How Brands Can Overcome the Challenges of Delivering Predictive Personalization at Scale
Today, with consumer loyalty up for grabs, personalization matters more than ever before. In fact, more than 71 percent of consumers now expect companies to deliver personalized interactions, and 76 percent express frustration when this doesn’t happen. If you’re trying to improve the consumer experience, enabling predictive personalization is a must.
Predictive personalization allows you to use historical and real-time data to predict what content or products an individual consumer wants to see when they visit your website. Predictive personalization goes beyond demographics and can factor in consumers’ changing preferences and external factors, such as location, time, date and weather. The more personalized the experience, the more likely consumers are to sign up for your CRM, make more purchases, and remain loyal to your brand.
Unfortunately, while many marketers and brands are talking about consumer experiences and personalization in their digital marketing efforts, there are very few that are doing this effectively and at scale. Here’s a look at how brands can overcome the challenges.
Determine Where You Stand With Personalization
Personalization is more than a strategy or task that marketers need to check off their list — it’s a journey. It’s important to understand where you are on the journey in order to effectively deliver personalization across marketing channels in a scalable way.
Instead of approaching personalization as a multiyear, extremely complex project, breaking it down into three stages can help brands effectively integrate predictive personalization into their digital strategy and see measurable results at each stage.
While the personalization journey isn’t necessarily a linear process, the three main stages are:
- Collecting data and extracting actionable consumer insights.
- Optimizing the consumer journey with simple personalization.
- Enabling predictive and dynamic personalization at scale.
Define Your Business Goals
Brands often set all-encompassing goals such as “digital transformation” or “implement predictive personalization.” Like any other marketing endeavor, personalization needs a well-constructed road map, with both goals and quantifiable and measurable objectives — i.e., individual actions you take to achieve a goal.
While thinking long term can help you set high-level strategic priorities, clear short-term business objectives can serve as key milestones for what you intend to accomplish through your personalization journey and facilitate quick results and learning.
Examples of good short-term goals:
- Increase conversions from cart to checkout by 5 percent in the next quarter.
- Increase new CRM registrations by 30 percent in six months.
Identify Use Cases and Build a Foundation for Predictive Personalization at Scale
Once you've set clear goals, it’s much easier to find use cases for personalized content. For example, if your goal is to increase cart-to-checkout conversions by 5 percent in the next quarter, you might try one of these:
- providing personalized product recommendations; and/or
- sending discount emails to consumers that have items sitting in their cart for a week.
Both of these are simple use cases that can have a measurable and quick impact, and can help you build a foundation for predictive personalization at scale.
Leverage Technology and AI Solutions to Deliver Personalized Experiences
Predictive personalization is a complex process that needs to be built on a foundation of data science. It’s impossible to do manually. With thousands or millions of customers, curating experiences for every single individual isn’t feasible — that’s where the right personalization tools come in.
As you develop a personalization road map, look for AI-driven predictive solutions to maximize resources and improve marketing return on investment. With the right predictive personalization tools, you can curate a unique experience for each individual customer. If you have a million customers, your solution should be able to produce a million variations of the consumer experience. This is predictive personalization done right and at scale.
Diane Keng is the CEO and co-founder of Breinify, an AI-powered predictive personalization platform that helps consumer enterprises deliver relevant and personalized experiences at scale.
Diane Keng is the CEO and co-founder of Breinify, an AI and predictive personalization engine that helps brands curate dynamic, meaningful experiences for their consumers at scale. Diane is on Forbes’ 30 Under 30 for enterprise technology and has been featured in The Wall Street Journal, HuffPost, TechCrunch, OZY, and Inc. Magazine. Diane ran three successful businesses before she was 18 and is a noted software innovator who frequently speaks on the intersection of AI, personal data, privacy, and the future of smarter products. Breinify works with retailers and consumer packaged goods brands to enable data science in marketing campaigns that secure 51% year-over-year online sales, 20 times the click rate, and six times the reaction rate.