Customer Rise Acquisition costs (CAC) put a significant dent in marketing budgets, putting marketing teams in a position to do more with less.
When it comes to user acquisition campaigns, there are a few small fires that need to be extinguished first. Many organizations’ problems stem from major premature decisions made based on incomplete data, and this is a problem that weighs more heavily on startups that sell to different companies than those that sell to consumers.
For starters, B2B startups tend to have longer funnels than their counterparts, as their offerings often include freemium options and free trials. As a result, these startups don’t see many conversions in the first few weeks after acquiring new subscribers. That’s not to say there won’t be more conversions — B2B startups following a product-driven growth model just need more time.
Ultimately, marketing teams at such B2Bs struggle to make key campaign decisions based on early CAC or ROAS (return on ad spend) metrics that are based on historical averages. They need some extra help in the form of predictive marketing, some elements of which can easily be done in-house.
To help you better evaluate your campaigns early, our data science team has developed a Ad group probability simulator.
Marketers can use this tool to estimate the probability that a campaign will deliver a high ROAS over time by simply entering a few numbers.
As the name implies, marketers can use this tool to estimate the probability that a campaign will deliver a high ROAS over time by simply entering a few numbers.
How to use the simulator?
Step 1
Based on your historical campaign data, complete the Quality Group Ranking, which divides your campaigns into Quality Cluster Groups 1-5, with 5 being the best quality (highest probability of converting) and 1 being the least favorable (lowest probability of transferring).
Obviously, campaigns have a higher chance of being among the latter. If you don’t have this data available, ask your BI team to extract it for you by following the instructions below:
Choose the Average Conversions quality cluster group. Let’s assume you have 500 ad group history and are interested in conversions that occurred within 12 months.
Option 1
Take all of your 500 ad groups and calculate the 10th, 30th, 50th, 70th, and 90th percentiles of the 12-month conversion rate. These are the centers of the conversion rates of your five cluster groups.
Option 2
