Account-based advertising is hot right now — 66% of marketers cited account-based advertising as a top delivery channel in Demand Gen Report’s “ABM Benchmark Survey,” and one-third indicated they plan to implement account-based advertising tools throughout the year.
But collecting data to inform targeted advertising remains choppy. Organizations are still polishing the new metrics they adopted to account for the Covid-19 pandemic and its subsequent challenges, such as IP addresses being attached to home networks and “The Great Resignation.” However, it remains a lucrative strategy to engage accounts, as serving up a targeted advertisement to an in-market account is more likely to result in a conversion.
To dive deeper into the modern world of account-based advertising, we sat down with Jon Miller, CMO of Demandbase, to discuss the ins and outs of advertising and the role multiple data sources play in creating relevant assets.
ABM In Action: With so many practitioners utilizing account-based advertising to deliver content, how can they ensure they’re ads are working efficiently?
Jon Miller: There are two things to figure out to ensure ads are working efficiently: Identifying the account you want to show an ad too and figuring out how to get it in front of them. In the past, organizations mainly just used the IP address. You’ve probably heard of “reverse IP,” where you can analyze a database and see which actions are tied to certain companies.
But when the Covid-19 pandemic hit and everybody started working from home and other places, IP data became a lot less valuable and a lot more inaccurate. So, we had to figure out how to combine cookies and IP data to identify an account — and it’s a nuanced process.
ABMIA: Can you expand on the nuances of targeted advertisements and discuss the importance of infusing multiple data sources?
Miller: It’s all about how companies pair their first-party data with the third-party — third-party data without great first-party data is still somewhat limited. Third-party data can tell you an account has 50 employees that all work for a SaaS company headquartered in New Jersey, so that highlights some key features of the account to tailor the advertisement itself. You might even find that one of the targets was a customer at another company and they just switched careers.
You then need to combine those insights with first-party data to see if the account is already a customer, engaging with your sales team already or if a current employee just moved over from another one of your customers.
Essentially it comes down to this: Any good ABM or ABX advertising program really centers on doing smart things with the data you have on your customers and prospects.
ABMIA: Why is it so important for companies to rely on intelligence and data to inform campaigns and content?
Miller: What’s been missing from most ABM platforms is data intelligence. If you go back to old automation tools, you were just served an empty application — similar to a blank Excel spreadsheet — that you had to fill with data manually if you wanted it to do anything. Due to AI and machine learning, we’re seeing this blend of data intelligence into workflows to ultimately make the tools smarter, more effective and automated.
If you don’t have good data or intelligence, you’ll have to use hunches to make decisions around target accounts. You won’t know what to say to them either and it’ll end up feeling like spam — even if it’s not. When you can bring data and intelligence to the table, you’re able to spot better opportunities and more intelligently engage with them to create a better overall customer experience (CX), which includes personalization.
Reaching out at the right time is a form of personalization — and not reaching out to a prospect if they’re not in-market is personalization. And by “reaching out” I don’t just mean the outreach itself; I mean the wording you use to describe a prospect’s problem, recognizing what company they’re at and knowing the context of their history and relationship with your company. And that’s all powered by good intelligence and workflow.
ABMIA: What are some best practices you’ve seen for identifying in-market accounts to target, and what are some outdated ones?
Miller: Typically, people have tried to target accounts by homing in on titles — for example, I might want directors and above in the marketing department. But that’s not a reliable metric, as research has shown that only generates about a 30% hit rate.
At Demandbase, we combine the cookie and IP address on our website and then mine our data sets. And while we might not know who the individual is, we can identify they work at Company X. If we’re able to find a pattern that multiple people from Company X are researching a certain topic, that signals that it’s time to serve them an advertisement.