AI-assisted ad campaign management
Less time on repetitive ad work, more on strategy. One system for campaigns on Google, Facebook and Instagram.
+90%
time saved (repetitive work)
+84%
saved on analysis (Google)
+87%
saved on analysis (Meta)
+85%
saved on adjustments (Meta)
Client
Fallow Deer is a marketing agency that runs ad campaigns for its clients. It works on the two largest platforms: Google (search, display and video ads) and Meta (Facebook and Instagram).
Problem
Running ad campaigns is largely repetitive, manual work. The specialist has to:
- collect results from the ad platforms and arrange them into readable reports,
- go through hundreds of numbers to find what works and what burns budget,
- set up new campaigns and write ad copy,
- make adjustments and make sure nothing slips through.
The more clients, the more of this work. Time that could go into strategy and ideas disappears into clicking and copying data.
Solution
We built a system that connects to the agency's ad accounts and takes over the repetitive work. The person stays in charge of decisions: they approve, correct and set the direction.
It runs on the same process behind almost every automation we build: first gather the data, then analyse it, then review the available options and finally pick the best one. For ads it looks like this:
Gathering data
The system pulls the results of all campaigns on its own and turns raw numbers into short, readable summaries. What a specialist used to compile by hand for hours is ready in moments.
Analysis
It checks which campaigns make money and which burn budget. It compares results with what is normal for the industry and flags problems and opportunities on its own, even if nobody asked about them.
Reviewing the options
The system draws on a large set of proven rules and checklists, gathered from experience and from official advertising guidance. This is its catalogue of solutions: ready approaches to typical situations.
Choosing the best solution
From these options it picks the ones that will help the client most and prepares them: new campaigns, ad copy, switching off what wastes money, budget changes. Everything waits for a person's approval.
Once the changes are live, the system records what it did and why. The following month it compares the results with the previous ones and proposes the next adjustments on that basis. The process runs in a loop and gets more accurate with every turn.
Safety
- The system never deletes campaigns. At most it pauses them, so they can be restored.
- It does not make larger budget changes without explicit confirmation from a person.
- Every change goes into a log, so it is always clear what happened, when and why.
- Nothing is published without the specialist's approval.
Results
We compared two months of work by the same team: one month without the system and one with it (after the team had learned to use it). The numbers show how much less time each task takes.
Google (search, display, video)
- publishing text campaigns: 91% less time,
- publishing display and video ads: 38% less time,
- making adjustments to campaigns: 91% less time,
- analysing data and drawing conclusions: 84% less time.
Meta (Facebook and Instagram)
- publishing campaigns: 36% less time,
- making adjustments to campaigns: 85% less time,
- analysing data and drawing conclusions: 87% less time.
Result: the system saves the most time where there is the most work: analysing data and making adjustments. The team handles more clients without growing and gains time for what a machine cannot do: strategy and ideas.
Technology
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