Account-based marketing (ABM) has its roots in the 1990s as both B2C and B2B companies realised the importance of a more personalised marketing model.
The term took off in popularity in the 2000s and the phrase ‘account-based marketing’ made it into Google Trends in 2013.
In 2015, SiriusDecisions unveiled the results of its 2015 State of Account-Based Marketing Study where it proudly announced that: “More than 90 per cent of marketers believe that account-based marketing is a B2B must-have.”
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Unlocking and managing data with account-based marketing
Data is everything when it comes to marketing. Indeed, it is this access to customers’ data itself that made ABM such an appetising prospect in the first place.
Following on, the richer the datasets a business can monitor and follow, the more personalisation of conversation and content can take place, which is the cornerstone of ABM.
Let’s take marketing content as an example. Tracking IP addresses allows businesses to discover the number of visitors coming on to their domain, and how they navigate around it.
But with IP Enrichment, you can determine the domain name associated with each IP address, splitting out public-facing, yet vital data about which company or business was visiting your website.
This means you are only talking to people who have shown an interest in you in the first place.
So we are dealing with quality over quantity; many businesses believe their account list in its thousands is up-to-date and relevant. It probably isn’t.
ABM practitioners need to be plugged into the constant changes and developments in data across both account and individual levels. What if a company changes priorities? Or an individual leaves or is promoted?
After all, there is rarely just one decision-maker in a B2B sale, so managing your datasets is vital.
Getting intent data
Once your dataset is well managed, the next step is clear – predictive insights, otherwise known as intent data.
By monitoring a business user’s web content consumption – both on your platforms and across other B2B sites – you can provide insights into their interests and where they sit on the buying cycle.
Essentially, each anonymous website visitor is actually a user persona that can be tracked and targeted. And those marketers offering the richest data, give marketers the best targeting capabilities and visibility.
Not only can a campaign be structured towards a specific company, but it can also be ranked against other companies within their industry.
But it is not enough to have access to all this intent data if you are relying on marketing teams to track every single interaction in real-time, and fully understand the needs and actions of customers, unless technology is underpinning it all.
Account-based marketing: making use of intent data
To optimise your ABM campaign, intent insights must be predictive and offer critical information in real-time. The aim is, after all, to stay ahead of the potential buyer so you know when they are most likely to act.
And in reality, it is only smart technology that can turn this deluge of data into meaningful actions.
Propensity modelling tries to mathematically predict whether visitors, leads and customers will sign-up, buy or accept an offer.
Machine learning (computer-based algorithms that improve automatically through experience) allied with propensity modelling enables markets to target readers at the moment they are likely to take positive action.
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