How Monte Carlo Used Campaign Experimentation to Create $6.1M in Pipeline

$6.1M Pipeline created
$25.7M Pipeline influenced
14.5X Return on investment

Challenge

Monte Carlo has a very strict ideal customer profile and felt limited by LinkedIn’s native targeting.

They needed to get in front of more accounts that fit their criteria and advertise the right content at the right time.

Monte Carlo’s one person paid media team spent way too much time building experiments and figuring out which campaigns were working.


Solution

Monte Carlo used Metadata to create new custom audiences and exclusion lists while staying laser focused on their strict ICP.

They started testing multiple ad creative and copy variations against each audience to figure out which experiments showed early signs of success. With Metadata, they no longer had to manually do this analysis and could automatically put their remaining budget behind their top-performing experiments.

Monte Carlo’s Digital Marketing Manager spent 3x less time launching and reporting on new campaigns by automating this entire process.


How they used Metadata

STEP 1

Create audiences using native LinkedIn targeting, plus firmographic targeting and dynamic Salesforce account targeting from Metadata

STEP 2

Add contact-level audience criteria including seniority, job functions, and member skills to each audience

STEP 3

Set campaign and budget goals to create auto-pause rules

STEP 4

Build campaign experiments testing multiple audiences, ad creative and ad copy against each other

STEP 5

Advertise to in-market contacts and accounts on LinkedIn

STEP 6

Let auto-pause rules turn off under-performing experiments and scale top-performing experiments


More case studies for you

How Eightfold.ai Triggered 26 Opportunities and $8M in Pipeline
How Pendo Reduced Their Cost-per-MQL on Facebook by 80%