AI-powered relevance is driving digital transformation
Once an internet buzzword, digital transformation is now a strategic initiative being pursued across every industry. Companies in all sectors are investing significant time and capital into the digital infrastructure necessary to compete in tomorrow’s business landscape.
However, enabling digital business and increasing digital presence is not enough. Leading companies understand that they also need to address their relevance across all digital interactions. Digital success is binary, and 100 percent driven by relevance.
Prospects either convert or abandon, customers either self-serve or call, or worse change provider, employees either get what they need or reinvent the wheel. In a world where choice is a commodity and digital interactions happen in a split second, being relevant to every audience, every time, is what drives business and company success. We call this the Relevance Transformation.
What is the Relevance Transformation?
Relevance Transformation, simply put, is a strategy to succeed at every digital interaction, across the entire customer, employee or trading partner journey.
Whether that’s a prospective customer being presented with relevant content on a web site, helping them to convert on your e-commerce site by recommending exactly what they need; or it’s an existing customer getting just what they need to solve a product issue; or an employee having the right information to act on, helping them become more proficient in their role - all of these experiences require that they interact with content that is relevant to them, and them alone.
Being relevant to every individual, every time they engage with your company, at every interaction, means thinking beyond just digital enablement. It means thinking about all of the touch points your prospects, customers and employees interact with throughout their unique journeys, and what content is available throughout your company that could be of optimal value at each of those touch points.
Finally, thanks to the power of AI, it means understanding that all of those touch points capture very valuable data about how and what users engaged with and in what context; precious behavioral data that can be used by machine learning to drive the next best content within the next interaction.
In other words, understanding both their exact context as well as their likely intent, using AI, and being able to pair it with the best available data, and recommend what’s needed within a split second.
How AI, Search and Analytics Makes the Relevance Transformation Possible
Let’s take the following scenario: Two customers might be searching for the same item or two employees might need the same information, but each may have a completely different underlying context and intent for making the request and will need content to be tailored to them.
As people, and using traditional programming of rules, we are only intellectually capable of handling a limited number of differing scenarios. We, and the systems we program, have the ability to organize problems into segments, and as we create those broad categories we typically lose solution relevance in the process. In reality, a million interactions should command a million different, personalized, relevant responses.
AI is a game changer. In simple terms, before machine learning, the world of software was about programming rules associated with data models, for example similar to how we segment audiences in marketing and apply a set of campaign rules against each segment, or how we would program a set of rules to teach an algorithm how to play chess moves facing a set of circumstances. Hence, machines were isolated as “specialists” built to complete specific, repetitive tasks while versatile tasks requiring more flexibility and situational judgment were reserved for humans.
The fundamental paradigm shift with AI is that machine learning finds the rules. Feed it with 100,000 chess games, tell it who won, and within a few hours the algorithm will likely beat Gary Kasparov, Bobby Fischer and Magnus Carlsen, combined.
This same concept applies to business interactions. Recent advances in machine learning and deep learning have led to artificial intelligence (AI) capable of effectively handling relevance at scale across all interactions. In simplified terms, the sum total of interactions throughout customer, trading partners and employee journeys provide all the data necessary to feed machine learning and predict the likely next best content to deliver on the next interaction.
Now, where humans might be able to organize and deliver dozens or hundreds of unique solutions, AI can deliver millions, and adapt continuously. Each new request only improves and expands machine learning capabilities, meaning the companies that invest in AI for relevance transformation gain a permanent competitive edge.
Moreover, this same data continuously provides the analytics and insight into what is most relevant to every person you do business with, so you can act the next time around.
In this architecture, search technologies play a critical role. The ability to securely reach, index and unify, and query data enriches the ability to understand context. Moreover, it provides the infrastructure to mash-up the most relevant content from anywhere, and deliver it within every interaction.
Putting data to work, consolidated from every data silo, to act on each individual interaction’s context and intent. Leveraging the unified data index to connect information, and combining behavioral data and machine learning to infer intent and automatically recommend, promote or suggest the most relevant information to users as they engage with your company throughout any channel. These interactions can take the form of recommendations, query suggestions, chat bots, etc., and within any device or channel.
In summary, enabling a relevance transformation strategy is about making every interaction count. First, by focusing on the importance of delivering relevance within every interaction, recognizing that only relevant interaction drives digital success. Second, by learning from every interaction’s data to improve the relevance of the next interaction, figuring out what is the next best content to offer to that next user. Achieving this at any scale requires taking advantage of the advances in AI technology - machine learning in particular - to process it all in real time, every time, everywhere.
Why is the Relevance Transformation So Important?
This ability to individualize each business interaction is what makes relevance transformation of such vital importance. By offering up the most relevant product information to a shopper they are more like to convert, which drives revenue growth.
By helping a customer find self-service exactly what they need to troubleshoot a problem, they are happier, driving up customer satisfaction and net promoter scores, ultimately improving lifetime value. By equipping an employee with the information they need to exercise judgment facing a situation, they become more proficient, they can do more on their own, they can answer more complex problems faster - and they stay in their role longer, with lower churn and lower replacement costs. From that perspective, relevance has profound economic value across commerce, service and the workplace.
How Are Companies Already Adopting a Relevance Transformation Strategy?
Leading companies have already begun to realize the benefits of the relevance transformation. Netflix, for example, is using AI to power its recommendation engine, profiling unique users and recommending relevant content the user is likely to find interesting based on prior behavior.
Each recommendation also comes with a confidence level and provides the basis for the recommendation, demonstrating an openness to consumers that allows them to see why certain items are being selected. Today, more than 80 percent of the shows people watch on Netflix are discovered through its recommendation engine, and in the past five years shareholders have seen share value rise by a factor of ten.
Amazon is eating the commerce world not because of its digital presence, but rather because its primary focus has been around relevance and immediacy. According to a recent McKinsey study, “35 percent of Amazon.com’s revenue is generated by its recommendation engine algorithms”. Amazon is about telling buyers what they need next, using data about the user, but also data from the sum total of interactions from digital soulmates, combined with machine learning to understand their likely intent and what would be the best offer.
Digital publishing platform CMS WiRE has overhauled its digital infrastructure to make content more relevant to individual audiences and 100 percent searchable. As a result of its relevance optimization and focus on responsive digital infrastructure, CSM WiRE has seen a 125 percent increase in mobile conversion rate over 12 months.
Following its acquisition of recommendation engine Hunch in 2011, Ebay has continually refined its search function to recommend items based on queries that include similar items other users have clicked on after using the same search term. Ebay’s AI also notices word cloud search patterns, and can provide relevant search results based on similar search terms used by others whose browsing behavior matches the user. As a result, in 2017 Ebay earned nearly $10 billion in revenue.
Furthermore, Walmart has leveraged its AI to recommend relevant solutions and products based on location as well as user behavior, showing digital customers what items are important to nearby users. The results can be seen in the growth of its net worth, currently a whopping $386 billion.
Business executives need to ask themselves how they are maximizing their relevance at every point in the customer journey, understanding that every time they are not relevant in digital, that they just wasted money and lost another opportunity. They need to take the time to understand the art of the possible with the current state of technology and in particular how they can leverage AI to improve both the business experience - for customers, trading partners and employees alike - and its massive impact on the bottom line.
By building on digital transformation and implementing AI to meet relevance transformation goals, companies can directly affect their business metrics by delivering value to prospects, customers and employees throughout every stage of the business process, driving conversions and revenue growth, lifting customer satisfaction and cost reductions through self-service intelligence, and improving employee proficiency and the capability for less people to do more on their own.
AI will be a brutally transformative technology, as the companies adopting it will experience quantum leaps in business and financial performance, while the others will face stiff competition from the former group of adopters, radically more effective.
It’s not enough for executives to settle on digital transformation as a goal for its own sake: being relevant at every interaction is the central reason for pushing a digital transformation initiative. Forward-thinking companies are already using AI-powered recommendations and insight engines to drive business value and capture market share across a variety of sectors and industries.
Investing in Relevance Transformation is fundamental for companies, regardless of their vertical, to maximize the value they’re delivering customers, the value creation they are getting from their employees, and ultimately remain competitive in a digital world.