Spotted: Account-Based Marketing Strategies

This year, we've spotted a lot more SaaS teams talking about and trying to implement account-based marketing strategies.

Amongst them, there's a common set of challenges we've seen:

  • Teams struggling to assemble the data they need, particularly amongst a huge landscape of different data vendors
  • Teams struggling to take a list of target accounts and engage them
  • Teams struggling to engage the right people within accounts and say the right thing

But we've also seen teams who adopt account-based marketing strategies adopt other best practices too. Since account-based marketing involves many shared tools, teams & data, there's more alignment between sales & marketing and a more mature customer data management strategy .

In this Spotted post, we'll share the best practices we've seen amongst scaling B2B SaaS companies who are building out their account-based marketing strategies.

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Account-based marketing is not a license to pursue blind cold outbound

Many teams building account-based marketing strategies pursue accounts based on a "fit" model only. Inbound leads are qualified, and outbound chases a list of target accounts. This is a problem.

As we've discussed ranted about previously, if you're debating (or dividing) inbound vs outbound marketing, you don't understand how your customers buy from you.

Instead, every account (and lead) can be expressed in terms of:

  • Fit: Do they match your ideal customer profile
  • Intent: Are they in the market to buy?
  • Engagement: Have they interacted with your brand or product?

Best-fit, highly-engaged, high-intent accounts are the easiest to talk to and close. But to build that picture of accounts, you need to be able to track and make sense of the entire buying journey.


Target account lists should be dynamic, not static. As accounts start to engage, they should enter and exit accounts. Your lists should update each day based on engagement & intent data.

To organize accounts, tier accounts by engagement (as well as fit). Group similar conversions (like Demo requested and Trial signup) into tiers to understand at a high level.

Track buying signals too. Not all engagement indicates (or correlates with) a likelihood to buy. For instance, viewing blog posts vs viewing pricing pages, or a lead agreeing to a discovery call vs a lead agreeing to a pricing negotiation call.

The goal is to build separate programs for different types of accounts with different fit, intent & engagement states.

Accounts have three types of person; you must track them all.

The challenge with account engagement and intent is most of the activity that can be tracked is tied to a person.

Broadly spreading, there are three categories of people you can tie to an account.

Type Example
Anonymous Website visitor. “Someone from ACME corp”
Identified Prospect. “Job Bloggs from ACME corp”
Identified & permission given Trial signup. Subscriber. Demo request.

The last is subtle, but particularly with GDPR you need to be able to tag people who have given their direct permission to be messaged separate from those who haven't (yet) - more on that in our Spotted post on data enrichment & prospecting.

You need to be able to tie them together around the common account-level identifier like the domain name. We see teams using data enrichment services like Clearbit Reveal to identify website visitors and associate different email addresses with the correct parent domain (e.g.'s team has used email addresses.)

You then need to be able to transform this person-level, event-level data into account-level attributes. Instead of " purchased a subscription on 2018-12-31" you need ACME Corp Became_Customer_Date: 2018-12-31.

By bubbling up data to account level, you get more visibility with a unified company profile, you can segment accounts (for tiering), and can easily sync across all your tools - not every tool manages event streaming seamlessly.

Tactical Takeaway #1: Prioritize your own engagement & intent data

The easiest and most valuable engagement data is not the data you buy in - it is your own data.

There are more vendors than ever selling "B2B intent data" and "buying signals" that "plugs right into your CRM". This might be helpful, but we don't see the most successful teams starting here.

Remember, the goal is to identify engaged, ready-to-buy accounts. Usually, the most obvious signals for this come within your own product usage data. Whether you have a freemium, free trial, or upsell-driven sales model, you likely capture far more of the buying journey within your product than elsewhere.

What's a bigger buying signal? Maxing out usage credits halfway through a free trial, or downloading a relevant white paper on a 3rd party site triggering a "surge" score to rise?

(Trick question: You don't know until you run the analysis. But remember, one of those has a sales rep advocating for it. The other doesn't.)

Your own data is also much more cheaply and readily available. Analytics tools, databases, and perhaps data warehouses are likely to already be setup by your product, engineering, and operations teams. We see teams running scheduled queries with SQL Importers and then syncing any detected changes to their sales & marketing tools.

Finally, your product usage data is likely to be easier to understand. It's easier to focus messaging based on an action someone did (or didn't) do within your product than it is with an abstract, intent-data source.

If you can't make account-based marketing and sales outreach work based on how people use your own product, you're unlikely to succeed with intent data either. This is the quickest, cheapest, and easiest data source to start with.

Voila! You now have a simple data source for tracking account engagement and intent.

Tactical Takeaway #2: Track the entire B2B buying journey

Starting with data within your product, gradually expand your data sources to track the entire B2B buying journey.

We call this universal lead tracking - any possible touchpoint or lead source should be tracked and combined into a unified lead and unified company profile.

Remember, you need to associate with an account level identifier like domain. This means you can associate all the data together with your own data and historical data in a unified company profile. Besides giving a complete picture of account engagement & intent, this is also the key to building multi-touch attribution models.

Website visitors are usually only identified if they convert (give their email) with the same cookie or under the same session. Using reverse IP lookup tools, you can dramatically increase the number of accounts you can identify on your website, although you won't know who exactly.

Emails might be widely captured through forms, chat, sales reps, event registrations, and so on. If it's not straightforward to parse the domain, data enrichment can sometimes return an associated company these aren't 100% accurate but can help to fill-in-the-blanks)

Social mentions can be tracked via their username or @handle. Using a service like FullContact, you can return a name and email to lookup against an account.

Free trials can be detected when a company installs a new piece of software. Datanyze reindexes their database of technologies each day enabling daily alerts of trials in your industry so you can detect accounts in the market to buy.

Product & category reviews and topical interests be detected using intent data sources like G2 Crowd and Bombora.

Voila! You can now track the entire buying journey with an account

Tactical Takeaway #3: The fastest-growing, fastest-closing teams take their account-based marketing tactics omnichannel

Omnichannel marketing is not just marketing across multiple channels. It's coordinate messaging & campaigns across multiple channels to maximize the engagement.

We've spotted teams try a number of individual account-based marketing tactics like:

The best teams use these all together all at once. This takes a high degree of coordination.

The danger with omnichannel marketing is that some channels are left behind with out-of-date messaging for an account. For instance, retargeting ads with a free trial extension offer still displaying after the account has become a customer. Some call this "communication clash".

You need something in the middle of your marketing stack to orchestrate all your tools and data - a "single source of truth". The complete B2B buying journey needs to be tracked & consolidated into a unified company profile, but then that data needs to be used to update all the rules (segments, templates, workflows...) across all your tools.

Think of a "closed loop" system - wherever you are messaging, you must be able to track & update.

To prevent "communication clash", it is vital that this data processing happens in as close to real-time as possible. This ensures that at any one time your system of tools & teams only ever has the most recent, most complete, most up-to-date data. This also makes sure you have sales and marketing alignment on your messaging - no one likes reps asking all the same questions over and over.

The bonus too is you can engage accounts across more channels like web personalization and triggering personalized live chat.

Voila! You can now maximize engagement with target accounts with omnichannel account-based marketing.

Subscribe for a future Spotted post on omnichannel marketing.

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Tactical Takeaway #4: Build sales outreach & messaging around engagement, not fit

With fit-based sales outreach, the focus for the sales rep is on asking someone to do something they haven't done. With engagement-based sales outreach, the focus on is what someone has already done and discussing their next best move.

Whether you have fit-based messaging or engagement-based messaging, at some point sales has to reach out.

But there's a difference between "I saw you just raised a Series A - congrats! You must be hiring. Would you like to try our HR software?" or "I notice you're using Salesforce. Our AppExchange product can..." compared to "I spotted people from your team checking out our docs. Can I answer any questions?" or "Thanks for coming to our webinar. Is {topic} a challenge at your company?".

We've talked previously about sales enablement (and our Triggers-Content-Action framework) here and where measures of fit do come into messaging, but you need to structure your sales outreach around your accounts engagement.

Engagement-driven account-based marketing strategies make for much simpler sales outreach. For each engagement trigger, develop specific messaging and suggestions for the next step in the customer journey. It's also much easier to position sales outreach as consultive, helpful content than a cold sales pitch.

Organize your deal stages by your leads actions, not your reps actions.

John ShererDirector of Sales, Appcues

Voila! Your sales can now engage your target accounts with in-context, helpful messaging

Tactical Takeaway #5: Intelligently reach out across an account

Instead of focusing on just inbound leads, account-based marketing tries to profile all possible stakeholders within an account. But who should sales reach out to and when?

Remember, the focus shouldn't only be on finding the best-fit people but those who are most engaged and showing likelihood to buy.

One way we've seen this represented is on a two-by-two chart. If you plot job roles within target accounts on an axis of likelihood to buy and likelihood to activate, you can see two distinct groups.

For most products, the senior management are not the biggest users but they do hold the most sway in buying decisions. If they're buying, they're more likely to "raise their hand" and be considered a marketing qualified lead (MQL).

Within most teams, most users of products aren't in senior management but they'll be trying and setting it up. They're more likely to activate and be considered a product qualified leads (PQL).

The challenge is these often form separate groups - of people.

Chart from MadKudu's presentation at INBOUND 2018

By bubbling up this data to account level, you can score & spot engagement amongst associated leads & prospects. This creates new opportunities to reach out to accounts who wouldn't otherwise have an MQL or PQL for sales - "marketing qualified accounts" (MQAs).

Marketing qualified accounts become the accounts that marketing considers qualified enough to pass to sales for any reason (has_mql, has_pql, account_score > 100).

By broadening the definitions of lead qualification and scoring from person-level to account-level engagement, marketing is able to increase the number of accounts ready for sales to engage & how to prioritize them.

The hardest part remains in rendering the data actionable. This is where big data can help personalization at scale. Lead scoring tools have been built with this in mind. They leverage the multitude of data points available to automate the qualification historically run by SDRs.

Francis BreroCRO & Co-Founder at Madkudu

Best-fit criteria for account-based marketing

There are a few pre-requisites we'd recommend before pursuing an ABM strategy like this.

Clear ideal customer profile

Whilst we don't recommend you target & pursue accounts solely based on fit, you do need to have clear, common, objective definition of the types of accounts you are going after first.

Core tracking already in place

To track the full B2B buying journey, you need to have the core tracking elements in place first. One quick way to get started here is to sketch a customer journey map and use that to assign tools to track each of the channels' conversions.

Teams & resource dedicated to targeting accounts

Account-based marketing cannot be an add-on to other strategies. You need to have resources dedicated to engaging your most engaged target accounts before they go cold again.

Single source of truth

It is essential you have a centralized database to unify all your tools, tracking & database around each account, then update all your rules, and sync to all your other tools in real-time.

Without this, it isn't possible to react to your engaged accounts and create new sales opportunities.

Get started with account-based marketing orchestration

Orchestrate account engagement across all your sales & marketing tools in real-time from Hull’s B2B customer data platform.

Explore Hull's account based marketing playbook

What you should do now

  1. Request a custom demo - and see how to unify & sync all your tools, teams & data (like we did for all these companies), or book a demo with a product expert.

  2. If you'd like to learn our best practice for customer data integration, read our free Guide to Getting Started with Customer Data Integration.

  3. If you enjoyed this article, perhaps your team will too? Why not share it with the links below.

Ed Fry

Prev 'Ed of Growth at Hull, working on all things content, acquisition & conversion. Conference speaker, flight hacker, prev. employee #1 at (acq. HubSpot). Now at Behind The Growth

If you've questions or ideas, I'd love to geek out together on Twitter or LinkedIn. 👇