How do you connect Attio to your product data?

The gap between what your product knows and what your sales team sees is where deals die. Connecting product data to Attio closes that gap so your CRM reflects what customers are actually doing, not just what a rep last typed into a notes field.

Product usage attributes on an Attio company record give your sales team real-time context on customer behavior.

Why product data belongs in your CRM

Product-qualified leads are the highest-converting signal most SaaS teams have, and most CRMs never see them. A free trial user who's invited three teammates, connected an integration, and hit your usage threshold is a fundamentally different lead than someone who signed up and logged in once. If your sales team can't distinguish between the two inside Attio, they're prioritizing blindly.

The same applies to existing customers. Usage drops, feature adoption stalls, billing downgrades -- these are churn signals that your product tracks but your CS team only discovers at renewal time. Getting this data into Attio means you can build workflows that trigger on product events, not just CRM events.

I have seen this play out dozens of times: a CS team runs a quarterly business review with a customer, everything seems fine, and then the customer churns two weeks later. When the team looks at the product data, usage had been declining for three months. That signal was available the entire time. It just was not in the CRM where the CS team could see it and act on it.

What to track

Be selective. The mistake I see most often is teams dumping every product event into their CRM and creating noise that nobody acts on. Focus on the signals that change how your team behaves:

Signup events and account creation. Feature adoption milestones -- the two or three actions that correlate with conversion or retention. Usage thresholds that indicate a product-qualified lead. Billing events: upgrades, downgrades, payment failures. Seat count changes that signal expansion opportunities.

If a data point wouldn't change what a rep does next, don't put it in Attio.

Here is a practical framework for deciding what belongs in your CRM versus what stays in your analytics tool. Ask three questions about each data point:

1. Would a rep or CSM act on this today? If a user hits your activation threshold, a rep should reach out. That belongs in Attio. If a user clicked a button 47 times yesterday, that is analytics data. Keep it in Amplitude or Mixpanel.
2. Does this change the priority of an account? Usage spikes signal expansion. Usage drops signal churn risk. Both change how your team allocates time. Feature adoption percentages might be interesting but do not change priorities by themselves.
3. Can you automate around this? If the data point can trigger a workflow that saves your team manual work, it earns its place in Attio. If you are just storing it for reference, think hard about whether it needs to be there.

For most SaaS teams, the sweet spot is five to ten product attributes on each Company record. More than that and reps start ignoring all of them.

How to model it

The cleanest approach is adding custom attributes directly to your Company or People records. A "Signup Date" date attribute, an "Active Users" number attribute, a "Plan Tier" select attribute, and a "Product-Qualified" checkbox attribute give your team everything they need at a glance. These attributes then feed into lead scoring and calculated attributes that automate qualification.

For teams that need more granularity, a dedicated Events custom object works well. Each record represents a product event linked back to the Company via a relationship attribute. This gives you a timeline of product activity on each account. It's more complex to set up but powerful for teams running product-led growth motions.

Here is how I structure the attributes for most clients:

On the Company record:

• Signup Date (date) -- when the account was created in your product
• Plan Tier (select) -- Free, Starter, Pro, Enterprise, or whatever your pricing tiers are
• Active Users (number) -- updated daily or weekly via API
• Total Seats (number) -- how many seats they are paying for
• Product-Qualified (checkbox) -- set to true when activation criteria are met
• Last Active Date (date) -- when anyone from the account last used the product
• Feature Adoption Score (number) -- a composite score you calculate based on which key features they have used

On the People record:

• Product Role (select) -- Admin, Member, Viewer, based on their permissions in your product
• Last Login (date) -- when this specific person last logged in
• Invited By (text or relationship) -- useful for understanding how accounts grow

The Events object approach is worth the extra complexity when your sales motion depends on timing. If you need to know that a user connected their first integration yesterday (not just that they have connected one at some point), individual event records with timestamps give you that granularity. Each Event record links to a Company and optionally to a Person, with attributes for Event Type (select), Event Date (date), and Event Details (text).

Integration patterns

Three approaches, from simplest to most robust:

Direct API calls from your product backend to Attio's API. When a user hits a milestone, your code updates the corresponding Attio record. This is the fastest to implement and works well for a handful of key events.

The direct API approach works best when you have a small number of high-signal events. Your product code calls Attio's API to update a record attribute when the event fires. The implementation looks something like: user completes onboarding, your backend finds or creates the Company record in Attio by domain, and updates the relevant attributes.

Watch out for rate limits and error handling. Attio's API has rate limits that you will hit if you are sending high-volume events. Batch updates where possible, and build a retry queue for failed calls. Never let an Attio API failure block your product's user experience.

Event pipeline through Segment or a similar CDP. Your product instruments events once, and a Segment destination pushes the relevant ones to Attio. This scales better and decouples your product code from your CRM.

The CDP approach is the right choice for teams that already use Segment, RudderStack, or a similar tool. You define which events should flow to Attio, map the event properties to Attio attributes, and the CDP handles delivery, retries, and batching. This also means your product team does not need to know anything about Attio's API. They instrument events according to their tracking plan, and the CRM integration is configured separately.

If you do not already have a CDP, do not add one just for this integration. The overhead of setting up Segment for a single destination is not worth it. Go with direct API calls or reverse ETL instead.

Reverse ETL from your data warehouse. Tools like Census or Hightouch query your analytics tables and sync aggregated metrics to Attio on a schedule. This is the right approach when your product data lives in a warehouse and you want derived metrics -- like "7-day active users" or "feature adoption score" -- rather than raw events.

Reverse ETL shines when your product data team has already built the tables and metrics you need. Instead of recreating that logic in API calls or CDP mappings, you write a SQL query that calculates the values you want in Attio, and the reverse ETL tool syncs the results on a schedule (typically hourly or daily).

This approach also gives you the most flexibility for calculated metrics. A "feature adoption score" that combines five different product signals into a single number is trivial to compute in SQL and hard to compute anywhere else. Sync that score to Attio as a number attribute, and your sales team gets a single actionable metric without needing to understand the underlying signals.

Common mistakes with product data integrations

Syncing too frequently. Unless your sales team acts on data within minutes (and most do not), daily syncs are fine for most metrics. Real-time syncs for every attribute update generate unnecessary API load and can cause race conditions when multiple events fire for the same account simultaneously.

Not handling record matching correctly. Your product identifies accounts by internal IDs. Attio identifies companies by domain. You need a reliable mapping between the two. The cleanest approach is storing the Attio record ID in your product database after the first sync, so subsequent updates do not require a lookup. If you cannot do that, match on domain, but make sure your product stores a clean, normalized domain for each account.

Ignoring the human side. Product data in your CRM is only valuable if your team knows it is there and understands what it means. Add the product attributes to your default record views. Explain what "Product-Qualified" means and what action to take when it flips to true. Include product signals in your Slack notifications so reps see them in their normal workflow, not just when they happen to open a record.

Building for perfection instead of shipping. Start with two or three signals: signup date, active users, and product-qualified status. Get those flowing reliably and confirm your team uses them. Then add more. Teams that try to build a comprehensive product data integration before shipping anything end up with a project that takes months and delivers value never.

Using product data to drive action

Once the data is in Attio, wire it into your workflows. Here are the automations that generate the most value:

PQL alerts. When the Product-Qualified checkbox flips to true, trigger a workflow that creates a Deal record (if one does not already exist), assigns it to the right rep based on your routing rules, and sends a Slack notification with the account details and usage metrics.

Churn risk flags. When Active Users drops below a threshold or Last Active Date is more than 14 days ago, trigger a workflow that notifies the account's CSM and creates a task to reach out. Pair this with your renewal tracking to prioritize accounts approaching renewal with declining usage.

Expansion signals. When seat utilization exceeds 80% (Total Seats is close to the plan limit) or usage of premium features spikes, flag the account for an expansion conversation. This is how the best SaaS teams turn product data into revenue.

The principle: your CRM should reflect what customers do, not just what reps record. Connect the systems, be ruthless about which signals matter, and build your GTM engine on real usage data instead of gut feel. For a deeper look at building reports around product data once it is in Attio, start with the metrics your team already reviews and add product signals as a dimension.