B2B营销归因最佳实践（英文版）_16页_688kb

B2B Marketing Attribution Best Practices Summary

I. Purpose

B2B marketing attribution is a critical component of the marketing technology stack, as it connects marketing activities to down-funnel sales data, including revenue. This white paper outlines best practices for effectively implementing and utilizing marketing attribution, covering metrics, touchpoint tracking, attribution models, reporting, and forecasting.

II. Choosing the Right Metrics

B2B marketers should identify their ultimate goal and key indicators to measure marketing performance. The ultimate goal could be revenue, pipeline revenue, or sales opportunities, depending on the organization. Key indicators should be spread across the buyer's journey (TOFU, MOFU, BOFU) to assess funnel health at different stages.

• Impressions, clicks, visits, leads, qualified leads, demos, sales, and revenue are common metrics.
• Marketers must understand that these are indicators, not the ultimate goal.

III. Tracking Touchpoints

Touchpoint data is essential for marketing attribution. It includes:

• Date/time
• Marketing channel
• Source
• Landing page
• Ad campaign (if applicable)
• Keyword (if applicable)
• Type (web visit or form submission)

a. Acquiring Touchpoint Data

Touchpoint data can be collected through:

1. UTM Parameters: Standard web tracking mechanism, but prone to user error.
2. Technology Integrations: Native integrations with ad networks provide more accurate and automated data collection.
3. CRM Campaigns: Touchpoints can be tracked via list uploads, event registrations, and co-marketing efforts.
4. Custom Channels: Organizations should customize their marketing channels to better reflect their unique audience and strategy.

b. Treating Touchpoint Data

• Touchpoint Position: Indicates the stage in the buyer journey (e.g., first touch, lead creation, opportunity creation).
• Deletion and Suppression: Marketers may choose to delete or suppress touchpoints from certain sources or stages to avoid misleading credit allocation.

IV. Account-Based Attribution

B2B buyers are typically groups (accounts), not individuals. Attribution should reflect this by:

• Rolling up touchpoints to the account level.
• Ensuring only one first touch, lead creation touch, opportunity creation touch, etc., per account.
• Using lead-to-account mapping to align touchpoint data with CRM data.

V. Attribution Models

There are two main types of attribution models:

• Single-touch models: Assign full credit to one touchpoint (e.g., first or last touch).
• Multi-touch models: Assign fractional credit based on touchpoint positions, offering more nuanced insights.

a. Attributing Leads

• U-Shaped model: 40% to first touch, 40% to lead creation touch, 20% to others.
• Ideal for measuring lead generation effectiveness.

b. Attributing Opportunities and Pipeline Revenue

• W-Shaped model: 30% to first touch, 30% to lead creation, 30% to opportunity creation, 10% to others.
• Suitable for measuring the impact of marketing on sales opportunities.

c. Attributing Revenue

• Full Path model: 22.5% to each of the first touch, lead creation, opportunity creation, and closed-won touchpoints, with 10% to others.
• Best for measuring revenue impact across the entire buyer journey.

VI. Reporting

Reporting using attribution data helps marketers assess performance across different dimensions.

a. Cross-Channel Reporting

• Provides a high-level view of marketing performance across all channels.
• Highlights which channels drive leads, opportunities, and revenue.

b. Demand Reporting

• Offers granular analysis at the channel or function level.
• Useful for evaluating specific campaigns, ads, or keywords.

c. Content Reporting

• Focuses on content performance, such as landing pages or form URLs.
• Helps content marketers understand how specific content drives leads or revenue.

d. Adding Filters

• Filters can be applied to reports for deeper analysis.
• Example: Filter by marketing channel or lead status (e.g., MQL) to understand specific performance metrics.

VII. Forecasting

Attribution data can be used to forecast future performance by leveraging historical data and machine learning.

a. Using Historical Data

• Historical conversion rates (e.g., lead-to-opportunity) can be used as a baseline for future forecasts.

b. Leveraging Machine Learning

• Machine learning enhances forecasting accuracy by analyzing large datasets and deriving insights beyond historical trends.
• It helps predict prospect behavior and improve marketing strategy.

Conclusion

Marketing attribution in B2B requires careful selection of metrics, accurate tracking of touchpoints, appropriate use of attribution models, and effective reporting. With the right tools and strategies, marketers can better understand and optimize their full-funnel marketing efforts. As the industry evolves, the integration of machine learning and more sophisticated data collection methods will continue to improve the accuracy and value of marketing attribution.