5 steps for better attribution — and better measurement
Posted: Wed Sep 24, 2025 9:26 am
Start by clarifying what you want to learn or achieve — whether it’s driving incremental sales or boosting brand awareness. Align these goals with stakeholders across teams so your data collection and metrics directly support cross-channel measurement (XMM) and MMM decision-making.
2. Clarify and document each channel’s role in the journey
Be clear about what every channel is belarus cell phone database supposed to do (awareness, consideration, conversion or re-engagement). Structure and weight your inputs for MMM and XMM to reflect each channel’s intended purpose and impact across the funnel — not just conversion volume.
3. Measure assisted and last-touch conversions together
Don’t focus solely on what closed the sale. Analyze both assisted and last-touch conversions and feed your models with a diverse set of metrics:
Conversion rates.
Qualitative brand survey results and consumer insights.
4. Segment, structure and enrich your data
Organize data by audience, region, creative and strategy to ensure clean tagging. This helps link exposures to outcomes and gives your XMM/MMM models a more robust, less biased set of inputs.
2. Clarify and document each channel’s role in the journey
Be clear about what every channel is belarus cell phone database supposed to do (awareness, consideration, conversion or re-engagement). Structure and weight your inputs for MMM and XMM to reflect each channel’s intended purpose and impact across the funnel — not just conversion volume.
3. Measure assisted and last-touch conversions together
Don’t focus solely on what closed the sale. Analyze both assisted and last-touch conversions and feed your models with a diverse set of metrics:
Conversion rates.
Qualitative brand survey results and consumer insights.
4. Segment, structure and enrich your data
Organize data by audience, region, creative and strategy to ensure clean tagging. This helps link exposures to outcomes and gives your XMM/MMM models a more robust, less biased set of inputs.