The future of personalisation – necessary preparation. Personalisation will be the prime driver of ecommerce marketing success within five years. Here are the capabilities companies need to develop to stay ahead of the curve.
The exciting promise of personalisation 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 reconceived, 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 personalisation.
But there’s a big incentive to figure it out. Today’s personalisation 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 personalisation and building up the necessary skills and capabilities to respond to them.
Three major shifts will make personalisation more personal
Over the next five years, we will see three major shifts in personalisation:
Physical spaces will be ‘digitised’
Fewer than 10 percent of the companies we surveyed currently deploy personalisation 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 personalisation. 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, personalisation 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 analysing 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 personalisation 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.
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 next level of personalisation, 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—personalisation 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, developments 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
Personalisation tends to be thought of as an important capability for marketing, but we believe it must become the core driver of how companies do their marketing. Here’s where successful brands need to focus now:
Invest in customer data and analytics foundations: Personalisation 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 analyse structured and unstructured data, algorithms that can identify behavioural patterns and customer propensity, and analysis capabilities to feed that information into easy-touse 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. SwiftERM are already in this space this for email marketing prediction software for imminent products purchase.