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An attribution model is a rule or algorithm that determines how conversion credit is distributed across the marketing touchpoints a customer encountered before completing a purchase. A touchpoint might be a Google search ad click, a Meta retargeting impression, an email open, or an organic search visit. Because most customers interact with multiple channels before buying, you need an attribution model to decide: which channel gets credit for the sale? The answer has direct budget implications — channels that receive credit get investment; channels that don't, get cut.
The model you choose changes what your data tells you — and therefore what you spend. A brand running on last-click attribution will systematically cut brand awareness and email campaigns (which rarely get last-click credit) and over-invest in retargeting and branded search (which often are the last click). That's a budget allocation problem disguised as a performance insight.
For D2C brands in India, where customer acquisition paths often involve Instagram discovery → Google search comparison → SMS/WhatsApp coupon → purchase, a last-click model credits only the coupon channel while ignoring the Instagram and search spend that made the sale possible.
Understanding attribution models also matters for A/B testing: if you're testing landing pages, make sure you're measuring conversions against the same attribution window for both variants.
A D2C brand selling premium yoga gear priced at ₹2,500–₹6,000 runs three channels: Instagram awareness ads (top of funnel), Google Shopping (mid-funnel), and email retargeting to past browsers. Under last-click attribution, email retargeting shows a ROAS of 18x and Instagram shows 0.8x. Under a data-driven model that accounts for assisted conversions, Instagram's actual contribution scores 4.2x (it initiates 60% of purchase paths that later convert via email). The brand that only looks at last-click cuts Instagram spend, sees new customer acquisition fall 40%, and can't understand why email retargeting performance drops — because there are fewer new prospects to retarget.
When running experiments, attribution model consistency matters. If your control and variant are measured against different attribution windows or models, you may see apparent differences that are just reporting artifacts. Always confirm your testing platform and analytics platform agree on what counts as a conversion.
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