Machine Learning And Predictive Analytics In B2B

Kevin Carlson

A little over three years ago, I wrote a series of posts about the pressures online retail was placing on traditional brick-and-mortar stores. From behemoths of the past, like Borders, to those at risk today such as Sears, companies that failed to react to broad changes in consumer expectations have closed their doors or are struggling to keep them open.

Let’s be fair, though. Physical retail isn’t doomed. It’s a piece of the puzzle in the omnichannel world. It’s just not the centerpiece anymore. Companies that are succeeding are paying close attention to customer data, learning or predicting what their customers’ actions will be and driving customer experience to an entirely new level. A level that is the new baseline for how to compete and succeed in retail.

And if you’re in the B2B space, I hope you’re paying attention.

Over the past 20 years, I’ve helped many companies implement both B2C and B2B sites. The challenges are different in some ways, but the expectations of those interacting with a site have been converging for years. And as companies like Amazon continue to expand in the B2B world, you can be sure that the demand for a “smarter” experience will grow.

Most companies in the B2B space are using Web analytics to track the basics: page views, visitors, bounce rate, and similar metrics. Today, that is the low bar.

The true value that can be extracted from the way in which customers interact with a site isn’t lurking in an obscure Google Analytics report. It’s buried in the wealth of data that can be collected from customer interactions. For companies both small and large, the world of data science can be intimidating, and knowing where to start can leave one’s head spinning.

But ignore this at your own peril.

OK, so a full discussion of data science is beyond what’s possible in a blog, but here are some key points that all in the B2B space should know:

  • Being able to respond quickly and accurately to changes in customer behavior is critical. The days of taking a week or longer to peruse reports and figure out what your customers want are long gone.
  • Predictive analytics and machine learning are here to stay, and companies that employ these techniques will outmaneuver those that don’t.
  • Your commerce platform may or may not be able to gather all the data you need. It’s a piece of the puzzle and there are other sources to consider.

So how can B2B companies use predictive analytics and machine learning? Here are a few use cases that I’ve seen.

Customer classification

Understanding behavioral tendencies of customers and grouping them with similar customers can be an effective way to focus marketing and merchandising efforts. One classification that most have seen is the “VIP customer,” but too often, B2B companies simply decide, using a single metric, what constitutes a VIP. One implementation I’ve seen simply classifies as a VIP any customer that purchases more than $100; a little badge is placed on the customer’s online profile. That’s not really classification; it’s closer to gamification, which gives the customer some sort of sense of achievement and hopefully boosts their loyalty.

Real classification is based on a set of data features measured across all recent customer activity. In other words, whether someone is a real VIP customer or not is going to change when compared to the behavior of the entire customer base.

Knowing customer classification and being able to use a classification model to predict a group to which a new customer is likely to belong can help greatly in determining which promotions to show a customer while they’re on the site or via email. In a recent machine learning implementation for a B2B firm, we grouped customers into several segments by using data from a recent time period:

  • VIP shopper: The highest tier of customers based on value and number of conversions.
  • Engaged shopper: A large number of visits, an active cart, numerous product views, and at least one conversion.
  • Window shopper: Several visits, numerous product views

This information was not shared with the customer – it’s not a public distinction, rather it’s an internal indication of how to interact with a customer. Having this information allows marketers to display targeted promotions onsite during a visit. For example, if a customer falls into the “Engaged Shopper” classification and has an active cart that is above the average order value (AOV), a coupon for free shipping or a discount could be displayed to move this shopper toward conversion.

It’s also important to note that models aren’t static. They must be recalculated frequently. In the B2B firm’s implementation, the models recalculate every few hours to ensure they are as current and accurate as possible.

Prescriptive product and content placement

There are many reasons buying behavior may change. Seasonality is one such reason. Other reasons that may drive short-term behavioral changes are weather, news, shortages, and manufacturer promotions. Using models to detect conditions that are “outside of expectations,” and adjusting site merchandising to respond to time-sensitive anomalies, are becoming more common. This technique can be used to alter search results, homepage item placement, and category item placement, and guide users to product information when a search for a competitive product is conducted.

Does a given product perform better when visible at the top of the homepage? Some do, some don’t. Detecting optimal placement for products, given a recent history of activity such as conversions and cart additions, can be accomplished through machine learning. When introducing a new product, similar models can be used to predict best placement for optimal performance based on product attributes.

Improving personalization

Personalization on most B2C sites has a long way to go. And on B2B sites, it’s still in its infancy. While it’s possible to group shoppers into a cohort and show them similar items, machine learning makes it possible to make these cohorts smaller, approaching a more unique experience for your B2B customer. This can be especially impactful when there are multiple B2B buyers from a single customer.

To begin moving toward a more unique experience, machine learning models can be developed starting at a high level, then progressing to more granular levels that deliver unique insights into a customer.

For example, beginning with geolocation as a factor, a B2B seller of industrial HVAC equipment should feature different products for customers in Minnesota vs. those in Florida. The buying seasons are not only different, but events in Florida, such as an impending hurricane, may influence buying behavior in the short term. A properly designed model can help spot these changes and alert the B2B marketer to changes that may require a change in site merchandising for a geographical region.

To add on, a company could develop models that can predict the optimal sort order for search results for a customer, the ideal products and categories to feature for them, along with suggested promotions based on their recent behavior.

In summary

It’s a fascinating time to be working in commerce and with data in particular. The convergence of low-cost cloud-based computing and the abundance of data available from a wealth of sources (not the least of which is a company’s B2B site) provide a lot of actionable intelligence with a lower investment – by orders of magnitude – than a decade ago. That not only puts this technology within reach, it positions it to become a core part of how you relate to your customers and how you operate your business.

Those that ignore the call today may become tomorrow’s Borders.

For more on setting your business up to better compete, see Why Machine Learning and Why Now?

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Bringing Ethics To Artificial Intelligence With Crowdsourced Innovation

Jochen Schneider

The ongoing debate over the value of artificial intelligence (AI) swings between two extremes. There’s a utopian vision, where AI results in limitless prosperity and health, the end of poverty and hunger, elimination of crime, human immortality, colonization of the galaxy, and the Omega Point at which people become all-knowing and ever-present. But eventually, a skeptical vision emerges as people anticipate a grim future of human enslavement into mass poverty and endless despair, terminating centuries of scientific, technological, and social progress.

As the benevolent potential of AI brightens, the shadows of unethical possibilities become longer and darker. However, digging deeper into this black-and-white dialogue can help companies blur the lines between the two extremes to deliver significant AI-driven advantages for everyone.

Blending the extremes of AI helps create a future of more human-centered experiences

An excellent way to assess AI is to look through the lens of the end user and customer. Think about it: Would you want to enter a store without a single retail associate? Sure, it’s quick and efficient to have a robot retrieve the item, make algorithm-driven product suggestions, and complete the transaction. But this kind of shopping experience eliminates the personal interaction that often motivates buyers to visit a store in the first place instead of shopping online.  As shoppers peruse the store floor, talk to an associate about product options, and get honest opinions, they often feel more comfortable about the purchasing decisions they’re making. So, human emotion rather than fact becomes the more significant influence.

The same degree of reflection is needed when considering AI for any aspect of the business. While the technology can optimize processes to make work faster and simpler, decision makers often miss the opportunity to create more human-centered experiences, which later increase employee and customer engagement, unify people in a collaborative environment, and build a culture of trust. It’s not enough to adopt technology as a replacement for human tasks; businesses need to think about the customer experience and use existing capabilities and resources to interact more closely with every customer.

Companies can engage in this line of thinking by leveraging a platform for digital innovation. Executives, stakeholders, and technology experts can work together to seamlessly integrate AI capabilities into the business network, now and in the future, to proactively respond to the behaviors and needs of customers, employees, and suppliers. Through open dialogue and multi-perspective thinking, the team can uncover new value by creating improved processes, truly unique shopping experiences, industry-disruptive business models, or entirely new companies.

As knowledge about AI technology grows, this unified approach can help companies become more efficient and engaging without losing the human touch that people demand. The platform can also safeguard the privacy of employee and customer information by providing smart apps and processes that comply with proper business conduct policies and regulations.

In the near future, the government will need to pass legislation to help prevent harmful and immoral outcomes that may become possible with AI. However, companies can’t stand still and hope for the best until then. AI technology and market dynamics will undoubtedly continue to change. And for businesses, this means they must evolve with those shifts – responsibly, ethically, and sustainably – to establish a relationship of trust and loyalty with their potential and existing employees and customers.

If we want to retain humanity’s value in an increasingly automated world, we need to start recognizing and nurturing skills that are uniquely human. Learn about Human Skills for the Digital Future.

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Jochen Schneider

About Jochen Schneider

Jochen is chief digital officer at SAP Innovative Business Solutions in EMEA/ MEE. He dedicates his time to innovation, the next generation’s demand and influence. First and foremost, he processes knowledge on how to make innovation real for customers. Second, he cares about how young professionals can prepare for business and influence the future of work.
He studies modern leadership, entrepreneurship, and exponential growth drivers.
In his present role, he is responsible for devising new go-to-market strategies for innovative custom-tailored solutions. His focus is on solutions that leverage innovation technology, foster new business processes, or business models, consequently growing customer value.

Follow him on LinkedIn: https://www.linkedin.com/in/sjochen/

Faster Product Recalls: How To Reach More Customers

Eric Somitsch

There is nothing more disruptive to a manufacturer than dealing with a quality issue. Production lines come to a screeching halt if the severity of the problem could cause the product to injure a customer. This situation is especially true for products that cause immediate harm such as cars, toys, pharmaceutical products, and especially food/beverage products. In the United States, about 2 million illnesses occur annually caused by contaminated poultry and meat products that reach consumers’ plates, according to the Pew Charitable Trusts.

For manufacturers, the best case scenario is that the problem is discovered before one product gets sent out. Unfortunately, safety and health issues could take days, weeks, months, and even years before they are discovered.

The complexities of consumer recalls

Once a product problem is discovered, informing the public is a top priority. Yet again, as a manufacturer, you can run into a roadblock. Reaching as many customers as you can has become a daunting task, since sometimes there is no way of knowing how many products have been sold or used. Also, finding a means to contact people about the recall requires a concentrated effort.

Most manufacturers normally post product recalls on the main company website. You may also issue recall information to the media and through print media. Local, state, and federal governments may become involved either through contact from the manufacturer, through other government organizations, or by discovering the product issue during inspections of the manufacturer’s facility. They then will issue a press release, hold a news conference, and post the recall on their website. Yet not all recalls are announced to the general public.

Even if you post the information online and talk about it in the media, there still may be people who miss the recall alert. They may be at work during the hours when the news is on television or at home watching a different channel. They may also have no reason to visit the manufacturer’s website for product updates or visit any online sites where they will learn about the recall.

In these instances, you need a better way of reaching out to customers. A more direct method of communication can lower the risks of people using unsafe products. You can provide information on how a customer should return the product or how to get a refund. Other times, you can provide emergency instructions in case they use a defective product and become injured or eat a food that makes them sick, such as instructing them to go to the emergency room or see their doctor immediately.

Product registration platforms streamline the emergency recall process

What would you say if there was a central platform where you can connect with consumers who have purchased your products? With this central platform, you can immediately send out information about a particular batch of items that have quality issues. This information can go to the person’s email or be sent via SMS to their mobile devices. You could even use push notifications through company mobile apps or through app messaging systems such as WhatsApp.

Through this platform, you could provide all the information that the person needed to deal with the defective product. Your company can tell people how to return products to stores where they were purchased. You can also warn people to check food packages in their refrigerators for batch numbers that would indicate contaminated food.

Such a central platform can do more than what news media sources can: reach people anywhere and at any time. A manufacturer would know who to target with their emergency recall information by allowing customers to register their product through the centralized platform. They could type the serial number into the registration form on the platform or simply scan the product’s barcode using a mobile app on their smartphone.

But what about people who don’t want to share personal information during registration? With the central platform, they could register their purchase anonymously. The platform would record the product’s serial number and the mobile app’s unique identifier. When a recall occurs, the central platform issues the recall alert based on serial number batches and sends out the message through the mobile app.

Reaching more people to increase safety during product recalls

While ensuring that only safe and high-quality products reach consumers is manufacturers’ primary goal, appropriate risk management when issues are discovered needs to also take center stage in a company’s policies and procedures.

Seeking out new technologies that will help alert people about product recalls can be a tremendous benefit. Central platforms, anonymous registrations, and mobile apps can be used by these manufacturers to reach everyone who is affected by the emergency recall. These central platforms can lead to greater alert coverage so people are protected if a quality issue is discovered.

Learn how to bring new technologies and services together to power digital transformation by downloading The IoT Imperative for Consumer Industries. Explore how to bring Industry 4.0 insights into your business today by reading Industry 4.0: What’s Next?

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Eric Somitsch

About Eric Somitsch

Eric Somitsch is Senior Director of Solution Management for Agribusiness and Commodity Management at SAP.

Human Skills for the Digital Future

Dan Wellers and Kai Goerlich

Technology Evolves.
So Must We.


Technology replacing human effort is as old as the first stone axe, and so is the disruption it creates.
Thanks to deep learning and other advances in AI, machine learning is catching up to the human mind faster than expected.
How do we maintain our value in a world in which AI can perform many high-value tasks?


Uniquely Human Abilities

AI is excellent at automating routine knowledge work and generating new insights from existing data — but humans know what they don’t know.

We’re driven to explore, try new and risky things, and make a difference.
 
 
 
We deduce the existence of information we don’t yet know about.
 
 
 
We imagine radical new business models, products, and opportunities.
 
 
 
We have creativity, imagination, humor, ethics, persistence, and critical thinking.


There’s Nothing Soft About “Soft Skills”

To stay ahead of AI in an increasingly automated world, we need to start cultivating our most human abilities on a societal level. There’s nothing soft about these skills, and we can’t afford to leave them to chance.

We must revamp how and what we teach to nurture the critical skills of passion, curiosity, imagination, creativity, critical thinking, and persistence. In the era of AI, no one will be able to thrive without these abilities, and most people will need help acquiring and improving them.

Anything artificial intelligence does has to fit into a human-centered value system that takes our unique abilities into account. While we help AI get more powerful, we need to get better at being human.


Download the executive brief Human Skills for the Digital Future.


Read the full article The Human Factor in an AI Future.


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About Dan Wellers

Dan Wellers is founder and leader of Digital Futures at SAP, a strategic insights and thought leadership discipline that explores how digital technologies drive exponential change in business and society.

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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How Manufacturers Can Kick-Start The Internet Of Things In 2018

Tanja Rueckert

Part 1 of the “Manufacturing Value from IoT” series

IoT is one of the most dynamic and exciting markets I am involved with at SAP. The possibilities are endless, and that is perhaps where the challenges start. I’ll be sharing a series of blogs based on research into knowledge and use of IoT in manufacturing.

Most manufacturing leaders think that the IoT is the next big thing, alongside analytics, machine learning, and artificial intelligence. They see these technologies dramatically impacting their businesses and business in general over the next five years. Researchers see big things ahead as well; they forecast that IoT products and investments will total hundreds of billions – or even trillions – of dollars in coming decades.

They’re all wrong.

The IoT is THE Big Thing right now – if you know where to look.

Nearly a third (31%) of production processes and equipment and non-production processes and equipment (30%) already incorporate smart device/embedded intelligence. Similar percentages of manufacturers have a company strategy implemented or in place to apply IoT technologies to their processes (34%) or to embed IoT technologies into products (32%).

opportunities to leverage IoTSource:Catch Up with IoT Leaders,” SAP, 2017.

The best process opportunities to leverage the IoT include document management (e.g. real-time updates of process information); shipping and warehousing (e.g. tracking incoming and outgoing goods); and assembly and packaging (e.g. production monitoring). More could be done, but figuring out where and how to implement the IoT is an obstacle for many leaders. Some 44 percent of companies have trouble identifying IoT opportunities and benefits for either internal processes or IoT-enabled products.

Why so much difficulty in figuring out where to use the IoT in processes?

  • No two industries use the IoT in the same way. An energy company might leverage asset-management data to reduce costs; an e-commerce manufacturer might focus on metrics for customer fulfillment; a fabricator’s use of IoT technologies may be driven by a need to meet exacting product variances.
  • Even in the same industry, individual firms will apply and profit from the IoT in unique ways. In some plants and processes, management is intent on getting the most out of fully depreciated equipment. Unfortunately, older equipment usually lacks state-of-the-art controls and sensors. The IoT may be in place somewhere within those facilities, but it’s unlikely to touch legacy processes until new machinery arrive. 

Where could your company leverage the IoT today? Think strategically, operationally, and financially to prioritize opportunities:

  • Can senior leadership and plant management use real-time process data to improve daily decision-making and operations planning? Do they have the skills and tools (e.g., business analytics) to leverage IoT data?
  • Which troublesome processes in the plant or front office erode profits? With real-time data pushed out by the IoT, which could be improved?
  • Of the processes that could be improved, which include equipment that can – in the near-term – accommodate embedded intelligence, and then communicate with plant and enterprise networks?

Answer those questions, and you’ve got an instant list of how and where to profit from the IoT – today.

Stay tuned for more information on how IoT is developing and to learn what it takes to be a manufacturing IoT innovator. In the meantime, download the report “Catch Up with IoT Leaders.”

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Tanja Rueckert

About Tanja Rueckert

Tanja Rueckert is President of the Internet of Things and Digital Supply Chain Business Unit at SAP.