Multi-touch attribution models explained for Indian marketing teams
Your customer saw a Facebook ad, searched Google, clicked an email, and then converted. Which channel gets credit? Attribution models answer that question and change how you allocate budget.
Here is a scenario I see constantly in Indian e-commerce and services businesses. A customer sees a Meta ad, does not convert. A week later they Google the brand name, click an organic result. Two days after that they receive an email offer and click through to buy. The order gets recorded. Now which channel gets the credit?
If you are using last-click attribution, the email gets 100% of the credit. But the Meta ad started the journey. The Google search showed intent. The email just happened to be the final push. This matters because if you only look at last-click data, you will underfund Meta and over-invest in email, and eventually wonder why performance drops.
The most common attribution models
Last-click attribution gives all credit to the final touchpoint before conversion. It is simple and still the default in many platforms including older Google Ads setups. The problem is it systematically undervalues awareness and consideration channels.
First-click attribution does the opposite. All credit goes to whatever first brought the customer to your site. This overvalues top-of-funnel channels and ignores what actually sealed the deal.
Linear attribution splits credit equally across every touchpoint. If someone interacted with your brand five times before buying, each interaction gets 20% of the credit. This is more balanced but still feels arbitrary.
Time-decay attribution gives more credit to touchpoints closer to the conversion. A touchpoint that happened one day before conversion gets much more credit than one that happened three weeks earlier. For businesses with short sales cycles, this model often reflects reality well.
Position-based attribution, sometimes called U-shaped attribution, gives 40% to the first touchpoint, 40% to the last touchpoint, and splits the remaining 20% among everything in between. This recognises that both acquisition and closing matter.
Data-driven attribution: what it is and when it applies
Google Analytics 4 and Google Ads offer data-driven attribution, which uses machine learning to assign credit based on actual patterns in your conversion data. This is the most accurate model but requires a minimum volume of conversions to work properly. If you are converting fewer than 300 sales per month, data-driven models are not reliable for your account.
How to apply this in India-specific contexts
In India, customer journeys often involve WhatsApp. A customer might see an ad, send a WhatsApp message to enquire, and then receive a follow-up that converts them. Most attribution tools cannot track WhatsApp touchpoints natively. This means a significant portion of Indian business journeys are invisible to standard attribution models.
The practical workaround is to tag WhatsApp conversation starters with UTM links and track enquiry form submissions separately from sales, then reconcile the two manually. It is imperfect but better than pretending WhatsApp does not exist in the funnel.
What to actually do with attribution data
The goal is not to find the "correct" model and declare victory. It is to run the same campaign data through two or three models, notice which channels look different across models, and investigate why. A channel that looks poor on last-click but strong on first-click is an awareness driver that deserves budget even if it never appears to close deals.
Review your attribution model settings quarterly and revisit them when you change your channel mix significantly.
Frequently asked questions
What attribution model does Google Analytics 4 use by default?
Google Analytics 4 defaults to data-driven attribution for conversions. For older Universal Analytics, the default was last interaction (last click). This is an important difference if you are comparing reports between the two platforms.
Does Meta Ads Manager use the same attribution as Google Analytics?
No. Meta uses its own attribution window and counting methods, which is why Meta and Google Analytics often show different numbers for the same campaign. Meta typically shows a 7-day click, 1-day view window by default. Set these windows consistently across platforms when comparing performance.
Can a small Indian business with limited conversion data use multi-touch attribution?
For low-volume businesses, position-based or time-decay attribution applied manually in a spreadsheet is more practical than in-platform models. Track every touchpoint you can measure, and focus on qualitative feedback from customers about how they found you as a supplement.