses were traditionally, but taking it in a new direction based on some of these technological benefits that we’ve talked about -- poster children for innovation perhaps.

Atzberger: When I think about poster children for innovation, I think about companies that are really looking to the network as infrastructure. What are the other things I can do through this network in order to change my business or add new capabilities?

What I love is when we have customers who talk about the fact that they can actually change their industry. Or their entire supply chain. We have a one high-tech manufacturer who thinks about how they can get demand signals much faster to their supplier base so they can actually impact the end customer. I like that thinking a lot.

Gardner: Greg, last thoughts on what's to come, how technology and business combine to transform how we get things done and perhaps even improve our quality of life?

Solving big problems

Williams: That’s obviously the fundamental end result, one hopes, of all technological change -- that people have better lives and we solve big problems. Looking forward, we're going to see, as Alex has been describing, a real joining of the dots. There aren’t necessarily going to be things that are dramatic, but we're going to see increasing amounts of AI, for instance, offering us insights in industries such as healthcare that only machines are capable of determining because of the sheer volume of data that they can analyze.

I was talking to a guy who worked in the security industry recently. They do a lot of work for the Pentagon. He was telling me that they did an analysis of tweets about ISIL during one week in August last year and they noticed that most of them were about security or the security situation in various parts of Northern Iraq and Syria, account promotion, religion, and strategic updates, but then they came across an outlier that they never noticed before.

That’s obviously the fundamental end result, one hopes, of all technological change -- that people have better lives and we solve big problems.

The official ISIS accounts were re-tweeting any mention of female fighters or women in ISIL -- there was clearly a big push by ISIL to recruit women. What happens? Six weeks later, we had the first female suicide bomber in Europe in Paris. Now, those things probably are not linked, but I think we're able to see things in the data now that we have never been able to see before and I think they increasingly will be putting those things to use.

Gardner: I’m afraid we’ll have to leave it there. You’ve been listening to a BriefingsDirect thought leadership podcast discussion on how major new trends and technology are translating into disruption, and for the innovative business -- opportunity. And we’ve heard how technology innovations translate into business impacts, and how consumers and suppliers of services and goods can best prepare.

So please join me in thanking our guests, Greg Williams, Deputy Editor, WIRED UK in London, and Alex Atzberger, President of SAP Ariba.

And a big thank you too to our audience for joining this SAP Ariba-sponsored business innovation thought leadership discussion.

I’m Dana Gardner, Principal Analyst at Interarbor Solutions, your host and moderator. Thanks again for listening, and do come back next time.

Listen to the podcast. Find it on iTunes. Get the mobile app. Download the transcript. Sponsor: SAP Ariba.

Transcript of a discussion on how major new trends and technology are translating into disruption, and for the innovative business -- opportunity. Copyright Interarbor Solutions, LLC, 2005-2016. All rights reserved.

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'Extreme Apps’ Approach to Analysis Makes On-Site Retail Experience King Again

Transcript of a discussion on how technology providers have teamed as an ecosystem to develop new dynamic and rapid analysis capabilities for the retail industry.

Listen to the podcast. Find it on iTunes. Get the mobile app. Download the transcript. Sponsor: Hewlett Packard Enterprise.

Dana Gardner: Hello, and welcome to the next edition of the Hewlett Packard Enterprise (HPE) Voice of the Customer discussion series.

Gardner

I'm Dana Gardner, Principal Analyst at Interarbor Solutions, your host and moderator for this ongoing discussion on IT innovation and how it’s making an impact on people’s lives.

Our next big-data use case discussion explores how technology providers have teamed as an ecosystem to deliver new dynamic and rapid analysis capabilities to the retail industry. 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, 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.

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Built on SAP HANA

To learn more about how individual buyer information and group buying behavior inferences combine to customize the buying experience anywhere and anytime, please join me in welcoming our guest, Frank Wammes, Chief Technology Officer, for Capgemini Continental Europe.

Frank Wammes: Thank you, Dana. It’s great to be on the show.

Gardner: We're delighted to have you with us. Frank, 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.

Wammes

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?

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.

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.

Beacon technology

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.

This is something that we're also actively looking into, making sure that the retention of the consumer will be increased.

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.

New combinations

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.

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HPE and Capgemini Collaborate
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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 comes in with their traditional Business Suite, but the Extreme Apps are explicitly built into the SAP HANA platform.

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.

Adding value

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.

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.

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.

Improving promotions

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.

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.

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.

Better analysis

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.

That’s where the learning on the multiple clients and multiple different retail stores will kick in.

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?

Business cases

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.

They saw the potential to do an increase of about 25 percent, because they could better target the promotions that they gave.

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.

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On Extreme Applications for Retail
Built on SAP HANA

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?

Personalized experience

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.

Technology is not threatening that. If you apply it in the right way, you strengthen it.

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.

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.

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 frank.wammes@capgemini.com 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)

Explore How
HPE and Capgemini Collaborate
On Business Solutions

Gardner: I'm afraid we'll have to leave it there. We've been discussing how technology providers have teamed as an ecosystem to develop new dynamic and rapid analysis capabilities for the retail industry. And we've seen how the Extreme Apps for Retail Initiative puts new knowledge in the hands of on-site sellers to the customized benefit of shoppers at the very point of sale and in real- time.

So please join me in thanking Frank Wammes,Chief Technology Officer for Continental Europe for Capgemini. Thanks so much, Frank.

Wammes: Thank you, Dana.

Gardner: And I'd also like to thank our audience for joining this Voice of the Customer big data use case discussion. I’m Dana Gardner, Principal Analyst at Interarbor Solutions, your host for this ongoing series of Hewlett Packard Enterprise-sponsored interviews. Thanks again for listening, and do come back next time.

Listen to the podcast. Find it on iTunes. Get the mobile app. Download the transcript. Sponsor: Hewlett Packard Enterprise.

Transcript of a discussion on how technology providers have teamed as an ecosystem to develop new dynamic and rapid analysis capabilities for the retail industry. Copyright Interarbor Solutions, LLC, 2005-2016. All rights reserved.

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