Imagine the following scenario: You’ve got a great B2B product or service (think of your best one), and you’re ready to sell it.
You know it’s awesome, you’re confident it works – and damn well.
So you call in your best sales rep, give them the full brief, and have marketing produce top-notch material. Then, you instruct your sales rep to step outside your office and start selling it to every pedestrian who walks by dressed in a suit.
Needless to say, that would constitute a monumental waste of time, and of a valuable sales rep, to boot. Most tragically of all, your proudest solution will never reach its potential.
But without building an Ideal Customer Profile, that scenario may actually be playing itself out in your company right now (albeit to a slightly lesser degree of absurdity).
How to build an Ideal Customer Profile
Whether your company has adopted account-based marketing (ABM) or not, no B2B marketing strategy can achieve results without an intelligent, focused approach. Firing blindly is just plain amateur, and will achieve minimal ROI. You need to be strategic in who you target and how.
The obvious dilemma, of course, is where to begin. How can you target your best leads – never mind generate sales-qualified net new – without knowing who you’re meant to be targeting? Or, in plainer terms: how can you know who your ideal customers are before you’ve even engaged with them?
That’s what an Ideal Customer Profile is for.
An ICP is a model of your ideal buyer, which enables both sales and marketing to work in-sync and efficiently prioritize and guide the best leads through the sales funnel.
It isn’t an arbitrary construct. Building an Ideal Customer Profile is a science – one which requires cutting-edge Artificial Intelligence to execute effectively. Once your company has done so, the applications are endless; for example, you can build multiple ICPs to guide multiple campaigns.
An effective AI solution will identify your Ideal Customer Profile utilizing three main ingredients in particular.
The first is predictive analytics, a topic we’ve dealt with at significant lengths on this blog already.
Watch – Predictive analytics in action: predictive scoring
Predictive analytics uses machine-learning to build a model of your ideal customer, based on the past behavior of your existing leads. A quality predictive model will use that intelligence to identify which behavioral patterns and traits which constitute a Sales-Qualified Lead or potential customer.
It should also be able to tell you the opposite – which low-potential leads not to waste your limited resources on.
Why traditional predictive analytics alone isn’t enough
While clearly a hugely valuable tool, predictive analytics alone isn’t enough. Even the most advanced predictive model will struggle to deliver results if you aren’t properly utilizing two other, even more fundamental ingredients.
The good news? You’re already sitting on them right now.
The first is quality data. Predictive models are totally reliant upon the quality of the underlying data in your CRM and marketing automation systems. If – as is inevitably the case – a proportion of your data is either inaccurate or has for various reasons become obsolete, that will skew the model and lessen its effectiveness. It’s one of the key challenges of AI for B2B marketing.
An effective end-to-end AI solution must begin with highly accurate, and granular-level, data enrichment. By way of illustration, RingCentral rescued 200,000 leads from within its systems, which had been overlooked due to faulty or incomplete data fields, after employing Leadspace data enrichment.
One-off or periodical enrichment is only a start, though. Real time and on-demand enrichment is the only way to counteract the surprisingly rapid deterioration B2B databases tend to undergo.
The final ingredient requires even less effort: the collective knowledge and experience of your team.
A common pitfall when it comes to Artificial Intelligence solutions is the tendency towards “black box” models. In terms of predictive analytics, for example, that means a platform which doesn’t allow for any human interfere with their machine-learning model.
The problem with such a “black box” approach is that there are only so many variables that even the most advanced Artificial Intelligence can account for. Nothing can replace the personal intuition and experience of your own people.
To get the most out of your predictive analytics, make sure they offer a semantic model; i.e. one which combines Artificial Intelligence with the real, human kind. Something else RingCentral quickly appreciated.
The interface between AI and your staff needs to work in real time as well. Your sales and marketing teams must be ready to update your ICP as they learn more about your customers. They should be constantly looking out for changing trends, new or previously unnoticed yet important characteristics, and even errors within the initial Profile. All of that information should then be fed back in to further refine the model.
It might sound technically complicated, but it’s actually relatively simple to implement on an operational level.
Leadspace customer BloomReach for example utilized predictive analytics to build a highly effective Ideal Customer Profile – and generate 78% more net-new pipeline.
Image credit: iStock
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