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Categories: Marketing, Predictive,

Running an ABM campaign is more straightforward than you might think. But if there’s one thing to bear in mind while pursuing account-based marketing, it’s that guesswork simply won’t work.

That may be stating the obvious, but if your company doesn’t have an accurate, detailed Ideal Customer Profile, that’s essentially what you’re doing.

Consider the following: How do you segment your leads and target audiences? Job title? Seniority? Industry?

These are all important pieces of intelligence to have — but what’s the sum total of it all? How does it inform your decision regarding who to market or sell to?

Let’s say you’ve got a VP Sales or CMO who’s been a customer before — a name and some details which comprise a statistical probability to buy. Based on that information, in theory they could well have a need for what you’re offering. Or maybe not.

In short: You’re still guessing.

Sure, it may be a well-informed guess. And if you apply predictive modeling to your ABM campaign, you’ll even have a clear statistical model to work off of. But it’s still a guess, relatively-speaking.

The essential challenge here is that you’re not actually dealing with a statistical model. You’re dealing with a person, and people’s decisions are influenced by more than a handful of superficial professional traits.

ABM, at its essence, is the practice of marketing to companies (accounts) as units of one, via key influencers within those accounts. So for your ABM campaign to even get off the ground, you need to remember that — despite the name — “accounts” are only half the story.

Everything, from your content marketing to your sales pitches, needs to be personalized on an individual level. In fact, an ABM campaign can in some ways require even greater degrees of personalization in your marketing and sales materials, given that you’re pinpointing only a select group of decision-makers within their company.

But with only a few superficial data sets you’ll be doomed to pitching generically, no matter how many times you segment your contacts.

So how do you graduate from guesswork to something truly targeted — even predictive?

To sell to accounts, your data must be human

It goes without saying that you’ll never be able to predict the future with 100% accuracy. However, the more information you have, the closer you’ll come. So first of all, you need as much accurate data as possible.

But it’s not just about the sheer amount of data, but the variety as well. To create a reliable predictive model you need personal, professional and firmographic information on a very granular level.

Traditional data vendors won’t provide you with all of that. To obtain that quality of information you’ll need to mine data and behavioral signals from across the open web as well, and particularly from your leads’ social media profiles. Since you can’t expect sales and marketing reps to spend their days trawling through Facebook and LinkedIn, you’ll need a data solution which does it all for you.

But even that’s not enough. All of that enriched data needs to be regularly refreshed, or else your database will deteriorate in quality remarkably quickly.

Stop guessing, start predicting

An Ideal Customer Profile built on the basis of that information, with plenty of human input from your Sales and Marketing teams as well, will be far more intelligent. Far more effective. Far more human.

Take BloomReach. Their cloud marketing platform provides organic search, personalized site-search and digital marketing and merchandising applications, to help clients surface relevant content through search engines.

Unsurprising, then, that such a data-driven company would be an early adopter of ABM.

Yet Chief Marketing Technologist Jason Seeba soon realized they couldn’t accurately pursue ABM with the data at hand.

Using Leadspace Audience Modeling, BloomReach created customized Ideal Customer Profiles for each of their campaigns.

That level of personalization made their lead generation far more efficient throughout the sales funnel. By segmenting in such a personalized way, BloomReach generated 78% of their net-new pipeline.

For marketing, Leadspace’s predictive scoring assessed the quality of every lead source. This enabled more accurate analysis of the ROI of programs like tradeshows or webinars the moment they ended.

BloomReach were also empowered with the intelligence to direct their SDRs to target only the most worthwhile prospects. And sales more generally benefited from the fact that all of this intelligence was fully integrated into their CRM.

You can find out more about BloomReach’s ABM success with Leadspace predictive analytics by downloading the full case study:

BloomReach sources 78% net-new using Leadspace B2B Audience Management Platform

Picture by Scott Taylor from Flickr | CC BY 2.0 | No changes

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