Six things to do before using predictive analytics to drive ABM

Predictive analytics in B2B marketing. It’s new (to many), it’s shiny, it’s exciting.

Naturally, you’re super keen. When you explain it (in layman’s terms) to your boss and your bosses’ boss, they’re excited too. New accounts we don’t know about? Buyers who are in market right now? Three times the response rate? When can we start? What are you waiting for?

Whoa there. Stop. Think.

This could indeed change the world of account based marketing as we know it. But did you ever hear the one about the sexy new technology that didn’t quite deliver on grossly inflated expectations? Yes, we know: it’s not funny anymore.

Before you write the big check, open up those APIs and start adjusting those filters, you’d be wise to take these six simple-ish steps to ensure success.

1. Get your first party data in order

If predictive analytics pinpoints an account as being in market, you need to be sure you can identify the contacts within your database associated with those accounts. That means combining data from relevant systems – CRM and MAP and maybe ERP too (you don’t want to be approaching an existing customer like a brand new prospect). Contacts may be mapped to any one or more of these and there should ideally be a common identifier across all systems. We go into much more depth on this topic in this article.

2. Develop relevant sales and marketing ‘plays’

You’ve identified a set of accounts that are hot. But hot for what? Don’t let this take you by surprise. If you have email addresses and telephone numbers for contacts at these accounts, you’ll need a calling guide covering drivers, triggers, barriers and potential objections, relevant late-stage content offers for information fulfilment, promotions to push, vertical industry use cases to refer to, or maybe a means to deliver demos or a process for provisioning trial offers.

3. Train business development reps to go in cold

Yes, the account may be red hot, but the first person you speak to may be icy cold. Inside sales teams used to doing inbound lead qualification, or key account managers used to dealing with decision makers may not be ready to deal with a frosty first response. Are they equipped to engage an influencer who may never have heard of your company, or won’t acknowledge they have issue? If you have doubts, then engage a specialist for the pilot program at least.

4. Proactively manage consent

The people you’re going to be dealing with aren’t your usual “hand-raisers” – they may not be in your database, or have even visited your website. You don’t even have tacit permission to communicate. Job #1 in this era of GDPR is to systemize the gathering of consent across all channels you plan to use: phone, email, advertising and social media so that you’re set up to continue the conversation long after the first call. Ensure you can log the date and context of consent within a ‘consent certificate’ and ideally send a ‘consent’ receipt to the individual concerned.

5. Implement a rigorous test methodology

It’s all too easy to jump in hoping for the best. Ensure you have established a proper methodology for designing experiments and analyzing both quantitative reports and qualitative feedback. Otherwise it’s all just guesswork. Campaigns get implemented, they may work, but you don’t know why. Models never get refined or improved and you’re starting from scratch every time.

6. Set internal expectations

Predictive analytics is not a magic wand. It’s not a silver bullet or a gold mine either. Start drilling into the data and you may strike a sales-ready seam early, but you may also spend the first few months coming up dry. Both the budget ratifier and the internal customers (your sales team) need to understand and accept this.

Now you’ve under-promised, you’re ready to start over-delivering. Just take the time to select the right tool (hint: some predictive analytics vendors are way better than others) and if you’re still lacking the confidence to go it alone, there are some great partners out there willing to ride along. Giddy-up!