Marketing To An Audience Of One: Personalizing My Flavor Profile

Carol Mackenzie

It’s Sunday afternoon, my usual time for the weekly grocery shop. Yes, in person in an actual store. (I buy non-perishables online but still prefer choosing my own meat and fruit.) I have in hand coupons that have been personalized for me, and I’ve dutifully used my Cozi app to organize what I need based on the layout of the store. This is not my favorite task of the week, so I try to be efficient – get in, get it done!

I look closer at the coupons I dug out of my purse. Were these really meant for me? I see there are coupons for several products that I have never bought and don’t have any interest in. As I push the cart around the store, I think further about personalization for groceries. Why can’t it be more like personalized, digitized music, eBooks, even the promotions pushed to me on Facebook? Shouldn’t what I like to eat – the tastes I prefer – drive what retailers and consumer packaged goods (CPG) manufacturers suggest for me to buy?

I get home, pull out the iPad, do a bit of research (glass of vino in hand; it is Sunday afternoon after all), and discover that 87% of food shoppers say taste is the most important driver for selection, according to a 2016 survey of American consumers by the International Food Information Council Foundation. Like me, 59% of shoppers want more personalization. Clearly, I am not alone in my reflections.

What if my taste preferences could drive personalized recipe recommendations, personalize the retail and CPG manufacturers websites I visit, and improve the promotions pushed to me? According to my research, it turns out there is a way to actually “fingerprint” your taste profile using new digital technologies. By answering a few simple questions on foods I prefer, my own “flavorprint” is quickly developed. Based on machine learning and food science, this innovative approach is adding the all-important (yet long-neglected) taste dimension to consumer profiles.

There are clear benefits to this technology, whether you are a grocery shopper, an R&D expert at a CPG company, or a brand manager seeking the next level of personalization. To hear more of the story, come check out our demo theater session, “Build brand loyalty with precision targeting based on flavor preferences,” at SAPPHIRE NOW on May 18th at 4:00pm (session #IN 43493). #sapphirenow2017

Learn more about CPG industry trends at SAPPHIRE NOW in Orlando, Florida (May 15-19). Secure your spot today!

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Carol Mackenzie

About Carol Mackenzie

Carol Mackenzie is vice president of Business Development for Consumer Goods Industry Solutions at Sap. In this role, she works with consumer products firms, partners, and industry thought leaders globally on driving innovation into their operations utilizing the SAP Consumer Products solution portfolio.

Will AI And Machine Learning Spell The End Of Retail As We Know It?

Joerg Koesters

According to IDC, artificial intelligence (AI) is expected to become pervasive across customer journeys, supply networks, merchandizing, and marketing and commerce because it provides better insights to optimize retail execution. For example, in the next two years:

  • 40% of digital transformation initiatives will be supported by cognitive computing and AI capabilities to provide critical, on-time insights for new operating and monetization models.
  • 30% of major retailers will adopt a retail omnichannel commerce platform that integrates a data analytics layer that centrally orchestrates omnichannel capabilities.

One thing is clear: New analytic technologies are expected to radically change analytics—and retail—as we know it.

AI and machine learning defined in a retail context

AI is defined broadly as the ability of computers to mimic human thinking and logic. Machine learning is a subset of AI that focuses on how computers can learn from data without being programmed through the use of algorithms that adapt to change; in other words, they can “learn” continuously in response to new data. We’re seeing these breakthroughs now because of massive improvements in hardware (for example, GPUs and multicore processing) that can handle Big Data volumes and run deep learning algorithms needed to analyze and learn from the data. Ivano Ortis, vice president at IDC, recently predicted:

“Artificial intelligence will take analytics to the next level, and will be the foundation for retail innovation, as reported by one out of every two retailers globally. AI enables scale, automation, and unprecedented precision, and will drive customer experience innovation when applied to both hyper micro customer segmentation and contextual interaction.”

Given the capabilities of AI and machine learning, it’s easy to see how they can be powerful tools for retailers. Now computers can read and listen to data, understand and learn from it, and instantly and accurately recommend the next best action without needing to be explicitly programmed. This is a boon for retailers seeking to accurately predict demand, anticipate customer behavior, and optimize and personalize customer experiences. For example, it can be used to automate:

  • Personalized product recommendations based on data about each customer’s unique interests and buying propensity
  • The selection of additional upsell and cross-sell options that drive greater customer value
  • Chat bots that can drive intelligent and meaningful engagement with customers
  • Recommendations on additional services and offerings based on past and current buying data and customer data
  • Planogram analyses, which support in-store merchandizing by telling people what’s missing, comparing sales to shelf space, and accelerating shelf replenishment by automating reorders
  • Pricing engines used to make tailored, situational pricing decisions

Particularly in the U.S., retailers are already able to collect large volumes of transaction-based and behavioral data from their customers. And as data volumes grow and processing power improves, machine learning becomes increasingly applicable in a wider range of retail areas to further optimize business processes and drive more impactful personalized and contextual consumer experiences and products.

The transformation of retail has already begun

The impacts of AI and machine learning are already being felt. For example:

  • Retailers are predicting demand with machine learning in combination with IoT technologies to optimize store businesses and relieve workforces.
  • Advertisements are being personalized based on in-store camera detections and taking over semi-manual “clienteling” tasks of store employees.
  • Retailers can monitor wait times in checkout lines to understand store traffic and merchandising effectiveness at the individual store level—and then tailor assortments and store layouts to maximize basket size, satisfaction and sell through.
  • Systems can now recognize and predict customer behavior and improve employee productivity by turning scheduled tasks into on-demand activities.
  • Camera systems can detect the “fresh” status of perishable products before on-site employees.
  • Brick-and-mortar stores are automating operational tasks, such as setting shelf pricing, determining product assortments and mixes, and optimizing trade promotions.
  • In-store apps can tell how long a customer has been in a certain aisle and deliver targeted offers and recommendations (via his or her mobile device) based on data about data about personal consumption histories and preferences.

A recent McKinsey study provided examples that quantify the potential value of these technologies in transforming how retailers operate and compete. For example:

  • Retailer supply chain operations that have adopted data and analytics have seen up to a 19% increase in operating margin over the last five years. Using data and analytics to improve merchandising including pricing, assortment, and placement optimization is leading to an additional 16% in operating margin improvement.
  • Personalizing advertising is one of the strongest use cases for machine learning today. Additional retail use cases with high potential include optimizing pricing, routing, and scheduling based on real-time data in travel and logistics, as well as optimizing merchandising strategies.

Exploiting the full value of data

Thin margins (especially in the grocery sector) and pressure from industry-leading early adopters such as Amazon and Walmart have created strong incentives to put customer data to work to improve everything from cross-selling additional products to reducing costs throughout the entire value chain.

But McKinsey has assessed that the U.S. retail sector has only realized 30-40% of the potential margin improvements and productivity growth their analysts envisioned in 2011—and a large share of the value of this growth has gone to consumers through lower prices. So thus far, only a fraction of the potential value from AI and machine learning has been realized.

According to Forbes, U.S. retailers have the potential to see a 60%+ increase in net margin and 0.5–1.0% annual productivity growth. But there are major barriers to realizing this value include lack of analytical talent and siloed data within companies.

This is where machine learning and analytics kick in. AI and machine learning can help scale the repetitive analytics tasks required to drive better leverage of the available data. When deployed on a companywide, real-time analytics platform, they can become the single source of truth that all enterprise functions rely on to make better decisions.

How will this change retail analytics?

So how will AI and machine learning change retail analytics, as they are currently defined? We expect that AI and machine learning won’t kill analytics as we know it, but rather give analytics a new and even more impactful role in driving the future of retail. For example, we anticipate that:

  • Retailers will include machine learning algorithms as an additional factor in analyzing and monitoring business outcomes in relation to machine learning algorithms.
  • They will use AI and machine learning to sharpen analytic algorithms, detect more early warning signals, anticipate trends and have accurate answers before competitors do.
  • Analytics will happen in real time and act as the glue between all areas of the business.
  • Analytics will increasingly focus on analyzing manufacturing machine behavior, not just business and consumer behavior.

Ivano Ortis at IDC authored a recent report, “Why Retail Analytics are a Foundation for Retail Profits,” in which he provides further insights on this topic. He notes how retail leaders will use new kinds of analytics to drive greater profitability, further differentiate the customer experience, and compete more effectively:

“In conclusion, commerce and technology will converge, enabling retailers to achieve short-term ROI objectives while discovering untapped demand. But implementing analytics will require coordination across key management roles and business processes up and down each retail organization. Early adopters are realizing demonstrably significant value from their initiatives—double-digit improvements in margins, same-store and ecommerce revenue, inventory positions and sell-through, and core marketing metrics. A huge opportunity awaits.

So how do you see your retail business adopting advanced analytics like AI and machine learning? I encourage you to read IDC’s report in detail, as it provides valuable insights to help you invest in—and apply—new kinds of analytics that will be essential to profitable growth.

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Joerg Koesters

About Joerg Koesters

Joerg Koesters is the Head of Retail Marketing and Communication at SAP. He is a Technology Marketing executive with 20 years of experience in Marketing, Sales and Consulting, Joerg has deep knowledge in retail and consumer products having worked both in the industry and in the technology sector.

We Were Kings (Or When Things Went To Zero)

Spiros Margaris

The title of this post was inspired by the 1996 documentary “When We Were Kings,” about the heavyweight fight of 1974 between two boxing legends, Muhammad Ali and George Foreman. In the not-so-distant future, it will also be a fitting phrase for many in the banking and insurance industries.

Readers may ask why I am talking about banking and insurance in such doomsday terms. My bleak forecast does not stem from the notion behind the common fintech (financial technology) and insurtech (insurance technology) industry pitch that they will change their respective industries with innovation and better customer experiences, although I firmly believe that some of the startups will cause significant pain to the incumbents and will indeed change their respective industries. One day, some of the existing and as-yet-unlaunched fintech and insurtech companies will also become incumbents that other startups aim to disrupt.

The real threat to the financial industry will come from a radical approach to penetrate the financial market—an approach that I believe has not yet been addressed or even conceived by the competition. The emphasis is clearly on “yet.”

What is this new concept? It is simply this: offering financial services at or below cost. I have mooted this idea at many think tank events, and I thought I should write it down to share it more broadly. It is, and should be, a terrifying thought for many, and I strongly believe this approach will be implemented in the near future. It will bring many of the incumbents to their knees, unless they prepare for what is to come by investing in technology and adapting radical business models.

People talk about the limited impact of fintech and insurtech on the incumbent business model. I must agree that at this point many startups have little influence, if you look only at the customers they have taken away from incumbents. What the startups are already doing, however, is forcing many incumbents to lower their fees to better match what the smaller players offer to their clients.

Moreover, startups have also changed customers’ expectations of the user experience. Startups will also use artificial intelligence and machine learning to compete against the established financial players that have more resources—such as money, data and clients—at hand to compete. There is no way around investing in AI and machine learning to compete successfully against tech-savvy competitors. Many startups and large companies already use machine learning algorithms to build better credit risk models, predict bad loans, detect fraud, anticipate financial market behavior, improve customer relationship management, and provide more customized services to their clients. Arguably, the biggest effect of startups is that they continuously put pressure on incumbent profit margins. Startups will continue to try to change the status quo because they smell blood in the incumbent water.

The real and biggest threat to incumbents will likely originate from tech giants, such as Amazon, Apple and Facebook, and other big non-tech companies that have large customer and employee bases. These organizations will use their customers and employees to sell banking and insurance solutions, and the big financial institutions will become at best dumb pipes. The technical approaches to doing business within the fintech and insurtech industries may provide some of the tools tech giants and other large companies need to execute this strategy.

I know some readers will say that regulators will stop any attempt by non-traditional players to provide many banking and insurance services. However, I do not think regulators can or will stop the new competitors, because these companies will either obtain the necessary licenses to operate or have a bank or insurer provide third-party financial services to them. This strategy is not unlike the way some fintech challenger banks use the licenses of an existing bank to operate.

Why should we expect this scenario of financial industry disruption to happen? In our case, we all seem to agree that the tech giants are the ones to fear because of the Big Data platform and technology knowledge they possess. In addition, tech giants have several advantages, such as the trust factor and the constant interaction with satisfied customers. Furthermore, studies have shown that millennials would prefer to bank with tech giants such as Amazon, Facebook, or Google than with the existing banking players. And last but not least are the tech giants and startups that keep setting the bar higher for exceptional customer experience (for instance Apple’s simplicity or Amazon’s instant gratification) and shape the client behavior and expectations, not the incumbents.

All that speaks to tech giants’ favorable circumstances as serious competitors that are not yet ready to come in at full speed and hit the financial industry broadly, but it does not point to the need to fear an extreme disruption as I projected. I do not believe we will see those tech giants providing whole-spectrum financial services anytime soon, but they have the potential to offer services in certain segments, such as providing payment, lending, or insurance options for their customers and employees.

What is terrifying to imagine is a situation in which tech giants or other big companies provide financial service solutions at or below production costs. No, that is not a typo; I mean providing financial services for nothing—for free.

If we take this scenario to its extreme—that is, selling banking or insurance services for nothing (yes, for zero pounds, euros, dollars, or renminbi)—then we have a situation in which financial institutions in their present forms will die or be reduced to shadows of their current selves.

That can and will happen, and here’s why: Large companies could do exactly that—sell at or below cost—to win or keep customers. The new competitors would not need to earn money and could even afford to lose money in offering financial solutions if these features entice customers and new potential clients to use the companies’ core offerings. Remember that Facebook, for instance, earns the biggest portion of their profits through advertising because they have created a great platform through which people love to interact. Financial solutions would be just another great offering (especially if they are offered for free) to entice many people to join the tech giants’ ecosystems.

Alternatively, car companies such as GM could provide their employees and customers with very cheap or no-cost (no cost to customers, at cost for the company) banking or insurance solutions. Don’t forget that banking and insurance solutions can be provided at very little cost as white-label services from third parties that already have all the necessary licenses, technology and infrastructure.

All is not lost for banks and insurers, but it will be very hard for them to compete against savvy tech giants on their technological home turf. The financial industry must think fast to find ways to compete before their business oxygen runs out.

One solution that banks and insurers should pursue aggressively is to embrace the fintech and insurtech industries for their innovative business spirit and fast, direct execution approach to new ideas. That means financial institutions should buy what they can or partner with startups to make up for all the shortcomings that legacy brings. Size and regulation will not be enough to protect incumbent financial institutions against new competitors, as we have seen in many other industries.

Another idea might be for financial institutions to place advertisements on their websites or apps to compensate for loss of profit margins. I do not think this is the only solution, but financial institutions must innovate beyond their core areas of expertise and standard industry practices. Why do you think Amazon, Uber, and Airbnb have been so successful at disrupting their industries? Because they thought and acted as if they had nothing to lose and everything to gain.

The “at or below cost” approach to financial service solutions is not a far-fetched scenario for tech giants and other companies that are trying to find new ways to attract and keep clients. The banking and insurance industries must at least get very comfortable with the idea that low-cost or free financial services are coming.

A tsunami is often unnoticed in the open sea, but once it approaches the shore, it causes the sea to rise in a massive, devastating wave. The financial industry needs to determine if the threat by tech giants and non-tech companies is a small wave or a tsunami and prepare accordingly. My recommendation to all financial institutions is this: You’d better prepare for a tsunami, even if all you see is a small wave on the horizon.

For more on digital transformation in the insurance industry, see Preparing For Digital Transformation: What The Insurance Industry Needs To Know.

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Spiros Margaris

About Spiros Margaris

Spiros Margaris is a Venture Capitalist & Thought Leader in the FinTech and Insurtech scene. He was ranked No. 1 FinTech and No. 2 Insurtech global influencer by Onalytica and regularly appears in the top three positions in several industry rankings. He is also ranked worldwide the No. 11 AI influencer by Jay Palter Social Advisory. Previously, he worked in banking and money management (hedge funds) and launched two start-ups in New York, one of which would nowadays be termed a fintech. He is a frequent speaker at international fintech and insurtech conferences and publishes articles on his innovation proposals and thought leadership. www.MargarisAdvisory.com Twitter @SpirosMargaris

Diving Deep Into Digital Experiences

Kai Goerlich

 

Google Cardboard VR goggles cost US$8
By 2019, immersive solutions
will be adopted in 20% of enterprise businesses
By 2025, the market for immersive hardware and software technology could be $182 billion
In 2017, Lowe’s launched
Holoroom How To VR DIY clinics

From Dipping a Toe to Fully Immersed

The first wave of virtual reality (VR) and augmented reality (AR) is here,

using smartphones, glasses, and goggles to place us in the middle of 360-degree digital environments or overlay digital artifacts on the physical world. Prototypes, pilot projects, and first movers have already emerged:

  • Guiding warehouse pickers, cargo loaders, and truck drivers with AR
  • Overlaying constantly updated blueprints, measurements, and other construction data on building sites in real time with AR
  • Building 3D machine prototypes in VR for virtual testing and maintenance planning
  • Exhibiting new appliances and fixtures in a VR mockup of the customer’s home
  • Teaching medicine with AR tools that overlay diagnostics and instructions on patients’ bodies

A Vast Sea of Possibilities

Immersive technologies leapt forward in spring 2017 with the introduction of three new products:

  • Nvidia’s Project Holodeck, which generates shared photorealistic VR environments
  • A cloud-based platform for industrial AR from Lenovo New Vision AR and Wikitude
  • A workspace and headset from Meta that lets users use their hands to interact with AR artifacts

The Truly Digital Workplace

New immersive experiences won’t simply be new tools for existing tasks. They promise to create entirely new ways of working.

VR avatars that look and sound like their owners will soon be able to meet in realistic virtual meeting spaces without requiring users to leave their desks or even their homes. With enough computing power and a smart-enough AI, we could soon let VR avatars act as our proxies while we’re doing other things—and (theoretically) do it well enough that no one can tell the difference.

We’ll need a way to signal when an avatar is being human driven in real time, when it’s on autopilot, and when it’s owned by a bot.


What Is Immersion?

A completely immersive experience that’s indistinguishable from real life is impossible given the current constraints on power, throughput, and battery life.

To make current digital experiences more convincing, we’ll need interactive sensors in objects and materials, more powerful infrastructure to create realistic images, and smarter interfaces to interpret and interact with data.

When everything around us is intelligent and interactive, every environment could have an AR overlay or VR presence, with use cases ranging from gaming to firefighting.

We could see a backlash touting the superiority of the unmediated physical world—but multisensory immersive experiences that we can navigate in 360-degree space will change what we consider “real.”


Download the executive brief Diving Deep Into Digital Experiences.


Read the full article Swimming in the Immersive Digital Experience.

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Kai Goerlich

About Kai Goerlich

Kai Goerlich is the Chief Futurist at SAP Innovation Center network His specialties include Competitive Intelligence, Market Intelligence, Corporate Foresight, Trends, Futuring and ideation. Share your thoughts with Kai on Twitter @KaiGoe.heif Futu

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Jenny Dearborn: Soft Skills Will Be Essential for Future Careers

Jenny Dearborn

The Japanese culture has always shown a special reverence for its elderly. That’s why, in 1963, the government began a tradition of giving a silver dish, called a sakazuki, to each citizen who reached the age of 100 by Keiro no Hi (Respect for the Elders Day), which is celebrated on the third Monday of each September.

That first year, there were 153 recipients, according to The Japan Times. By 2016, the number had swelled to more than 65,000, and the dishes cost the already cash-strapped government more than US$2 million, Business Insider reports. Despite the country’s continued devotion to its seniors, the article continues, the government felt obliged to downgrade the finish of the dishes to silver plating to save money.

What tends to get lost in discussions about automation taking over jobs and Millennials taking over the workplace is the impact of increased longevity. In the future, people will need to be in the workforce much longer than they are today. Half of the people born in Japan today, for example, are predicted to live to 107, making their ancestors seem fragile, according to Lynda Gratton and Andrew Scott, professors at the London Business School and authors of The 100-Year Life: Living and Working in an Age of Longevity.

The End of the Three-Stage Career

Assuming that advances in healthcare continue, future generations in wealthier societies could be looking at careers lasting 65 or more years, rather than at the roughly 40 years for today’s 70-year-olds, write Gratton and Scott. The three-stage model of employment that dominates the global economy today—education, work, and retirement—will be blown out of the water.

It will be replaced by a new model in which people continually learn new skills and shed old ones. Consider that today’s most in-demand occupations and specialties did not exist 10 years ago, according to The Future of Jobs, a report from the World Economic Forum.

And the pace of change is only going to accelerate. Sixty-five percent of children entering primary school today will ultimately end up working in jobs that don’t yet exist, the report notes.

Our current educational systems are not equipped to cope with this degree of change. For example, roughly half of the subject knowledge acquired during the first year of a four-year technical degree, such as computer science, is outdated by the time students graduate, the report continues.

Skills That Transcend the Job Market

Instead of treating post-secondary education as a jumping-off point for a specific career path, we may see a switch to a shorter school career that focuses more on skills that transcend a constantly shifting job market. Today, some of these skills, such as complex problem solving and critical thinking, are taught mostly in the context of broader disciplines, such as math or the humanities.

Other competencies that will become critically important in the future are currently treated as if they come naturally or over time with maturity or experience. We receive little, if any, formal training, for example, in creativity and innovation, empathy, emotional intelligence, cross-cultural awareness, persuasion, active listening, and acceptance of change. (No wonder the self-help marketplace continues to thrive!)

The three-stage model of employment that dominates the global economy today—education, work, and retirement—will be blown out of the water.

These skills, which today are heaped together under the dismissive “soft” rubric, are going to harden up to become indispensable. They will become more important, thanks to artificial intelligence and machine learning, which will usher in an era of infinite information, rendering the concept of an expert in most of today’s job disciplines a quaint relic. As our ability to know more than those around us decreases, our need to be able to collaborate well (with both humans and machines) will help define our success in the future.

Individuals and organizations alike will have to learn how to become more flexible and ready to give up set-in-stone ideas about how businesses and careers are supposed to operate. Given the rapid advances in knowledge and attendant skills that the future will bring, we must be willing to say, repeatedly, that whatever we’ve learned to that point doesn’t apply anymore.

Careers will become more like life itself: a series of unpredictable, fluid experiences rather than a tightly scripted narrative. We need to think about the way forward and be more willing to accept change at the individual and organizational levels.

Rethink Employee Training

One way that organizations can help employees manage this shift is by rethinking training. Today, overworked and overwhelmed employees devote just 1% of their workweek to learning, according to a study by consultancy Bersin by Deloitte. Meanwhile, top business leaders such as Bill Gates and Nike founder Phil Knight spend about five hours a week reading, thinking, and experimenting, according to an article in Inc. magazine.

If organizations are to avoid high turnover costs in a world where the need for new skills is shifting constantly, they must give employees more time for learning and make training courses more relevant to the future needs of organizations and individuals, not just to their current needs.

The amount of learning required will vary by role. That’s why at SAP we’re creating learning personas for specific roles in the company and determining how many hours will be required for each. We’re also dividing up training hours into distinct topics:

  • Law: 10%. This is training required by law, such as training to prevent sexual harassment in the workplace.

  • Company: 20%. Company training includes internal policies and systems.

  • Business: 30%. Employees learn skills required for their current roles in their business units.

  • Future: 40%. This is internal, external, and employee-driven training to close critical skill gaps for jobs of the future.

In the future, we will always need to learn, grow, read, seek out knowledge and truth, and better ourselves with new skills. With the support of employers and educators, we will transform our hardwired fear of change into excitement for change.

We must be able to say to ourselves, “I’m excited to learn something new that I never thought I could do or that never seemed possible before.” D!

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