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On this page

• About Bizible
• Bizible Touchpoints
  • Touchpoint Generation Methods
  • Touchpoint Mapping Methods
    • Sales Activity Tracking in Bizible
  • Online vs. Offline Touchpoint Management
    • Online
    • Offline
• Attribution Models
  • U-Shaped
  • W-Shaped
  • Full Path
  • Linear Attribution
  • Custom Attribution (Based on Bizible Machine Learning Algorithm)
    • How GitLab Calculates Linear Attribution IACV
• Bizible Channel and Sub-Channel Mapping
  • Online Channel Mapping
  • Offline Channel Mapping
• Bizible Attribution with Pathfactory
• Bizible Reports in SFDC
  • Use case: Track registration by source for GitLab owned gated landing pages

About Bizible

Bizible unifies behavioral and ad data with sales outcomes and machine learning so you can make the right marketing decisions.

Bizible Touchpoints

Bizible defines a touchpoint as: Touchpoints refer to the interactions a prospect/lead has with your online or offline marketing efforts.

They capture a multitude of information about the interaction such as utm parameters, date/time of the interaction, and type of interaction (web visit, form submission, campaign response, etc.).

In Salesforce, the abbreviations for the various touchpoints are as follows (see more in the diagrams below to see how these work in the different models):

• FT - First Touch, a prospect's first-ever interaction
• LC - Lead Created, prospect submits information
• OC - Opportunity Creation, prospect intentionally moves toward a purchase decision
• Closed - Customer Close, prospect makes a purchase

Here is Bizible's breakdown of how Touchpoints are generated and mapped:

Touchpoint Generation Methods

The touchpoint generation process answers the question, “How is Bizible going to know that this occurred?” Depending on your feature set and the types of interactions your prospective customers can have, there are up to three ways Bizible can pick up on an interaction and create a touchpoint to represent it.

Touchpoint Mapping Methods

The touchpoint mapping process answers the question, “Once this touchpoint’s been created, how is Bizible going to know what channel and subchannel it belongs to?” Each method of touchpoint generation has its own method of touchpoint mapping.

There are two types of Bizible Touchpoints as explained below.

Sales Activity Tracking in Bizible

Bizible looks at two key fields on a Task/Event to determine if a BT/BAT should be created for it, Disposition and Type. Type will become what system/activity category (IQM for example) created the touchpoint. Disposition is used by Bizible to determine when a touchpoint should be created.

In an ideal scenario, all systems will use one of two options for the Disposition. Either meaningful engagement/move to SAO or meaningful engagement to tell Bizible when to create a touchpoint. This will reduce the manual effort on the backend and simplify both reporting and system administration. meaningful engagement/move to SAO will be used when there was a quality engagement with the prospect and this lead to the creation of a SAO whereas meaningful engagement will be used when there was a quality engagement that did not lead directly to a SAO (such as a meeting that was useful, but didn't indicate the prospect was ready for a sales accepted opportunity).

Example: Initial Qualifying Meetings (IQMs) When an IQM is had, the Type is set to IQM and the disposition currently is set to Quality Meeting to indicate that the meeting/IQM should be counted as a touchpoint and was useful to the sales/marketing process.
Ideally, Quality Meeting would be changed to the more generic, menaingful engagement, so that we have parity between the tools and can use a tool-agnostic approach to touchpoint generation.

Online vs. Offline Touchpoint Management

Online

Online channels are any channels that would be directly associated with your website or any integrated site – any display advertising, paid search, paid social, organic search, organic social, email, chatbots, etc.

Bizible touchpoints are automatically created when a form is submitted on one of the GitLab.com domains for these online channels, therefore we do not need to create SFDC campaigns to track these.

Offline

Offline channels are associated with initiatives like direct mail, trade shows or hospitality events, marketing or sales research, and pretty much any other channel where a person’s engagement cannot be tracked digitally or where they physically attend. A quick rule of thumb: if there is a list uploaded and they person took an action, they are offline touchpoints.

Bizible touchpoints are created by dispositioning Enable Bizible Touchpoints field on the Salesforce Campaign as Include only "Responded" Campaign Members for these offline channels.

See progression statuses for the standard Campaign Types that will have offline bizible touchpoints enabled

Attribution Models

Marketing attribution is the process of assigning revenue credit to a marketing touchpoint. This is done by tracking a prospect’s entire customer journey, starting from their very first interaction with your company to when the deal closes. The revenue generated from the deal gets attributed back to the marketing touchpoints that drove the sale. The amount of revenue credit given to a touchpoint depends on how much that touchpoint influenced the customer’s purchasing decision.

Here is Bizible's breakdown of the major attribution models.

Note: GitLab uses a blended approach when it comes to attribution models. The Custom Attribution Model (Based on Bizible Machine Learning) is going to be used side-by-side the other attribution models in order to get us closer to the "true" attribution answer.

Below are the major attribution models that GitLab marketing uses of that list:

U-Shaped

U-Shaped attribution is a great lead generation focused attribution model. It tracks every single touchpoint up to the point of lead creation. It emphasizes the importance of two touchpoints: the first touch that brought the lead to us (while anonymous) - First Touch and the touchpoint that converted the lead to a known prospect - Lead Creation. These two touches get 40% of the attribution each and the remaining touches equally share the remaining 20%.

W-Shaped

W-Shaped attribution takes U-Shaped and expands it to opportunity creation. It emphasizes the importance of three touchpoints: the first touch that brought the lead to us (while anonymous) - First Touch, the touchpoint that converted the lead to a known prospect - Lead Creation, and the touchpoint that converted that prospect to an opportunity - Opportunity Creation. These three touches get 30% of the attribution each and the remaining touches equally share the remaining 10%.

Full Path

Full Path attribution (or the Z-Shaped model) tracks the entire lifecycle of the record from First Touch through the point of becoming a customer - Customer Close. Full Path expands on the W-Shaped model by adding a 4th important touchpoint of Customer Close, adding it to the 3 important touches of W-Shaped. Each of these 4 touches get 22.5% of the attribution and all of the other touches equally share the remaining 10%.

Linear Attribution

Linear Attribution is the simplest and most all-encompassing of the multi-touch attribution models and is the model that GitLab is focused on for longterm attribution modeling. In this model every touchpoint from First Touch through to Customer Close and beyond share equal credit as shown below.

Custom Attribution (Based on Bizible Machine Learning Algorithm)

Custom Attribution based on the Bizible machine learning attribution model uses our touchpoint data to calculate how much attribution weighting should be assigned to each stage. This is determined by how important each stage was in driving deals to close.

How is the Machine Learning model Calculated?

Bizible calculates the importance of each custom stage by using the touchpoint data from your account. The criteria used to determine the importance of each stage are:

• Model Accuracy: If we build a predictive model with the touchpoint data to predict whether we will win a deal eventually, how accurate will the model be? Higher predictive accuracy means that the details of this stage correlates more with whether a deal will close
• Conversion Rate: If Leads or Opportunities at this certain stage convert to the next stage at a high rate, this suggests that the marketing activities that occurred at this stage didn't matter very much. Conversely, if a certain stage converts to the next stage at a low rate, this can suggest that the marketing activities that occurred at this stage were influential in driving the conversion.
• Touchpoint Uniqueness Weight: If a stage occurs as a standalone transition, meaning there weren't any other stage transitions that occurred at the same time, this stage could receive a higher attribution weight. Conversely, if a touchpoint for a stage is shared with other stages (e.g. the touchpoint shares the First Touch, Lead Conversion, and Opportunity Conversion stages) this stage could receive a lower attribution weighting. The final weight for a custom stage is calculated as such:

Model Percentage = Model Accuracy x Conversion Rate x Touchpoint Uniqueness Weight

At the end, all the custom stage weights are normalized and converted to % as shown below.

In our case, the custom machine learning model has the following weights for each stage:

• First Touch - 27.5%
• Lead Creation - 27.7%
• Opportunity Creation - 19.6%
• Closed (Lost, Won) - 15.2%
• Middle Touches - 10.0%

How GitLab Calculates Linear Attribution IACV

Here is an example of how we calculate linear IACV:

There are two opportunities, Opportunity A and Opportunity B.
Opportunity A has an IACV of $10,000. There are a total of 100 touchpoints (attribution touchpoints) associated with that opportunity. Each touchpoint is valued at $100 ($10,000 / 100 touchpoints). We call that the Weighted Linear IACV

Opportunity B has an IACV of $15,000. There are a total of 100 touchpoints (attribution touchpoints) associated with that opportunity. Each touchpoint is valued at $150 ($15,000 / 100 touchpoints). We call that the Weighted Linear IACV

Of the Touchpoints on Opportunity A, they are split among 2 different campaigns - 60 touches in Campaign Y and 40 in Campaign Z.

Of the Touchpoints on Opportunity B, they are split among 2 different campaigns - 40 touches in Campaign Y and 60 in Campaign Z.

To calculate the linear attribution IACV in each of those two campaigns we use the following calculation:
Campaign Y : 60 touches (from Opp A) at a Weighted Linear IACV of $100/touch = $6,000 + 40 touches (from Opp B) at a Weighted Linear IACV of $150/touch = $6,000. Total: $12,000 linear IACV.

Campaign Z : 40 touches (from Opp A) at a Weighted Linear IACV of $100/touch = $4,000 + 60 touches (from Opp B) at a Weighted Linear IACV of $150/touch = $9,000. Total: $13,000 linear IACV.

The Checksum is to combine the IACV of both opportunites ($10,000+$15,000=$25,000) and compare it to the sum of the linear IACV of all campaigns those opportunities are a part of: ($12,000+$13,000=$25,000). The results of the sums are equivalent - we are good.

Note: If the IACV of the opportunity/opportunities is/are negative, then the resulting Weighted Linear IACV and final linear IACV of the campaign(s) could be negative.

Bizible Channel and Sub-Channel Mapping

Online Channel Mapping

The channels and sub-hannels are mapped based on 5 attributes:

1. Campaign - this is the utm_campaign value on the form URL the form submission occurs on.
2. Medium - this is the utm_medium value on the form URL the form submission occurs on.
3. Source - this is the utm_source value on the form URL the form submission occurs on.
4. Landing Page - this is the landing page url that the form submission occurs on.
5. Referring Website - this is the website that referred the submitter to the form submission page.

These rules are hierarchical and will operate in a top-down fashion, so the first rule is checked and then the second, and so on.

Offline Channel Mapping

These channels and subchannels are pulled into Salesforce and can be further filtered by using medium for those channels with overlap or with Ad Campaign name to search for specific UTMs or campaigns. The Bucket Mapping is a high-level grouping of each subchannel set to allow for additional reporting.:

Bizible Attribution with Pathfactory

This diagram is to be used internally and with Pathfactory to understand the attribution touchpoints created through our setup of Pathfactory listening campaigns and how the tracks are used in integrated campaigns and other tactics that drive straight to pathfactory.

Bizible Online an Offline Touchpoints with Pathfactory by Jackie Gragnola

Bizible Reports in SFDC

Below are some frequently used bizible reports in SFDC and their use cases.

Use case: Track registration by source for GitLab owned gated landing pages

Bizible report type used: Bizible person with Bizible touchpoints (Custom)

Below are step-by-step instructions on how to track registration by driving channel for zoom webcasts, where the registration is set up on about.gitlab or Marketo pages.

• Step 1: Click SFDC report tab.
• Step 2: Create new report. In the search bar, type and select report type:Bizible person with Bizible touchpoints (Custom) and click create.
• Step 3: Within the SFDC report, make sure at the top the filters as set as Show All bizible persons and Date Field Range is set to All Time.
• Step 4: Add filter: Form url contains [insert webcast landing page unique identifier] (e.g: Form url contains automate-security-ci)
• Step 5: Select summary format and group by Marketing Channel - Path.

Training Video: