For much of the internet’s existence, a website was simply an online marketing and sales brochure designed to relay generic information about a company and what it sells. Thanks to technology advances such as reverse-IP data enrichment and firmographic enhancements, website personalization is now possible, and in fact a staple in differentiation against the competition.
Website personalization means serving content specific to the visitor. The website experience becomes an experience that is defined by the visitor’s demographic, location, or profile that is built over a period of time and interaction.
There is a general consensus among marketers that website personalization drives higher conversion rates and more revenue. If these two benefits aren’t compelling enough, McKinsey analysts estimate that personalization stands to create $1.7 to $3 trillion in new value for companies. It stands to reason companies that do not personalize their website experience will lag behind the leaders in their respective industries and ultimately lose market share to those who embrace website personalization.
While the importance of website personalization may be perfectly clear, the misconceptions on getting started suggest a difficult planning and implementation effort. In reality, it’s best to keep it simple out of the gate. Here are a few things to consider as you examine website personalization as an option.
Importantly, there are some fundamental differences between rules-based personalization, which relies on marketers to create segment-specific definitions for each personalization, and machine-learning-based personalization which leverages the power of AI to deliver unique experiences personalized to every single visitor. These are two important considerations for any marketer that is evaluating a machine-learning based approach:
Because of the incredibly high technical bar in creating AI-driven personalization systems, until now, this approach has only been possible for some of the largest and most familiar technology companies in the world, like Google, Amazon, Microsoft and others. The good news is that Intellimize is now bringing the power of advanced personalization technology to marketers at scale. Today, conversion obsessed marketers can implement an AI-driven personalization solution by adding just a single line of code to their website. With this approach, getting started with personalization can be as simple as providing the creative ideas that will be unique to each audience or segment..
With these aspects of website personalization in mind it’s time to get started and that means exploring data. Data is the fuel that powers personalization.
With some of the misconceptions of website personalization behind us, it’s time to start thinking about implementation and that starts with data. How do marketers use data in their efforts? We’ve culled some best practices thanks to working relationships with a host of organizations across a number of industries. Here are 5 best practices for using data to build successful website personalization:
As we’ve pointed out above, website personalization doesn’t have to be a daunting ordeal. Gone are the long, arduous and lengthy A/B testing protocols that many marketers have relied on to date. With AI and machine learning technologies now available, website personalization can truly take hold.
Much of the work of conversion rate optimization is in managing experiments; design, setup, implementation, analysis, and application of learnings. This work is important, but also sometimes administrative and repetitive. Predictive personalization automates the rote tasks involved in website testing, automatically applying learnings and freeing up marketers to focus more energy on generating new ideas.
Automation does more than take the mechanical work off of marketers’ collective plates. In some cases, automation is able to do those jobs to a degree no marketer ever would. Where teams using A/B testing are typically able to test a few ideas at a time, predictive personalization allows teams to test dozens of ideas over a comparable period. In fact, in our first year, our average customer tested 25 times what they could have achieved using A/B testing.