Case Study Snapshot
656 Conversions at ₹109 Cost per Conversion for an EdTech Brand
In this Google Ads campaign for an EdTech brand, approximately ₹71,200 in advertising spend generated 655.98 attributed conversions at an average cost per conversion of ₹109.
The campaign also generated approximately 8,720 clicks, giving us enough traffic and conversion volume to evaluate performance beyond a small sample of results.
The screenshot below is taken directly from the Google Ads account and shows the reported campaign performance.
Campaign results:
Clicks: 8.72K
Attributed Conversions: 655.98
Average Cost per Conversion: ₹109
Advertising Spend: ₹71.2K
I’m Deepak Singh, Performance Marketing Expert. Over the years, I have worked extensively with education and EdTech businesses across Google Ads, YouTube Ads, Meta Ads, lead generation, conversion optimization and paid acquisition strategy.
This case study breaks down the campaign performance, what the numbers actually tell us and the marketing decisions I would evaluate behind a result like this.
Business Context and Campaign Objective
This campaign was for an EdTech brand promoting a frontend course through Google Ads.
The objective was not just to drive traffic. The goal was to generate a meaningful volume of conversions at a cost level that could support profitable scaling.
What the Campaign Needed to Achieve
For an education offer like a frontend course, traffic alone is not enough.
The campaign needed to bring in users who were genuinely interested in learning frontend development and were likely to respond to the course offer.
That means the campaign had to balance volume, efficiency and conversion quality.
Why This Result Matters
According to the Google Ads screenshot, the campaign generated 8.72K clicks and 655.98 conversions from approximately ₹71.2K in ad spend.
That produced an average cost per conversion of ₹109, which is a useful benchmark for evaluating how efficiently the campaign turned paid traffic into measurable action.
What I Look At in a Result Like This
When I review a frontend course campaign like this, I do not look only at the final conversion number.
I also want to understand:
- Whether the traffic was relevant to the frontend course offer
- Whether the ad messaging attracted the right kind of learner
- Whether the landing page supported conversion well
- Whether the campaign maintained reasonable cost efficiency while generating enough volume
This is what makes the case study useful. The result is not just that conversions happened, but that the campaign generated them at a measurable scale with controlled acquisition cost.
What the Google Ads Results Tell Us
The screenshot gives us four important performance signals: 8.72K clicks, 655.98 attributed conversions, ₹109 cost per conversion and ₹71.2K in advertising spend.
Approximately ₹8.17 Average Cost per Click
Based on the reported ₹71.2K spend and 8.72K clicks, the campaign generated traffic at an approximate average cost of ₹8.17 per click.
For me, CPC becomes useful only when it is connected with what happens after the click.
Cheap traffic would not matter if those users were not converting into meaningful actions for the frontend course.
Approximately 656 Attributed Conversions
The campaign recorded 655.98 attributed conversions in Google Ads.
Google Ads can sometimes display fractional conversion values depending on the attribution setup, which is why the dashboard shows 655.98 rather than a whole number.
For readability throughout this case study, I refer to this as approximately 656 conversions while preserving the exact dashboard figure when presenting the source data.
₹109 Cost per Conversion
The most commercially useful number in the screenshot is the average ₹109 cost per conversion.
This connects media spend directly with the action the campaign was optimized to generate rather than judging performance only by clicks or traffic volume.
Strong Conversion Volume Relative to Traffic
A simple comparison of the reported clicks and attributed conversions gives a ratio of approximately 7.5 conversions for every 100 clicks.
I would treat this as a directional calculation rather than substitute it for the Google Ads conversion-rate metric because the exact platform calculation can depend on campaign type, conversion settings and attribution.
Why I Look at the Numbers Together
The useful story here is not any single metric.
The campaign combined traffic volume, conversion volume and controlled cost per conversion within the same acquisition system.
That is how I prefer evaluating performance marketing results: not by asking whether clicks were cheap, but whether paid traffic was consistently producing the business action the campaign was designed to generate.
What I Would Check Before Scaling This Campaign
The screenshot shows efficient conversion generation, but I would not scale the campaign based only on the ₹109 cost per conversion.
Before increasing budget, I would want to confirm that the conversions were commercially useful and that the performance remained stable as spend increased.
1. Conversion Quality
The first question is whether the reported conversions were turning into the right kind of customers for the frontend course.
A low cost per conversion is valuable only if those conversions have genuine business value.
2. Conversion Rate Stability
I would check whether the conversion rate remained reasonably consistent across different days, audiences and campaign segments.
A strong average can sometimes hide periods of weaker performance.
3. Cost per Conversion by Campaign Segment
The overall average was ₹109, but I would still want to know which campaigns, audiences, keywords or asset groups were producing the most efficient conversions.
This helps identify where additional budget may have the strongest chance of maintaining performance.
4. Search and Audience Quality
For an EdTech course, I would want to understand whether users were arriving with genuine learning intent or whether some of the traffic was too broad.
Relevant traffic becomes increasingly important as budget scales.
5. Landing Page Performance
With 8.72K clicks and approximately 656 attributed conversions, the post-click experience was clearly contributing to the result.
I would still review the landing page for opportunities to improve message match, clarity, trust and conversion efficiency before pushing significantly more spend.
6. Performance at Higher Spend Levels
A campaign that performs well at ₹71.2K in spend does not automatically maintain the same ₹109 cost per conversion at a much larger budget.
I prefer scaling gradually and watching whether conversion efficiency, traffic quality and downstream customer value remain healthy.
The objective is not simply to spend more. It is to increase spend while preserving as much of the underlying acquisition efficiency as possible.
What This Result Proves and What It Does Not
I prefer being careful with performance marketing case studies because a dashboard screenshot can verify some things very clearly while leaving other business outcomes unanswered.
What the Screenshot Clearly Shows
The Google Ads account reports approximately 8.72K clicks, 655.98 attributed conversions, ₹109 cost per conversion and ₹71.2K in advertising spend.
That gives us direct evidence that the campaign was able to generate a meaningful volume of measurable conversions for the frontend course at the reported acquisition cost.
It Shows More Than Traffic
This was not simply a campaign that generated thousands of clicks.
The traffic was also producing attributed conversion actions, which makes the result much more useful than reporting impressions, views or clicks alone.
It Does Not Tell Us Revenue or Profitability
The screenshot does not show course revenue, customer lifetime value, refunds, payment completion rate or profit.
I would therefore not use this screenshot alone to make a claim about ROAS or overall campaign profitability.
It Does Not Tell Us Which Campaign Element Caused the Result
The dashboard confirms the outcome, but it does not independently prove whether targeting, creative, bidding, landing-page optimization or another factor contributed most to the performance.
Those conclusions require additional campaign-level and funnel-level data.
Why I Still Consider This Strong Campaign Evidence
The value of this screenshot is that it provides direct platform evidence of spend, traffic, conversion volume and acquisition cost in the same campaign environment.
For a performance marketing case study, that gives us a much stronger foundation than a result stated without supporting account data.
This is also how I prefer presenting my work more broadly: show the result, explain the context and avoid claiming more than the available data can support.
What I Take Away From This Frontend Course Campaign
This campaign is useful because it shows how quickly paid acquisition becomes more meaningful once traffic and conversion data are viewed together.
Traffic Volume Alone Is Not Enough
Generating 8.72K clicks would not be particularly useful if those users were not taking the conversion action the campaign was designed to produce.
The approximately 656 attributed conversions are what turn the traffic number into a meaningful acquisition result.
Cost per Conversion Is More Useful Than CPC Alone
The estimated CPC of approximately ₹8.17 helps explain traffic efficiency, but the ₹109 cost per conversion gets much closer to the actual business objective.
This is why I prefer moving beyond click metrics as quickly as reliable conversion data becomes available.
EdTech Campaigns Need Intent, Not Just Reach
For a frontend course, the goal is not to reach everyone interested in technology.
The campaign needs to attract users whose intent is close enough to the actual course offer that they are willing to take the next step.
Enough Conversion Volume Creates Better Decisions
A handful of conversions can be heavily influenced by short-term variation.
With approximately 656 attributed conversions, there is substantially more data available for analysing campaign segments, identifying stronger patterns and deciding where additional budget may be justified.
Scaling Should Follow Evidence
The result gives a strong starting point, but I would still scale based on what happens to conversion cost and customer quality as budget increases.
I would rather grow a campaign gradually with clear performance feedback than increase spend aggressively simply because the current average looks attractive.
That same principle guides how I evaluate paid acquisition across Google Ads, YouTube Ads and Meta Ads: generate enough data, identify what is actually driving useful outcomes and scale only when the economics continue to make sense.
Frequently Asked Questions About This EdTech Google Ads Case Study
What Was Being Promoted in This Campaign?
The campaign was promoting a frontend course for an EdTech brand.
How Much Was Spent on Google Ads?
The Google Ads screenshot shows approximately ₹71.2K in advertising spend.
How Many Clicks Did the Campaign Generate?
The campaign generated approximately 8.72K clicks.
How Many Conversions Were Reported?
Google Ads reported 655.98 attributed conversions, which I refer to as approximately 656 conversions for readability.
What Was the Cost per Conversion?
The reported average cost per conversion was approximately ₹109.
Why Does Google Ads Show 655.98 Conversions Instead of a Whole Number?
Google Ads can report fractional conversion values depending on the attribution model and how conversion credit is distributed across ad interactions.
What Was the Approximate Cost per Click?
Based on ₹71.2K spend and 8.72K clicks, the approximate average cost per click was ₹8.17.
Does This Case Study Show Revenue or ROAS?
No. The screenshot does not show revenue, ROAS or profitability, so I would not use this evidence alone to make those claims.
Does a ₹109 Cost per Conversion Mean Every EdTech Campaign Should Achieve the Same Result?
No. Performance can vary significantly based on the course, pricing, audience, competition, landing page, campaign structure, conversion definition and market conditions.
What Is the Main Lesson From This Campaign?
The main lesson is that paid traffic should be evaluated through conversion outcomes rather than clicks alone.
In this case, the combination of 8.72K clicks, approximately 656 attributed conversions and ₹109 cost per conversion provides a clear view of how traffic translated into measurable acquisition activity.
Sources & Methodology
This case study is based on a real Google Ads dashboard screenshot from an EdTech campaign promoting a frontend course.
The screenshot directly shows approximately 8.72K clicks, 655.98 attributed conversions, ₹109 cost per conversion and ₹71.2K in advertising spend.
Where I refer to approximately 656 conversions, I am rounding the platform-reported 655.98 figure for readability while keeping the exact dashboard value visible in the source evidence.
The approximate ₹8.17 cost per click mentioned in this case study is calculated from the reported spend and click volume rather than copied directly from the screenshot.
I have intentionally avoided making unsupported claims about revenue, ROAS, profitability, customer lifetime value or sales because those figures are not visible in the available screenshot.
The interpretation in this case study reflects how I personally evaluate paid acquisition performance across traffic quality, conversion volume, acquisition cost, landing-page performance and scaling potential.
The purpose of this case study is to document a real campaign result with clear evidence and explain what can and cannot reasonably be concluded from the available data.
Want Help Improving Your EdTech Google Ads Performance?
If your EdTech campaigns are generating traffic but the cost per conversion is too high, I would first look at the complete acquisition path rather than changing bids randomly.
The biggest opportunities usually sit across audience quality, campaign structure, ad messaging, landing-page conversion, tracking and downstream customer value.
You can also review my YouTube Ads for EdTech lead generation framework, my YouTube Ads metrics guide, or my campaign structure framework to understand how I think about paid acquisition more broadly.
If you want help auditing or improving your Google Ads, YouTube Ads or overall EdTech acquisition system, you can work with me.
