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Playbook: Chain-Based Attribution

Chain Based Attribution

How Chain-Based Attribution Effectively Reports Customer Journey Touch Points

Chain-Based Attribution (CBA) is rooted in probability and statistics, which uses the Markov model to assign value to each touch point the customer has with your brand on their journey. Using machine learning, CBA is always listening and improving the model over time. It processes your account’s conversion data to gauge the real impact of every channel along the conversion path. By contrasting the pathways of accounts that convert with those that don’t, it identifies the key factors for each conversion path.
CBA works backward from Closed opportunities to analyze the complete chain of events and computes the contribution of each marketing channel to revenue. See how companies are finally unlocking their buyer journey with Chain-Based Insights with Calibermind’s 2020 Guide to Chain-Based Attribution.

Who Chain-Based Attribution Is Valuable For

Demand Generation Teams

Understand which touchpoints and channels are most effective at driving conversions and work to optimize your campaigns for better results.

Business Intelligence or Marketing Analyst teams

Help allocate resources to campaigns that are driving the most revenue by understanding which touchpoints and campaigns have the most ROI.

VPs of Marketing

Accurately measure the effectiveness of your marketing strategies by knowing which campaigns have the highest impact on conversions.

Chief Marketing Officers

Improve marketing spend and decision-making with data-driven insights into the impact of all marketing channels and campaigns.

AVPs, VPs of Sales, Chief Revenue Officers

Optimize your sales strategies and customer segmentation by understanding what channels are most effective and which accounts are quality leads.

*If you’re an Enterprise organization, you can expect this data to be valuable for all the above roles, aside from VPs of Product and VPs of Sales.


Why You Should Use Chain-Based Attribution

Traditional attribution models may not accurately assign credit to different channels in a customer’s journey, as each customer’s path varies and so does the impact of each interaction. To truly comprehend how each channel affects revenue in line with marketers’ objectives, it’s essential to combine historical performance data with predictive insights.

This model enables marketers to predict sales opportunity conversion with a much higher level of accuracy than previous marketing attribution models. CBA can take less-than-perfect data and combine it with web tracking and identity graph partners to give B2B marketers full-funnel visibility throughout the entire customer journey, from anonymous to new revenue.

You can learn more by reading Calibermind’s Ultimate Guide to Chain-Based Attribution.

What Is Needed For Account-Based Marketing Funnels

Data You Need

Data Sources Required

The more data you have access to, the better the “machine” can learn from the optimal customer journey path to revenue.

The Difference of Utilizing Chain-Based Attribution

The beauty of CBA is that as long as your CRM has enough (100 or more) Closed-Won and Closed-Lost opportunities, you’re eligible for Chain-Based Attribution. With that data in hand — we automatically train a model that’s unique to each of our customer conversion types. The model observes what your accounts do before converting, and what they do when they don’t convert, to measure what’s important.

The process is not a one-and-done event — it utilizes machine learning, which means the models enhance and improve over time as they receive more data.
Unlike the existing Multi-Touch Attribution (MTA) method, Chain-Based Attribution (CBA) can work with imperfect data. By integrating this data with web tracking and identity graph partners, provides B2B marketers with a comprehensive view of the entire customer journey, from the point of anonymity to new revenue generation.

Want to close more leads? Increase e-newsletter subscriptions? Determine the event that generates the most revenue? A Markov chain model can help you do that.

Say you ran a webinar campaign. You could utilize a Markov chain model to compare the contributions of organic search traffic and paid ad traffic. This model can help determine which event had the highest probability of generating webinar traffic and predict how to best allocate your marketing budget for future webinars. Moreover, by examining the Removal Effect report, you can identify the events that would cause the largest drop in traffic if they were to end.

Here’s an example Chain-Based Attribution dashboard in CaliberMind:

CBA Graph