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Mastering Your Data Strategy to Support a Shared Revenue Goal

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This episode of the Revenue Marketing Report features Paul McKay, Marketing Operations Specialist at Visier. Paul shares with us what it takes from a Marketing Ops perspective to move towards a common shared goal between Marketing and Sales... which is, of course, Revenue. 

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Paul McKay
Based out of New York City, Paul is the Marketing Operations Specialist at Visier.
Revenue Marketing Report

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Sales and Marketing Move Toward a Common Goal: Revenue

It was a new revenue-focused CMO joining the team that prompted Visier’s marketing team to reassess its KPIs. The logic plays out pretty simply: if what matters most to the business is revenue, what matters most to Sales is revenue. Since Marketing is a key player in driving demand for Sales, well, there you have it. 

Less simple but of utmost clarity is that the shared common revenue goal between Sales and Marketing meant that achieving better alignment between both was crucial.

The strategy to achieve this common revenue goal? Stop random acts of marketing (dispersed campaigns or campaigns running on one channel but not aligned with the others), and move toward an integrated campaigns approach.

Now the question became: How?

It Starts with a Data Strategy

Rethinking the data strategy was the first step. In particular, Marketing Operations (MOPs) needed to reassess how they were supporting the campaign managers who are running integrated campaigns to drive demand for Sales.

They needed to build a data strategy for ensuring marketing is targeting the right people, and that MOPs can track the buyer's journey from unknown to customer, in turn enabling program managers to target the right people at the right time in their buyer’s journey.

Not only this, but they needed to be able to score prospects in an insightful way to ensure Sales is receiving them at the right time in their decision making process.

ICP Identification, Revised Scoring, and Attribution Modeling Come Together for Success

Included in Visier's new data strategy was a revised Ideal Customer Profile definition, a new scoring model, and finally, tying it all together with attribution modeling. 

Ideal Customer Profile (ICP) Re-definition

Since the team was no longer running at MQL numbers as a metric of success, and instead cared more about who is actually turning into revenue, their first step was to identify who their ideal customer is in terms of:

  • Who is actually turning into revenue and therefore,
  • What characteristics to look for when targeting at the top of the funnel

How Visier Did It 

Paul and the team did a live analysis on their database to look at the attributes of the accounts that have turned into customers. They identified positive and negative sets of data from their database of who became customers and who didn't, to see what the actual customer install base looked like.

Paul's team also worked with a vendor to identify characteristics that weren't available to them through their own database, to get a fuller picture of what their ICP was. With these two sources together, they mapped the combined attributes to their intent data, to identify where each customer was in its journey.

Visier's strategy also extends beyond just identifying who their customers are; it also considers what data points in the ICP actually have an impact.

Within the ICP lie thousands of data points, some of them not relevant to your line of business. Paul's recommendation is to pick and choose which data points to bring in, so instead of just taking in all available data, you're viewing data points almost as channels that you can push and pull, depending on the variables in the ICP matter.

New Scoring Model

Most companies operate a scoring model mainly based on just behavioral scoring and a little bit of demographic scoring (typically all done through a MAP). Which, is okay, but it's not very prescriptive, strategic, or robust, and only tells you what happened as opposed to who's most likely to go on and become an opportunity.

With the ICP rating on the account and contacts in Visier's database, they are now using account scoring, context scoring, and behavioral scoring.

For example: you can have an account rated as a AA in your ICP (meaning this account is the most ideal customer for you), and combine that with a numeric context and behavioral score. This gives a much more robust picture of an contact or account's engagement, but also how likely they are to convert.

The end goal is to tie them together into one account score, so they can be more targeted to the accounts themselves, rather than just a linear journey of bringing in a specific contact.

We all know that multiple decision makers exist in any buying process, so the ideal scoring model would tie together engagement from all contacts associated with an account, across all touch points and channels. This brings in the account to the sales cycle, instead of a single person, giving your sales team a fuller, more accurate picture of where the prospective company is in their journey.

Attribution Modeling

Visier has layered in this data strategy with its attribution model as well, as a method of determining whether its ICP definition is accurate, based off of how prospects are actually converting into customers. 

From the attribution model, they can see which milestones are occurring, and how attribution is being assigned to these milestones.

And, with the ICP ranking of those accounts layered in, they can get a better sense of how they’re engaging with top tier accounts (before they even start assigning credit back to revenue).

"We did that with a couple different models, and we found that our ICP did match our A and B accounts, which is pretty important. The key for us will be to measure that over time, to make sure that it's not shifting and getting farther away from that concentration (the top A's and B's), or else we'll have to shift our marketing strategies (or either something's wrong with the ICP model)."

Visier's Initial Results and Indications of Success

As with any shift involving mass amounts of data, it’s a process. You’ll get a feel for what’s working as you iterate, and find value where you may not have expected it along the way — like major confidence- and trust-building between Marketing and Sales. 

As you start building these layers of data into an organization that various people need access to in different ways, you want to provide enough data, and in a way that gives those people confidence in it enough to use it in their outreach. 

Ensuring the underlying data strategy is accurate gives players from Marketing and Sales confidence in the data being delivered to them. When Marketing Operations takes on a position of “business partner” to sales, Sales receives data it can have confidence in.

"We kicked off a project to redesign a lot of the operational campaigns in our MAP to say, 'here's all the data that's being created. How do we build the profile of this lead or contact in a meaningful way so that a sales rep could pick it up, a program manager could pick it up, or an executive could pick it up and have some value in it?'"

Visier currently has just one smart list in its entire MAP that is responsible for creating data in their systems. That same smart list enriches and normalizes the data, and pushes to Salesforce. In the span of three months, lead records and contact records created were 95% complete (as opposed to previously missing phone numbers, addresses, LinkedIn records, websites, etc).

So, now when a salesperson sees a record passed in from Marketing, and all the fields are filled out, they feel more confident in that data and contact, than in a contact that only has half of the necessary fields filled out.

Data is at the heart of everything businesses do. If you don't get a good handle on your data, you're not going to be able to make good decisions on it. With the right data strategy, and Sales and Marketing focused together on driving revenue, you’re guaranteed to see the opportunity for success widen. 

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