This is one in a series of blog posts that will tap into our own ABM experts to guide you through the questions you should be asking when considering ABM technology.
In the world of ABM platforms, there really isn’t anything more important than how well a platform can identify, segment, and surface relevant insights to enable account selection. Intelligent and informed targeting is ultimately at the heart of every good ABM program, so it makes sense that when reviewing ABM platforms, this is where a company should spend a good deal of time asking the right questions.
For this article, let’s define ABM Platforms as a technology able to provide account-based targeting capabilities and incorporate some level of intent and/or predictive AI. Companies relying on purely firmographic modeling are really not relevant to most of these questions because they lack the ability to look beyond basic account profiling, which already greatly limits their ability to fuel today’s “intelligent targeting” practices.
Having worked with various ABM tools, and having led multiple ABM platform reviews and implementations before, I want to share my perspective on the key questions and topics any prospective buyer of an ABM platform should dive into during any evaluation.
3rd Party Data
Start by looking at a provider’s network of data to understand where they source data and how they surface it to you with:
1. “How do you source anonymous intent data and at what level can I access that data?”
With this question, you’re seeking to dig into the expanse of a vendor’s intent data network and understand what kind of signals they can provide to both their AI engine (should they have one) and as insights to your revenue teams.
It’s important to understand some key aspects of this data. Is the data global? What KIND of data are they sourcing (e.g. bidstream/impression data or content metadata) and is it truly indicative of intent? Is the data uniquely their own or available through other vendors? Is the data meeting today’s compliance standards? How granular can your platform be when surfacing intent data?
In my experience, this last point is the most important – the more flexibility you have with the data, the better you are able to tailor targeting for unique programs around very specific intent and the better insights your reps have for a conversation. For example, if a customer is researching CASTL compliance standards, that’s far more useful than knowing they’re just searching “compliance” related topics.
Interpreting Anonymous Activity
I believe this is one of the most important areas to question and is where you need to ‘look under the hood’ a bit to understand what you’re getting by asking:
2. “How do you identify account activity in the digital universe and how good are you at doing it?“
Plenty of companies can throw big numbers around about their database size, intent signal volume, etc. but it really comes down to how well they can tie all that anonymous traffic back to real accounts. This is critical because it represents how good they are at translating raw anonymous signals into usable account-level insights. It also reflects a vendor’s ability to actively target against those same accounts in the digital universe when it’s time to go back out into the market with your programs.
You want to examine how they match signals to accounts (IP? Cookie? Other?), how well their system adapts to changes in account mappings (new office, new ISP, new device, etc.,) and how accurate their system is at identifying traffic with confidence.
For some providers, this may be a moot point because they only gather data from self-identified (though maybe not verified) contacts inside their walled-gardens, but for vendors with expansive data networks and first-party web monitoring, this is critical. A bunch of intent data without the ability to make sense of it is useless noise at best, or false signal improperly matched at worst. A great way to test this is to ask a vendor to do a match-rate test on your website and compare their results to other vendors.
Here’s where the rubber meets the road when it comes to an ABM platform – the ability to feed tons of intent signals into an AI platform that is able to interpret the data and provide usable predictions. So dive in by asking:
3. “What data informs your predictions and how are your models built?”
A good predictive ABM model needs to be able to predict for ‘when’ – when is an account truly in-market, when are they hitting certain stages of their buyer’s journey, and when is the right time to activate the right GTM channels and messages. This is what you should be aiming for when evaluating an ABM platform – segmentation is just pushing data around, profile models are guesses at who looks like a buyer, predictive is helping you hone your targeting to be efficient and effective based on who wants to buy.
Unraveling how a model is built is important – not just “are they using machine learning” (that’s just a python library file these days), but what data is informing the model? How well is the model being trained to fit your systems and your data? What is being scored by the AI – just accounts? What about leads and contacts? These are important distinctions that make an AI engine truly unique and tailored to your business.
Be wary of the promise of ‘quick’ or ‘easy’ AI platforms – a good engine will incorporate more than just 3rd party intent signals or firmographic data. They will also look at your first-party data, real engagement with your brand and campaigns, as a primary driver of real intent. Also, be cautious of a ‘self-serve’ approach to this. An AI is only as good as its ability to understand the signal you provide it, and if you don’t have help interpreting your systems’ data for the AI and just ram data into a platform, the model will not be able to pull out the truly valuable signal. AI systems are generally not smart enough to understand that a lead from an event looks different from a lead from a webform until you tell it that they are different based on certain rules. If you skip this step you lose granularity that makes the AI truly tuned to your company’s unique data.
Once you understand how a platform identifies and brings in key account-level insights, the next area to investigate is how well you can target using those insights. Ask a vendor:
4. “What dimensions does your platform allow me to segment on?”
The segmentation engine is where targeting happens, and you want the flexibility to segment and analyze accounts across a wide range of dimensions. Paired with the right data, this is the engine that allows a revenue team to build very unique audiences or TAL’s to achieve your specific GTM objectives.
Understanding the data available in the segmentation engine, how many segments you’re allowed to create and target within a platform’s license fee, how dynamic a segment is day-to-day, and how easy the engine is to use are all critical points to further investigate to ensure a platform can support your long-term needs.
A good segmentation engine should provide the ability to target on multiple account dimensions including account firmographic and technographic details (profiling), behavioral insights on an account’s activities and engagement, intent data that reflects specific topics of interest, and predictive insights that provide an interpretation of all the data and its relevance to your buyers’ journeys. This is a system that should be able to pull in key data, and even other segments or lists, from all your core systems – and then be able to dynamically manage your target audiences across a variety of parameters. The combination of the right data in a robust segmentation tool is what allows your teams to leverage all the account-level insights brought into an ABM platform, and turn them into usable target audiences to execute GTM programs against – so make sure this works for your needs today and where you anticipate you’ll be tomorrow.
Account Insights & Analytics
Finally, it’s important to understand what your revenue team can learn from your segments. This may seem like an afterthought compared to the segmentation tool, but it’s incredibly important to help ensure your targeting strategy is both viable and actionable. Explore this area by asking:
5. “What insights do you provide, at what levels and how does my team access them?”
Segmentation is one thing, but the ability to then evaluate your account selection is how a revenue team can determine if a given target account list or segment is worth going to market with.
Digging into a vendor’s offering here, you want to look to see what kind of insights they can surface and if they can help evaluate a segment beyond the parameters you selected. Can the platform offer insights about the segment’s composition? Can you throw additional parameters on top of a segment to quickly understand specific behavioral patterns? Are you able to quickly dive into individual accounts you’ve targeted? What about the contacts within those accounts?
It’s important to understand what kind of insights are offered by the platform and how you can use a vendor’s platform to analyze a segment’s viability ahead of time. For example – it’s probably good to know if you have the right contact persona coverage for the accounts you’re targeting before you send them to sales. It’s also important to understand HOW and WHERE your team can access those insights. The availability of platform integrations into tools like Salesforce can be a huge differentiator when enabling a sales team, while the ease of use of a core platform UI may be important for marketers trying to quickly review their targeting strategy for a program. Understand how the vendor can empower your teams to make good decisions around their targeting BEFORE going to market.
The account selection process is the lifeblood of good ABM, without the right targeting and insights, the larger promise of ABM falls apart and can instead make an ABM project a source of discord in a GTM engine. It makes sense to look very closely at how a platform enables your revenue teams to select the right accounts for ABM – the data they use, their ability to match it to accounts, how the platform processes the data into insights, the range of segmentation capabilities, and their ability to enable segment analysis – are all critical components to understand when evaluating ABM platform offerings.
By asking the right questions, you’ll be able to build a clear understanding of how each vendor’s technology works, and how it might best fit your needs. At the very least, you’ll ensure you aren’t in for any nasty surprises when you start to build your targeting lists for your ABM program.
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About Jeffrey Siegel
At 6sense, Jeffrey Siegel is the Director of Strategy and leads the Strategic Advisory Services practice. He has focused his career on driving go-to-market innovation and implementing cutting-edge operational strategies to deliver on the vision of an effective and coordinated revenue engine. At 6sense, he brings the same vision to our customers every day, helping make our customers successful with the right strategies for implementation, and helping guide 6sense to continue to be the best technology partner it can be in our market. Jeff brings years of B2B GTM experience to 6sense, having worked in global GTM strategy & planning roles at both Box and DellEMC – where he was a three-year, two-time customer of 6sense, responsible for leading two global implementations of the 6sense Account-Based Orchestration platform and piloting many of our use cases.
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