Support the Account Based Marketing (ABM) motion within your organization by leveraging the MadKudu platform.
This article highlights how you can support the Account Based Marketing (ABM) motion within your organization effectively by leveraging the MadKudu platform. We will share some best practices employed by various MadKudu customers and discuss pain points and successes for both marketing and sales.
We’re sure you’ve heard the saying, “ABM isn’t something you buy, ABM is something you DO.” No tool or marketing platform can magically deliver you turn-key execution (if you find one, let us know…). In MadKudu’s experience, the aforementioned has proven true as we’ve observed many fast-growing SaaS companies committing to the development of an ABM solution in 2020-2021 - and they are going big on it.
We’ve tabulated many of the common mistakes that GTM teams have made and documented some of the more successful patterns. Outbound and Account Based Marketing remain big and somewhat complex topics so we’ll focus in and start with outlining best practices for an ABM approach from the Sales perspective.
Sales typically have two key questions when it comes to the warm or cold outbound:
Question 1. How do I know which account I should reach out to?
Question 2. Within that account, who should I be speaking with?
Sales reps face many challenges when they begin working accounts. Especially in the case of cold outbound, there’s very little or no presentation or segmentation expressing the fit of the account - does it look like an account from the target list or similar to an existing customer? Do they use tech that integrates with your product, or maybe they are already using a competing product? What indicates that the account will not only convert but will also potentially be more likely to renew?
Individual SDRs or AEs don’t usually have access to this enrichment data (except maybe through multiple, exhaustive manual searches of LinkedIn) and it’s something that needs to be coordinated by the Ops team. Purchasing enrichment and aggregating those data points in a meaningful fashion provides the foundation for intelligence, but other challenges such as scattered data across disparate sources, objects and fields become apparent. Additionally, manually or even semi-automatically culled data takes too much time to produce and then further needs to be massaged into meaningful insights that can be appreciated by busy reps.
The obstacle for Sales when it comes to the warm outbound motion is that it’s hard to know what’s happening within the account overall and how individual actions roll up at the account level. Questions like “Are contacts from this account checking out email campaigns, registering and attending our webinars?” or “How many actions were performed in our app this month?” often remain unanswered (or, at best, decentralized) and that leads to fruitful conversations being missed all together.
In MadKudu’s Data Science Studio, you have the ability to build an Account Fit model for your Sales team addressing the first pain point of, “Which Account?” Specifically, this model would help answer the question, “Out of the whole universe of accounts, which ones look the best?"
The model helps you predict the fit of the account matching the accounts from your target account list, or your existing customers, or those accounts that produce your mid-stage pipeline. MadKudu enriches the accounts with data from partners in the enrichment space and provides actionable insights in the form of Signals passed directly to your CRM.
Signals enable sales personnel to understand the scoring better and clarify the reason for outbound prioritization. Moreover, they can be used to drive meaningful conversations with prospects and help to more easily identify their pain points to resolution.
This information can be customized and some of the features can be combined, e.g. if you generally have the characteristics of your core ICP already cataloged (employee size, industry, revenue, tech used, etc.) you can create a computation from the various enrichment data our partners provide and add it to your Signals. Or, you can upload the list of your target accounts and signals would be produced for them.
Such predictive segmentation is not only beneficial to Sales but also Marketing in preparing accounts for warm outbound. Growth and Demand Generation teams can sort by Customer Fit Segment for campaign prioritization and run different content and test different value propositions to different accounts.
Once the fit is clear and you have an index of accounts Sales should be going after, the next step is to address the behavioral side of the question and provide an aggregated view of all the activity that’s going on. In the Data Science Studio you have the functionality to build your own account behavioral model that would support your Marketing Qualified Account (MQA) or Product Qualified Account (PQA) motion and provide a relative prediction of likelihood-to-buy based on the engagement and momentum within the account.
You can connect any marketing campaign or product event data sources you’d like (data warehouse, Segment, Amplitude, Marketo, etc.) and have it analyzed by MadKudu’s algorithm to determine what events are correlated to conversion, but also statistically significant in that correlation. This model provides you with the holistic view of people within an account - using your product or trial, visiting your website, interacting with your brand in all possible ways - limited only by what you track.
The scoring itself helps to prioritize Sales’ efforts, allowing them to identify which account is the hottest and potentially time your engagement with that account when it is most ready to move toward purchase. From what we’ve seen, it’s best when Marketing and Sales work together on driving the account’s engagement, mapping out the account touchpoints and deciding on the threshold that would mark an account as MQA or PQA. This ensures that the account is properly warmed up and ready to convert or upsell.
This comprehensive behavioral model nicely complements the goals of AM and CS organizations as you can create aggregations and indicators for existing customers, helping your team identify which accounts are showing strong signs of being ready for upsell, based on their engagement. Or, potentially setting a floor around logins or activity and surfacing those accounts as potentially at risk.
Now that we’ve resolved the first big question of, “Which Account?” It’s time to talk about, “Who should we be speaking with?” Answering this question is the key to establishing the buying committee and giving sales a well defined route by which they can engage.
Not every company can afford massive database enrichment subscriptions such as ZoomInfo. We’ve seen so many Sales personnel manually researching Linkedin, Googling company names and job titles - spending so much time just to get a bit more insight into the general account map, specific roles and the decision-making authority of their contacts. Getting the wrong person results in a huge waste of time, and a low-likelihood of direction or forward referral. Sales reps would of course rather spend their time closing deals and SDRs would be better off booking real meetings. Even if your company actually has the enrichment already plugged into the CRM, you probably already know how painful it is to analyze that data and present it in a meaningful, concise way. So we agree that it’s hard to view this data, let alone plug it into automated workflows e.g. execute structured sales cadences to identified personas.
People’s digital activity is another aspect that can’t be ignored when we address, “Who?” MadKudu has helped many customers with Product-Led motions create demographic segmentations (for persona definition) as well as behavioral aggregations (for relative product or feature interest) and thereby establish the various members of the buying committee - champion, buyer, IT influencer, admin, etc. For Sales, not being able to understand the dynamics of people's interaction with the product and across marketing channels and content, can minimally cost time and maximally, the deal.
In the Data Science Studio, Computations tab, you are able to define the logic behind personas - based on their demographics/ firmographics and behavior, and push this data to your CRM or MAP. The definitions are highly customizable and you can update them on a regular basis as your approach and selling strategy evolve. You can thereby empower Sales to contact the correct individuals within the buying committee or champions ready to upsell.
The same feature called Aggregations also allows our customers like Lucid to differentiate their product users and identify cross-sell opportunities e.g. if a user of Product A is actively exploring the marketing materials about Product B, taking advantage of a free trial and performing actions that indicate high likelihood to convert, a Sales rep can be notified and reach out either directly to that user or their manager who’d be identified as buyer/ decision maker of Product B.
Ultimately, your outbound GTM motion involves coordinated data at multiple strata of the account, contact, and lead. These orchestrated processes and signals need to align with your ICP as well as the behavioral indicators, both individually and en masse throughout the target organization. If you get it right, you alleviate major barriers for sales and reduce friction around general account engagement. Explainability and a clear approach to engagement are facilitated by comprehensively scored and prioritized accounts and their personas within. Sales organizations are trying to reach your company's potential customers and help them. By using MadKudu, marketing operations can help sales, marketing’s primary operational customer, in doing exactly that.