How We’ve Implemented (Parts of) The Demand Unit Waterfall for ABM

Last week at the 2017 SiriusDecisions Summit, research analysts Kerry Cunningham and Terry Flaherty revealed in the morning keynote on the second day the newly updated and highly anticipated demand waterfall — which they call the Demand Unit WaterfallTM. If you haven’t seen it yet, here it is. Tada!

Source: SiriusDecisions

A Shout Out From My Homies

So I was busy live-tweeting the event during their presentation trying to capture every key takeaway and social media gem that I almost missed it when Kerry gave me a nice shout out from the stage. I think he said something along the lines of “some marketers like Tony Yang from Mintigo and Dawn Colossi from Commvault are already experimenting with this new Demand Unit Waterfall…” (Thanks Kerry for my five seconds of fame! Side note: Commvault is also a Mintigo customer — #justsaying.) During the remainder of the event I’ve had several people come up to me and ask how we’re doing this and what manner of sorcery did we cast over Kerry and Terry. I can assure you that the answer to the latter is “nothing”, and the former came as a result of necessity for executing our own ABM approach. Let me explain.

We Live In An ABM World

A change happened sometime last year in our marketing organization — rather than being measured against the number of leads or MQLs, we were told that marketing is now going to be held accountable to opportunities and pipeline. While my initial thoughts were to push back on this — mostly because we were killing it from an MQL standpoint (hitting over 150% our quota every quarter for over a year) and that marketing does not have direct control over converting opportunities because that part of the funnel is “owned by sales” (on a side note, I’m a huge proponent of the SDR/BDR function reporting to marketing…perhaps more on this in another blog post) — deep down inside I knew that this was the right move to align the efforts of the two teams in light of our own ABM strategy. Many thought leaders in the B2B community have been pushing for this change and several of my marketing peers have been adopting this as well. I remember attending an event hosted by our friends at Integrate not too long ago, and one of the panelists who I don’t recall (I’ll need to ask Scott Vaughan to refresh my memory) made a comment that “MQLs is a vanity metric”. Ouch!

Back to the waterfall — the premise for revising the previous waterfall is based on the fact that marketing and selling enterprise solutions to companies involves a buying group or buying center that is comprised of decision makers, economic buyers, influencers, possibly end users and other key stakeholders. The previous version of the demand waterfall was revolutionary at the time because it provided a concrete framework for how demand marketers and sales work together in a B2B organization.

However, practitioners realized that there was gap in this approach — marketing worked with (and was measured against) leads and person-centric metrics such as AQLs, MQLs and TQLS higher up in the funnel, while sales operated from an account perspective. Lower funnel metrics and terms such as opportunities and pipeline were all in the context of a company or account, and unless you’re selling low ASP transactional type of offerings, you’re selling to a group of people at an account rather than a single individual paying with a credit card. B2B selling, especially in an ABM approach, is a consensus sale, so if we in marketing talk about leads at the top of the funnel and sales is focused on accounts and buying centers, it inadvertently creates a riff or misalignment between the teams. Probably more importantly though, I believe it doesn’t allow for the customers’ journey as a buying group representing their company to be aligned smoothly with how they are engaging with your brand through marketing and sales.

The new Demand Unit Waterfall thus introduces the notion of accounts and buying groups (aka “Demand Units”) early in the funnel. Matt Heinz pointed out in his blog that the new waterfall does not include nomenclature specifically around marketing or sales but just focuses on pipeline — I’d add that it also does not infer exclusive and static ownership of specific parts of the funnel by different teams. Kerry and Terry described this as such in their presentation by talking about multiple potential paths through the new waterfall, showing one example where marketing leads engagement at all stages with “tele” and sales taking part at mid funnel stages, and another example where sales owns engagement across all stages and marketing with tele contributes at certain stages. If you are a SiriusDecisions client then you can get access the full presentation in their resource center along with other great content from the event.

This new framework aligns well to an ABM approach — Trisha Winter at Amplifinity and Brandon Redlinger at Engagio would agree with me as they share their points-of-view in their respective posts.

That is the “why” — let me share the “how”.

More Than Just Blocking & Tackling — Setting Up The Infrastructure For Success

I’m a process-oriented guy. I like data — not for the sake of implying that I’m a data-driven marketer but because I believe that you need to set up the right infrastructure — workflows, integrations with core systems and best-of-breed tools, data views and captures, metrics, etc. — in order to succeed in marketing and sales. I guess that’s why a big part of my role focuses on marketing operations. That’s also why I believe wholeheartedly that you need to set up your infrastructure to be able to execute an effective ABM strategy. As I was developing this ABM strategy, we spent a lot of effort into building out this infrastructure. I won’t go into our whole martech stack and what kind of campaigns or plays we’re running (perhaps yet another post!) but instead focus on what we’ve built within our Salesforce instance to support this infrastructure and how it aligns with the new Demand Unit Waterfall… even before we knew it was called that.

First, here’s a snapshot of a view we created in the Account tab. This is a roll-up view that each of our sales reps has for their target accounts — management gets a consolidated view. This view serves as a central dashboard or console so that we know where we stand with each of our target accounts.

Source: Mintigo. Click on image to see larger version

The application of the new Demand Unit Waterfall is loosely aligned with an Account field we call “Account Status” — remember that we implemented this before we were introduced to this new waterfall…we’ll probably consider some updates to align more closely with SiriusDecisions nomenclature.

Target Demand Stage

The first stage of the new waterfall called Target Demand is defined by SiriusDecisions as the potential Demand Units believed to exist for the solution in the market, or the Total Available Market (TAM). For us, this is equivalent to the set of named accounts we are focusing on for our ABM strategy. We use our own predictive platform to identify this set of target accounts. Aside from the fact that I obviously work at a predictive vendor, I’m a firm believer that you need to take a data driven approach to selecting the right target accounts — otherwise you’ll be potentially wasting a ton of time and resources on the wrong targets. I discuss this in more detail along with concepts such as TAM in this interview with Martech Series.

I do want to point out that though we used technology and data to identify our target accounts based on high predictive account scores, I gave our sales team the freedom to change their list with their understanding of what went into our predictive model — this helps with getting sales buy-in and alignment at this crucial phase.

For us, we have about 425 target accounts split into two tiers. We made sure these target accounts are identified in Salesforce with the Account Status field set to “MQA — Target”.

Active Demand Stage

According to SiriusDecisions, this stage represents Demand Units that are showing evidence of acute need or buying intention — those that are either in market or need to be in market for our solutions. We identify accounts in this stage not by an Account Status but rather by a combination of looking at their predictive score (which represents propensity to buy) along with intent data signals that our platform collects from all over the web and “curates”. I won’t go deep into the topic of intent data here (feel free to reach out to me or my colleagues if you’d like to understand our POV on this topic). I’ll just mention that there are specific intent data categories that pertain to our business — such as “predictive analytics”, “predictive lead scoring”, “account based marketing” and “sales enablement” to name a few — that tells us when a target account has been recently researching that category and is potentially “in market” to purchase a solution.

This is a time-sensitive data type, and I get this data piped into our Marketo and Salesforce instances directly from Mintigo. If you don’t have a predictive solution that offers intent data, you can consider working with Bombora, TechTarget or The Big Willow to name a few alternatives.

Source: Mintigo

Engaged Demand Stage

This stage is reached when members of a Demand Unit respond to a marketing, tele or sales stimulus. We identify accounts in this stage by Account Status as “MQA — Interest Shown”. Accounts are auto-updated to this status when the Engagement Score at the account level is positive. Here’s how we do this.

First of all, mapping lead records to accounts is an absolute must for ABM and for this particular waterfall stage because we need full visibility into engagement with all prospects at a target account. Those of you who are familiar with Salesforce architecture know the challenges and limitations of having the lead object as the orphaned step-child from the other standard objects. I won’t get into the debate between whether or not you should auto-convert all leads into contact records — there are plenty of discussion threads and content that talk about the pros and cons of going full “contacts” only or preserving lead records (here’s a great ebook by LeanData on this from pages 10 to 14, another good piece by Jay Jennison at Full Circle Insights, one by Jon Miller at Engagio and another one from InsightSquared). We decided to keep the use of lead records and thus needed a lead-to-account matching tool. We use RingLead internally, but LeanData, Engagio, and Full Circle Insights are also great alternatives.

Having leads now associated to the appropriate account records enables us to do several things. First, we have a basic engagement scoring model set up in Marketo that tracks engagement activity by prospects (leads and contacts) with our marketing programs, such as downloading an ebook, watching a webinar, visiting us at an event booth, or attending a case study session. We also have a score decay rule that subtracts points for periods of inactivity so that engagement scores don’t keep increasing or stay the same over time. Because we can now see all leads as well as contacts associated to an account, we wrote a simple rule that sums up the engagement scores for all “people” records associated to that account to come up with the account engagement score. You can also do this if you have Engagio or the Marketo ABM module. As another side note for a future project, I’m thinking of creating a predictive model based only on activity data so that we’ll have a better understanding of what activities matter more than others — I only have so many hours in a day and I do like to sleep!

As you can see in the below screenshot, the Engagement Score is the sum of all engagement scores for people identified for each account. The column to the right of it labeled “Persons Engaged by Marketing” shows the number of persons that comprise of that engagement score. The column to the right called “Total Leads & Contacts” shows all the people records that we’ve identified and matched to that account. In the future we may enhance this to represent only the personas that we target — it might not make sense to build engagement with people at a target account who aren’t the right audience for us. This will then help us focus our marketing and sales efforts on those who are identified as good targets but have yet to engage with us.

Source: Mintigo

Prioritized Demand & Qualified Demand Stages

According to SiriusDecisions, Prioritized Demand is when the level of engagement from the Demand Unit has reached a threshold that justifies additional interactions from tele or sales resources. Qualified Demand is defined when, based on interactions with Demand Unit members, the fit and urgency of prospect needs, as well as potential purchase resources and willingness to engage, have been verified.

To be honest, we don’t have a set threshold for either of these two stages and thus do not have a corresponding Account Status. The way I see it, ABM requires a certain level of proactivity by sales and tele/BDRs — you can’t wait for an account to reach a certain level of expressed activity before you engage with them from a sales perspective.

Marketing can certainly help by providing air cover on these target accounts so that sales/BDRs don’t have to call into an account cold. As you can see from the previous screenshot, we use Terminus to run specific ad campaigns that are driven by characteristics and intent of each target account along with where they are in the sales cycle with us. We run “top of the funnel” programs such as content syndication to initiate engagement with target accounts; we have nurture programs to increase that engagement with target account prospects; and we take advantage of face-to-face events and meetings to help with engagement at all stages of the waterfall. Currently it is up to the sales rep or BDR to prioritize their efforts based on the intel and data provided in the account record and roll-up view.

Now we do have a specific workflow through our BDR function that tracks “qualified” meetings set with target accounts. If you’re curious, we use a custom object to manage this. In light of the newly defined waterfall, we may consider adding another Account Status to define when a qualified meeting has been set at a target account. This should match the “Qualified Demand” stage.

Pipeline & Closed Stages

These two stages in the new demand waterfall are pretty self-explanatory, and are obviously mapped appropriately to our two Account Statuses — when there is an open opportunity record tracked to a target account, the Account Status field will auto-update to “SAA — Opportunity” and any Closed Won opportunities will auto-update it to “Customer”. This also accommodates for customers that we engage with in upsell/cross-sell opportunities. You’ll be able to see how many open opportunities in the column labeled the same way in the previous screenshot. In the future we’ll need to give further thought into whether or not we’ll want to differentiate between upsell/cross-sell at an existing Demand Unit and an entirely new opportunity with another Demand Unit at the same account.

Caveats, Challenges & Closing Thoughts

One thing you may have noticed while reading through to this point (bravo!) is that while the new waterfall talks about Demand Units, my descriptions of how we’ve implemented our infrastructure is centered around Accounts…and accounts may have multiple Demand Units. Most of the Account Statuses that I described above doesn’t directly reference or identify a specific Demand Unit, buying center or buying group. I believe this is one of the challenges right now, which is identifying relationships and org structures that can help crystalize a buying group. For example, if we say that we sell to marketers at HP Enterprise, there are probably hundreds of people who fit that persona at HPE.

The way we’re thinking of possibly addressing this is allowing for a “stage 0” opportunity stage that is omitted from pipeline that sales can use for account planning purposes and identifying Demand Units. Doing this may require significant changes to our reports and dashboards in Salesforce, and identifying Demand Units in this way is a manual process. I’m open to any ideas for addressing this in an automated way!

Here’s my caveat to what I’ve written thus far — like many of you we’re still figuring things out and we believe what we’ve established so far is a good approach for us. This might not be the right approach for you, and we certainly don’t claim to be the final authority on this topic…though it’s nice to get some validation from SiriusDecisions!

Lastly, I want to give my own well-deserved shout out to my fellow marketing team member Ana Semenina. We’ve pieced together and built all the functionality described above between the two of us, though honestly she did most of the heavy lifting in Salesforce while I’m on the sidelines spouting forth crazy ideas. You’re awesome Ana! (And poachers stay away!)

Thanks for reading to the end. Would love to hear comments, feedback and ideas!


Originally published at www.mintigo.com.