Optimizing your campaign for lead volume and CPL

Now that you have your campaign up and running, there is definitely more to do. As time passes, you’ll notice either a drop in leads or stagnant lead volumes and your CPL is increasing.

Variables to look out for when optimizing for lead volume and CPL

  1. Audience saturation, this metric serves to tell you if you need to increase you audience pool
  2. Cost Per Lead
  3. Lead Volume
  4. Budget
  5. Location
  6. Timing

Optimizing for lead volume and CPL involves

  • Establishing your Hypothesis
  • Establishing your Methodology and Key Metrics
  • Establishing your Hypothesis Testing
  • Drawing a Conclusion
  • Implementing learning’s in your campaign

With the usage of Peasy, you don’t really have to worry about this at all. As the AI itself will consume data from your campaign learnings and use other similar campaigns in order to formulate the best strategy in bid, budget split and audience targeting. It follow the same scientific and methodical process that one would take in running experiments and determining whether it should be implemented or not in your campaign.

Optimizing for lead volume and CPL in all honesty is a lengthy process, but if you maintain a scientific and methodical approach, you’re sure to find the answers that makes for a successful campaign. So let’s take a scenario that we ourselves have experienced, we ran a property campaign targeting middle income audience segment for 3 month, but we found that our lead volumes were dropping and that our CPL was increasing. Based on experience, we know that there are a couple of factors that causes such an issue; which are audience, bid or budget.

Steps we took

  1. We first tried to understand whether budget was the issue, looking at the budget allocation and budget usage, we found that only 20% of the total budget was utilized, therefore we knew that the budget wasn’t the issue.
  2. We then took the next step in looking at our bid, having changed our bid 25% higher for the next 1 week, our lead volume remained has returned to previous levels but our CPL continued to increase. This in turn tells us that in order for us to stay competitive in the auction, our bid has to increase. This is less than ideal as it means we have to spend more in order to gain more.
  3. We then checked on our audience saturation, we noticed that the frequency increased over time and the CPL increased over time but the first time impression ratio has dropped. This is indicative that the audience pool is drying up at the given bid. We now know that our issue here is the audience pool, so we begin with the expansion of our audience pool to capture.

Establishing the Hypothesis and Methodology

  1. We then hypothesize that if we were to add in targeting criteria’s of those who has interest in developers of residential properties and platforms, our lead volume increases and the CPL will become lower.
  2. Then established our Methodology and Key Metrics. Our methodology was to run 2 to campaigns in Peasy, one being the control and the other being the audience with added targeting criteria’s
  3. Next is the hypothesis testing, we disprove the hypothesis statement when lead volume has not increased and CPL has increased if we add interest in developers of residential properties and platforms
  4. We proceed to run the experiment for a week through Peasy, given within this 1 week time frame. Peasy collected enough data to understand that the audience segment with the added interest in developers of residential properties and platforms has increased lead volume and decreased CPL. An automatically stopped the previous audience segment.
  5. We now know that in future campaigns with the same product, this audience targeting is ideal

Peasy takes this a step further as well with the SWO Analysis Report for you campaign. Where it will tell you What are your best performing audience segment, your worst performing audience segment and which audience segment look promising.

Best thing yet, with Peasy takes it a step further by offering insights, recommendations even predictions on how an audience segment can be improved and what are the estimated performance gains.