How we optimized our Google Ad Campaigns to bring down the CPL?
At ABC Corp. (name changed), we were heavily dependent on paid acquisition to drive the demand side of our marketplace. In this blog, I will share how we brought down the CPL from almost 150 Rs to about 90 Rs on our paid acquisition channels.
Before we go into the details, I will give some context which will then help us to understand the overall approach better.
Company context
ABC Co. is a Marketplace for the B2B Services category. Initially, the focus was on 4 main categories Design, Content, Web Development, and Digital marketing. To pilot the concept faster, Paid Search was chosen as the medium for acquisition.
Marketing Objective: Increase conversions
Communication Objective: Trusted service where we match you with the most suitable service provider based on your location, price point, and industry.
Assumption while writing this article: Campaigns are already setup. The process of setting up campaigns will be discussed in another blog
Having set up the context pieces, its time we discuss the optimization approach
Measure. Monitor. Improve
As they rightly say, if you cannot measure, you cannot improve. We measured everything around our acquisition and in fact till lead closure. Certain things were measured daily, certain weekly, and some things were measured monthly.
Visualize your funnel and optimize for each part of the funnel phase-wise
Our funnel looked something like this (We measured this for the overall marketplace and category wise as well)
Impressions > Clicks > Users > Add to Cart > Final Requirement sharing> Qualify the lead > Vendor connect > Evaluation of vendor > Final Purchase
From a marketing perspective, the first 5 steps are important while the last 3 are important from a sales perspective. The step where we qualify the lead is a shared responsibility depending on the kind of keywords it comes in the scope of the marketing team and at the same time on sales ops depending on their exploration calls.
The first five steps were measured for both mobile and desktop separately as well.
Optimize: Start with the lowest hanging fruit first
The first thing which we solved was reducing the Cart Abandonment Rate. When we started our cart Abandonment rate was about 40%, which means out of every 10 people who filled in their requirement details only 6 completed the complete form. (The form was a 2 step form where we first ask the requirement details and then client’s details like Name/Phone No/Email so that we can reach out and qualify the lead)
From our category-wise analysis, we saw that for the Design and Content Category, the abandonment rate was as high as 50% but for categories like Digital and Website, it was around 30%. To dig deeper we saw user videos on a tool named LuckyOrange and saw the heatmap on step 2 to solve this.
We solved the problem (It is not yet completely solved) by taking these steps
a. We realized that most people were wary of sharing their phone numbers, so we strengthened our communication on why we needed the phone number and tried to reinforce that the data will not be shared and there will be only one person from our team calling.
b. Since this was a marketplace and people at times do not prefer an intermediary, we re-enforced their trust on this page by communicating how many freelancers we have found basis their requirement and the details will help them to access those freelancers. In the FAQ section, we addressed how the payment was kept secure.
c. Now we are trying to add a testimonial carousel right beside the form so that the trust is built further.
We now have about a 25% cart abandonment rate and hopefully, this will increase.
After doing this, the second thing was to focus on Add to cart rate. Since this was traffic, we are already getting, it is better to increase the conversion first, achieve better capital efficiency and then focus on more traffic.
For this, one can refer to the blog here. Although the blog refers to the design category the overall approached remained the same. The overall approach included asking relevant but not high cognitive load questions, increase trust by adding relevant testimonials and how many clients took that service.
We also tried to reduce the clutter on the webpage by reducing distractions (if any)
The third thing(s) were getting better on the click-to-user rate and increasing the click rate. The click-to-user rate is less of a marketing thing, but more of an engineering thing where the focus was on increasing the page load speed by and large so that more clicks get converted to actual users.
Since the clicks were already made, these steps were the first thing that we solved so as to bring capital efficiency in place with what we had already in place.
The next step was to optimize for clicks since we were already getting impressions. Clicks optimization dependent on writing trustworthy ad copies and since we were focussing on mobile how much was our top-of-the-page rate because a lower position ad will not get as many clicks.
The approach was not to get to the top of the page for all the keywords at one go, but phase-wise, from better-performing keywords to worse ones.
Optimizing for impressions, we will take it in the end, before which will walk you through how we used UTM parameters to measure keyword-level data as the fourth thing
Even though in this blog, we are only talking about CPL and not about CPQL (Cost per Quality Lead), but the quality of leads is an important factor in bringing CPL down as well.
For this, we relied heavily on a strong UTM-based framework. We passed on UTM parameters like Source, Campaign, and Keyword in the URL and captured them in our back-end database.
So at the back end, we will clear data about date-wise, campaign and keyword-wise conversions, their relevance, closure status, and average ticket size. So apart from conversion, we had strong data about which Campaigns/Keywords were doing well in terms of closures and ticket size. This helped us to channelize our focus and approach for performing and non-performing keywords differently, helping to bring the CPL down.
The fifth and the final few things which we did and not limited to are as below:
- Use automated bidding strategies like Target CPA when the CPA is below your profitability threshold. This helps you to save time and get leads at the rate you want.
- Once decent conversions are coming, divide the campaigns for Mobile+Tablet and Desktop separately so that you can optimize for each platform
- Look Region-wise data in your target geography, divide the campaigns into different regions basis logic like regions that have good results but not getting enough impressions due to the target region being a larger one.
- See your conversion rates and cost per conversion day-wise and time-wise and reduce your bids accordingly. (Something like bid less on Weekends and night etc.)
These were some of the things which we did to optimize our campaigns. These were not the only things but in the interest of time, I have tried to summarize this in a crisp manner. If you have any suggestions etc., please reach to me at ashutoshgarg273@gmail.com
