Customers want more personal connections with brands. That’s what we’ve come to expect with many of the most respected brands today. Lululemon, Amazon and Starbucks, for example, are building their companies on the ability to forge emotional connections at scale.
As more brands rely on digital channels like chatbots, messaging apps and email campaigns to build relationships, AI and machine learning are critical in being able to use the big sets of data amassed to create more personalized experiences that demonstrate empathy and authenticity.
It almost seems counterintuitive: Automation is required to do more “human-focused” marketing? In a word, yes.
The reality is that empathy happens when brands tap into an individual customer’s wants and motivations continually, connecting with them on a deeper level than just a one-off exchange. That’s just not possible without automatically understanding the context and intention behind each individual customer interaction — and doing that across the entire customer journey.
COVID-19 Raised the Bar
Customers had sophisticated demands for digital experiences prior to COVID-19, and the pandemic raised those standards even higher.
Brands learned they need to see the world from their customer’s eyes and treat every action as part of a growing relationship, not a series of transactional exchanges. It’s not enough to just have the information about what customers have done in the past and use it sporadically. To build more personal connections, businesses need to step into their customer’s shoes and anticipate their future needs or wants on a regular basis.
Accomplishing this means analyzing customer data to detect wider patterns and changes in preferences. Brands cannot wait around for data scientists to explain what customers are telling them. They need to be active listeners and respond in real time.
Making Big Data Actionable
With more interaction points, content types and digital moments in the mix than ever before, predicting customer behavior and the next best action has also become increasingly complicated. Simply having a large swath of data coming in from different sources isn’t enough to build a valuable and trustworthy relationship with customers.
With billions of potential experiences to choose from, selecting the best sequence of events is an impossible task for a human to accomplish on their own. That’s why AI and machine learning are key to building more personal connections. Whether standalone or within a larger platform like a customer data platform (CDP), AI and machine learning can help brands make sense of enormous volumes of customer data across multiple channels and offer sophisticated recommendations based on past customer interactions.
Related Article: 3 Customer Trends AI Can Spot Before Marketing
Putting AI to Work
Blending data intelligence with prescriptive AI and predictive machine learning techniques gives a view of customers that encompasses all of their previous histories with a brand, whether that was via the website, in-person browsing, or leaving a review on social media. Only when brands have built this more holistic view are they able to work toward improving each future interaction through predictive modeling.
I’ve worked with enough companies to know this analysis differs greatly across industry, audience and many other variables, but here are a few examples:
- Question: Is this customer an avid in-store shopper who suddenly has been browsing the e-commerce site, maybe due to store closures?
- Action: Acknowledge their adjustment with a free shipping offer to lessen the inconvenience of needing to change their shopping channel to online.
- Question: Has the customer showed a strong interest in a luxury item, but also failed to pull the trigger on the purchase?
- Action: Use AI to detect their pattern of behavior and alert them to a price drop through SMS notification.
- Question: Has this customer recently called the customer service hotline to report a delayed package or wrongly delivered item?
- Action: Send them an exclusive free shipping “apology” code for their next order.
All of these actions go beyond simple customer acquisition and help build trust. These kinds of data-driven insights should also be fairly simple to achieve by leveraging machine learning and uniting data from across all customer touch points to shape a vision of customers in real time.
When a person trusts a brand is using data in a thoughtful and intentional way, rather than to just drive a sale, they are more likely to offer that brand more data to inform experiences in the future. Each interaction generates more value for both the organization and the customer, and it’s essential to do this at scale to grow deeper connections and foster customer loyalty in the long term. That’s only possible with AI.
Omer Artun is the Chief Science Officer at Acquia. He was previously the founder and CEO of AgilOne, which was acquired by Acquia in December 2019.