We already wrote an article about why you should invest in predictive marketing rather than traditional marketing. The interesting (or depressing) fact about scaling a business is that what worked earlier will no longer work. This is true for tech companies. If you are a demand gen or marketing head of a growing tech company, you'd relate to this very well. As if the challenge of getting your annual marketing budgets approved wasn't enough already, you now have to find a foolproof way to generate ROI, even while experimenting. At the NRF that happened in Feb 2018, I asked tech company marketers what their priority for 2018 is. 8 out of 10 mentioned that 'account-based marketing' is a priority. They are already thinking about it or are experimenting.
'Account-based marketing' initiatives invert the funnel
The success of an account based marketing initiative lies in identifying the right accounts and knowing enough context about each one of them to engage them meaningfully at several levels across several media touch points at different stages of the buying cycle. Here is where 'Predictive marketing' comes into the picture.
Predictive marketing, done right, delivers highly relevant contextual information about very relevant accounts so that your 'account based marketing' initiatives generate the best results.
Let's take the case of a tech company selling omni-channel e-commerce suite to large retailers. Let's say that the ideal customer is an omni-channel retailer with $50M+ annual revenue. In traditional marketing, you would reach out to all retailers that qualify with the same message across the same channels. Predictive marketing helps you do better. It helps you qualify leads for fit, intent and opportunity size. With this qualification, downstream 'account based marketing' efforts become much more precise. Zero wastage marketing, ahoy! Predictive marketing starts with understanding the 'ideal segments in the market' and the 'ideal buyer profiles'. This understanding comes from analyzing the CRM & other systems (to not just consider 'win/loss' data but also assess which conversions led to 'high lifetime value'). Where such data is not available, you could start with your organization's 'subject matter experts' (typically product, sales, and marketing teams). The outcome of this initiative will be:
Based on the customer segments, buyer profiles and buying patterns identified, you'd then build playbooks for engaging each account and the buyers in each account. A playbook or cadence or drip is essentially a series of touch points for accounts in different stages of the funnel at different levels of buying capacity.
The predictive models then learn from campaign outcomes and update themselves to serve better accounts and better buyer profiles based on how the previous cohorts engaged. In the case of a tech firm selling omni-channel software, this is golden because each deal will be few hundreds of thousands of dollars.
For all the promise that predictive holds, the rubber meets the road when the accounts are recommended. To recommend accounts though, you need as much context as possible and here's where most generic predictive marketing firms struggle.
Just how much context can you derive from past CRM data, revenue, headcount, and news? Industry-specific nuances matter.
We are of the opinion that predictive marketing companies will evolve to be vertical focused. Just as you won't hire a real estate sales executive to sell in the oil and gas industry, general purpose predictive platforms will be outsmarted by industry-specific predictive platforms. Here's an illustration of how precise your ABM campaigns could be when backed by a predictive platform that looks at the data unique to your industry.
Keen to dig deeper? How about an assessment of whether predictive makes sense for you and whether you could expect the RoI and if so, when? It's on us. Let's speak!
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