I 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, ABM 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 ISVs. They are pursuing an account-based marketing strategy and are in the process of developing a list of ISV targets – and are 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, they’re trying to agree on which field(s) to use (or to create new ones entirely). They tried using the NAICS classification system – but they found that tends to be not as accurate. For example, a company developing healthcare software could be classified as a “healthcare company.”
Here are a ways which good data drives sales and marketing alignment.
1. Identify “look-a-likes”
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 – like is Company A more likely to buy than Company B?
Most predictive analytics vendors will have their own databases of all the companies 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 they 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 this example, the company could look at their list of existing ISV customers (the “training data” for the predictive analytics system) and have predictive analytics provide a list of “look-a-like” accounts. Predictive analytics also provides a way to “back-test the model” (fancy way of saying verify its accuracy) so you can have confidence in the target list provided. A target list of your best-fit accounts is critical for sales and marketing alignment.
2. Find which accounts are in-market.
Your target accounts might already be engaging with you already on your properties or on 3rd party properties – in which case, marketing should flag that appropriately and put them into a special nurture track. Our example company was planning on running executive roadshows for its target accounts. They could look to see which target accounts are engaged via their inbound programs, identify what the title is for the engaged lead – if the lead is an exec-level lead, then invite them to the roadshow using a personalized invite – if not, send the contact to sales – and ensure they prioritize follow-up as this is a contact from a target account that has engaged with you.
A screenshot from Marketo showing nurture activities
3. Go deep on their 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:
- Title
- What keeps them up at night
- Where they “hangout”
- 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 “employee band.” Think credit scores, financial filings, quarter over quarter revenue changes, executive changes, office expansions, technologies used on their website and behind the firewall, etc.
In the context of our example, the company has different offerings for each of the different development platforms – Google Cloud, Amazon Cloud, Microsoft Azure, etc. 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 list by which companies have Google Cloud, Amazon Cloud, etc — 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 starts to get both marketing and sales thinking about “account-first.” That’s a great first step in driving sales and marketing alignment. But as I’ve shown with a simple example above, there are many other ways you can use predictive analytics and datato get sales and marketing on the same page with respect to who you 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’s latest whitepaper 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. This post is edited from an original version on the Terminus website.