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Tip: Use this tool before launching campaigns to model revenue potential under different budget and audience scenarios.
Impressions represent how often your content or ad is seen, but reach alone doesn’t equal revenue. By factoring in CPM and conversion rate, this calculator estimates how effectively your impressions turn into paying customers.
It bridges the gap between visibility and value, helping marketers evaluate the true impact of social media exposure. Whether you’re investing in paid ads or organic campaigns, tying impressions to revenue helps prioritise strategies that actually drive business growth.
Industry | Average CPM (₹) | Typical Conversion Rate (%) |
E-commerce (D2C) | ₹150 – ₹300 | 1.5% – 3.5% |
SaaS / B2B Services | ₹200 – ₹400 | 2% – 6% |
Education / EdTech | ₹120 – ₹250 | 1% – 4% |
Healthcare & Wellness | ₹100 – ₹200 | 0.8% – 2.5% |
Travel & Hospitality | ₹180 – ₹350 | 1.2% – 3.8% |
Note: CPM and conversion rates can vary significantly based on platform (e.g., Meta vs. LinkedIn), campaign objective, targeting, and ad creative.
Scenario:
A travel agency runs a campaign projected to generate 500,000 impressions. The CPM is ₹250, and the estimated conversion rate is 2%.
Calculation:
Revenue = ((500,000 ÷ 1,000) × ₹250 × 2) ÷ 100
= (500 × ₹250 × 2) ÷ 100
= ₹1,250,00 ÷ 100 = ₹12,500
Interpretation:
The campaign could potentially generate ₹12,500 in revenue from 500,000 impressions based on the selected inputs. Use this insight to evaluate ROAS before committing ad spend.
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Answers to Frequently Asked Questions
It estimates the revenue potential of a campaign based on impressions, CPM, and conversion rate inputs.
The formula multiplies total impressions by CPM and conversion rate, giving a projected revenue output.
CPM stands for Cost per 1,000 Impressions — a common metric used to price digital ads. It impacts your overall ad efficiency.
Yes. You can assign a notional CPM to organic impressions to estimate their business value.
It varies by industry and objective, but typically ranges between 1% and 5% for most sectors.
The accuracy depends on the quality of your input data. Use historical performance for best results.
It’s useful both for pre-campaign forecasting and post-campaign analysis when actual performance data is available.