The exciting promise of the future of personalization may not be here yet (at least not at scale), but it’s not far off. Advances in technology, data, and analytics will soon allow marketers to create much more personal and “human” experiences across moments, channels, and buying stages. Physical spaces will be re-conceived, and customer journeys will be supported far beyond a brand’s front door.
While these opportunities are exciting, most marketers feel under-equipped to deliver. A recent McKinsey survey of senior marketing leaders finds that only 15 percent of CMOs believe their company is on the right track with personalization. But there’s a big incentive to figure it out. Today’s personalization leaders have found proven ways to drive 5 to 15 percent increases in revenue and 10 to 30 percent increases in marketing-spend efficiency—predominantly by deploying product recommendations and triggered communications within singular channels.
Positioning businesses to win requires understanding the three main shifts in personalization and building up the necessary skills and capabilities to respond to them.
Read on to learn what it will take to win tomorrow’s personalization game.
Three major shifts will make personalization more personal
Over the next five years, we will see three major shifts in personalization:
Physical spaces will be ‘digitized’
Fewer than 10 percent of the companies we surveyed currently deploy personalization beyond digital channels in a systematic way. That presents a big zone of opportunity. One area where the implications could be significant is in store visits. Our survey data suggest that “offline” person-to-person experiences will be the next horizon for personalization. Some 44 percent of CMOs say that frontline employees will rely on insights from advanced analytics to provide a personalized offering; 40 percent say that personal shoppers will use AI-enabled tools to improve service; and 37 percent say that facial recognition, location recognition, and biometric sensors will become more widely used.
Some retailers have already started down this path to move beyond established, though still rudimentary, personalization practices. At Covergirl’s new flagship store, an AI-powered program, enabled by Google’s conversational Dialogflow platform, directs customers, while augmented-reality glam stations let customers “virtually try” products—by altering the customer’s image as if the product has been applied. But this doesn’t mean the end of the salesperson or stylist. These virtual experiences still need the human touch. Covergirl’s glam stations still need customers to tell stylists what products they’d like to try. As AI evolves, systems can generate recommendations based on analyzing a customer’s skin tone, facial features, and emotions in real time to tailor what to recommend or avoid offering.
Macy’s, Starbucks, and Sephora are using GPS technology and company apps to trigger relevant in-app offers when customers near a store. Other retailers have begun to provide sales associates with apps that generate personalized product recommendations for specific customers automatically. One retailer found an app like this generated a 10 percent lift in incremental sales and a 5 percent increase in transaction-size growth.
The next level of in-store personalization is likely to include providing these kinds of experiences to all customers as well as pulling in more advanced AR features to help customers experience products and services in different environments, such as trying hiking boots on a “virtual mountain.”
Empathy will scale
Empathy—the ability to relate to and understand another person’s emotions—is the basis of strong relationships. Understanding social cues and adapting to them is how people build trust. That’s not easy to do digitally or at scale.
Machine learning is changing that, or at least getting much better at reading and reacting to emotional cues. More sophisticated algorithms are allowing programs to interpret new kinds of data (visual, auditory) and extrapolate emotions much more effectively than in the past. Amazon has patented new features that will enable its Echo device to detect when someone is ill—such as nasal tones that indicate a stuffed nose. It will then offer a suitable recommendation, such as a chicken-soup recipe or cough drops, some of which could then be purchased over the device for at-home delivery. Other companies are getting into the game too. Affectiva, which spun off from work scientists were doing at the MIT Media Lab, is using machine learning to develop emotion-recognition algorithms to classify and map facial expressions, such as anger, contempt, disgust, fear, and joy. To date, the company has raised $53 million from investors including Kleiner Perkins, CAC Holdings, and the National Science Foundation.1
In time, these advances could help marketers communicate with customers in a way that’s tied to specific moods, offering specifically curated promotions for music or movies, for example, that match that mood.
Brands will use ecosystems to personalize journeys end-to-end
Different providers jointly own the customer experience. A mall operator, retail store, and brand product, for instance, all contribute to a shopper’s buying experience. But each sees and affects only a portion of the total buying experience. Creating connections between those points represents a big opportunity in the future of personalization, as expanding partner ecosystems allow brands to provide more seamless and consistent consumer experiences across all stages of their decision journeys. As AI gets better at predicting consumer needs—turning on the lights or turning up the heat shortly before someone comes home—personalization programs can navigate the transition from one system (car) to the next (home lights or home furnace).
While the share of global sales that move through the ecosystems is still less than 10 percent, we predict it will grow to nearly 30 percent by 2025. Perhaps the biggest frontier for consumer-ecosystem development is the home. As devices proliferate, they will need to work with each other and use platform standards. Consumer goods, home-mechanics systems, automobiles, and a vast array of digitized devices will need to be part of a seamless experience for the consumer or risk being completely shut out.
Industries as diverse as banking, healthcare, and retail are also forging ecosystems comprising a variety of businesses from different sectors to improve customer service and expand the quality and array of solutions offered.
How to turn the future into reality
Personalization tends to be thought of as an important capability for marketing, but we believe it must become the core driver of how companies do marketing. Here’s where successful brands need to focus now:
Invest in customer data and analytics foundations:
Personalization is impossible if marketers don’t have the means to understand the needs of high-value customers on an ongoing basis. So top marketers are developing systems that can pool and analyze structured and unstructured data, algorithms that can identify behavioral patterns and customer propensity, and analysis capabilities to feed that information into easy-to-use dashboards.
Setting up a centralized customer-data platform (CDP) to unify paid and owned data from across channels is essential to these efforts. Unlike traditional CRM solutions, CDPs provide built-in machine-learning automation that can cleanse internal and external data, connect a single customer across devices, cookies, and ad networks, and enable real-time campaign execution across touchpoints and channels. The best ones are also easy to use.
Individualizing outreach across channels also requires companies to develop and interact with new sorts of data, from voice to visual. The best are already actively experimenting with these technologies by developing use cases to understand how to best use them.
Making this technological leap forward requires marketing and IT to join forces. A product-management team, with representation from both IT and marketing, should be established to build and refresh the organization’s martech road map, develop use cases, track pilot performance, and compile a robust library of standards and lessons learned. Martech engineering should deliver needed capabilities to the team, including cybersecurity systems able to keep pace with the expansion of personalized experiences.
Find and train translators and advanced tech talent:
Personalizing spaces, moments, and ecosystems will require very different skill sets from those of the traditional marketing operation today. In addition to data scientists and engineers, marketing organizations will need analytics translators who can communicate business goals to tech stakeholders and data-driven outcomes to the business. As data become more complex and personalization use cases more advanced, the need for these translators will intensify. We predict a significant shortage of analytics translators. Today, only 10 percent of US graduates in business and STEM fields (science, technology, engineering, and mathematics) go into business-translator roles. By 2025, we estimate that figure will rise to 20 to 40 percent to meet the expected demand. Needless to say, the ability to recruit and develop translator talent will provide a significant competitive advantage in developing future personalization capabilities.2
Not surprisingly, the battle for AI talent will escalate. While organizations will need to figure out which talent to hire (those with core capabilities to drive creative problem solving) and which to access through an ecosystem of external talent, leading companies are moving aggressively to acquire relevant talent. Some 52 percent of the most-digitized companies build AI capabilities in-house, compared with just 38 percent of other companies, according to a recent McKinsey survey.3
The buildup of relevant tech talent will need to be matched by improved training so that people throughout the organization understand not just how to use new personalization tools but also how they can help them make better decisions. That means training call-center agents, sales teams, and marketers, for example, in how to use emotion and sentiment-analysis systems to better support customers. Rotation programs, in which people train in a variety of teams, will be particularly important to help spread knowledge and deepen organizational proficiency in key areas.
Build up agile capabilities:
Given the iterative, cross-disciplinary nature of personalization efforts, traditional siloed marketing teams won’t work.
Instead, the future of personalization requires a commitment to agile management, including cross-functional teams dedicated to specific customer segments or journeys with the ability to execute rapidly. Performance measurement must evolve similarly and become more focused on testing velocity, success rates, and creative boldness.
In addition to demonstrating needed expertise, the ability to collaborate and integrate information will be key to professional advancement. Equally important will be the ability to collaborate and solve problems with colleagues from across the organization, in IT, analytics, product development, and legal.
Annual budgeting and strategy processes must also become more flexible with frequent reviews to assess current initiatives, chart new ones, and realign funding and resources in support of key priorities
Protect customer privacy:
The nature of data privacy has rightly become a source of concern for consumers and brands alike. In the wake of repeated data-privacy scandals, 46 percent of customers want increased governmental regulation to protect data privacy.4 Companies at the cutting edge of personalization innovation are more likely to—rightfully or not—trigger privacy concerns among the customer base. As such, proactively managing customer privacy will be especially important.
Developing a personalization capability is a journey to get to the full suite of capabilities needed for true dynamic personalization: always-on, real-time, one-to-one communication across the consumer ecosystem. Often, the hardest part is just getting started. But in our experience, most companies have more than enough data and people to get value right away. The first step is to determine which use cases to focus on (converting new customers, increasing spend of loyal customers, etc.) and put an agile team on each of them to rapidly test and learn which offers and interactions best deliver. This hands-on approach quickly helps people develop real experience and expertise. Even as teams move quickly, however, it’s important to keep one eye on the end state so that you can effectively plan in order to make better decisions about people, technology, and investment.