Marketing Analytics: Build a Report You Can Make a Decision From

Your website dashboard says twelve inquiries, but the form system shows ten submissions, and the team can only find nine in its sales records. Meanwhile, someone wants to know whether you should increase the advertising budget.

You can't give them a useful answer until you understand those numbers. Are you counting people who asked for help, messages that actually reached the business, or clicks on a button? And even when you've sorted that out, a cheaper inquiry isn't necessarily a better use of your money if it rarely turns into work you can take on.

That's the job of marketing analytics: helping you understand what happened well enough to decide what to do next. A dashboard can make the numbers easier to see, but the useful work happens when you connect them to the business behind them.

Let's work through that connection, from three totals that don't agree to a budget decision you can explain.

Key takeaways

• Before comparing reports, check what each number represents, such as phone calls, completed contact forms, or scheduled appointments. Then check that the totals match what actually happened.
• Compare what you spend to attract inquiries with how many of those people eventually become clients. A campaign with cheaper inquiries can still cost more per new client.
• Use the report to decide what to fix or where to spend next, and explain what you still don't know so the team can judge how much confidence to place in that decision.

Why three reports can give you three different answers

Suppose those opening numbers belong to a hypothetical professional-services business. The website dashboard calls its twelve recorded actions “inquiries,” the form system contains ten valid submissions, and the sales system has nine corresponding records.

All three totals might accurately describe what's stored in each system, but they aren't automatically measuring the same thing.

An analytics event is a recorded interaction, such as a button click or a successful form submission. If the tracking records a click before the form checks whether required fields are complete, someone can trigger that event without sending a message. If the tracking runs twice after one submission, one message can produce two events.

Google describes Analytics as collecting and processing interactions with websites and apps. Whether a recorded interaction represents a useful business result depends on what you've configured it to measure. Calling an event an “inquiry” doesn't establish that anyone received a request.

I'd ask whoever manages the reporting to explain the number in ordinary language: “This counts successful form submissions,” for example, or “This counts clicks on the phone number.” Those are both useful measurements, but a phone-number click doesn't establish that a call connected, so you shouldn't quietly treat them as equivalent.

Follow a few inquiries through the systems

To find the cause of the disagreement, follow a few inquiries through the process, using only records you're allowed to review. Start by filling out the form with clearly labeled test details, so you can trace a submission without copying anyone's private information. You want to know whether the submitted form produced one analytics event and reached the place where the team handles inquiries.

In our hypothetical example, the investigation finds that two submissions each triggered the analytics event twice. That explains why ten valid submissions produced twelve events, but it doesn't explain why the sales system contains only nine records.

The missing sales record needs its own investigation. Perhaps the message arrived and nobody entered it into the system, or perhaps it never reached the team. Here, let's say it went to an old inbox that nobody monitors, so the business received the message without getting it to someone who would respond.

You now have two distinct repairs:

• Correct the tracking so one successful submission produces one event.
• Update the form's delivery route so inquiries reach the team that handles them.

Afterward, send another clearly labeled test submission and follow it through every step. Does the form accept it, does the analytics system record it once, and does the right person receive it? Note when the fixes went live, because the reported inquiry count may fall after duplicate counting stops even though demand hasn't changed.

You don't need to wait for a larger sample before fixing a confirmed delivery problem. You do need to avoid assuming that every missing record has the same cause, which is why tracing the process matters more than choosing whichever total looks most convincing.

Agree on what makes an inquiry useful

Once the records make sense, you can ask the question behind that budget request: are the campaigns bringing in people the business can actually help?

For our hypothetical business, let's define a suitable inquiry as a request for a service it provides from someone in the area it serves. That's an initial fit check, not a promise that the person will become a client. Your own definition might also need to account for other practical requirements, but the team handling inquiries should help set those criteria before you compare campaigns.

Keep “we couldn't establish fit” separate from “this doesn't fit.” If nobody reached a person and their original request doesn't establish whether they fit, you don't yet know whether the inquiry was suitable. Recording it as unsuitable would hide that uncertainty, and it could also hide a problem with how the team responds.

You also need to decide whether you're counting contacts or distinct inquiries. A person who submits a form and then calls about the same request has contacted you twice, but that doesn't necessarily mean you've acquired two new opportunities. Both contacts matter when you're assessing staff workload; counting them separately can mislead you when you're assessing the cost of attracting new business.

The definition should fit the decision, and everyone comparing the campaigns needs to use the same one.

Put the cheaper inquiry to the test

Now imagine the records are reliable enough to compare two campaigns over a larger period. This is a separate, hypothetical example: both campaigns ran during the same period, promoted the same service, and used the same fit criteria. Their inquiries have had the same amount of time to reach an outcome, and the report assigns them to campaigns using the same method. For simplicity, all their outcomes are recorded by the review date.

What you're comparing	Campaign A	Campaign B
Advertising spend	$3,000	$3,000
Distinct new inquiries	60	30
Inquiries meeting the initial fit criteria	20	20
New clients	4	6
Advertising cost per inquiry	$50	$100
Advertising cost per suitable inquiry	$150	$150
Advertising cost per new client	$750	$500

To calculate cost per inquiry, divide the advertising spend by the number of inquiries. Campaign A spent $3,000 to attract sixty inquiries, which works out to $50 each, while B's thirty inquiries cost $100 each.

If you stop there, Campaign A looks like the obvious winner: it brings in twice as many inquiries for the same spend. But both campaigns produce twenty suitable inquiries, so that apparent advantage disappears when you compare the cost of reaching people whose requests fit the business.

Campaign B also produces more clients in this example. Dividing the $3,000 spend by six clients gives you an advertising cost of $500 per client, compared with $750 for Campaign A.

That doesn't mean you should immediately move the entire budget to B. Four clients versus six is a small difference in actual people, and the report hasn't explained why it happened. A might be attracting more requests for services you don't offer, while a difference in response speed could help explain why fewer of its suitable inquiries became clients. Those possibilities call for different changes.

I'd start by reviewing the reasons behind A's inquiries and how the team handled them. If searches for services you don't offer are bringing in the wrong requests, change which searches the ads can appear for or make the ad wording clearer about the service you provide. If suitable prospects aren't getting a response, fix that process before expecting extra advertising to solve it.

What the table does establish is that “A has cheaper inquiries” isn't enough to justify increasing its budget. You've moved from admiring a low number to asking what would make the next dollar useful.

Check what the new business leaves after costs

There's another question to answer before calling either campaign profitable: what does serving those clients cost?

Suppose each new client in our example brings in $2,500 in revenue and costs $1,700 to serve. That leaves $800 per client before advertising and the business's other costs.

Campaign A's four clients therefore leave $3,200 after service-delivery costs. Subtract its $3,000 advertising spend, and $200 remains to help cover other business costs, such as rent and software, before there's anything left as profit. Campaign B's six clients leave $4,800 after delivery costs, or $1,800 after the same advertising spend.

Neither figure is the business's final profit, because we haven't yet deducted all its costs, including campaign management and the team's time handling inquiries. We've also assumed every client has the same value and delivery cost, which makes the illustration easier to follow but may not reflect your business.

The point is that revenue alone doesn't establish the return on your investment. A report showing $15,000 in new revenue against $3,000 in advertising can sound reassuring, but you still need to account for the cost of earning that revenue before deciding how much you can afford to spend acquiring the next client.

If the forecast relies on clients buying again, show how much of that future revenue is an assumption. It may be a reasonable expectation, but it shouldn't quietly become money you've already earned.

Give inquiries time to reach an outcome

Our comparison assumed the outcomes were known, but real reports often contain people who haven't decided yet. If someone inquires near the end of the month and hires you several weeks later, a month-end report won't include that eventual result.

To make a fairer comparison, follow the people who first inquired during the same period through to their later outcomes. That group is often called a cohort. For example, you could follow April's new inquiries through May and June, rather than comparing April's advertising spend with clients who signed in April but first contacted you in February.

Show how long you've followed the group and how many inquiries are still unresolved. If one campaign has ten open inquiries and another has none, their current cost per client tells you what you've observed so far, but it doesn't settle their eventual performance.

There isn't one waiting period that suits every business. Use what you know about how long people normally take to decide, and compare campaigns after a similar amount of follow-up. That way, you're less likely to penalize a campaign simply because its inquiries haven't had time to become clients.

Be clear about what source reporting can tell you

Even when the totals reconcile, you still need a consistent way to associate inquiries with campaigns. Attribution is the rule or model used to give marketing interactions credit for a result. Google Analytics explains how its available attribution models assign that credit.

For example, someone might first discover you through an article, return through a paid ad, and then contact you. A report that credits the last recorded interaction answers a different question from one that distributes credit across observed interactions. Neither should be presented as a complete account of everything that influenced the person.

Use the same attribution method when comparing campaigns, and explain it in the report. “Last recorded source before inquiry” tells the reader what you've measured; “the channel that caused every sale” claims more than those records establish.

Some sources will remain unknown because the available records don't capture every interaction. Keep that gap visible rather than assigning the unknown inquiries to a channel just to make the totals balance.

Keep the investigation within the right data boundaries

Connecting reports doesn't require copying every detail of an inquiry into every marketing tool. First establish which records you're allowed to use, who needs access, and where the information can go.

For a campaign comparison, an approved internal summary of inquiry counts, broad fit categories, costs, and outcomes may answer the question without exposing the person's name or the details they shared. Google's guidance on personally identifiable information explains information that must not be sent to Analytics, including names, email addresses, and personal phone numbers.

If your business handles sensitive legal or health information, have the appropriate privacy or legal reviewer check what information each system would send, where it would go, and who could access it before you connect the systems. A technical change, such as sending data through your own server, doesn't by itself establish permission to disclose it.

Leave the meeting knowing what happens next

A useful report should let you explain the decision in ordinary language. In our examples, that might sound like this:

“We've confirmed that the form was double-counting two submissions and sending one message to an unmonitored inbox. We've repaired both issues and tested the full route. Separately, Campaign A's lower inquiry cost hasn't translated into cheaper suitable inquiries or clients, so we're reviewing its search traffic and response records before increasing its budget.”

Give that follow-up an owner and a review date. If the evidence later supports a budget test, agree on the change you'll make and the results you'll examine, while accounting for capacity and the time inquiries need to reach an outcome.

You don't have to solve every reporting problem at once. Start with a decision you're being asked to make, then trace the numbers that decision depends on. When you can explain what the numbers represent, what they leave out, and why they support a particular next step, your report is doing something useful for the business.

Morpheus Consulting Inc. can help you connect your marketing reports to the decisions behind them.