Personalization has come a long way since Amazon started leveraging personalized book recommendations in 1999. Half of companies worldwide have identified it as a priority investment, and globally 92 percent of marketers believe there’s value in 1-to-1 marketing technology. If you’re part of this majority but haven’t figured out how to advance your personalization efforts,…
Personalization has come a long way since Amazon started leveraging personalized book recommendations in 1999. Half of companies worldwide have identified it as a priority investment, and globally 92 percent of marketers believe there’s value in 1-to-1 marketing technology.
If you’re part of this majority but haven’t figured out how to advance your personalization efforts, you’re not alone. Personalization has gained popularity in recent years, but not all marketers feel like their activities in this area are effective.
Let’s demystify where personalization is now and where it’s heading this year.
2018 was heralded by many as the year when personalization would go mainstream, especially in retail. Salesforce analyzed the shopping activity of 150 million shoppers across millions of websites and identified personalized product recommendations as a key revenue driver in the industry. The assumption was that we’d see unique product recommendations, dynamic email and website content, and even personalized pricing emerge as the norm in most e-commerce experiences.
On the B2B front, Gartner predictedthat advances in AI would result in up to 40 percent of B2B sites using price optimization algorithms to personalize prices for each customer. They also predicted revenue increases of 15 percent for B2B companies that employed personalization in digital experiences.
Yet, despite analysts’ enthusiasm, more than half of marketers in a recent Evergage studyreported that they don’t have sufficient data and insights for effective personalization.
Poor data quality and lack of integration often get in the way of personalization efforts. Marketers are housing their customer data, on average, in four different systems, making it incredibly difficult to deploy personalized experiences across channels. Using commonly available data like age, gender, and zip codes won’t cut it.
You need deeper data sets to draw more accurate conclusions about your audience segments and you need to systematically combine data sources (referral, purchase data, interests, etc.
