If you’re reading this, you already track your product data every day. You see how users interact with your features and which ones they use most. But are you really turning that data into clear, useful insights?

Most teams rely on general analytics tools as a basic query interface. You might even push data to Segment for sharing across teams. Yet raw data, by itself, is hard to act on and tough to scale beyond your data analysts.

Why your existing product data needs more context

Raw data is fine for deep dives. But when you need fast decisions, it falls short. Imagine a creditor sifting through your entire credit history every time you apply for a loan. It’d take ages and likely scare lenders off.

Here’s a snippet of raw credit data:

January 2010: Opened credit card

May 2010: Car loan issued – principal $13,500

June 2011: Late payment on car loan

July 2012: Applied for mortgage

August 2012: Late payment on car loan

February 2013: Opened second credit card

February 2014: Credit limit extended

May 2014: Car loan paid in full

May 2014: Mortgage issued – principal $159,400

July 2014: Credit card balance paid in full

December 2015: 90-day delinquency on mortgage

Would you know at a glance whether this borrower is high or low risk? What if most borrowers have six late payments over five years? Or if 8% have zero late payments? Or if this person falls in the bottom 25% by credit history length? Without context, you can’t tell.

That’s why credit scores exist. They weigh payment history, total debt and other factors, then spit out a single number from 300 to 850. That score makes it easy for any lender to decide in seconds.

Adding context to your product data

You need the same clarity for your product metrics. A clean engagement score gives each user and account a clear, comparable number. Then every team can use it:

Helping Sales assess trial leads.

Letting Marketing tailor messaging to engagement levels.

Guiding Customer Success on upsells, renewals and risk.

Informing the board on where to invest next.

With context, your data becomes a foundation for better decisions and faster action.

How Accoil handles engagement scoring

Accoil is built to score product engagement the way credit scores rate borrowers. You pick your key events and assign weights based on their impact. Accoil then processes your raw event data and returns a normalized score from 1–100.

You’ll see engagement scores for:

Every active user

Each customer account

Individual product features

Custom segments

Overall engagement trends over time

Those scores make it easy for everyone—from founders to product managers to customer success teams—to speak the same language and focus on what matters.

Start taking your product data to the next level

Don’t let raw data slow you down. Give your metrics the context they need. When your whole organisation works with clear, actionable scores, you’ll make smarter decisions faster and keep your product momentum strong.