Getting More From Your ABM Strategy with Personalization
How to increase lead quality by connecting your company’s ABM with website personalization
So many of your B2B website's visitors are unknown. If only you could address those visitors and engage them you could improve your business results. But they're lurking in the shadows.
You've deployed an Account-based Marketing (ABM) strategy and technology to help engage those visitors but that's still leaving a lot of opportunity on the table. What you really need is a way to engage visitors regardless of what you know about them.
AI technologies can help make sense of visitor behavior data without needing to know the visitor. Once you've engaged them, it's more likely they'll self-identify through a download or form. The visitors have stepped from the darkness into the light.
Learn how to increase your website engagement and conversion rates by marrying AI-powered personalization with your existing ABM stack in order to unlock data you’re not using today.
By attending this webcast, viewers will be able to:
- Differentiate between the three types of website visitors and technologies that engage each;
- Understand how AI — machine learning and natural language processing — can be applied to drive personalized experiences for both known and unknown visitors; and
- Develop a strategy to utilize personal, firmographic, and anonymous data together to engage website visitors.
Who Should Attend: Managers, Directors, and Vice Presidents of marketing, digital marketing, web operations, and customer experience responsible for increasing website visitor engagement and conversion. Also, technical leaders looking to increase the value proposition of the marketing IT stack.
January 20, 2021 01:00 PM ET [13:00] (New York)
January 26, 2021 04:00 PM CET [16:00] (Brussels)
January 26, 2021 11:00 AM ET [11:00] (New York)
January 27, 2021 01:00 PM ET [13:00] (New York)
January 28, 2021 03:00 PM ET [15:00] (New York)
February 03, 2021 02:00 PM ET [14:00] (New York)
February 10, 2021 02:00 PM ET [14:00] (New York)
January 14, 2021 01:00 PM ET [13:00] (New York)
January 21, 2020 03:00 PM ET [15:00] (New York)