Case Study: Multiverse

Building an inbound pipeline in a new region with account-based marketing principles

How we helped an ed-tech unicorn build an agile growth approach by quickly testing and optimizing to scale results from paid channels while maintaining flat budgets.

The Partner

Multiverse is a global ed-tech leader that develops apprenticeship programs to help close the tech skills gap in the workforce. They work with enterprise organizations to build their own pipeline of new talent or reskill their existing teams into tech and data roles.

Tuff Services

A Quick Look

up arrow 189%

increase in MQLs in 6 months

down arrow 22%

decrease in monthly ad spend

up arrow 38%

increase in known account engagement in the US

TL;DR: Between October 2022 and March 2023, we built an inbound pipeline in a new market by testing over 70 campaigns on LinkedIn to establish a benchmark CPL. Then, we constructed testing frameworks at the campaign, audience, and creative levels. Once a campaign hit our threshold for acceptable conversion costs, we either validated the idea or refined our approach to keep learning. By adopting this methodology, we were able to test rapidly while hitting MQL targets – all on a decreased ad budget.

The Challenge

Validate a scalable paid acquisition strategy in a new region, all while maintaining lead quality as spend increases.

Multiverse has strong brand equity in the United Kingdom, but isn’t well known in the United States. After closing their Series D in 2022, they knew that in order to grow aggressively, they had to build an inbound pipeline in the U.S. market. Our challenge? Finding quick wins while building a foundation for long-term growth in a saturated market for tech talent solutions.

So, how did we build an inbound lead pipeline using account-based marketing?

Let’s dig in ↓

The Strategy

Leverage ABM and rapid social ads testing to scale qualified leads with reduced marketing budgets.

Our team created a testing framework for LinkedIn where we bucketed campaigns and creative concepts into “validated, non-validated, and needs to be validated” groups based on our CPL benchmarks.

What We Did:

  1. Identify our benchmark CPL after an initial round of testing.
  2. Build value props that resonate with each ABM segment based on brand engagement.
  3. Automate reporting to enable speedy collaboration with sales to vet lead quality quickly.
  4. Optimize copy, creative, and campaign type based on validated vs. non-validated framework.
  5. Repeat the process, scaling up what worked and scrapping what didn’t.

We used 6Sense as our ABM platform and worked with the Multiverse team to identify a list of target accounts in the US. Because of 6Sense’s capabilities and Multiverse’s TAM, LinkedIn presented the clearest path to success. It offers robust B2B targeting to narrow in on specific positions, departments, and seniority levels working at our target accounts. LinkedIn also offers highly-visual placements, giving us the timeline real estate to creatively convey Multiverse’s most compelling value props.

We knew that key learnings could be muddied without a structured testing framework, so we focused on testing three specific things:

  • Content: We tested different types of content – from case studies, flagship industry reports, whitepapers, and more – to see what content types were most important to our target audience.
  • Messaging: Once we found the types of content most likely to convert, we narrowed our testing to focus on specific messages, copy and CTAs based on the ABM segment’s intent.
  • Campaign Optimization Events: We amplified winning content and messages in multiple campaigns simultaneously, optimizing for multiple optimization events to see which strategy was most effective at generating qualified leads.

Our primary KPI, cost per marketing qualified lead, guided our testing framework. No matter what we tested, we knew that once we neared our cost per MQL threshold without actually generating a lead, we’d need to make aggressive optimizations or kill it completely. Collaborating closely with Multiverse’s in-house marketing team, we decreased cost per MQL by 57% by identifying the types of content and messages that convert. Here’s a deeper look at those results ↓

The Results

Iterative testing at scale to incrementally improve results, manifesting into a 189% increase in MQLs while reducing monthly budgets by 22%

In six months, we saw significant growth in all key performance indicators, with 30% increases in MQL target volume quarter over quarter  while budgets were cut because of economic conditions.

Initially, we found that our top message “solve the tech talent shortage” became irrelevant overnight when all the major US tech organizations began layoffs in late 2022. After pivoting our creative strategy to stay relevant in a changing market, we found that promoting content that solves a timely pain point keeps Multiverse top of mind when our target audience is actually in-market for an apprenticeship-based talent solution.

The Data

Tuff has truly been a strategic partner for Multiverse as we’ve built and scaled our B2B marketing across both our US & UK markets. The Tuff team’s ability to pivot and execute quickly has been a game-changer for us, enabling us to stay nimble and quickly adapt to feedback from the market. Tuff takes the time to understand our unique business challenges and key objectives, and their expertise in both leveraging data to achieve our KPIs and implementing testing and learning methodologies has been instrumental in driving our growth. Tuff provides invaluable support in executing across key growth marketing channels, and our partnership has allowed us to consistently scale & exceed our quarterly targets.

Katie Swoap McLane, Head of Growth at Multiverse

Key Takeaways

Speed wins in B2B marketing: Brands with systems set up to gather data, build insights, and make quick decisions are most efficient with ad dollars.

Make sure you’ve spent enough to validate a test: When your CPL target is $1,000+, and you’re testing multiple audiences and creatives, it’s easy to look at the entire investment divided by total leads and call it inefficient. However, when you dig deeper, you’ll see that some concepts haven’t spent enough to validate that it’s effective – or ineffective. Applying a validated vs. non-validated framework to all elements of your campaign helps you learn rapidly.

Build reporting methods that keep you pivoting proactively: If you’re reporting on ABM campaigns on a weekly basis, you’re setting yourself up to be reactive. You’ll always be one step behind. By automating reporting (in our case, building out a Zap that posts a Slack message when a lead comes through), marketing and sales can stay in lock step with each other to make optimizations based on lead quality faster.

You can scale with static budgets: While investing more ad dollars can certainly help achieve more significant results, it’s not the only way to grow. Efficiency is the game, and you can increase lead volume by optimizing every element of your current campaign setup.

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