Google Ads Review
A Practical Framework
If you are working with Google Ads, you already know that performance does not just come down to good creative or big budgets. Real success depends on structure, clarity, and discipline , and that means tight targeting, rigorous exclusions, well-segmented campaigns, and flawless tracking.
In this article, we will walk through how to run a thorough Google Ads audit that cuts through the clutter and identifies what’s working, what’s not, and how to fix it. This guide is aimed at marketers, agencies, and in-house teams looking to optimise spend and improve conversions.
Don’t Trust Google To Optimise Everything.
Let’s start with a blunt truth: you should never trust Google to optimise your campaigns by default. Google’s machine learning tools are powerful, but they are built to serve Google’s own incentives just as much as your performance metrics. This is especially true for broad or automated campaign types like Performance Max, where targeting signals are treated as “suggestions” rather than filters.
That’s why the best advertisers take a hands-on approach. The goal is simple:
✅ Tighter targeting
✅ Clean, structured campaigns
✅ Smart use of all available ad types and platforms
Always run one low-cost, broad campaign. This ‘training’ campaign gives the algorithm room to learn and helps surface new insights for better audience segmentation across the rest of your account.
Oliver Gwynne, Data Strategist
Targeting: Who Sees Your Ads
What is Targeting?
Targeting determines who your adverts are shown to. It can be set at:
- Account level (e.g. audience lists, shared exclusions)
- Campaign level (e.g. demographics, location, devices, scheduling)
Ad Schedule (Campaign Level): Ensure that your adverts are only shown when your audience is most likely active. (e.g. weekdays, working hours). Avoid running ads 24/7.
How to identify problems: In the overview page you may see a schedule like the one below which indicates that no targeting has been applied to the ad schedule. This can be verified by going to Campaign > Settings > Ad Schedule, if this is blank there will be no schedule applied.
Where no scheduling has been applied, you can at least use this data to help optimise your future campaigns, set schedules at times that show darker blue colours.
Location Targeting: Location should be set to “Presence: People in or regularly in your included locations” and not “Presence or interest: People in, regularly in or who’ve shown interest in your included locations (recommended)” The reason is that the “Interest” category will includes tourists or people researching this location, which may lead to wasted budget.
How to identify problems: Campaign > Settings > Locations > Location Options.
We would generally advise that it is better not to target whole countries or large areas. Instead set up different campaigns for different geographical areas and then cross-compare them. For advanced use-cases consider how you
Location-based targeting, if used properly, can transform your campaigns. On platforms like Google Ads and Meta (Facebook and Instagram), you can target within a one-mile radius of a specific address. That level of precision is astonishingly underused.
People Costs
The cost that is almost unavoidable, however, is hiring the right people to set up all this infrastructure correctly, especially for scaleups aiming to grow fast and stay competitive.
Data remains a highly specialised field, requiring expertise in data architecture, data engineering, and analytics. The demand for skilled professionals has long outstripped the supply, making experienced people both scarce and expensive. Even for established businesses, the cost of hiring data people is high, and with that high cost also comes a high risk of hiring the wrong person, especially where that skill is not present in the business or they might not fully understand what is needed, and so how can you mitigate that risk?
The Skill Barrier
With data talent being so costly, how do scaleups try and adapt to data today and where do we see them most often going wrong?
Use Excel/Sheets: Traditional reporting still has a place in most businesses. Whilst obviously excel has some limitations in terms of manual input leading to errors, and a latency wherever people are having to update spreadsheets – excel is still a great tool for reporting that nearly everyone knows how to use.
Use Off-The-Shelf: There are many ETL tools that allow you to get started with data quickly and without heavy technical knowledge. These platforms are aimed at business users and can help you centralise and visualise your key metrics from popular data sources. Whilst often these tools will not scale – they can be a great way of getting value from your data in your early stages so long as you think of them as a short-term solution.
Don’t Hire A Unicorn: A mistake that we often see growing businesses make is that they try to hire one amazing person to handle their entire pipeline. The problem with this is that building a data stack requires a team with diverse skills and knowledge. Data engineers are needed to design and maintain the infrastructure, data analysts to interpret and visualise the data, and data scientists to develop advanced models and algorithms. A multidisciplinary team brings a well-rounded perspective and expertise, ensuring a robust and effective data infrastructure. What we often see happening is that businesses will try and hire a unicorn, someone with a bit of all-round experience. They get the stack up and running, but often when data volumes start to scale, what they have built has not been optimised for this and so the cost increases significantly and the stack often has to be completely redesigned.
The Complexity Barrier
Each different element of your data stack will have different considerations in order to ensure it is set up for optimal performance, and this leads to a high level of complexity in set up. There are often business requirements that, on the surface, seem simple, but in practice may have huge ramifications later on. A great example of this would be ‘Real-time’ analytics. Nearly every stakeholder would tell you they want or need this, but might be unaware of the significant complexity and cost they would be adding to your analytics infrastructure.
It is only really when someone has extensively used a particular tool that they will know all of the considerations in optimising its performance. Most often this means that data professionals will recommend a stack that they are most comfortable with – but in some cases this may not align closely to the needs of the business, or means they are the only one who understands the logic within.
Perhaps the biggest element which adds complexity is scalability. Data is such a fast-moving world with so many different tools that it can be difficult to understand the implications as to whether that stack will still be useful in a few years time. Embedding the best practices around documentation, quality and governance to ensure this healthy growth requires a senior data person who has done it before. Here we come back again to our cost barrier in attracting this talent for start-ups.
Alternatives
Instead of hiring expensive people to build out your analytics, hoping you can find a unicorn or making do with point solutions for a few years – we truly believe that working with an agency like 173tech offers a cost-effective alternative. Our experienced team have launched data stacks for 40+ businesses with scalability and longevity in mind.
Expert help is only a call away. We are always happy to give advice, offer an impartial opinion and put you on the right track. Book a call with a member of our friendly team today.
Aligning Data Strategy With Company Growth
Aligning data infrastructure with business growth is critical to ensuring that the right tools, processes, and governance are in place to manage and leverage data effectively for informed decision-making.
If Your Company Is Generating $500K – $1M Annually
At this early stage, businesses prioritise quick, cost-efficient solutions. Off-the-shelf tools offer a convenient starting point for data management, providing pre-built functionalities with minimal setup. These tools enable businesses to establish a data strategy without heavy investment. However, their limitations in scope and flexibility can hinder deeper insights, leading to missed opportunities for optimisation. We estimate that an effective data strategy should contribute to at least a 20% annual performance boost through efficiency gains, revenue improvements, and cost optimisation. Relying solely on basic tools can impose an opportunity cost by keeping your company blind to areas of improvement – but we understand that at this point, often your focus is on market-fit and nothing else.
If Your Company Is Generating $2M Annually
Hitting the $2M revenue mark is a pivotal moment for your data strategy. The limitations of off-the-shelf tools become increasingly evident as costs rise and fragmented systems create inefficiencies in data integration, reporting, and decision-making. At this stage, relying on multiple tools for different use cases leads to operational complexity and lost opportunities. This is the point where implementing a data warehouse can be a game-changer. A centralised data infrastructure enables bespoke analytics, automated reporting, and deeper operational insights, helping businesses optimise strategies and drive additional revenue growth. Companies that fail to take this step risk a growing opportunity cost, as their ability to extract meaningful insights remains constrained by rigid, pre-built tool limitations.
If Your Company Is Generating $5M Annually
By the time a company reaches $5M in annual revenue, its data strategy must evolve significantly. Custom and centralised reporting are no longer optional—they are essential. A dedicated data stack provides complete control and flexibility, allowing businesses to move beyond off-the-shelf tools that no longer meet their needs. Companies that invested in a data warehouse early find themselves at a strategic advantage here. Instead of scrambling to build a centralised reporting system, they are already leveraging custom analytics to optimise operations, increase efficiency, and unlock new revenue streams. Those that delayed this investment will now face a more significant and urgent transition, potentially falling behind competitors that made data a priority earlier on.
Over our five years in business, we noticed two interesting trends…
The first is that a lot of the startups that we have helped have second-time founders. These are people who have typically built up and sold their business and now, armed with that experience, understand the power of data and how it can be leveraged to gain a competitive edge.
The second is that while you might be familiar with the likes of Numan, Treatwell, MUBI, and Plend today, they were not big names when we first worked with them! By utilising data from the beginning, they were able to make informed decisions, optimise their operations, and scale effectively.
Adrian Macias, VP Engineering
Analytics goes well beyond reporting. As your customer base scales, it becomes the glue that ties together your business.
Ensuring ROI From Your Investment
If now is the time to implement your data stack, make sure you have the right strategy in place.
Data Needs A Purpose
Your data strategy should be guided by your business growth objectives. Everyone on your team must be clear on what they aim to achieve and how success will be measured. Begin with an overarching view of your metrics, then apply the same approach to each business function and product squad. Consider these questions:
• What are your short-term and long-term growth goals?
• Can you identify a ‘North Star’ metric that reflects the health of your growth?
• What factors contribute to your North Star metric? Define each one precisely, as this will set measurable KPIs.
How do you put this into practice? Document your KPIs in a data dictionary, a glossary of business terms, their definitions, how they are calculated, and where the data is stored. This creates a common data vocabulary and facilitates discussions across the organisation. The document need not be exhaustive; it will naturally evolve as your business and analytics capabilities grow. Once you have these definitions in place, you can build an analytics toolbox to help your team achieve their goals more swiftly.
Automation Saves Time
Automating processes today will save you time in the long run. The pursuit of efficiency is essential for growing your analytics, with automation at its core. Our guiding principle is: if you find yourself repeating a task, automate it, it is likely you will need to do it again. Automation can save your team thousands of manual hours, which can be redirected to fuel growth. This is akin to increasing your headcount by 10% (a conservative estimate) without having to recruit additional staff!
Data automation occurs in several layers. First, establish a robust infrastructure to ensure an automated, efficient flow of data from various sources into your centralised data warehouse (e.g. Snowflake or BigQuery). The raw data is then modelled according to your data dictionary definitions and automatically transformed into meaningful aggregations within your warehouse. For maximum analytics efficiency, we recommend that all data processing logic is captured within the modelling layer (a single point of work, for example, dbt) Why is this beneficial? Modelled data can serve all analytics purposes including automated dashboards, in-depth analysis, the development of data science algorithms, or even sending predictive data signals to various applications (such as churn propensity alerts to your app). Beyond saving time, automation ensures data accuracy and consistency in insights, minimising human error.
Empower Everyone
When all teams and applications query the same set of data models, you achieve a single source of truth. With all data sources centralised, you also gain a complete 360-degree view of the customer, capturing every touchpoint of the user journey. Data democratisation empowers everyone to make informed decisions, arguably the most important aspect of building a data-informed culture. So, what can you do to speed up data adoption?
Make data insights as accessible as possible: Most of your team will consume data via automated dashboards, so provide training and ongoing support to ensure they make the most of your reporting tools. Develop layered reporting that starts with a high-level overview and allows for drill-down into specific areas. Ensure that key metric definitions are easy to find to prevent any confusion.
Designate data champions within each business function: Identify the most data-savvy individual on each team and form a power-user group. Hold regular sessions to update and train them on the latest analytics developments (for example, addressing their most pressing questions). These champions will act as the go-to resource for day-to-day data queries and help spread the power of data across the organisation.
Continue to highlight the value and benefits of data: As with any product or service, make the value proposition clear and consistently add and communicate new features. Consider a monthly newsletter or commercial report showcasing the latest analytics milestones, successful use cases, and practical how-to tips.
Finally, make data engaging: Data is much more than charts, trends and predictions, it tells a story about your customers and their behaviours. Take the time to look beyond the quantitative ‘what happened’ and explore the qualitative ‘why it happened’.
Mai Nguyen – Data Analyst
Implementing analytics is normally a change from the norm and needs to be considered as a change management process.
Which Projects To Start With?
For most companies a ‘data-driven culture’ is a change from the norm, even now. And where there’s change, there needs to be management. This means that before you do anything, you need internal support from the c-suite, a project leader and a clear outcome.
Customer Journey: Map out your customer journey to better understand your data touchpoints, KPIs and decisions across different departments. You will need a clear understanding of what is important to your business, and where the gaps are today before anything else.
KPIs: Decide on which data points you need to track, how they are calculated and what the source of this data is. Don’t just track everything and anything! Think about key decisions and actions your team can actually take and work from there.
Data Stack: With a sound understanding of your business needs, consider the technical side. You should think about expected data volumes, your existing tech ecosystem, any specific security or data privacy concerns as well as considering the cost when starting to plan out what you need to build.
Core Management Reports: Your first use case should be a master dashboard for your C-Suite. We like to think of this as a five minute health check on how the business is doing. By focusing your initial outputs in this way, you can demonstrate value without needing to integrate every single data source across your customer journey.
Marketing Channels: For growing businesses, marketing is a big area of spend and so centralising and overlapping data from different channels helps to create a holistic picture on performance.
Online Advertisement Optimisation: If every business in a market relies on the same algorithm for their ads, they will all be optimised to the same customer profiles and so instantly lose competitive advantage outside of increasing their budget. Leverage your first-party data to optimise campaigns towards high LTV customers which will lower CAC and improve ROI.
Advanced Attribution: With marketing and advertising already incorporated, you can think about how sales teams win new business and apply advanced attribution to help identify winning trends and focus efforts.
From here the possibilities are endless, but we have seen that for most growth-focused companies, focusing on these areas represents a significant return from their analytics spend.
Of course if you are a scaleup and you want to lower the time to value – why not get in touch with 173tech.
Our core team previously led analytics at dating giant Bumble and we have since been using data to accelerate growth for the likes of MeUndies, Plend Loans, Wizards of the coast, MUBI and many more.
