The Next Stage of Account-Based Marketing is Predictive


The business world coined the term “account-based marketing” back in 2004. But B2B marketers only started to take notice in the last four or five years. From a strategic perspective, we’re still in the earliest days of ABM adoption and maturity.

Research by ITSMA found that half of ABM programs are less than a year old, with just 17% being in place for three or more years. However, adoption is set to skyrocket in the coming years. This is due to ABM delivering double the ROI of traditional marketing for almost 50% of B2B companies.

The challenge is figuring out how to reach the next stage of ABM — where personalization and budget allocation aren’t the major challenges that they are today. That’s where predictive insights and intent data come into play.

3 Benefits of Predictive Insights in ABM

The whole purpose of account-based marketing is to shift from high-volume lead gen programs to strategies that are designed to address the needs of individual target accounts. Doing so requires a deep pool of intelligence at both the account and individual levels.

That’s why so many B2B marketers have been turning to third-party intent data. With third-party purchase intent insights, you can understand the true behaviors and needs of in-market target accounts. Infusing that information into your ABM strategy will lead to better targeting, better content, and higher conversion rates.

But not all intent data is created equal. To get to the next level with ABM, intent insights must be predictive and offer critical information in real time. Your intent data partner can introduce artificial intelligence (AI) to their services to unlock predictive insights. Using AI, you can stay ahead of buyer expectations and further optimize your ABM processes.

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By leveraging predictive analytics and intent data, you can boost ABM performance with the help of three key benefits:

  • Better Account Prioritization: Timing is everything in ABM. Even if your target account list is accurate, mistiming when they’re actually in-market for your solutions can result in wasted marketing budgets and low conversions. In contrast, predictive insights and intent data give you real-time intelligence of the accounts that are most likely to convert. This streamlines account prioritization and fully optimizes your budget allocation.
  • Accurate Intent Signal Scoring: With real-time intelligence should come accurate intent signal scoring. As contacts and accounts move along the buyer’s journey, predictive analytics helps project the best times for sales outreach. Without predictive intent insights, there are several risks. Your competitors could reach target accounts before you do, or your messages could be outdated.
  • Deeper Content Personalization: One main reason personalization is such a challenge for ABM practitioners is that we’re often reacting to account intelligence. Every time you learn about a particular buyer behavior, you scramble to put together a personalized piece of content that will address specific account needs. Predictive analytics get you ahead of buyer behavior, giving you the means to proactively personalize content without wasting marketing resources.

Finding Intent Insights That Are Predictive

It’s one thing to talk about bringing AI to your ABM program — it’s another thing entirely to actually do it successfully. This is where so many B2B marketers fall short. Trying to unlock the potential of AI and machine learning on your own will take valuable time and energy away from the ABM program you’re building.

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When you invest in intent data sets built on predictive analytics and machine learning, you get the benefits of high-quality data without having to implement and understand the underlying technologies yourself.

This all starts with choosing an intent data provider that goes beyond behavior tracking to truly predict purchase intent. And that means knowing what to look for from an intent data provider.

Check out our eBook, Demystifying B2B Purchase Intent Data, for a primer on how predictive analytics factor into intent data.



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