How to Report on Most Effective Customer Journey Touch Points
Chain-Based Attribution (CBA) is rooted in probability and statistics and uses the Markov model to give credit to each touch point along the customer journey. It uses your account’s conversion data to calculate the actual contribution of each channel along the conversion path and, by comparing the paths of accounts that convert to those who don’t, determines what truly matters for each conversion path.
CBA works backwards from Closed opportunities to analyze the complete chain of events and computes the contribution of each marketing channel to revenue. Using machine learning, CBA is always listening and improving the model over time.
Why You'd Use This Model
Traditional attribution models run the risk of unfairly giving credit to different channels in a customer’s journey, as no customer journey is always the same and therefore, no touch point’s influence is, either. In order to understand how each channel truly influences revenue based on marketers’ desired outcomes, the combination of both historical performance and predictive insights are needed.
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.
WHO THIS is valuable for
Depending on the size of your organization, you may have different stakeholders. If you're a small business or midsize enterprise, this data will be valuable for:
- Demand Generation teams
- Business Intelligence or Marketing Analyst teams
- VPs of Marketing
- Chief Marketing Officers
- AVPs, VPs of Sales, Chief Revenue Officers
If you're an Enterprise organization, you can expect this data to be valuable for all of the above roles, aside from AVPs, VPs of Sales, and Chief Revenue Officers.
DATA You Need
- Anonymous and/or known website data
- Paid media data (impressions, clicks, etc...)
- Any touch point (we call this an event graph) from across the customer journey
- Email Performance
- Form Data (conversions)
- Opportunities created (pipeline)
- Revenue
- Time Frame (based on model and touch point)
data sources required
The more data you have access to, the better the "machine" is able to learn about the optimal customer journey path to revenue.
- Marketing Automation Platform (Marketo, Hubspot, Eloqua, Pardot, etc)
- CRM (Salesforce, SAP, etc)
- AnalyticsJS / product behavior
- Web Analytics (Google, Adobe)
- Ad Platform Connectors (LinkedIn, Facebook, Google Ads)
More on Chain-Based Attribution
Chain-Based Attribution (CBA) uses your account’s conversion data to calculate the actual contribution of each channel along the conversion path. By comparing the paths of accounts that convert to those who don’t, CBA determines what truly matters for each conversion path.
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.
And it’s not one-and-done -- using machine learning -- with more data the models continue to improve over time.
Unlike existing MTA (Multi-Touch Attribution), CBA can take less than perfect data, 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.
Using a Markov chain model, each touch point is fairly given credit for their influence based on the outcome you want to achieve.
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 use a Markov chain model to see how organic search traffic contributed as compared to paid ads traffic, determine which event had the highest probability of generating webinar traffic, and then predict how to best allocate your marketing budget for the next webinar. You can also find out which events would cause the largest drop in traffic if they were to stop by looking at the Removal Effect report.
Here's an example Chain-Based Attribution dashboard in CaliberMind:
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