How Sales and Marketing Alignment Increased Revenue at Pitney Bowes

Artificial IntelligenceB2B Demand Generation

10 Min Read

J. David Green

Sales and marketing alignment eludes mahny companies.  So I am always looking to learn something from those who have built collaborative relationships with sales teams.

Allison Smith Terrey is such an executive.  She has been a pioneer of marketing operations, most recently with Pitney Bowes where she was Vice President of Global Marketing Operations and Technology. She has been evolving operations strategies and technologies for 15 years.

I spoke to her on two podcasts, which you can listen to here and here.

Accepting and Moving Past the Marketing-Sales Culture

The marketing-sales disconnect is often simply a matter of definitions. Allison put it this way: “Marketing and sales have a lot of disagreements because neither team knows what the other one is talking about.”

The word “lead” is a perfect example. To marketing, a lead is someone who shares their contact information. For sales, it’s someone in the target market interested in discussing a relevant problem with sales.

Without a common language, the conversation with sales can become the corporate equivalent of a food fight.

To increase funnel velocity and closed-won deals, she suggests marketing and sales need to be “aligned through continual conversation.”

Pinpointing the Problem

One of the most frequent problems for marketers is lack of understanding or awareness of how sales plans to meet quotas.

Another issue is that prospecting techniques are advancing faster than the tools. “There’s a movement towards looking at leads and opportunities from the account perspective, or the buying team, which is the right thing to do. The problem is that systems today really don’t enable that to happen.”

Allison gave an example scenario:

“There may be five people on a buying team and five separate lead records for each person in the systems, but only one of those records is going to get converted to an opportunity. There are four other people who are also part of that buying team, so marketing’s contribution is greater than just that one person sales converted,” she said. She then discussed how marketing often gets no credit for opportunities at all. “It just so happens that Record 2 is the one that converted. We need to capture this information and understand this team of people who collectively decided they wanted to go forward with your company, at least in the exploration phases. The systems today don’t provide that depth of information for marketing or that intelligence.”

Allison (who is not a customer of LeadCrunch) believes artificial intelligence (AI) and machine learning (ML) are going to fix these issues at some point, but until then, there are ways to better clarify and organize definitions and measurements.

Marketing-Sourced Versus Marketing-Influenced Pipeline

In the never-ending battle to get revenue credit, Allison and her team looked at two different scenarios involving attribution.

In one case, leads converted into opportunities and ultimately into closed-won deals. She referred to such wins as “marketing sourced revenue.”

Straightforward.

In the other, Allison had noticed that leads sales did not convert into an opportunity were often part of the buying committee. Obviously, marketing was not getting appropriate credit for the revenue.

Not so straightforward.

Because Allison could not find the tools to automate the identification of these marketing-influenced leads, she and her team took a manual approach.

Allison’s team looked at opportunities marketing didn’t source and took them out of the mix so she and her team could identify contacts (or buying teams) that had a marketing touch. From there, Allison’s team would “go to the contact record, identify which had a marketing campaign attached, and then the team would pull these records into that metric for what we called marketing influence revenue.”

Following these two metrics helped determine some of the impact marketing was making with demand generation efforts.

But Allison needed to go deeper for account-based marketing campaigns.

Measurement of Account-Based Marketing (ABM) Campaigns

ABM campaigns gained a lot of traction at Pitney Bowes. To track sales and marketing success within those accounts, Allison’s team created custom reports around account-based marketing campaigns. Sales told marketing what solutions the existing accounts were considering so the system could flag them. It was harder with incoming prospects, but marketing flagged them also. When flagging them correctly, marketing could create reports that would look at the pipeline and leads at the account level.

There was a snag in this method. Manual input was cumbersome for sales reps who spent most of the time in the field, not at a desk. That’s where creating a lead management system came in.

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The Lead Management Framework

Allison helped create a new culture between sales and marketing. “We had what I call the triumvirate: marketing operations, demand generation, and sales operations. We formed a very close partnership, and we had some lead management issues to address.”

The idea was to “create a consistent methodology for marketing to collect their prospects, nurture them, identify who goes to sales, send them to sales, and then have sales progress the prospects through the pipeline.”

Allison worked with marketing and sales operations to create a lead management framework.

The Advantage of Frameworks Rather than Systems

According to Allison, a “framework” allows for better flexibility than a system. Allison knew there would be differing timetables, buyer cycles, qualifications, and other criteria. The framework had to flex with multiple factors.

Even though the framework had to be flexible, it also required some well-defined parameters. Marketing needed to qualify leads more thoroughly.

Here’s what marketing and sales did:

  • Agreed on a shared language and shared definitions.
  • Set up common reports using that shared language and those definitions, understanding that funnels might have different conversion rates for each business unit.
  • Collected the data and reviewed the results with special attention on volumes and conversion rates against goals and over time.
  • Shared information that allowed people to see their individual goals and the bigger picture of shared goals.

The marketing team could then review the data and identify weaknesses in the funnel. Next, marketing worked closely with sales to mitigate those weaknesses. “We found that honest, hard, deep conversation paid off in revenue growth.”

That conversation revealed that marketing thought it was reaching goals and sending over qualified leads, but sales didn’t see it that way. When marketing looked deeper into the issue, the team realized it was sending leads over too early. Marketing nurtured leads that were not sales-ready.

“We had to beef up our scoring model to make marketing’s qualified lead criteria more stringent.” Marketing took lead qualification one step further by adding an inside sales development team to qualify and nurture early-stage prospects for sales reps.

These approaches and conversations enabled marketing to target the prospects sales missed. Sales saw how marketing bridged the gaps and became motivated to pitch in.

“Sales leadership saw we were willing to do what it took, so sales management sat down with the sales reps and said, ‘Okay, why aren’t you picking these leads up?’ Then, sales ops, marketing ops, demand gen, and the sales team started to determine who to send the leads to. We started to have a lot more success that way.”

Creating Your Own Lead Management Framework

  • Define funnel stages. Collaborate with sales to create definitions for Marketing Qualified Leads (MQLs), Sales Accepted Leads (SALs), Sales Qualified Leads (SQLs), Opportunities, and Closed Won Deals.
  • Visualize the data. Create graphs of key measurements so you can visually track your progress.
  • Meet regularly. Set up regular meetings between demand generation, marketing operations, and sales operations. Meet monthly at first to define factors, then quarterly and yearly to review and discuss how performance is progressing over time.
  • Store Funnel Snapshots. Take a snapshot at the end of each month and each quarter and then store the data. If you don’t have big system, you can save the report in an Excel file and add more columns as you move forward, monthly. These reports and snapshots are useful because a lot of systems cannot go back and show you what happened retroactively. “In some systems, especially with lead data and opportunity data as they progress, the record itself changes and you can’t get the history; you can’t get a snapshot.”
  • Track baselines. Collect that snapshot data for a year (and continuously thereafter) to measure progress across time, this month to last month, this quarter to the same quarter last year, and so on. The idea is to measure your improvement over time and to identify leads in your funnel by creating these comparisons in time. These comparisons provide context.
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The Future Holds Easier Solutions

In a recent post for Modern Marketing Today, Allison discussed how AI and ML will improve how we collect and review data.

I asked her how that would impact the B2B marketer.

“It will enable marketing to understand what catches people’s attention and then how to convert them to pipeline. I also think AI and ML will help with the best way to score leads and to identify marketing’s true contribution to revenue. I’m sure we leave a lot on the floor because we can’t measure this revenue easily.”

According to Allison, feeding the programs more data and more response information over time will help AI and ML to grasp more focused criteria for qualifying leads. These techniques will also look at multiple elements for each prospect and compare them to those factors in other prospects. The resulting precision will give marketing a better idea of how to structure the marketing mix to attract the perfect prospect.

How does that AI-driven approach differ from automation platforms?

Per Allison, lead scoring through automation platforms works on what we know. AI/ML will find things we don’t recognize as factors based on behavior and other data streams. The new systems will find correlations from sales data, marketing automation data, and web data to build better pictures of what’s going on.

“That’s not something we can easily do today. I believe AI will give that extra edge to enable marketing to score someone more accurately. I believe that AI, or whatever the algorithm is, will be part of the scoring model, and it’ll enable marketing.”

Learn more about how we measure data and what’s emerging in marketing operations by keeping up with these two podcasts here and here.