As the customer journey continues to evolve, brands and marketers have to constantly question their assumptions to fully understand how to deliver value in the moment—based on where customers are and what they’re doing in the moment.
Consumers today are being inundated with marketing content from every direction. If your brand is missing the mark in terms of what’s most important to them, it’s not only going to impact your engagement, but also your bottom line.
Simply put, broad segmentation by marketers no longer works. While segmentation is still helpful for marketers as a summary, the reality for most brands today is moving to micro-segmentation or personalization to effectively engage customers.
While most companies have already moved to personalize products though data, insights and machine learning, the reality is that many brands struggle to effectively scale personalization. For a company with millions or hundreds of thousands of customers, it can be a daunting challenge to get the right people, processes and technology in place to effectively make the leap.
Here are four things marketers can do to effectively segment and personalize marketing at scale:
Focus on relevancy first and building trust with customers
Today, there’s a long list of buzzwords to describe smarter, more targeted marketing such as personalization, hyper-personalization, customization, predictive insights and contextual relevance—just to name a few.
In many cases, personalization down to the individual customer level isn’t needed, and there are some realities and roadblocks that come into play for any brand.
One of the biggest impediments to personalization is an oversimplification of the customer experience. Brands also need to build trust with their customers through relevant content and CX.
While personalization may work well online (via an account login), inside an application or at retail, personalization in other channels such as email, mobile messaging or social can act as a detractor for brands if it’s not done in the right way. For example, the predictive ads we see in our Facebook feed or other social channels (based on our past shopping preferences) can often come across as tone-deaf or borderline creepy.
So how do marketers use data responsibly to deliver relevance? It starts with understanding the customer journey.
Understand the customer journey
To effectively personalize and improve relevance, marketers have to understand the customer journey. While any marketer would tell you this, it’s much easier said than done, because there is no one single journey, except at the individual level.
The reality today is that even the most well-tested content strategy isn’t going to guarantee lead generation. The reason why is that human attention is limited. Both the experience and content must be immediately relevant to customers.
As the first step in their evolution to more relevant marketing, most digital brands today are moving to micro segments to understand each customer journey—the series of interactions with their brand from initial consideration, to purchase, use and subsequent purchases.
In addition to backwards-facing data such as visits to the company website, purchases at a store or customer-service interactions, brands are increasing tapping into real-time experience data and mobile to understand exactly where and what customers are doing the moment.
Understanding and personalizing CX requires digital brands to create hundreds, and sometimes even thousands, of micro-segments in order to identify journeys that are likely to have similar patterns. From there, they design experiences to meet and improve each of those customer needs.
Leverage data and AI to create relevance at scale
Today the majority of digital brands are leveraging some form of marketing technology, machine learning or artificial intelligence to customize the content and experience customers see online and via mobile. For example, Amazon and Netflix gather information and preferences from visitors to personalize their experience down to the micro-segment or individual level. Over time the experience evolves as data influences the output.