The Shoppers Of Tomorrow: Subscription In The Age Of Amazon

Jenn Vande Zande

There’s no denying that Amazon has changed the way that traditional retailers do business. In fact, an argument could be made that traditional retailers don’t exist anymore, and that the disruption economy is here to stay.

Marketing experts Bill and Steve Bishop of Brick Meets Click discussed the results of their extensive research on the subscription economy, and how subscriptions can help businesses grow and stand out against their giant competitors.

Even the experts are surprised: the time to add subscriptions is now

One of the most surprising aspects of subscriptions is, “how truly broad and expansive they are now,” said Steve, “until we really dug into the topic, we didn’t have a full appreciation of the magnitude of it.”

The way consumers shop has changed drastically in the last few years, and it’s going to keep changing. Even if you view it as a landscape of uncertainty, Bill said, one thing is clear: “If you’re interested in growth, you have to look at subscriptions, there’s just no way around it.”

Subscriptions bear a variety of fruit, including customer loyalty

Subscriptions shouldn’t be viewed as merely an additional source of revenue. Bill noted that their first purpose should be to offer personalized and curated experiences to the customer. “If you’re trying to build a business that is consumer-centric, you have to ask the right questions: what is your consumer looking for and not getting, what is their feedback on each interaction, and how can you make their input have authentic meaning?”

Once you’ve demonstrated that you want to know your consumer and that you’re able to provide them with outstanding experiences and conveniences tailored to their needs, they’ll be far less likely to stray to competitors.

Standing out in a jungle of competition

Many retailers are adding subscriptions as a necessity in order to stay afloat with their competition. When they view subscriptions through this singularly focused lens, they are missing the panoramic view of the opportunity to differentiate themselves.

If you’re only seeking to be like your competition, there really isn’t a reason for consumers not to check out your competition. Subscriptions can be used to clarify your unique services and products to shoppers, according to Steve.

Hone in on your specialties, and market them with your subscription services. Bill explained that if you offer an outstanding and personalized subscription for niche products, such as ties, shoes, clothing, or personal care items, your consumer is very unlikely to ever search for those items on Amazon or with other competitors.

For more insight on online retail strategies, see What Retailers Can Learn From Amazon’s New Reactionary Attitude.


Jenn Vande Zande

About Jenn Vande Zande

Jennifer is an experienced writer, editor and content strategist, and is the Managing Editor of The Future of Customer Engagement & Commerce. For over 16 years, she has worked with brands to provide copy writing, marketing content, technical writing, corporate communications, and grant writing.

How Real-Time Consumer Intent Is Changing The Sales Cycle

Ralf Kern

As a marketer, it’s your job to know what a consumer bought, thought, and did a month ago, a week ago, and even yesterday. But do you know what a consumer is thinking right now?

Understanding real-time consumer intent allows marketers to deliver targeted information, like special product promos or offers, at the exact moment in time when a consumer is searching for this information. Real-time intent is a powerful marketing tool that helps businesses close more deals by providing the information consumers need at the exact moment they’re open to receiving this information. This drives decision-maker consensus, reduces barriers to sales, and primes consumer to take immediate action.

The contemporary sales cycle: More options, more stakeholders, more challenges

Motivating a customer to complete a purchase has always been a challenge for sales and marketing professionals at both B2B (business-to-business) and B2C (business-to-consumer) companies. At B2B companies, for example, shrinking budgets and a greater wealth of available options has led to longer sales cycles. More decision-makers means everyone needs to weigh in on a potential decision. This makes it more difficult to drive stakeholder consensus and also lengthens the decision-making process. Everyone wants to have a voice in the decision, but no one wants to be the deciding voice and risk getting blamed if the decision is a mistake.

The B2C decision-making cycle is also changing, thanks to social media and smartphone technology. Mobile shopping is disrupting the consumer decision journey for retail. Google reports that while foot traffic in retail stores has declined by 57% in the past five years, the value of every visit has nearly tripled. With smartphones in tow, consumers are researching product information, checking social media for product reviews, and doing a quick price comparison search before they make a purchase. Most significantly, these mobile searches are happening at the exact moment consumers are in stores. They’re holding the product in their hands, asking, “Should I buy this right now?”

How does intent targeting increase sales?

Intent targeting allows businesses to reach consumers at these critical, decision-making moments. Whether it’s delivering a case study that drives decision-maker consensus at a B2B company or targeting consumers with a special promo discount while they’re doing a mobile price comparison, intent targeting delivers the right information at the right moment.

Intent targeting is different than showing an ad on a website that simply mirrors that website’s content. When businesses truly understand real-time customer intent, they can target a customer with information aligned with the consumer’s needs across a variety of touch points. Most importantly, the timing is spot-on: marketers can target a customer at the exact moment that customer is open to receiving information.

Targeting consumers in the moment with dynamic, contextually relevant experiences

Successful customer engagement must be proactive, tapping into the emotional drivers behind the buying process and delivering relevant customer experiences in real time. The more compelling and relevant the experience, the more likely customers will stay engaged (or re-engage) during the buying journey.

In order to successfully engage with today’s customers, businesses need more than just historical data. They need knowledge of “in-the-moment” customer activity to accurately target and deliver relevant and engaging customer offers and promotions. They need to understand the real-time intent of each customer and dynamically deliver contextually relevant experiences across channels. This requires a 360-degree view of customer’s behavior and actions. Businesses need to be able to process large volumes of structured and unstructured data, score implicit and explicit behavior across channels, and convert this information into real-time insights that drive marketing decisions.

Static customer segmentation approaches don’t cut it anymore. Progressive customer profiling taking into account each and every customer interaction and sentiment are required to show empathy, build trust, and earn customer loyalty. Customer happiness is the ultimate currency in retail. A concise and contextual brand experience based on a deep understanding of the customer is a prerequisite to creating customers for life.

Learn how to innovate at scale by incorporating individual innovations back to the core business to drive tangible business value: Accelerating Digital Transformation in Retail. Explore how to bring Industry 4.0 insights into your business today: Industry 4.0: What’s Next?


Ralf Kern

About Ralf Kern

Ralf Kern is the Global Vice President, Business Unit Retail, at SAP, responsible for the future direction of SAP’s solution and global Go-to-Market strategy for Omnicommerce Retail, leading them into today’s digital reality.

Meet Machine Learning, Your New Favorite Colleague

Kirsi Tarvainen

What if you had a colleague who would take care of all the dull, routine tasks without complaining? A colleague who lets you do interesting and challenging tasks, helps you solve them, then happily lets you take all the credit. A colleague who stays after office hours doing prep work for you so you will have a good start the next morning?

Meet machine learning, your new favorite colleague, who will dramatically change customer service both for customers and for customer service personnel.

Machine learning boosts customer service

Think about insurance companies. It’s estimated that 70%-80% of insurance claims are pretty straightforward, so this is an area where machine learning algorithms can find the right solution. For humans, it is hard to stay motivated if you have to repeatedly work through tons of claims for stolen bikes or broken mobile phones. But if you have machine learning as a colleague, you can let it solve the simple cases so you can focus on the more challenging ones – and you will have more time to carefully address each one since you don’t need to worry about the bikes and phones.

Or think about contact centers. For customer service agents, it is difficult to answer similar, repeated questions over and over again. What if you let machine learning field the routine questions while you take the more inspiring cases where customers want to speak with a live agent? A great example of this is Finland Post, which created a Christmas bot to help handle pre-Christmas peaks in customer service demands. Customers could chat with the bot to get answers to the easy, but frequent questions like, “What is the last day to send my packet to France,” which freed a lot of human resources to help customers with more complex queries.

Add more time to your day with machine learning

Machine learning is a colleague who can make you look smarter and perform better in your work. About 25% of contact center agent’s time is spent searching for information from different systems. That’s one-fourth of the workday! It is a total waste of time and shifts attention away from the customer interaction.

What if you had a chatbot that digs the information you need from all the data sources and conveniently provides it in a matter of seconds? You could fully concentrate on listening and understanding the customer, thereby providing first-class customer service.

Machine learning is a colleague we will all know very soon. It will help us get quicker and smarter – and it will help us transform our business in ways we can’t even imagine right now. But the key is to start imaging and experimenting now.

Technology is evolving; in the future almost anything will be possible, but we need to start envisioning how our customer service will look in the era of intelligent machines. There are no ready answers yet, as we are all creating the future together.

For inspiration, here is a great, short video on vision, future, and machine learning.

This article originally appeared on The Future of Customer Engagement and Commerce.


Kirsi Tarvainen

About Kirsi Tarvainen

Believing strongly that we all deserve good customer service, Kirsi has been working in customer service field for more than fifteen years. In her current role in SAP Hybris she works for SAP Hybris Service Solutions, helping companies worldwide improve their customer experience.

The Blockchain Solution

By Gil Perez, Tom Raftery, Hans Thalbauer, Dan Wellers, and Fawn Fitter

In 2013, several UK supermarket chains discovered that products they were selling as beef were actually made at least partly—and in some cases, entirely—from horsemeat. The resulting uproar led to a series of product recalls, prompted stricter food testing, and spurred the European food industry to take a closer look at how unlabeled or mislabeled ingredients were finding their way into the food chain.

By 2020, a scandal like this will be eminently preventable.

The separation between bovine and equine will become immutable with Internet of Things (IoT) sensors, which will track the provenance and identity of every animal from stall to store, adding the data to a blockchain that anyone can check but no one can alter.

Food processing companies will be able to use that blockchain to confirm and label the contents of their products accordingly—down to the specific farms and animals represented in every individual package. That level of detail may be too much information for shoppers, but they will at least be able to trust that their meatballs come from the appropriate species.

The Spine of Digitalization

Keeping food safer and more traceable is just the beginning, however. Improvements in the supply chain, which have been incremental for decades despite billions of dollars of technology investments, are about to go exponential. Emerging technologies are converging to transform the supply chain from tactical to strategic, from an easily replicable commodity to a new source of competitive differentiation.

You may already be thinking about how to take advantage of blockchain technology, which makes data and transactions immutable, transparent, and verifiable (see “What Is Blockchain and How Does It Work?”). That will be a powerful tool to boost supply chain speed and efficiency—always a worthy goal, but hardly a disruptive one.

However, if you think of blockchain as the spine of digitalization and technologies such as AI, the IoT, 3D printing, autonomous vehicles, and drones as the limbs, you have a powerful supply chain body that can leapfrog ahead of its competition.

What Is Blockchain and How Does It Work?

Here’s why blockchain technology is critical to transforming the supply chain.

Blockchain is essentially a sequential, distributed ledger of transactions that is constantly updated on a global network of computers. The ownership and history of a transaction is embedded in the blockchain at the transaction’s earliest stages and verified at every subsequent stage.

A blockchain network uses vast amounts of computing power to encrypt the ledger as it’s being written. This makes it possible for every computer in the network to verify the transactions safely and transparently. The more organizations that participate in the ledger, the more complex and secure the encryption becomes, making it increasingly tamperproof.

Why does blockchain matter for the supply chain?

  • It enables the safe exchange of value without a central verifying partner, which makes transactions faster and less expensive.
  • It dramatically simplifies recordkeeping by establishing a single, authoritative view of the truth across all parties.
  • It builds a secure, immutable history and chain of custody as different parties handle the items being shipped, and it updates the relevant documentation.
  • By doing these things, blockchain allows companies to create smart contracts based on programmable business logic, which can execute themselves autonomously and thereby save time and money by reducing friction and intermediaries.

Hints of the Future

In the mid-1990s, when the World Wide Web was in its infancy, we had no idea that the internet would become so large and pervasive, nor that we’d find a way to carry it all in our pockets on small slabs of glass.

But we could tell that it had vast potential.

Today, with the combination of emerging technologies that promise to turbocharge digital transformation, we’re just beginning to see how we might turn the supply chain into a source of competitive advantage (see “What’s the Magic Combination?”).

What’s the Magic Combination?

Those who focus on blockchain in isolation will miss out on a much bigger supply chain opportunity.

Many experts believe emerging technologies will work with blockchain to digitalize the supply chain and create new business models:

  • Blockchain will provide the foundation of automated trust for all parties in the supply chain.
  • The IoT will link objects—from tiny devices to large machines—and generate data about status, locations, and transactions that will be recorded on the blockchain.
  • 3D printing will extend the supply chain to the customer’s doorstep with hyperlocal manufacturing of parts and products with IoT sensors built into the items and/or their packaging. Every manufactured object will be smart, connected, and able to communicate so that it can be tracked and traced as needed.
  • Big Data management tools will process all the information streaming in around the clock from IoT sensors.
  • AI and machine learning will analyze this enormous amount of data to reveal patterns and enable true predictability in every area of the supply chain.

Combining these technologies with powerful analytics tools to predict trends will make lack of visibility into the supply chain a thing of the past. Organizations will be able to examine a single machine across its entire lifecycle and identify areas where they can improve performance and increase return on investment. They’ll be able to follow and monitor every component of a product, from design through delivery and service. They’ll be able to trigger and track automated actions between and among partners and customers to provide customized transactions in real time based on real data.

After decades of talk about markets of one, companies will finally have the power to create them—at scale and profitably.

Amazon, for example, is becoming as much a logistics company as a retailer. Its ordering and delivery systems are so streamlined that its customers can launch and complete a same-day transaction with a push of a single IP-enabled button or a word to its ever-attentive AI device, Alexa. And this level of experimentation and innovation is bubbling up across industries.

Consider manufacturing, where the IoT is transforming automation inside already highly automated factories. Machine-to-machine communication is enabling robots to set up, provision, and unload equipment quickly and accurately with minimal human intervention. Meanwhile, sensors across the factory floor are already capable of gathering such information as how often each machine needs maintenance or how much raw material to order given current production trends.

Once they harvest enough data, businesses will be able to feed it through machine learning algorithms to identify trends that forecast future outcomes. At that point, the supply chain will start to become both automated and predictive. We’ll begin to see business models that include proactively scheduling maintenance, replacing parts just before they’re likely to break, and automatically ordering materials and initiating customer shipments.

Italian train operator Trenitalia, for example, has put IoT sensors on its locomotives and passenger cars and is using analytics and in-memory computing to gauge the health of its trains in real time, according to an article in Computer Weekly. “It is now possible to affordably collect huge amounts of data from hundreds of sensors in a single train, analyse that data in real time and detect problems before they actually happen,” Trenitalia’s CIO Danilo Gismondi told Computer Weekly.

Blockchain allows all the critical steps of the supply chain to go electronic and become irrefutably verifiable by all the critical parties within minutes: the seller and buyer, banks, logistics carriers, and import and export officials.

The project, which is scheduled to be completed in 2018, will change Trenitalia’s business model, allowing it to schedule more trips and make each one more profitable. The railway company will be able to better plan parts inventories and determine which lines are consistently performing poorly and need upgrades. The new system will save €100 million a year, according to ARC Advisory Group.

New business models continue to evolve as 3D printers become more sophisticated and affordable, making it possible to move the end of the supply chain closer to the customer. Companies can design parts and products in materials ranging from carbon fiber to chocolate and then print those items in their warehouse, at a conveniently located third-party vendor, or even on the client’s premises.

In addition to minimizing their shipping expenses and reducing fulfillment time, companies will be able to offer more personalized or customized items affordably in small quantities. For example, clothing retailer Ministry of Supply recently installed a 3D printer at its Boston store that enables it to make an article of clothing to a customer’s specifications in under 90 minutes, according to an article in Forbes.

This kind of highly distributed manufacturing has potential across many industries. It could even create a market for secure manufacturing for highly regulated sectors, allowing a manufacturer to transmit encrypted templates to printers in tightly protected locations, for example.

Meanwhile, organizations are investigating ways of using blockchain technology to authenticate, track and trace, automate, and otherwise manage transactions and interactions, both internally and within their vendor and customer networks. The ability to collect data, record it on the blockchain for immediate verification, and make that trustworthy data available for any application delivers indisputable value in any business context. The supply chain will be no exception.

Blockchain Is the Change Driver

The supply chain is configured as we know it today because it’s impossible to create a contract that accounts for every possible contingency. Consider cross-border financial transfers, which are so complex and must meet so many regulations that they require a tremendous number of intermediaries to plug the gaps: lawyers, accountants, customer service reps, warehouse operators, bankers, and more. By reducing that complexity, blockchain technology makes intermediaries less necessary—a transformation that is revolutionary even when measured only in cost savings.

“If you’re selling 100 items a minute, 24 hours a day, reducing the cost of the supply chain by just $1 per item saves you more than $52.5 million a year,” notes Dirk Lonser, SAP go-to-market leader at DXC Technology, an IT services company. “By replacing manual processes and multiple peer-to-peer connections through fax or e-mail with a single medium where everyone can exchange verified information instantaneously, blockchain will boost profit margins exponentially without raising prices or even increasing individual productivity.”

But the potential for blockchain extends far beyond cost cutting and streamlining, says Irfan Khan, CEO of supply chain management consulting and systems integration firm Bristlecone, a Mahindra Group company. It will give companies ways to differentiate.

“Blockchain will let enterprises more accurately trace faulty parts or products from end users back to factories for recalls,” Khan says. “It will streamline supplier onboarding, contracting, and management by creating an integrated platform that the company’s entire network can access in real time. It will give vendors secure, transparent visibility into inventory 24×7. And at a time when counterfeiting is a real concern in multiple industries, it will make it easy for both retailers and customers to check product authenticity.”

Blockchain allows all the critical steps of the supply chain to go electronic and become irrefutably verifiable by all the critical parties within minutes: the seller and buyer, banks, logistics carriers, and import and export officials. Although the key parts of the process remain the same as in today’s analog supply chain, performing them electronically with blockchain technology shortens each stage from hours or days to seconds while eliminating reams of wasteful paperwork. With goods moving that quickly, companies have ample room for designing new business models around manufacturing, service, and delivery.

Challenges on the Path to Adoption

For all this to work, however, the data on the blockchain must be correct from the beginning. The pills, produce, or parts on the delivery truck need to be the same as the items listed on the manifest at the loading dock. Every use case assumes that the data is accurate—and that will only happen when everything that’s manufactured is smart, connected, and able to self-verify automatically with the help of machine learning tuned to detect errors and potential fraud.

Companies are already seeing the possibilities of applying this bundle of emerging technologies to the supply chain. IDC projects that by 2021, at least 25% of Forbes Global 2000 (G2000) companies will use blockchain services as a foundation for digital trust at scale; 30% of top global manufacturers and retailers will do so by 2020. IDC also predicts that by 2020, up to 10% of pilot and production blockchain-distributed ledgers will incorporate data from IoT sensors.

Despite IDC’s optimism, though, the biggest barrier to adoption is the early stage level of enterprise use cases, particularly around blockchain. Currently, the sole significant enterprise blockchain production system is the virtual currency Bitcoin, which has unfortunately been tainted by its associations with speculation, dubious financial transactions, and the so-called dark web.

The technology is still in a sufficiently early stage that there’s significant uncertainty about its ability to handle the massive amounts of data a global enterprise supply chain generates daily. Never mind that it’s completely unregulated, with no global standard. There’s also a critical global shortage of experts who can explain emerging technologies like blockchain, the IoT, and machine learning to nontechnology industries and educate organizations in how the technologies can improve their supply chain processes. Finally, there is concern about how blockchain’s complex algorithms gobble computing power—and electricity (see “Blockchain Blackouts”).

Blockchain Blackouts

Blockchain is a power glutton. Can technology mediate the issue?

A major concern today is the enormous carbon footprint of the networks creating and solving the algorithmic problems that keep blockchains secure. Although virtual currency enthusiasts claim the problem is overstated, Michael Reed, head of blockchain technology for Intel, has been widely quoted as saying that the energy demands of blockchains are a significant drain on the world’s electricity resources.

Indeed, Wired magazine has estimated that by July 2019, the Bitcoin network alone will require more energy than the entire United States currently uses and that by February 2020 it will use as much electricity as the entire world does today.

Still, computing power is becoming more energy efficient by the day and sticking with paperwork will become too slow, so experts—Intel’s Reed among them—consider this a solvable problem.

“We don’t know yet what the market will adopt. In a decade, it might be status quo or best practice, or it could be the next Betamax, a great technology for which there was no demand,” Lonser says. “Even highly regulated industries that need greater transparency in the entire supply chain are moving fairly slowly.”

Blockchain will require acceptance by a critical mass of companies, governments, and other organizations before it displaces paper documentation. It’s a chicken-and-egg issue: multiple companies need to adopt these technologies at the same time so they can build a blockchain to exchange information, yet getting multiple companies to do anything simultaneously is a challenge. Some early initiatives are already underway, though:

  • A London-based startup called Everledger is using blockchain and IoT technology to track the provenance, ownership, and lifecycles of valuable assets. The company began by tracking diamonds from mine to jewelry using roughly 200 different characteristics, with a goal of stopping both the demand for and the supply of “conflict diamonds”—diamonds mined in war zones and sold to finance insurgencies. It has since expanded to cover wine, artwork, and other high-value items to prevent fraud and verify authenticity.
  • In September 2017, SAP announced the creation of its SAP Leonardo Blockchain Co-Innovation program, a group of 27 enterprise customers interested in co-innovating around blockchain and creating business buy-in. The diverse group of participants includes management and technology services companies Capgemini and Deloitte, cosmetics company Natura Cosméticos S.A., and Moog Inc., a manufacturer of precision motion control systems.
  • Two of Europe’s largest shipping ports—Rotterdam and Antwerp—are working on blockchain projects to streamline interaction with port customers. The Antwerp terminal authority says eliminating paperwork could cut the costs of container transport by as much as 50%.
  • The Chinese online shopping behemoth Alibaba is experimenting with blockchain to verify the authenticity of food products and catch counterfeits before they endanger people’s health and lives.
  • Technology and transportation executives have teamed up to create the Blockchain in Transport Alliance (BiTA), a forum for developing blockchain standards and education for the freight industry.

It’s likely that the first blockchain-based enterprise supply chain use case will emerge in the next year among companies that see it as an opportunity to bolster their legal compliance and improve business processes. Once that happens, expect others to follow.

Customers Will Expect Change

It’s only a matter of time before the supply chain becomes a competitive driver. The question for today’s enterprises is how to prepare for the shift. Customers are going to expect constant, granular visibility into their transactions and faster, more customized service every step of the way. Organizations will need to be ready to meet those expectations.

If organizations have manual business processes that could never be automated before, now is the time to see if it’s possible. Organizations that have made initial investments in emerging technologies are looking at how their pilot projects are paying off and where they might extend to the supply chain. They are starting to think creatively about how to combine technologies to offer a product, service, or business model not possible before.

A manufacturer will load a self-driving truck with a 3D printer capable of creating a customer’s ordered item en route to delivering it. A vendor will capture the market for a socially responsible product by allowing its customers to track the product’s production and verify that none of its subcontractors use slave labor. And a supermarket chain will win over customers by persuading them that their choice of supermarket is also a choice between being certain of what’s in their food and simply hoping that what’s on the label matches what’s inside.

At that point, a smart supply chain won’t just be a competitive edge. It will become a competitive necessity. D!

About the Authors

Gil Perez is Senior Vice President, Internet of Things and Digital Supply Chain, at SAP.

Tom Raftery is Global Vice President, Futurist, and Internet of Things Evangelist, at SAP.

Hans Thalbauer is Senior Vice President, Internet of Things and Digital Supply Chain, at SAP.

Dan Wellers is Global Lead, Digital Futures, at SAP.

Fawn Fitter is a freelance writer specializing in business and technology.

Read more thought provoking articles in the latest issue of the Digitalist Magazine, Executive Quarterly.



The Differences Between Machine Learning And Predictive Analytics

Shaily Kumar

Many people are confused about the specifics of machine learning and predictive analytics. Although they are both centered on efficient data processing, there are many differences.

Machine learning

Machine learning is a method of computational learning underlying most artificial intelligence (AI) applications. In ML, systems or algorithms improve themselves through data experience without relying on explicit programming. ML algorithms are wide-ranging tools capable of carrying out predictions while simultaneously learning from over trillions of observations.

Machine learning is considered a modern-day extension of predictive analytics. Efficient pattern recognition and self-learning are the backbones of ML models, which automatically evolve based on changing patterns in order to enable appropriate actions.

Many companies today depend on machine learning algorithms to better understand their clients and potential revenue opportunities. Hundreds of existing and newly developed machine learning algorithms are applied to derive high-end predictions that guide real-time decisions with less reliance on human intervention.

Business application of machine learning: employee satisfaction

One common, uncomplicated, yet successful business application of machine learning is measuring real-time employee satisfaction.

Machine learning applications can be highly complex, but one that’s both simple and very useful for business is a machine learning algorithm that compares employee satisfaction ratings to salaries. Instead of plotting a predictive satisfaction curve against salary figures for various employees, as predictive analytics would suggest, the algorithm assimilates huge amounts of random training data upon entry, and the prediction results are affected by any added training data to produce real-time accuracy and more helpful predictions.

This machine learning algorithm employs self-learning and automated recalibration in response to pattern changes in the training data, making machine learning more reliable for real-time predictions than other AI concepts. Repeatedly increasing or updating the bulk of training data guarantees better predictions.

Machine learning can also be implemented in image classification and facial recognition with deep learning and neural network techniques.

Predictive analytics

Predictive analytics can be defined as the procedure of condensing huge volumes of data into information that humans can understand and use. Basic descriptive analytic techniques include averages and counts. Descriptive analytics based on obtaining information from past events has evolved into predictive analytics, which attempts to predict the future based on historical data.

This concept applies complex techniques of classical statistics, like regression and decision trees, to provide credible answers to queries such as: ‘’How exactly will my sales be influenced by a 10% increase in advertising expenditure?’’ This leads to simulations and “what-if” analyses for users to learn more.

All predictive analytics applications involve three fundamental components:

  • Data: The effectiveness of every predictive model strongly depends on the quality of the historical data it processes.
  • Statistical modeling: Includes the various statistical techniques ranging from basic to complex functions used for the derivation of meaning, insight, and inference. Regression is the most commonly used statistical technique.
  • Assumptions: The conclusions drawn from collected and analyzed data usually assume the future will follow a pattern related to the past.

Data analysis is crucial for any business en route to success, and predictive analytics can be applied in numerous ways to enhance business productivity. These include things like marketing campaign optimization, risk assessment, market analysis, and fraud detection.

Business application of predictive analytics: marketing campaign optimization

In the past, valuable marketing campaign resources were wasted by businesses using instincts alone to try to capture market niches. Today, many predictive analytic strategies help businesses identify, engage, and secure suitable markets for their services and products, driving greater efficiency into marketing campaigns.

A clear application is using visitors’ search history and usage patterns on e-commerce websites to make product recommendations. Sites like Amazon increase their chance of sales by recommending products based on specific consumer interests. Predictive analytics now plays a vital role in the marketing operations of real estate, insurance, retail, and almost every other sector.

How machine learning and predictive analytics are related

While businesses must understand the differences between machine learning and predictive analytics, it’s just as important to know how they are related. Basically, machine learning is a predictive analytics branch. Despite having similar aims and processes, there are two main differences between them:

  • Machine learning works out predictions and recalibrates models in real-time automatically after design. Meanwhile, predictive analytics works strictly on “cause” data and must be refreshed with “change” data.
  • Unlike machine learning, predictive analytics still relies on human experts to work out and test the associations between cause and outcome.

Explore machine learning applications and AI software with SAP Leonardo.


Shaily Kumar

About Shaily Kumar

Shailendra has been on a quest to help organisations make money out of data and has generated an incremental value of over one billion dollars through analytics and cognitive processes. With a global experience of more than two decades, Shailendra has worked with a myriad of Corporations, Consulting Services and Software Companies in various industries like Retail, Telecommunications, Financial Services and Travel - to help them realise incremental value hidden in zettabytes of data. He has published multiple articles in international journals about Analytics and Cognitive Solutions; and recently published “Making Money out of Data” which showcases five business stories from various industries on how successful companies make millions of dollars in incremental value using analytics. Prior to joining SAP, Shailendra was Partner / Analytics & Cognitive Leader, Asia at IBM where he drove the cognitive business across Asia. Before joining IBM, he was the Managing Director and Analytics Lead at Accenture delivering value to its clients across Australia and New Zealand. Coming from the industry, Shailendra held key Executive positions driving analytics at Woolworths and Coles in the past. Please feel to connect on: Linkedin: Twitter: