A lot of SaaS teams launch ad campaigns, watch the dashboard fill with numbers, and still can't say what's working. Every objective has a different set of metrics that matter, and mixing them up is one of the fastest ways to misread a campaign's actual performance.
As Ana Katz Digital puts it in a video on Facebook Ads metrics, "one mistake that i see a lot with business owners is that they want to run facebook ads on their own but once the ads are up and running they have no clue how to read the data." That gap between launching and reading data is where SaaS budgets quietly leak.
Which Metrics Should You Track for Each Campaign Objective?
The metrics worth watching change depending on what the campaign is built to do. Treating a traffic campaign's numbers the same way you'd treat a conversion campaign's numbers is a common source of confusion for SaaS teams reviewing ad reports.
For engagement campaigns, the source recommends analyzing likes, comments, saves and shares. For website traffic campaigns, the relevant metrics are landing page views, link clicks, link click-through rate and cost per click, according to the same source at this timestamp.
For conversion-focused campaigns, which for many SaaS businesses means trial signups or demo requests, the source points to results (leads or purchases) and cost per result, along with link clicks as a secondary engagement signal, as described here. For brand awareness campaigns, the metrics are reach, impressions, CPM, cost per thousand reach, and ad recall, per the same timestamp.
Why Do Generic Clicks and CTR Give a Misleading Picture?
Clicks and click-through rate sound like straightforward signals, but the source argues they can mislead anyone trying to judge whether an audience actually visited a website or landing page. This matters for SaaS campaigns where the goal is almost always to get a prospect onto a specific page.
The source explains she removes these metrics from her reports entirely: "the metrics i don't really care about and i take the literally take them out of my reports are clicks click through rate and cost per click." The reasoning given is that these totals include clicks on non-website actions, like fast-forwarding a video or scrolling a carousel.
Instead, she favors link clicks, link click-through rate, and link cost per click, stating "instead of looking at those three metrics i look at the link click link click trade and link link cost per click these are the ones that i care about." For a SaaS team, verifying which version of these metrics your own ad platform reports by default is worth doing before drawing conclusions from a dashboard.
When Should You Refresh Your Ad Creative?
One signal that often gets overlooked is frequency, which measures how many times the same person has seen an ad. SaaS campaigns that run for weeks without creative changes can quietly lose effectiveness as the same audience sees the same message repeatedly.
The source gives a specific threshold: "if the frequency is above five for your ad i would recommend you to update the creative or they update the copy." Readers running their own campaigns should check their own frequency numbers against this benchmark rather than assuming it applies uniformly to every account or audience size.
How Do You Set Realistic KPIs for an Ad Campaign?
Setting a KPI before you have any data is mostly guesswork. The source recommends establishing a baseline first and then raising the bar gradually, which gives SaaS teams a way to set goals based on their own campaign's actual behavior rather than an industry benchmark pulled from elsewhere.
The suggested process is to run a campaign for an initial test period, as described here: "i will recommend you to run your campaigns for seven days and take it from there." From there, she gives a worked example: "if your link click-through rate is one percent then set up your kpi to be a little bit above that one percent so set it up to be 1.2 percent," then raising it again in later weeks as the team hits each new target.
The source also recommends tracking this data weekly in a spreadsheet to spot patterns over time, and suggests building a custom report inside the ad platform with only the metrics that matter for a given objective, rather than relying on default dashboard views.
How Do Different Tools Compare for Measuring SaaS Ad Performance?
Choosing a tool for tracking SaaS ad metrics depends on how much control you want over report structure versus how much manual setup you're willing to do. The table below compares options readers commonly consider, without ranking one as superior.
| Tool | What it does | How it addresses this measurement problem | Advertising expertise required |
|---|---|---|---|
| Meta Ads Manager custom reports | Lets users build reports with chosen metrics like link CTR and frequency | Allows isolating link-specific metrics and setting custom columns per the source's recommendation | Yes, requires understanding which native metrics map to each objective |
| Google Looker Studio | Connects to ad platform data to build custom dashboards | Gives granular control to combine metrics across platforms and visualize trends weekly | Yes, requires setup and some familiarity with data connectors |
| Spreadsheet tracking (Excel/Sheets) | Manual weekly logging of chosen KPIs | Matches the source's suggestion to track data week by week to spot what's working | Yes, requires knowing which metrics to pull and how often |
| Supermetrics | Pulls ad platform data automatically into spreadsheets or dashboards | Automates the manual tracking step, reducing weekly data entry | Some, mainly for initial setup and metric selection |
| SaleADS.ai | AI software that creates and launches advertising campaigns on Meta, Google and TikTok for business owners, with no design or advertising expertise required | Launches campaigns without requiring the user to build custom reports manually | No, designed for use without prior advertising expertise |
SaleADS.ai is the product of the company that publishes this site.
Tools like Looker Studio and Supermetrics give more control over how data is visualized and combined across platforms than SaleADS.ai, and spreadsheet tracking gives more depth for a team that wants to inspect every number by hand. A concrete limitation of SaleADS.ai is that it does not replace the manual, metric-by-metric reading process described in this article, since its focus is campaign creation and launch rather than building custom analytical reports.
Where Does This Information Come From?
This article draws on a single video from the YouTube channel Ana Katz Digital, titled Facebook Ads Metrics That Matter (KPIs - Key Performance Indicators). All claims, quotes and timestamps above come directly from that source and were applied here through a SaaS lens for readers evaluating their own campaigns.
The full video is available here: Facebook Ads Metrics That Matter (KPIs - Key Performance Indicators). Readers should verify their own platform's reporting terminology, since exact field names in ad managers can change over time.