How to set B2B online ad goals & budgets

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How to set B2B online ad goals & budgets

Is Your Google Ads Budget Enough to Prove a Significant Test?

Insights from a past B2B Client Discussion

Recently, I had a call with a B2B client who wanted to test the ROI of Google Ads with an initial £2,000 budget. They had previous reservations due to past experiences.


In summary, I advised the client that while £2,000 can provide some good initial data on cost per click (CPC), it’s typically insufficient to accurately gauge lead generation success.


Below is my rationale behind this summary:

B2B Client Data Shared with Me:

  • 100% of leads come from 5 X Business Development Managers (BDMs), who use cold calling to book meetings.
  • 60 sales qualified leads (SQL) a month
  • £17,000 cost for the 5 X BDMs
  • £283 cost per SQL
  • 33% conversion rate SQL > new customer
  • 20 new customers a month
  • £850 cost per new customer
  • The lead-to-customer conversion cycle is one month

1. Target Result:

For Google Ads to be viable as a lead generation channel, it must match or better the results of the BDMs – £283 cost per SQL and £850 cost per new customer. Let’s aim for Google Ads to achieve a £200 cost per SQL and a £700 cost per new customer.

2. Client’s Preferred Ad Budget:

Theoretical estimates based on the £200 target SQL suggest that the client’s £2,000 spend might yield around 10 leads. The conversion of these leads to SQLs is yet to be determined.

3. Minimum Conversions:

For a Google Ads test to be beyond random probability and statistically significant, Google recommends that a campaign should have at least 50 conversions (in this example, leads) in the last 30 days. Based on a £200 cost per lead, this would mean the budget would need to be at least £10,000.

4. Minimum Test Time Period:

Considering statistical significance, if the lead-to-customer conversion cycle is one month, it would be prudent to run the campaign for at least two months. This allows for a larger sample size and one month to gain leads and another month to observe how many become SQLs and convert to new customers.

5. My Recommendation:

  1. Month One: Set the ad spend budget to £2,000 to capture real CPCs and conversion rates, making necessary adjustments.
  2. Month Two: Increase the budget to £4,000, which should yield the lead > SQL rate, plus a small chance that the initial c. 10 leads from month one show signs of becoming new customers.
  3. Month Three: Propose an ad spend budget of £8,000, as we would have two months of data and potentially some new customer signups, indicating strong performance.

This structured approach, combined with diverse creative assets (30-second videos and images) and potentially using both Google and LinkedIn Ads with remarketing, provides a clearer picture of Google Ads’ potential for lead generation.
Starting with a £2,000 test budget is a reasonable step to gather initial insights. However, for a conclusive comparison with traditional methods like BDMs, a comprehensive budget and well-planned strategy are essential to ensure the test is statistically significant.

6. Other things to consider:

  • Clarify the Lead Definitions & CR Rates: It’s important to differentiate and measure between leads, sales qualified leads (SQLs), and the expected conversion rate for each stage within the Google Ads funnel.
  • Test Variability: The cost per SQL and cost per new customer can vary in Google Ads compared to traditional BDM methods due to market competition and keyword bidding.
  • ROI / LTV Calculation: The initial focus is on SQLs, but then it moves to customers, then to understand life time value (LTV), which can then all be factored into the budgeting equation.
  • Using Micro Conversions: Google’s recommendation of 50 conversions over a 30-day period – does not have to be leads and won’t initially be. We will use “micro conversions” as positive signals to feed the algorithm, like a click on the contact button or viewing the pricing page etc. But for the purposes of budgeting using leads provides an initial benchmark.

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