By David Yee
Understanding the customer’s needs and behaviours, and pairing it with the right content, delivery channel and message, dramatically increases the odds the content will pique their interest.
So, with technology playing a greater role in understanding consumer behaviour, it’s worth asking why so many brands still rely on rudimentary mass-market ads that largely target former customers: instead of delivering personalization at scale.
One obvious reason is the perceived difficulty brands face organizing and analyzing their customer data, which comes from many different sources and forms. Though brands have access to more granular details about their audiences, using the data fully requires cutting through an equally high volume of noise to extract the insights they need: a goal more easily articulated than achieved.
It’s clear an evolution is needed, as challenging as it may be. For brands to succeed in today’s marketplace, they must develop a strategy for personalization at scale. One that leverages artificial intelligence (AI)-powered analytics and is aligned with the brand’s needs and goals to transform customer data into genuine customer intelligence.
How AI can bridge the gap
While the terms are often used interchangeably, there’s a tangible distinction between customer data and customer intelligence. The former is the raw data that brands receive from interactions with their audiences. The latter refers to the actionable insights extracted from this data that help deliver a personalized experience.
AI-driven solutions, such as Adobe Sensei-powered Adobe Target, are helping today’s marketers uncover customer insights that were unimaginable a couple of years ago. These solutions help to expedite and simplify a multitude of processes, which allows marketers to focus on engagement and delivering unique experiences.
AI can help make recommendations more targeted and easier to scale. We’ve all witnessed the power of personalized recommendations on sites such as Amazon and Netflix; the more precisely your brand tailors recommendations to its customers, the more likely they are to use the products and services they want.
For example, leading Canadian retail travel agency RedTag.ca has incorporated AI technology, through Adobe Sensei, to better understand patterns in their customer demographic, behavioural and conversion data, in addition to detecting anomalies on its web site.
“Giving our customers a unique experience by leveraging AI and technologies like Adobe Target allows us to deliver recommendations that truly are personalized and have that one-on-one conversation,” says RedTag.ca chief digital officer Roberto Gennaro.
One of the most powerful tools available for automated recommendations is an algorithm known as item-based collaborative filtering. By ranking your digital assets by popularity, recency and frequency—and comparing them to attributes within each customer’s profile and purchase history—the algorithm provides each user with a list of personalized recommendations that carry the highest likelihood of engagement. You can add manual rules based on your own insights, and the algorithm will automatically incorporate those rules into its decisions too.
The more this algorithm learns from customer interactions, the better it becomes at recommending engaging assets, thus ensuring the right experience always reaches the right customer.
Another key component to effective personalization is delivering holistic experiences tailored to each customer. To maximize your clicks and conversions, you need to A/B test not only pages or products, but also large-scale variations in content, navigation, layout, timing and many other interconnected attributes.
That’s why today’s marketing technology platforms apply multi-armed bandit testing not only on individual products, services or content, but whole customer experiences, from app or web site layouts to in-store visits.
Engagement at scale
It’s worth noting that our recent Digital Trends report (https://adobe.ly/2JsPEYm), conducted with Econsultancy, found that 28% of brands are already using AI. And that a further 29% plan to implement it in the short term. It will be critical for brands seeking to achieve personalization at scale to adopt AI and machine learning, and to partner with firms that can help guide them through their transformation journey.
Personalization at scale requires more than adopting AI, however. It also has an impact on people and processes.
Developing true customer intelligence requires brands to reconstruct the way they develop customer profiles. That’s why, for example, instead of dividing the information they use to build their customer profiles into silos, leading brands are now combining their data into a single, unified platform. This gives all employees the context they need to deliver highly-targeted, engaging experiences, no matter their department.
In the end it all boils down to the same core principle, which remains in place whether technology is involved or not: speak to your customers the way they wish to be spoken to and they’ll reward you with their business.
David Yee is head of Enterprise Digital Marketing, Adobe Canada (www.adobe.com/ca/).