How a packaging equipment distributor went from explaining 5% of its orders to 99%

A case study on connecting ad spend, phone calls and email to the orders they actually produced.

Summary

A packaging equipment distributor spent many months putting between $10k and $35k a month into paid ads. In most of those months the ads brought back less than they cost. In some they probably worked. Nobody could say which.

The team knew the ads made phones ring. Which ad drove which quote, and which quote became a deal, was anyone's guess.

When they started with IdealData, they could explain where about 5% of their orders came from. Today they can explain 99%. Each order now carries its source and what drove the sale.

"This is what we think in our brains, but I've never seen it on paper."

The business

Ten people, seven-figure revenue, selling packaging equipment.

Sales start with a phone call or a quote request. Reps answer, follow up by phone and email, and close.

The stack is ten tools, not counting the store itself, for ten people:

• CRM: HubSpot
• Phone system: Aircall
• Call tracking: CallRail
• Ads: Google Ads, Bing Ads, Facebook Ads, LinkedIn Ads
• Analytics: Google Analytics, Google Search Console, Bing Webmaster Tools

Everything looked good

Ads made the phone ring, so putting money into paid ads made sense.

An agency partner ran those ads. They managed the campaigns, knew which ads were performing and which were not, and had tracking in place to report how much revenue came from ads. By those reports, everything looked good.

That is where the puck stopped.

The ad numbers did not line up with the deals in HubSpot. Every month the story was the same. The phones were still ringing, but there were fewer quality calls, and revenue was declining. There was no way to connect a ringing phone back to the ad that caused it, forward to the quote, and on to the deal.

The agency was doing its job. The problem was that nobody could see the full picture. Not the agency. Not the team. Not us.

Ten tools, ten sets of numbers

The team spent real time connecting HubSpot to Aircall, to CallRail, to the website and the analytics tools. When it was done, each system showed different numbers on its reports.

Some revenue was credited to ads and to organic traffic at the same time, so the same dollars got counted twice. It was a hot mess, and the same question kept coming back: which numbers do we trust?

Each system was set up exactly the way its vendor recommends. There were still gaps, and the gaps forced guesswork.

The team went as far as asking customers directly where they had found the business. The answers often did not match what the tools said.

The first and most important question, "where did this order come from?", turned into an oasis in the desert. Visible from a distance. Never reachable.

The second gap: why each customer bought

The team answered 99% of quote requests and closed about 30% of them. For their industry, that is normal.

So they asked a harder question: what made each of those customers buy?

Another gap. HubSpot can answer that question when the whole business runs inside HubSpot, with its full set of products and add-ons. This team, like most, runs tools outside it. Every one of those tools left a hole in the attribution and data in a silo, with no single screen to see it from.

Why "connect everything to ChatGPT" does not close it

The obvious modern answer is to plug every tool into an AI assistant and ask. It does not get you there. Many tools have no connector at all, and many of the connectors that exist are basic. Each missing piece is a gap in context, and that turns into a gap in the answer.

What changed

The IdealData team connected the two sides: traffic and activity on one, revenue on the other.

Much of that was already built. The IdealData platform connects to many of the tools this team uses today and standardizes their data into one clean model.

From there, IdealData audited the data across every sales origin, phone, email and web, and closed the attribution gaps. That answered the first question.

Then about 45,000 emails and about 21,000 inbound calls enriched the data further and answered the second one: what made each customer buy in the end?

From 5% of orders explained to 99%.

Today every order is matched to its origin, and they can see what drove each sale. A coupon code sent by email. A check-in email. A paid ad. An outbound call from a rep, made around the time that customer usually buys. Revenue by origin is tracked month over month, so the trend is visible.

The first thing they did with it was move their time. Out of the channels they had been hoping about, into the ones that are proven to work.

What's next

The team is measuring what that shift does to their return over the next three months. In the interim, if you have these tools above.Log into our platform and connect your tools. You don't need a developer to set it up. Most eCommerce platforms like Shopify, BigCommerce, WooCommerce have apps you install and it takes only a few clicks to connect your existing tools.

If this sounds like your business

If your ads make the phone ring and nobody can say which ad made which sale, or your tools each report a different revenue number, this is the problem IdealData solves.

You do not need a six-figure enterprise system for this anymore.