I recently had the pleasure of hosting Sangram Vajre, CMO and Co-Founder of Terminus, and Peter Isaacson, CMO of Demandbase in a webinar about scaling account-based marketing (ABM) using predictive analytics. A key point we kept coming back to was how sales and marketing alignment is a key factor in any ABM strategy.
Yes, account-based marketing does shift marketing’s focus from leads to accounts — which is how salespeople think about the business. So, I’d say that’s a step in the right direction. But that’s not enough. I’d argue that data is a key part of getting sales and marketing on the same page. Let me illustrate through a story.
I was talking to a B2B company last week who targets independent software vendors (ISVs). Let’s call them Sample Company. They are pursuing an account-based marketing strategy and are in the process of developing a list of ISV target accounts — and they’re struggling. Over the years, as they’ve grown, their Salesforce instance has proliferated with multiple fields, each serving to classify the “segment” into which an account falls (many of which were fairly arbitrary).
Now, Sample Company is trying to agree on which existing fields to use (or whether they should create new ones entirely). They tried using the NAICS classification system, but they found that it tends to be less accurate. For example, a company developing healthcare software could be classified as a “healthcare company,” but for this business’ purposes, that doesn’t work.
Here are four ways that good data drives sales and marketing alignment.
1. Identify lookalikes.
Predictive analytics relies on using training data and machine learning to identify patterns in large amounts of data. These patterns make it easier for you to make educated guesses about certain outcomes — for example, is Company A more likely to buy than Company B?
Most predictive analytics vendors will have their own databases of virtually every company out there. Using their technology, you can identify net new companies who “look” just like your existing customers. This not only helps immensely with sales and marketing alignment, but depending on how closely these net new accounts resemble your existing customers, you can assign them a score which you can ultimately use for prioritizing sales and marketing efforts.
In the context of Sample Company, the marketing team could look at their list of existing ISV customers (the “training data” for the predictive analytics system) and have their predictive analytics tool provide a list of lookalike accounts. Predictive analytics also provides a way to “back-test the model,” a fancy way of saying “verify the model’s accuracy,” so you can have confidence in the target list provided. A target list of your best-fit accounts is critical for ABM and sales-marketing alignment.
2. Find which accounts are in-market.
Your target accounts might already be engaging with you on your properties or on third-party properties. In this case, marketing should flag that appropriately and place those accounts on a special nurture track. For example, Sample Company is planning on running executive roadshows for its target accounts. They could look to see which target accounts are engaged via their inbound programs and identify what the job title is for the engaged lead. If the lead is an exec-level lead, then they could invite them to the roadshow using a personalized invite. If not, they could send the contact to sales and ensure they prioritize follow-up because this is a contact from a target account that has proactively engaged with them.
3. Go deep on buyer personas.
Product marketing typically creates buyer personas for go-to-market purposes. In most instances, however, they’ll be at the “person level” and include things such as:
- Job title
- What keeps them up at night
- Where they “hang out” online
- What metrics they care about
What’s needed, though, is a really close look at the companies that these people work for. And, by that, I don’t just mean company revenue, industry, or headcount. Think credit scores, financial filings, quarter-over-quarter revenue changes, executive changes, office expansions, technologies used on their website and behind the firewall, and so on.
Sample Company, for example, has different offerings for different development platforms: Google Cloud, Amazon Cloud, Microsoft Azure. They can use predictive analytics to identify the types of development platforms each of their target companies use.
Marketing can then further segment their target account list by development platform, enabling them to run hyper-targeted campaigns. Marketing can also provide this insight to sales so they can have more contextualized conversations with their target accounts. These types of conversations are only possible through sales and marketing alignment.
4. Going “account-first” is only the beginning.
Account-based marketing gets both marketing and sales thinking “account-first.” That’s a great first step in driving sales and marketing alignment. But as I’ve shown, there are many other ways you can use predictive analytics and data to get sales and marketing on the same page with respect to who your target accounts are, how to prioritize them, and ultimately how to engage them.
If you’d like to learn more, I invite you to download Lattice Engines’ latest white paper describing how you can scale account-based marketing at your company with predictive analytics.
Nipul Chokshi is the Head of Product Marketing at Lattice Engines and is responsible for messaging, product positioning, and sales enablement. Prior to Lattice, Nipul built and ran the Solutions Marketing and Sales Enablement functions at Yammer (acquired by Microsoft in 2012). Before Yammer, Nipul led product marketing at Marketo, Merced Systems (acquired by NICE Systems), and Siebel Systems (acquired by Oracle). He lives in San Francisco and is an avid runner.