Hi All,
For those involved in partnerships or channel programs, how are you using data to prioritize partner accounts and decide where to focus your team’s efforts?
I’m curious how others are approaching this. Are you using custom properties, reporting, workflows, a PRM, AI, or another process to help drive partner-related decisions?
I’d love to hear what’s working well and where the biggest challenges remain.
Thanks,
Mike
Hey @PFScore,
Welcome to the Community!
Great question! Here are some community threads that will hopefully point you in the right direction!
Tagging in some of Top Contributors who might’ve tackled this firsthand!
Hey @karstenkoehler, @danmoyle, @TomM2 -- is there anything specific you’d like to add here for @PFScore?
Thanks in advance!
Sam, Community Manager
Thanks, Sam, I appreciate the links and introductions.
One thing I’m particularly interested in understanding is how teams move beyond tracking partner activity and attribution to actually deciding where to focus their time and resources.
For example, are organizations using specific metrics, scorecards, health indicators, or other frameworks to help prioritize partner accounts and identify where action is needed?
Looking forward to hearing how others are approaching this.
Hi @PFScore. That’s a great question. As HubSpot is a CRM, not a PRM, it’s definitely a bit of customization and rethinking HubSpot set up. Do you have Enterprise? That will help for sure. Creating Custom Objects in HubSpot can help you manage the different classifcation like Affiliate or Partner, associating that specific record to its Contacts, Companies, or Deals. Then to help those Partners see their Deals and Commissions, you’ll need to create a custom portal (or use a tool like RocketPRM or other solutions) using HubSpot memberships and HubDB. That would look like this:
Or if I’m missing the mark here, let me know. If it’s not this complex, you can use association labels for your team along with custom properties.
Hi @danmoyle ,
Thanks for the thoughtful response.
Interestingly, I’ve been working with a client who manages partner relationships in HubSpot and asked me to explore an integration with an application I built focused on partner prioritization and account planning.
The integration is now complete, and one of the challenges we’re working through is determining which HubSpot fields are most valuable to synchronize for partner evaluation and decision-making. Standard company fields such as lifecycle stage, deal activity, pipeline value, owner, and recent engagement are useful, but they don’t always provide enough context to understand the health or strategic value of a partner relationship.
I’m starting to wonder whether Custom Objects may be the best long-term approach for organizations that want to manage partner-specific data separately from customers and prospects. It seems like that could provide greater flexibility for tracking things like partner tier, recruitment status, strategic fit, engagement levels, referrals, sourced opportunities, and other partnership-specific metrics.
For those using HubSpot to manage partner programs, what fields, objects, or data points have you found most valuable when evaluating and prioritizing partner accounts?
@PFScore I agree that Custom Objects is most likely the best scenario for you. I’ll be curious to see what others offer for your final question on which data points are valuable. Since I haven’t personally set up a PRM as you describe, I don’t have a good practical answer there. I’ve advised, but I’d love to also see what others say.
@danmoyle This discussion actually has me thinking about creating a “Recommended HubSpot Partner Management Schema” guide. Most CRM implementations are designed around customers and prospects, but partner data/analytics often require additional fields for partner tier, sourced opportunities, and recruitment status.
If I understand your question correctly, you’re essentially asking how teams use CRM data systems to prioritize partner accounts so effort is focused on the highest-value relationships.
To answer your quick side question first: a PRM (Partner Relationship Management) system is essentially a CRM built specifically for managing indirect sales channels, partner portals, and deal registration workflows.
Whether you use a dedicated PRM or build it inside your CRM using custom objects, partner prioritization usually comes down to a simple scoring model that combines partner attributes, engagement signals, and revenue impact.
In most cases, teams don’t rely on a single feature like AI or one specific tool. They build it in three layers:
1. Data layer (what you capture):
Custom properties like partner tier, partner type, engagement level, and revenue contribution. This defines your raw evaluation inputs.
2. Insight layer (what you understand):
Reporting and dashboards that show performance across the ecosystem, who is driving revenue, who is active, who is inactive, and which segments are actually performing.
3. Execution layer (what you do):
Workflows, tasks, and routing logic that turn insight into action, such as flagging stalled partner deals or prioritizing outreach for high-value accounts.
The biggest challenge is rarely the tooling: it’s data quality and consistency. If partners aren’t feeding reliable data into the system, even the best dashboards and automation will produce weak decisions.
Ultimately, prioritization isn’t a feature, it’s a system built on clean data, clear definitions, and consistent execution. Once that foundation is in place the focus becomes intentional instead of reactive.
Great points @Emedion.
One thing I’ve learned over the years working with CRM systems is that the quality of the output is almost always determined by the quality of the input. You can have sophisticated reporting, automation, AI, scoring models, and dashboards, but if the underlying data is incomplete, inconsistent, or outdated, you’re still making decisions on a shaky foundation.
I’ve always referred to it as “junk in, junk out.”
Whether it’s a CRM, PRM, or any other system, the technology can only be as effective as the data and processes supporting it. In my experience, data quality and user adoption are often bigger challenges than the platform itself.
That’s why I liked your point that prioritization isn’t really a feature—it’s a system built on clean data, clear definitions, and consistent execution.
Thanks for the input; it is valuable on many fronts.
To answer the main question: how do you make data-driven decisions about partner accounts?
It always starts with clarifying the decision you are actually trying to make. What defines a “valuable partner” in your ecosystem? Is it raw revenue contribution, partner-sourced opportunity rate, partner engagement, or strategic fit?
Once that is clear, the process follows a strict sequence:
Here is how to look at it through a HubSpot lens:
Tracking the Data (The Infrastructure): This is where custom properties vs. custom objects come in. If you just need to track partner attributes (like tier, status, or region), custom properties on the Company object are enough.
Tracking Complex Activities: If you need to track partner-specific activities with their own independent lifecycles (like MDF requests, co-marketing events, or partner plans), a custom object is the way to go. It lets that data evolve independently while staying associated with the partner account.
Making the Decision (The Insight): Once the data is structured and consistently collected over time, then you can use attribution reporting, partner scoring, and pipeline analysis to decide exactly where to invest your team’s energy, which partners to co-market with, and which ones to offboard.
Custom properties and objects only help you track the data. The data-driven decision happens afterward, when you use that structured data to change your channel strategy.
@Emedion I really like the way you’ve framed this around defining the decision first. In my experience, many organizations start with reporting and dashboards before they’ve clearly defined what a “good partner” actually looks like. As a result, they end up tracking dozens of metrics without a clear understanding of which signals should influence investment decisions.
The distinction you make between custom properties and custom objects is also helpful. I’ve seen teams over-engineer their data model too early, when a handful of well-defined properties would have solved the immediate problem.
Ultimately, the value isn’t in collecting more partner data—it’s in identifying the signals that correlate with outcomes and then using those signals to drive action. Otherwise, it’s easy to create lots of reporting that never changes behavior. That’s where I think many partner teams struggle: turning data into prioritization and prioritization into execution.