How IBM Uses Data To Power Account-Based Marketing Strategy
My neighbor was a math professor at a college in San Francisco. He once told me: “The longest distance between two points travels through a committee.”
His quote applies well to B2B sales. A Forrester survey found that 80% of respondents say at least three people are involved in the purchase process, and most of the other 20% say they buy in teams of two.
Caitlyn Wood, lead data scientist at IBM, says transitioning your B2B marketing strategy from targeting individuals to focusing on the account level is a must.
During her presentation at the Marketing Analytics & Data Science (MADS) conference, Caitlyn compares B2B buying to a family buying a house. Though the parents are the ultimate decision-makers, they aren’t the only voices in the process. “The teenage daughter might want a bigger bedroom, or maybe they want to live near a playground for the son, or maybe they want a nice fenced-in yard for the dog,” she explains.
In a B2B setting, the decision-makers and the other voices in the buying process have different needs. IT wants a product that complies with cybersecurity standards. Engineering wants an API to extend the product, and marketing wants the ability to have easy-to-create dashboards.
“In the typical lead-based (marketing) model, you’re looking at individual interactions with people, but in a B2B context, you really need to look at the full picture of what’s going on within an account, not just these little slices,” says Caitlyn, who creates data science models for sales and marketing at IBM.
Read on for her account-level methodology to renovate your B2B marketing strategy.
Follow a high-intensity account framework
When multiple people in an organization consume your content, you can quantify their interest in your products and services. Caitlyn looks for accounts with multiple marketing interactions in a 30-day period. You could use a month or longer, such as 90 or 120 days, depending on your sales cycle.
In the IBM sales pipeline, about 12% of accounts attain this multi-interaction status every quarter. About eight in 10 accounts involve two or more people, and 87% involve multiple product areas. These accounts are four times more likely to convert into an opportunity within 30 days.
“We want to learn from the accounts that are consuming our content rapidly and leading to conversion pretty quickly,” Caitlyn says. “These are the accounts that say, ‘I’ve looked at IBM, I really like what you’re doing, and I want to purchase from you.’”
With these high-intensity accounts identified, marketing can invite them to events and encourage them to sign up for free trials or demos. Sales can also prioritize follow-up interactions because these accounts are likely to convert quickly.
The framework also allows marketing to better understand the journey of the highly coveted customers. Caitlyn explains, “We can look at what are the most likely pathways for our accounts to get to high intensity. Are there sticking points in there? Can we fix those sticking points? Is this particular content resonating really well with the finance industry in Germany? Can we tweak this content to other industries or other geographies?”
Prioritize and highlight patterns for sales teams
Remember the family buying a house? A real estate agent would show them homes that met the needs of the parents and the wants of the children — a large second bedroom, a fenced-in yard, and a location near a playground.
In B2B, marketing and sales play the role of the real estate agent, delivering on the wants and needs of the multiple buyers in the process. Caitlyn says the journey might look like this:
- In early January, the potential buyers looked at a research report.
- Then, a few individuals at the organization did a free trial.
- A C-suite member and others attended an event hosted by the seller.
- In February, other individuals in the organization did another trial.
Given the surge in account activity, the marketing and sales teams would evaluate the interactions and tailor their follow-up conversations and activities accordingly.
“It’s not just one individual. It’s not just one trial,” Caitlyn explains. How do these products interact with each other? How do they mesh? What is this account really interested in looking at? And can we tailor our conversations to best match their needs?”
Use the data to optimize high-impact journey
Caitlyn’s study of the high-impact account data uncovered interesting patterns, which she used to improve the customer journey with IBM.
Over half (54%) of prospects whose first interaction with IBM is a trial followed by consumption of a research report are identified as high-intensity accounts. Yet only 26% of prospects who do just a trial are likely to become a high-intensity account. Similarly, prospects who received a research report within a week of a product demo were 3.2 times more likely to become a high-intensity account.
The data has shown Caitlyn that the research report provides important background and context to the product being tested or demonstrated. Prospects might think, “I’ve tried the product and the trial; I kind of get it, but then this research paper comes in and explains all of the benefits that I wasn’t really seeing within the trial itself.”
Use data science to find more high-intensity accounts
IBM doesn’t just use the data to maximize the current prospects’ journey; it analyzes data to find new accounts resembling those high-intensity accounts — also known as lookalike accounts.
It builds the strategy under the assumption that low-engagement accounts have not yet reached the content that will propel them into the high-intensity bucket. The blocks to accessing that content could be as simple as a broken link or the failure to follow up with a research report after a demo, Caitlyn explains.
To find lookalike accounts, IBM uses positive-unlabeled learning, a type of machine learning that uses positive accounts (i.e., high intensity), with unlabeled accounts. It analyzes the unlabeled accounts (usually low and medium intensity) to determine whether to label them as high intensity.
“We’re not trying to predict who will be high intensity yet, but who looks similar enough to our high-intensity accounts that we think they should behave similarly, and we can nudge them into continuing discussions with us,” Caitlyn explains.
The positive-unlabeled learning uses market activity, third-party industry data, geo/market data, and historical purchase data in the analysis. Caitlyn says it accurately identifies the lower-engagement accounts that can be nurtured into high intensity 93% of the time. These lookalike accounts also convert two times more than the average account.
Add account lessons into the system
After identifying the high-intensity and lookalike accounts, ensure that the account intelligence flows properly into the sales and marketing systems. Here’s a look at how IBM does it:
The high-intensity accounts flow to the sales rep to execute the Salesloft system’s inbound action at a cadence for the next-best actions, leading to the conversion.
The lookalike account triggers marketing actions that could prompt the outbound Salesloft cadence to drive additional activity or launch the nurture email stream through Marketo. This new activity could convert the lookalike account into a high-intensity account. Then, the new high-intensity account is funneled through that process.
Are you ready to focus on account-level insights?
If my neighbor transitioned from academia to B2B marketing, he’d say something like this: “The path from inquiry to close travels through a committee, so identify the constituencies on that committee and understand what’s important to them.”
In the old days, if the chief information security officer at a Fortune 500 company downloaded a white paper, the brand’s marketers used to jump up and down, thinking it was the opening play for a large sale.
But don’t get that excited about a C-suite download. That CISO might have her signature on the purchase order but likely has a committee of three to 10 or more influencers who collectively decide on a vendor and product offering. Like the parents buying a new home, the interests of the daughter, son, and dog are of high importance.
As the IBM journey shared by Caitlyn proves, it’s smart to transition from individual to account-level insights. Use the data gained from the pursuit of high-intensity accounts to identify new accounts likely to convert to high-intensity.
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Cover image by Joseph Kalinowski/Content Marketing Institute
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