Extract and score multiple stakeholders at MQAs

Overview#

As soon as an account is marked as marketing qualified by 6sense, we want to automatically retrieve the right stakeholders for this account, in order to enable sales reps to enroll it into a start a multi-touch, multi-person, triangulated outreach sequence.

This template will demonstrate how to retrieve, score and assign multiple stakeholder personas to assign to triangulated outreach sequences.

Extracting and scoring multiple target personas using Cargo#

Step 1 - Set variables#

Set up your input variables to the workflow

Inputs used in the workflow are set up in the variables node at the beginning of the workflow. This node is used to define the parameters that will be passed through the rest of the workflow as inputs to the rest of the nodes.

To power this workflow, the following variables are needed:

• domain: The domain of the company you are targeting (e.g., agorapulse.com).
• titlesList1: An array of job titles that you want to include in your search. These titles are used to filter for relevant stakeholders.
• titlesList2 and titlesList3: Additional arrays of job titles for further filtering.
• titlesExclusionList: An array of job titles that you want to exclude from your search. These titles help in filtering out irrelevant stakeholders.
• ownerCRMID: The sales representative best connected to the original champion contact.
• slackMessage: The Slack message you wish for your sales representative to receive asking for an intro to a prospect.

Step 2 - Cluster persona search nodes#

Retrieve different stakeholders using multiple search nodes

Different stakeholders play different roles in the sales cycle and have various levels of influence. In this template, we demonstrate a clustered approach of persona types, where there is a dedicated search node that targets a set of job titles, seniorities, locations, etc.

Step 3 - Score stakeholders#

Score stakeholders based on job title keywords

Since the output of the search nodes will be in the form of arrays. We can concatenate these arrays together and process the results inside a group node.

The nodes in the group node are dedicated to defining the scoring logic, we use a scoring node in Cargo to assign points for each inclusion keyword present in the job title and subtract points for each exclusion keyword present. The group node will eventually output a score attached to the contact data.

Keywords in job titles can indicate the level of influence, responsibility, and relevance of a stakeholder. For instance, including terms like "Director", "Manager", "Executive", "VP", "Head", etc., can help identify high-level stakeholders who are more likely to be decision-makers.

Here's an example of how you can set up the scoring:

• Inclusion Keywords: Director, Manager, Executive, VP, Head, CEO, CTO, CMO, CFO, etc.
• Exclusion Keywords: Intern, Assistant, Junior, Trainee, etc.

After the first group, we use a Javascript expression to sort the contacts by score so that the last group node will receive only the top N contacts, sorted by score. We use the following expression to handle the sorting: {{nodes.group_1.sort((a, b) => b.score - a.score)}}.

Step 4 - Enrich and assign stakeholders#

Enrich the sorted array of stakeholders

Once you have scored the stakeholders, the next step is to enrich the sorted array with additional details, like email, previous employers, current roles, contact information, and social media profiles. In the template we show a waterfall enrichment logic which ends with a filter ensuring that at least an email is found before a contact can be upserted.

Use this enriched data to add to the assignment logic, such as crafting personalized outreach messages, using the OpenAI integration.