Marketers have been talking about personalization since the first web browsers began tracking customer behavior using third-party cookies. For good reason. Customers increasingly demand that organizations deliver a relevant and personalized experience for them. This is a natural outcome of the explosion in digital channels, and the ease with which brands can blast customer inboxes and social feeds with their marketing. It’s overwhelming on the receiving end.
In response, customers are willing to reward brands that make it easy for them to find the signal through the noise. According to research by McKinsey, more than 75% of customers will recommend a brand, and purchase or repurchase from a brand that offers a personalized experience. When brands don’t take steps to make the experience relevant, in contrast, it can result in lost customers—around 40% of customers, according to data by Twilio.
The benefits of personalization are clear, but achieving them is difficult. Businesses struggle with the customer data requirements of delivering personalized experiences given the matrix of privacy laws and the loss of third-party cookies to augment in-house data sources. Another problem has to do with scale, given that many personalization solutions are designed for a specific channel. The result is that personalization initiatives often struggle to grow big enough to scale impact.
A solution exists. A combination of first-party data and language AI can overcome these challenges to enable personalized communication. More importantly, data leveraged by AI can add the element of motivation to drive performance to the next level from personalized communication.
Achieving personalization with first-party data and AI
The death of third party cookies is ushering in a new approach to personalization that relies on first party data. Customers are willing to share information with you and give you permission to use it to deliver relevant and convenient experiences. Leveraging first party data solves the problems of data and privacy that has stymied so many personalization initiatives. The problems of scale, in turn, can be solved by leveraging Artificial Intelligence and Machine Learning to extract insights from the data you have, deliver new experiences at scale and across channels, and continuously learn from what customers respond to.Yet even if your brand solves the challenges of data privacy, insights, and scale, it might not be enough. The reason is that personalization by itself can improve the experience and help consumers sort through the digital noise. But personalization alone doesn’t tap into motivation – a driving factor for 70% of consumer purchases. The Persado Motivation AI platform addresses all three. For brands that simply want higher performing messages at scale with little additional effort, Persado can predict the best language to use based on a decade of accumulated knowledge about what works in various contexts with different customers. Brands that choose to take the next step can leverage Persado to campaign experiments that compare 16 variants of the same message to understand what works best to motivate customers to engage. Campaign experiments result in an average 40% boost in campaign performance. And that is just the beginning. Because over time, as brands accumulate sufficient insights, their options for driving personalization improve first through segment-driven and later through more personalized campaigns.
Segmentation with Persado Segment Insights
Brands can leverage their experiments to identify the language that best motivates high-value customer segments. If you have already identified key customer segments using a customer data platform or other data source, Persado can integrate that segment knowledge into the process to identify whether different language, formatting, narratives, and other factors motivate different groups. (Hint: It often does.) For example, a Persado client may identify a group of clients as falling into a “frugal millennial” segment due to a combination of demographic and purchase behavior. Persado can integrate that information into the Motivation AI platform, to capture any distinct language preferences exhibited by members of that group in campaign experiments. For example, the frugal millennial segment may react to an exclusive offer and want more detailed information than a customer in a “financially mature” segment, members of which may be more motivated by concise facts and data. On average, Persado generates enough data to generate segment insights after 5 experiments – a process that can take less than 6 months.
Personalization With Persado Language Profiles
The next step is to leverage Persado language profiles, a more granular personalization technique that leverages insights based on individual customer engagement behavior to categorize customers into groups that behaviorally react to specific choices of words, narratives, formats, images, and other campaign elements. On average, Persado has enough data to generate and start using language profiles after 20 experiments – which can take 10 months or less.
The bottom line on personalization
Personalized experiences have become table stakes for businesses across industries. And the challenges of delivering them in messaging and communications can be overcome by adopting a focused strategy that leverages first-party data and Motivation AI. Whether you leverage higher level insights, or more granular language profiles, your organization can deliver more relevant messages and achieve stronger performance.