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August 2023 Release
Sales Pipeline Report & Lookalike Audiences
This week we're releasing one of our most requested features: the ability to view the impact of Primer audiences on revenue and pipeline via conversion reporting.
Note that the Sales Pipeline report is currently a beta feature.
To request access, contact your CSM.
Up until now, it’s been difficult to understand the impact of specific segments on your opportunities. Now, you can view a global summary of the impacted pipeline and impacted revenue for all audiences within a specified timeframe.
Once you define your conversion definition and desired attribution windows, head back to the Audiences page and review their performance!
Above your audience dashboard, you’ll now see a section displaying the impacted pipeline and impacted revenue of your Primer audiences. Additionally, we’ve introduced two new columns for pipeline and revenue that should help you quickly identify your most impactful audiences.
Inside each audience, there is a new “Performance” tab that displays trends in opportunities, pipeline, and revenue over a specified timeframe. We’ve also provided a table of all opportunities associated with the audience, including relevant fields like opportunity name, stage, date, and amount.
Finally, you’re able to download a CSV of audience performance as displayed in the dashboard.
Note that Lookalike Audiences is currently a beta feature.
To request access, contact your CSM.
A lookalike audience is created from a custom conversion event (e.g. opportunity “created” to “closed-won”). Primer compares converted and unconverted companies in the defined event and uncovers attributes most common among the converted companies.
- Click “Add New Conversion”. You will see a modal that asks you to “Add Conversion Criteria”. Here, you can define your conversion funnel.
- Unconverted State: This is the top of the conversion funnel, and we currently support fields from accounts and opportunity objects in Salesforce. For example, in a simple funnel of “Opportunity: Created” to “Opportunity: Stage Name = Closed-won”, the unconverted state is Opportunity Created.
- Converted State: This is the bottom of the conversion funnel, and it will inherit the criteria entered in the unconverted state, in addition to any new filter criteria you may add. In a simple funnel of “Opportunity: Created” to “Opportunity: Stage Name = Closed-won”, the converted state is “Opportunity: Stage Name = Closed-won”.
- Click “Save” to create a custom conversion dataset. At this point, the system will start building the custom conversion, and its status will become “Building”. After the system finishes processing the custom conversion, its status will become “Built”.
- In “Settings”, click “Create Lookalike” to start creating a lookalike audience. If you are in the “Audience Dashboard”, you can select “Lookalike Audience” from the dropdown to create audiences
- Once the select a custom conversion to use to create a lookalike audience, you will see the baseline “Baseline Attributes” and “Differentiating Factors” of that custom conversion funnel.
- Baseline Attributes: These are attributes that are most commonly shared across the entire unconverted dataset.
- Differentiating Factors: These are attributes that most separate the companies in the converted dataset from those that did not convert.
- You can click “Next” at this point to apply these attributes as filters to a brand-new audience directly. You can also use the checkbox next to each attribute to opt out of certain attributes, and you’ll have an opportunity to edit these attribute values further once they’ve been added to a draft audience.
- Once attributes for lookalike audiences have been added to an audience, you can edit and review them as you see fit.
- You are now ready to submit your first lookalike audience!
- 1.This feature is currently in beta, and there may still be some rough edges.
- 2.Attributes used to generate a lookalike audience are characteristics the system picks up based on statistical analysis but may not make the most intuitive sense right away. The current approach doesn’t assign causation to differentiating factors and is simply pointing out what attributes best differentiate a converted set from an unconverted set.