Data Analysis & Reporting

Which question does marketing analytics answer?

Marketing analytics is the work that shows which part of the budget you spend on channels such as ads, email, search and social media turns into sales and profit. Every platform has its own dashboard and each one tells its own story of success; what a business owner needs is to read these numbers together, on a consistent measure. Our aim is to give a data-based answer to the question “which campaign should I give more budget to and which less?”

The focus of this service is the return on your marketing investment. We cover the reach, engagement and content performance of your social media accounts in our social media analytics and reporting service. AI-supported forecasting and pattern analysis on large data sets is the subject of our data analysis with AI work.

Regular marketing analytics answers these questions:

• Which channel and campaign really bring sales or qualified leads?
• What does it cost us on average to win a customer?
• When ad spend goes up, do sales go up at the same rate?
• How closely do the conversions the platforms report match our own sales records?
• Which channel does it make more sense to move budget to in the coming period?

The steps of a marketing analytics project

1. Clarifying goals: We set your priorities, such as growth, profitability or new customer acquisition, and which indicators we will use to track them.
2. Measurement audit: We check conversion definitions, the UTM setup and data sources, and fill in what is missing.
3. Combining data: We bring ad platform, analytics and sales data together in a shared structure.
4. Analysis: We examine cost, return and trends by channel, campaign and customer group.
5. Reporting and recommendations: We share findings in regular reports and give concrete recommendations for the next period in every report.

ROI analysis: linking spend to profit

The most common mistake in marketing analytics is reading revenue as if it were profit. A campaign can seem to bring high revenue but be losing money once product cost, shipping, returns, commissions and agency fees are subtracted. ROI analysis compares spend not only with the sales it brings but with the profit left from those sales.

The difference between ROI and ROAS

ROAS divides the revenue earned from ads by ad spend; it shows how much revenue the ads brought. ROI divides the net gain from the investment, meaning what is left after all costs are taken from revenue, by the total investment. For products with a low profit margin, a campaign with a good-looking ROAS can be weak in terms of ROI. That is why, when evaluating campaigns, we also take into account the approximate profit margin of product or service groups.

The profitability threshold is different for every business. When we know your product margins and fixed costs, we can work out together the lowest ROAS a campaign must reach in order not to lose money. This threshold also guides us when setting the platforms’ automated bidding targets.

Customer acquisition cost and customer lifetime value

A campaign that loses money on the first order can be profitable in the long run if the customer shops again. That is why, in ROI analysis, we look not only at the first sale but at the total value the customer brings you over time. Comparing customer acquisition cost with customer lifetime value shows which channel brings loyal customers and which brings one-time discount hunters.

For this, we group customers by the period of their first purchase and the channel they came from, and track their purchases in the following months. This kind of cohort analysis makes visible the differences that short-term numbers hide.

Returns from organic channels

For SEO, content and organic social media, no pay-per-click is paid to a platform, but these channels are not free either. Content creation, shooting, design and agency fees are the investment in these channels. In ROI analysis, we evaluate organic channels together with these costs. Because the effect of organic channels usually appears later and lasts longer, we take care not to compare them on the same calendar as short-term ad results.

ROI for businesses that collect leads

In businesses where a sale moves forward by phone, form or quote, the conversion is not the sale itself but its first step. If the number of forms rises while sales do not, campaigns may be bringing in people who are not relevant. That is why we track leads by stage: incoming lead, qualified lead, customer who received a quote and won job.

Doing ROI analysis based on the last link of this chain lets you tell apart campaigns that bring cheap but worthless leads. For this, it is often enough for your sales team to mark the status of leads regularly.

Measurement setup: UTM, conversions and data sources

The quality of marketing analytics depends on the order in place at the moment the data is collected. Untagged links, inconsistent campaign names or wrongly defined conversions make even the best report unreliable. That is why we start marketing analytics by auditing the measurement setup.

A pre-campaign measurement plan for marketing analytics

Marketing analytics is done most soundly with a measurement plan prepared before the campaign goes live. Filling in missing data afterward, once the campaign is over, is often not possible. That is why we prepare a short plan for every important campaign and answer these questions from the start:

1. Goal: Is the campaign run for sales, leads, appointments or awareness?
2. Main indicator: Which single number will we look at to judge success, for example cost per order or the number of qualified leads?
3. Supporting indicators: Steps that help explain the main result, such as clicks, add to cart or form starts
4. Data source: Which tool each indicator will be taken from and which number will be treated as the reference when there is a difference
5. Tagging: The UTM values and campaign names of all links to be used in the campaign
6. Point of comparison: Whether the result will be compared with the target value, with a previous similar campaign or with a period without the campaign
7. Review time: When the interim checks and the final evaluation will be done

We test the plan before launch: are conversions really being recorded, do tagged links appear in the analytics tool with the right source, does source information reach the lead and sales records? This check can be done with a few test transactions and decides how reliable the data collected throughout the campaign will be.

At the end of the campaign, we return to the same plan for the evaluation. Because the goal and the measure are written down from the start, the result is read against the measure agreed in advance, not against indicators chosen after the numbers come in. This setup keeps ROI analysis and campaign reporting comparable across different campaigns. We also keep the prepared plan as a template for similar campaigns.

UTM parameters and naming rules

UTM parameters are tags added to a link that tell the analytics tool which source, which medium and which campaign the visitor came from. The basic parameters are utm_source, utm_medium and utm_campaign; with utm_content and utm_term, different ads or keywords in the same campaign can be told apart. Google Analytics’ guide to collecting campaign data with custom URLs explains in detail how to use these parameters.

These values are case sensitive; “Instagram” and “instagram” appear as two separate sources in a report. In addition, Google Analytics 4 looks at these values when it sorts traffic into default channel groups, and non-standard values can cause traffic to appear unassigned. That is why we set a shared naming rule that all teams will use, and make sure email, social media, influencer and ad links are tagged according to this rule.

When a Google Ads account is linked to Analytics and auto-tagging is on, this traffic is recognized even without UTM. For links coming from Meta, TikTok, email and partners, the UTM setup is the basis of campaign reporting.

Conversion definitions and values

Every business has a different conversion: a purchase, a quote form, a phone call, a WhatsApp message, an appointment or a newsletter sign-up. We define each of these steps separately and, where possible, assign a value. For purchases, the real order amount can be used; for businesses that collect leads, a reasonable estimate of the average value of a lead. Conversions with assigned values let you compare channels not only by count but by the value they bring.

Offline sales and CRM data

In businesses where the sale closes in a store, on the phone or in a meeting, the analytics tool does not see the sale. In this case, we look for ways to match your CRM or sales records with marketing data: recording the source on the lead form, the sales team asking customers where they came from and, where possible, sending sales results back to the ad platforms. A perfect match is not always possible, but a rough and consistent link is far more valuable than no link at all.

Cookie consent and missing data

The behavior of visitors who do not give cookie consent can reach analytics tools incompletely; browser restrictions and ad blockers also reduce the measured data. That is why it is not surprising for the number of sales in analytics to be lower than the real number of orders. We show this gap openly in reports, and when assessing trends we watch whether the measurement coverage changed between periods.

Attribution: which channel do we credit the sale to?

A customer often touches more than one channel before buying. First they watch a video on Instagram, a few days later they search for your brand on Google, and in the end they buy through a link in an email. How much of this sale you credit to which channel depends on the attribution method; it directly affects budget decisions and the result of marketing analytics.

Why do platform numbers add up to more than the total?

Ad platforms can credit to themselves, in their own dashboards, the purchases made within a set time by people who clicked or only viewed the ad. The same sale can appear in Meta, in Google Ads and in the email platform. Adding up the conversions the platforms report therefore often gives a higher number than the real number of sales. In campaign reporting, we read each platform’s number in its own context and judge overall success from the real sales on your site and in your accounting.

Attribution models

The last click model credits the whole sale to the channel just before the purchase; it is easy to understand but undervalues the channels that made the first introduction. Google Analytics 4 uses data-driven attribution by default, which distributes the contribution of touchpoints on the conversion path according to the data. More important than which model is chosen is that the report states clearly which model is used and that it is not changed between periods.

Incrementality: what would have happened without the ads?

Attribution shows after which touches the sale came, but it does not say whether that sale would have happened without the ad. For example, ads shown to people who are already searching for your brand can show high conversion, but some of these people could have found you without the ad.

At a suitable budget and traffic level, we try to measure this added effect with comparison tests that deliberately leave part of the audience out of the ads. Some ad platforms offer their own testing tools for this kind of measurement. These tests do not suit every business; with small budgets, results may not reach a meaningful difference.

Cross-channel campaign reporting

Reading each channel’s report separately makes it hard to see the big picture. Cross-channel campaign reporting brings Google Ads, Meta, TikTok, email, organic search and sales data together in the same table, with the same date range and the same definitions. This way you can see whether an increase in one channel came from another, or whether it really added to total sales.

What we watch for to keep a table consistent

• Same definitions: Terms such as “conversion”, “customer” and “revenue” being used with the same meaning across all channels
• Same period and time zone: Correcting time zone and date differences between platforms
• Same currency: Converting spend to a single currency in accounts that work with different currencies
• Tax and returns: Stating whether revenue is shown with or without tax and returns
• Source note: Writing clearly in the report which tool each number came from

What do we compare with?

A number is not good or bad on its own; what it is compared with matters. In campaign reporting, we read results against three points: the target set at the start of the period, the previous period and, where possible, the same period last year. In businesses with a strong seasonal effect, comparing only with the previous month can mislead; we note factors such as holidays, sale periods or the school calendar separately in the report.

What does a monthly performance report include?

We prepare the monthly performance report so that the reader can understand the situation in a few minutes. In campaign reporting, we adapt the content to your business, but the basic structure is made of these sections:

1. Summary: The three most important developments of the period and the status against goals
2. Overall table: Total marketing spend, revenue, conversions and customer acquisition cost
3. Channel breakdown: Each channel’s spend, results and change from the previous period
4. Campaign detail: The best and worst performing campaigns and the reasons
5. Recommendations: Budget, creative, targeting and page recommendations for the next period

Dashboards and automated reports

Alongside the monthly report, for businesses that want to follow data in real time, we can prepare dashboards that bring channels together with tools such as Looker Studio. A dashboard lets you see the numbers at any moment; commentary and recommendations come from the report. We use the two not as replacements for each other but as tools that complement each other. We also choose which indicators go into the dashboard by who makes which decision: total spend and return for management, creative and audience detail for the campaign team.

Data-driven recommendations: from report to decision

A report that does not change what the reader will do is only a list of numbers. In marketing analytics work, we present every finding with a recommendation: what we saw, what the likely reason is, what we suggest doing and how we will measure its effect. We rank recommendations by expected impact and ease of implementation, and mark uncertain conclusions as test proposals.

Examples of recommendation types are:

• Moving the budget of a campaign that stays below the profitability threshold to a more efficient campaign
• Reviewing a landing page that gets high traffic but does not convert under conversion rate optimization
• Setting up an email flow for a customer group with a high repeat purchase rate
• Finding the source of untagged traffic and completing the UTM setup

Budget allocation decisions

Getting good results in one channel does not mean unlimited budget can be added to it. As spend grows, platforms start reaching less relevant people too, and the cost of each extra sale usually rises. That is why we recommend budget increases in steps and separately evaluate the extra result that the extra spend brings after each increase. Marketing analytics helps you see where the budget can still grow efficiently and where it is nearing the saturation point.

Marketing analytics for small-budget businesses

Marketing analytics is not only for large ad budgets. In a business with limited spend too, keeping the monthly spend by channel, the number of leads or sales that came in and their approximate cost in a single table makes decisions easier. To start, tagging all links with UTM and asking new customers where they heard about you is often enough.

As data grows, this table can be turned into a dashboard fed by analytics and ad data. What matters is keeping records with the same definitions and regularly from day one; every later analysis is built on this accumulation.

What to prepare before we start

• Read access to your Google Analytics, Search Console and ad accounts
• A periodic sales or lead summary from your e-commerce or CRM system
• The approximate profit margins of your product or service groups
• Your monthly marketing spend by channel and any agency fees
• Your business goals: growth, profitability, a new market or a new product
• The dates of campaigns run before and any result reports

Common mistakes in marketing analytics

• Treating platform dashboards as the only truth: Each platform measures its own contribution by its own rules.
• Reading revenue as profit: An analysis that ignores margin can scale up campaigns that lose money.
• Naming campaigns at random: Inconsistent naming makes comparisons made months later impossible.
• Changing the measure and comparing periods: If the attribution model or conversion definition has changed, the numbers cannot be compared one to one.
• Tracking too many metrics: Numbers that do not help decision-making cause the important ones to be overlooked.
• Deciding on short periods: A few days of data can mislead, especially in businesses with a long sales cycle.
• Trusting averages: A single channel or product group may be losing money while the overall table looks good; marketing analytics needs to be done with channel and product breakdowns.
• Leaving the measurement plan until after the campaign: The data of links that went live without tags cannot be corrected afterward.

Frequently asked questions

What is the difference between marketing analytics and social media reporting?

Social media reporting focuses on the reach, engagement and content performance of your accounts. Marketing analytics ties spend across all channels to sales and profit and assesses where the budget should go.

What data is needed for ROI analysis?

You need marketing spend by channel, the sales or lead data from those channels and the approximate profit margin of your products or services. A calculation made without margin information is not ROI, only ROAS.

What is a UTM parameter?

UTM parameters are tags added to the end of a link that tell the analytics tool which source, medium and campaign the visitor came from. When used consistently, they make accurate comparison by channel and campaign possible.

Why do the sales in the ad dashboard not match Google Analytics?

Platforms and analytics tools count sales with different attribution rules, time windows and data sources. This difference is normal; what matters is knowing which number came from which source and making decisions on a consistent measure.

Is new software needed for marketing analytics?

Often it is not. When Google Analytics, the ad platforms’ dashboards and your sales records are set up correctly, most of marketing analytics can be done with these sources and free reporting tools.

How often should campaign reporting be done?

A monthly report suits most businesses for a full evaluation. For accounts with high spend or in an intense campaign period, following the key indicators more often helps catch problems early.

Let’s start your marketing analytics together

Let’s talk about which channels you spend on, where you track your sales and which questions you are looking to answer. Reach us through our contact page; let’s review your current measurement setup and build together a reporting structure where you can see the return on your marketing investment more clearly.