How to use intent data in your accounts-based marketing strategy
Intent data offers a wealth of opportunity to marketers. Ashley Evans dives deep into how to effectively use intent data in your account-based marketing strategy
Let’s get one thing straight. If you’re currently doing account-based marketing, or you’re thinking about doing ABM, and you think that there's a silver-bullet technology that will magically make it work, make it scalable, or just do the work for you, you’re barking up the wrong tree.
It doesn’t exist.
Moreover, using a marketing automation platform will not instantly create a solid accounts-based marketing programme.
Account-based marketing is a strategy, a methodology enhanced by technology.
It’s multi-touch; leveraging all media to ensure the most efficient use of marketing resource. Yes - a massive chunk of that output is digital these days, but DM, events and above-the-line traditional media all still have their place in the B2B marketing mix last I checked.
It’s true that marketing technology has helped us move on from the days when we could really only feasibly focus efforts on a select few named accounts, but if you’re looking to buy a piece of kit to do ABM on the cheap and your methodology is flawed you’re never going to get the most out of your tech stack, so it will end up costing you in the long term.
The most obvious example right now is purchase intent data, which seems to be the topic du jour in the B2B marketing space right now.
Everyone seems to be trying to get a handle on what it is and how to use it.
The truth is it’s an extremely powerful tool when used correctly; a way of building an outside-in, needs-based approach to account-based marketing.
But it’s not a crystal ball and very few know how to get the best out of it.
How to get the most out of intent data
For those unfamiliar with purchase intent data, I refer you to the Cyance ‘battleship’ grid below, a bit like a Rubix cube.
The horizontal axis (A,B,C) shows you level of purchase intent, or how ‘in-market’ a prospect is.
The vertical axis (1,2,3) represents target audience fit (e.g. sector, company size, revenue, geography, etc).
So, if your 194 ‘A1’ accounts are a perfect firmographic fit and heavily ‘surging’ around the most relevant keywords to your personas and value proposition, your ‘C3’ accounts would be less a fit and probably more top-of-funnel; not to be ignored, but not in-market right this second.
I’ve spoken with a number of marketing directors in the past few weeks who remain cynical about intent data, but when I dig deeper I find the methodology they’ve deployed is flawed and, as a result, the results are skewed.
Many people think simply chucking an SEO/SEM keyword list into an intent engine should yield the same positive results and it’s simply not the case. Just like any data-analysis tool, the output from these intent engines is only ever as good as the information being fed in. That requires a level of understanding few have, and a level of due diligence up-front few can commit to.
Cyance CEO Jon Clarke, says: “B2B Marketers have an opportunity to transform the results from demand generation and ABM campaigns. But you need an effective campaign strategy and activation plan to realise success.
Behavior-based marketing provides the insights that not only reveal that a prospect is more likely to buy, but highlights the active, intention-to-buy signals with pinpoint accuracy, and in real time.
By applying an effective approach to using buying intent data, you can begin to transform marketing results.
Follow this 3 step approach
1. Context – Ensure your intent signals truly reveal genuine intent and don’t paint a misleading picture.
2. Timing – Your intent strategy should help to establish the stage of the buying journey. This helps to target the right persona within each decision-making unit and adapt your message according to the buying stage.
3. Relevance – By combining context and timing you can ensure your content and engagement plan is more likely to resonate with your audience.
Good solid buyer persona work is a pre-requisite of any good B2B marketing programme, let alone account-based marketing. To ensure your personas are fit for purpose they should cover topics such as:
- Relevance to stage of the buying cycle
- Content consumption habits
- Trigger points
- Personal habits
- But above all, what keeps these people up at night?
But it becomes doubly important when building out an intent data set, as that persona work will likely fuel the keyword list being used.
If that leg-work hasn’t been done and you’re hoping for revelatory insights from the intent data on the back-end you’re likely to be disappointed.
It takes time and experience to understand how to manipulate these various tools to get to your desired result.
Knowing which ones deliver and which ones don’t - their various USPs and idiosyncrasies – is trial and error and being able to plug and play depending what you need to achieve is a rare skill; one honed over a painstaking amount of time.
However, it always comes back to a sound, fundamental ABM strategy with sound, measurable KPIs.
For instance, are you looking to drive:
- Net new client wins
- Pipeline acceleration
- Prospect engagement
- Account growth/engagement
- Marketing influenced/sourced/attributed revenue.
If these types of KPIs aren’t in place it doesn’t matter what tech you use to execute.
Ultimately, tech platforms such as Cyance, Demandbase, Marketo, Hubspot, etc exist for one reason - to take our good, solid marketing work and help us amplify our efforts in a way we can’t do manually.
When used correctly they can help us scale our efforts while maintaining the personalization at the heart of ABM. That’s the holy grail.
But if it’s being used to paper over the cracks in your methodology then it’s destined to fail. The irony therein being technology which is meant to deliver economies of scale ends up becoming a very expensive write-off.