Speed of Reality

Companies used to rely on (long) past data behaviors to predict future outcomes. In our fast-moving digital world that's no longer enough. Now the true differentiator—what can help a company rise to the top and go beyond—is data behavior and digitally enabled data science.

You don't know what you don't know—seriously! Data science proves that. Data science combines data collection, analysis, and intelligence to extract information on behaviors and provides more effective outcomes. In tech jargon, it's extracting data patterns with the goal of creating or making an intelligent experience. With data science, companies can quickly identify once-hidden patterns and potential in real-time.

Instead of waiting weeks, months, and even a full quarter for trend data collection and analysis that's already outdated, a business platform and its digital ecosystem can crunch the numbers and the data real-time for nearly instantaneous results. Those results enable company agility and swift change implementation as markets dictate.

Whitsons used to have to wait weeks for data and trend analysis; so did C&F, ACS, and so many other companies. In the beginning, the University of West London didn't have the data in the right format either. Now all these companies, with their digital operating models in place, can perform quick data analysis as part of their standard operating procedure. Better still, with the data that their digital ecosystems provide, outsmarting the competition not only becomes possible, but probable again and again. And, once you know the data well, it can be monetized multiple times.

Now, Whitsons knows almost instantly when a process or procedure falls short and can change or tweak it as necessary. That means less food waste, fewer wasted work hours, more personalized and better experiences, and most importantly happier employees, happier customers, and happier customers' customers.

And, at HHS, as discussed, the data is easily available and provides a clear picture of operations and shortcomings.

Knowing More

The most important aspect of getting ahead and staying ahead of the competition is to do things better, says CP Jois. To do that, one must know more. With that deeper and better knowledge, we're then empowered and can do better.

All this segues into the fundamentals of insights and intelligence. This is also where the concepts and constructs of machine learning and artificial intelligence begin to take practical shape. Knowing more about a customer, client, employee, or investor enables a more targeted approach to delivering value. The paradigm shifts when an enterprise changes the value equation from generically focused to hyper-targeted.

Even if two competitors provide the same service, combining targeted value with targeted pricing strategies makes a difference, says Jois. As simple as it sounds, too often performing better than the competition comes down to the more subtle aspects of a sale. Product and price usually have been cast as the dominant characteristics. Reality is that more subtle aspects can be effective elements to outsmart the competition. The simplicity of approach, pricing transparency, consistency of customer experience, speed, and agility collectively change the competitive ambiance.

Focus Internally

Andrew Zaleski and his company, Breakwater Treatment & Wellness, know the right way to outsmart the competition in the highly competitive cannabis industry. “We try to focus on ourselves and not what others are doing,” says Zaleski.

Instead of comparing and competing with others, Zaleski and his team believe in the quality of their product and in their business model. After all, if a company and its people don't believe in their own product, how can they expect their customers to do so?

Breakwater goes above and beyond for those customers, whether it's about educating them about their products and services, improving the shopping experience, or personalized service. The strategy is paying off, too. Despite many other cannabis companies in close proximity, Breakwater's customers drive up to 45 minutes for the experience.

Beyond Simple Data

This new way of thinking and doing with today's data science marries data with emotions and intuition to reveal the big picture. The result is a vibrant company that can react quickly to the changes and needs of its people and its customers.

Utilizing data science, a company can have real-time inventory management that ensures products are available when they're needed. No more incidents of a plane not flying due to the lack of availability of a small part; or the car production line at a standstill until a single part can be located. AI-powered inventory management can even make sure the corner coffee shop isn't out of your favorite flavored coffee.

Additionally, with the help of a digital operating model, data science capabilities can be unique to every company and among individuals. That's an ideal approach because every scenario differs for every business and person. All processes, for example, are not the same for everyone. Individual methods, cultures, and behaviors differ. Data science recognizes that and in real-time can factor all that into calculations, analysis, and conclusions.

Then, aligning those differences with the data creates layers of intelligence that can help companies take the right steps to better understand the product their consumers want and outsmart the competition. The bottom line is an intelligent company that makes decisions based on intelligent information and choices.

The Wild Card

With all this intelligence, data, and direction, outsmarting the competition can happen. But it happens only if a company has embraced innovation and isn't afraid to try something new that changes how things have always been done.

Higher education is an expensive and very competitive business, especially in London, where nearly 400,000 students study at about 40 institutions.⁶ Among the biggest challenges these universities (and others) face is attracting new talent and retaining students and the revenue they generate.

As mentioned earlier, to improve its users' experiences and outsmart the competition in the process, the University of West London decided to embrace a unified platform ecosystem. Before, UWL—and its competition—operated primarily on legacy systems that generally required multiple log-ins to access various services—from library information and access to finance, student life, and more.

Plenty of money was at stake. At UWL, for example, one student equated to more than £9,000 of income for the university each year. That's more than $12,000. Losing one student meant the loss of nearly $50,000 over a four-year period.

UWL's unified platform system provided its staff and students a smoother, better experience back then. Now and into the future the system continues to evolve. When the university decided to up its student-retention game, the platform's data science and analysis capabilities were front and center. “This wasn't UWL going out and saying let's do some analytics,” says Adrian Ellison. “Rather, we had a business problem to solve.”

Initially, UWL partnered with an outside technology vendor and rapidly deployed its analytics platform. Now the university can track real-time the latest student enrollment, attendance, and dropout trends. That enables the university to tweak its offerings, scheduling, staffing, and services to gain a competitive edge.

Next, UWL decided it wanted to track every time a staff member reached out to a student to encourage and help with engagement. These interventions, though, weren't back-office analytics. These were “in your face analytics,” says Ellison. So, once again, in line with the university's agile and caring culture, he and his team went to the students to make sure they were comfortable with using these analytics. The result was an analytics policy coauthored by UWL's student union.

That effort resulted in a rise in student completion rates, from 76% at the outset of the program to 85% in just four years, says UWL's Peter John. “That's an example of how we can use technology to adapt to the problems we face rather than be servants to them.”

This across-the-board support and implementation separates UWL from many organizations. Says Ellison, “The reason analytics worked for UWL is the chancellor backed it to the hilt and we got the whole organization behind it. Analytics is not an IT project; it's a business transformation project that IT is helping to deliver at the backend.”

Marketing Piece of the Puzzle

Consumer behaviors have transformed so businesses need the DOM now more than ever for the flexibility and management connections that a digital ecosystem provides. The new world is about influencers marketing along with product information management (PIM) and reviews.

Today before someone shops or dines, they likely check out reviews, read about products, or learn about them from independent resources. Some products or places, for example, could suddenly be the latest and greatest because an independent reviewer shared an experience real-time on Instagram.

That doesn't mean knowing and understanding your markets, and marketing what differentiates your product or service, aren't part of the path to outsmarting the competition. Rather, the key is how you approach the marketing.

With data analysis capabilities built in, business platforms enable companies to continually understand evolving consumer trends in real time and stay a step ahead in terms of knowing what the consumer wants.

A large British direct-to-consumer (D2C) company created D2C websites, not to boost sales, but to provide real-time data on how their messaging affects consumer purchasing. The company ultimately failed because its marketing technology was too complex. With the power of the business platform, companies already have access to that real-time data and can react immediately to customer messaging and preferences.

When it comes to marketing your product and service to outsmart the competition, when it's better, smoother, faster, and easier, consumers, customers, and customers' customers notice. It's not the competition that matters; it's giving consumers what they want. And your business will grow from there, says Joe Santagata, CEO of ACS.

Listen to Your Customer

Companies also have to pay attention and listen to the needs of their potential customers. That means understanding the demographics and how they relate to what you want to do, where, and why. No matter how good a product, if a potential customer doesn't need it, want it, or won't use it, it likely won't sell no matter the amount of marketing.

Consider a digital payment product, for example. India's population is young and demands the latest technology. Therefore, it makes sense demographics-wise to introduce a digital payment product there that can and likely would disrupt the market.

Name Recognition Matters

Brand recognition also can contribute to outsmarting the competition, especially when it comes to introducing and gaining acceptance of changes to processes and procedures. Consumers familiar with a brand are more likely to take changes—including digital upgrades—in stride.

Mastercard is a master at that kind of consumer acceptance, too. The technology behemoth's logo is everywhere at sporting events worldwide and on social media. Chances are most of us at least once have heard the advertising phrase “Start something priceless.”

Digital Onboarding for Employees

Typically bringing on new employees—onboarding—is a time-consuming and exhausting process for companies and their already-stretched-thin workforces. Over the years the hiring process has evolved to online applications as standard operating procedure.

But company onboarding hasn't always been as quick to latch onto the digital model. That could be because an employer still wants the human connection; they simply haven't kept up with technological changes, or a combination of both. Whatever the reason, competition today demands a new employee hit the ground at full speed. Technology enables that.

Faster Background Checks, Screenings

HHS hopes to take that digitalized onboarding a step further and outsmart the competition in the process. Because attracting enough labor is so tough, HHS's Bobby Floyd and his team are hoping technology can facilitate faster onboarding so that his company can draw on a larger potential labor pool.

The problem for HHS is that unlike many other businesses looking to hire unskilled labor, the healthcare field has time-consuming rules and regulations that must be followed. That includes preemployment screening and extensive background checks even for the simplest jobs.

“If I'm a cook or housekeeper or transporter and looking for a job, Walmart can hire me in three days,” says Floyd. “Whereas in the healthcare environment it will take three weeks because of rules and regulations.”

If someone needs a job quickly to pay bills that are due, that slower-to-hire company will lose that potential employee. “That's why we're looking into technology to automate that onboarding process,” he says.

Explaining the Differences

However, AI and machine learning are not really interchangeable. Let's clarify some of the terminology.

Artificial intelligence (AI) is simply intelligence exhibited by a computer/machine as opposed to a human. Machine learning (ML) is a type of artificial intelligence in which a machine can learn and become smarter on its own. In other words, the computer learns without the knowledge being input by a human.

Combining data science with machine learning nets automated machine learning (AML), which removes humans (and their ingrained biases and opinions) from the equation. The internet of things (IoT) adds internet accessibility (like a smart phone) to the equation and makes devices all around us intelligent. For example, with IoT, a coffee machine can have the ability to hold data and communicate with other platforms. Suddenly, usage skyrockets.

AML in Action

Automated machine learning is gaining popularity in the insurance industry when it comes to determining creditworthiness, risk assessment, and mitigation. For example, instead of asking potential policyholders lots of questions that can skew outcomes depending on the answer, insurers look to automated machine learning. These platforms examine vast amounts of data—financial as well as personal and more depending on the type of insurance or policy—for patterns and anomalies that can be flagged, and then allow for specific explanations.

The coffee machine maker earlier in the chapter counts on three-dimensional product visualization and augmented reality (AR) to market its products. The company knows that customers expect this enhanced experience. Like Best Buy, the company also recognizes traditional photography, regardless of angles or clarity, can be deceptive and is outdated as a method to display products digitally on a website.

With its unique approach to product display, the company provides potential buyers a realistic 3D picture of the product in their own space from all angles. The 3D model shows scale and interacts with the environment's lighting. It's a virtual beyond-the-in-store experience at home.

This digital-try-before-you-buy creates a better consumer experience and outsmarts the competition by leading to increased engagement. It also helps a potential buyer answer questions such as, “Does the machine look right or does it fit in the intended space?” This kind of user experience engages the customer more often and longer, and ultimately can lead to more conversions. The use of AR also reduces the probability of product returns and provides a significant reduction in overhead.

The University of West London uses automated machine learning to help identify and cut down on potential student dropouts. With dropout rates, the challenge always has been how to detect student disengagement before dropout occurs. With automated machine learning in play, early warning signs are visible months ahead of time. Those disengagement signals include student action points like access to assignments, library use or lack thereof, class attendance, and more.

With the data gleaned from a unified platform, a student's learning can be more individualized to their needs, including intervention when necessary to help someone who is becoming disengaged, struggling, and a potential dropout.

There Are Limitations

Before conjuring up images of machine learning taking over the world, keep in mind that machine learning—like most other technologies—isn't for every situation. That's because not all tasks are suited for the technology.

ML also raises certain ethical issues. Should we trust an algorithm instead of our own judgment? Matthew Stewart, a data science expert studying at Harvard University and an AI consultant and blogger, points to some of these ethical and data issues in a blog on the limitations of ML.

“Algorithms allow us to automate processes by making informed judgments using available data. Sometimes, however, this means replacing someone's job with an algorithm, which comes with ethical ramifications. Additionally, who do we blame if something goes wrong?” Stewart writes.¹¹