🔴 Challenge: Metadata’s target audience, B2B marketers, have high turnover rates. Without a systematic approach to track champion job changes and new executives joining accounts, the team was missing out on new pipeline opportunities and putting renewals at risk.

🟡 Solution: A few key reps were running job change plays independently, but there was no formalized program in place. After evaluating UserGems and finding that ~25% of their database had grown stale, they knew they wanted to build an intentional engine for tracking job change signals.

🟢 Results: 81% shorter deal cycles and result in 17% bigger contract sizes from UserGems leads. Plus, the signal-based approach created more alignment between the Head of Marketing and Sales!

The challenge

According to the team at Metadata – a B2B marketing advertising platform – the tenure for marketing roles in its ICP is about 18 months. They knew this highly mobile segment was an untapped pipeline source.

As part of the evaluation process, Metadata used the free data test to see what unknown opportunities they were missing. The results revealed that about 25% of their database was stale due to frequent job changes among their customer base. 

Previously, Metadata would find out when a past customer landed at another company because they would buy the product again, but this is never a sure thing. They wanted to streamline the process. 

The solution

In their search for a solution to generate new pipeline, Metadata’s team knew two things for sure:

  1. Past customers converted at a higher rate than net new leads, brought in higher ACVs, and were proven to contribute to a large percentage of pipeline. 
  2. UserGems had a solution they wanted to try and an excellent reputation to back up Metadata’s interest.

Interestingly, big wins from previous customers didn’t happen due to having systematic processes in place. Instead, Metadata was lucky to have some great reps who kept up with job changes and got the deal. 

This created an exciting prospect: what kinds of wins would they see with automated signal workflows to catch more leads?

🚨 Their key signals: 

  • Champions identified during closed opportunities (Sales)
  • Champions and key stakeholder job changes (Customer Success)
  • New Hires & Promotions (Sales + Customer Success)

Play #1: How Metadata operationalizes job change signals into new pipeline

The team ranks accounts from A to D using a combination of data from intent tools, technographic data, and, of course, champion job changes. This approach helps Metadata focus on accounts with the highest chance of closing.

AEs work high value champions from accounts A and B using a high-touch strategic approach and personalized messaging that references past relationships and past success with the product. 

SDRs work leads from accounts C and D. These are contacts associated with customer accounts and closed lost evaluators in Salesforce. SDRs use automated sequences (to ensure no leads fall through the cracks) with messaging that mentions the contact’s past relationship with Metadata. 

What’s the next iteration of this play? Using new hires joining target accounts as a signal for prioritized outreach. 

Metadata’s team recently realized the pipeline potential of targeting new hires at key accounts. They plan to target recent job changers with ad campaigns that increase brand visibility and generate interest in their offerings. The teams will focus heavily on research and personalized outreach for these new-in-role contacts. 

Play #2: Metadata’s signal-based churn prevention playbook

Job change signals are pipeline opportunities and alerts for churn risk. Metadata is taking this seriously by building a churn prevention playbook with UserGems. 

Whenever there is a job change in or out of the account:

  1. UserGems triggers automated notifications that alert Customer Success about the new job status/new-in-role executive.
  2. CS receives Slack notifications and Salesforce report updates.
  3. For champion job changes, the account owner contacts account stakeholders to manage changes and proactively catch churn risks.
  4. For new executives joining the account, CS can see new CMO contacts, take steps to build relationships, and show value early. 

Metadata’s goal for this playbook is to stay ahead of potential shifts in business. 

The results

50% of Q4 pipeline was attributed to returning customers. 

✅ The average sales cycle from UserGems leads is 60 days versus 143 days for other deals, an 81% decrease.

✅ The average contract length from UserGems leads increased from 13 months to 15.5 months. 

As a bonus, UserGems brought the Head of Sales and Head of Marketing closer together thanks to the new signal-based, systematic approach and increased visibility and trackability of closed won opportunity data!  

Get in touch to find out how to implement these playbooks for your team.

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