If you’re investing in marketing but still unsure what’s actually driving results, you’re not alone. In our latest report, we found that 36% of UK businesses would like more training in data analytics for marketing purposes.
Knowing where to start can feel overwhelming. Data is everywhere. Platforms provide dashboards, reports, and automated insights, yet despite this, performance can still feel unclear or inconsistent. As marketing becomes more AI-driven, understanding your data has never been more important.
In this blog we’ll provide an overview of what data analytics is, why it’s important, and the key metrics should be tracked.
- What is data analytics for marketing?
- Why is data analytics important for marketing?
- Data analytics in the era of AI-powered marketing
- Types of data analytics in marketing
- Key marketing metrics
- The role of first-party data in AI-driven marketing
- Data Analytics for Marketing: FAQs
What is data analytics for marketing?
Data analytics for marketing is the process of collecting, analysing, and interpreting marketing and customer data to understand performance and guide decision-making.
It allows marketers to move beyond surface-level metrics and see how campaigns, channels, and customer behaviour contribute to real business outcomes.
In practice, marketing data analytics brings together insights from multiple sources, including:
- Customer data, such as demographics, interests, and purchasing behaviour
- Campaign performance data across paid, organic, and offline channels
- Website and app analytics, showing how users move through journeys
- Sales and revenue data, connecting marketing activity to actual results
When these data points are combined, businesses can identify what’s working, what isn’t, and where opportunities to improve acquisition and conversion exist.
Why data analytics is important in marketing
Without analytics, marketing decisions are often based on assumptions or isolated metrics. Data analytics enables teams to shift toward evidence-based decision-making, where strategy is informed by real customer behaviour.
This is particularly important when campaigns run at scale across multiple channels and budgets can be spent quickly. Analytics helps marketers understand which channels drive profitable customers, not just traffic or engagement.
As marketing platforms increasingly rely on AI to optimise delivery and targeting, the quality of your data directly influences performance.
Data analytics in the era of AI-powered marketing
AI-powered marketing platforms now play a major role in how campaigns are targeted, optimised, and scaled. Automated bidding, algorithmic targeting, and AI-driven recommendations promise efficiency and performance gains.
However, whilst it may seem tempting to let AI take care of the analytical side of things, it’s worth remembering that AI is still very prone to making mistakes.
There’s also the fact that AI systems learn from the data they receive. If tracking is incomplete or conversion signals are unclear, platforms may optimise toward the wrong outcomes, such as cheap clicks rather than valuable customers.
For many businesses, this results in:
- Rising customer acquisition costs
- Declining lead or purchase quality
- Strong in-platform metrics that don’t translate into revenue
Businesses should still focus on collecting and analysing their own data, alongside the data they receive from AI-powered tools and platforms. This helps ensure that campaigns are being optimised to produce meaningful business results, not just surface-level activity for the sake of using AI.
Types of data analytics used in marketing
Different types of analytics support different marketing objectives. Here’s the type of analytics that are often utilised by marketers.
- Descriptive analytics
Explains what has happened by summarising historical performance, such as traffic trends, conversion rates, or campaign results. - Diagnostic analytics
Explores why performance changed by identifying patterns, correlations, or contributing factors, such as shifts in audience behaviour or creative fatigue. - Predictive analytics
Uses historical data to forecast future outcomes, including purchase likelihood or churn risk. Many AI-powered platforms now support this natively. - Prescriptive analytics
recommends actions to improve results, helping marketers prioritise changes that are most likely to drive growth.
Many businesses rely heavily on descriptive reporting, missing opportunities to use analytics as a strategic decision-making tool.
Key marketing metrics (and what they really tell you)
Tracking metrics is only valuable when they’re understood in context. Below are some of the most important marketing metrics for consumer-focused brands.
- Customer Acquisition Cost (CAC)
Customer acquisition cost measures how much it costs to acquire a new customer. Rising CAC often signals inefficient targeting, increased competition, or diminishing returns from existing channels. Analytics helps pinpoint where costs are increasing and whether those customers are delivering long-term value. - Customer Lifetime Value (CLV)
Customer lifetime value estimates the total revenue a customer generates over time.
CLV provides critical context for acquisition spend. If customer value is low or declining, even efficient-looking campaigns may not be sustainable. Understanding CLV helps brands invest confidently in growth. - Conversion Rate
Conversion rate shows the percentage of users who complete a desired action, such as making a purchase or signing up. Low conversion rates often indicate friction in the customer journey, unclear messaging, or poor alignment between traffic sources and landing experiences. - Click-Through Rate (CTR)
Click-through rate measures how often users click after seeing an ad or piece of content. CTR helps assess how relevant and compelling messaging is. However, strong CTR without downstream conversions can indicate that ads attract interest but fail to meet expectations. - Engagement Rate
Engagement rate reflects how users interact with content through actions such as time on site, scroll depth, or social interaction. Engagement metrics help marketers understand whether content resonates with the intended audience and supports the buying journey. - Return on Ad Spend (ROAS)
ROAS measures how much revenue is generated for each unit of advertising spend. While useful, ROAS should be analysed alongside other metrics, such as customer retention and repeat purchase behaviour, to understand true performance.
The role of first-party data in AI-driven marketing
As privacy regulations evolve and third-party data becomes less reliable, first-party data has become essential for effective marketing analytics.
For businesses, first-party data usually includes website behaviour, purchase history, email engagement, and customer account data. This information provides the signals AI-powered platforms need to optimise campaigns more accurately.
Without a strong first-party data foundation, AI-driven insights lack context, limiting performance improvements.
Why attribution is more complex in AI-powered marketing
Consumer journeys are rarely linear. Customers may first discover a brand through social media, return later via search, engage with content or PR coverage, and revisit multiple times before finally converting.
In an AI-powered marketing environment, this journey becomes even more fragmented. Platforms automatically adjust targeting, bidding, and creative delivery in real time, often based on signals gathered across multiple interactions. While this improves efficiency, it also makes it harder to clearly see which touchpoints are influencing decisions.
Traditional attribution models, such as last-click attribution, often oversimplify this process. By assigning all credit to the final interaction before conversion, they fail to reflect the earlier channels that introduced, educated, or influenced the customer.
Example scenario:
Imagine a small bakery looking to sell their subscription boxes online. Their marketing mix includes:
- Paid social ads highlighting seasonal boxes
- SEO-optimised blog content about baking tips
- Local press coverage and digital PR about their community initiatives
A potential customer might first discover the bakery via a Facebook ad, then research the bakery on Google, click through to a blog post that shows the quality of their products, and finally purchase a subscription after reading a PR article on a local news site.
If the bakery had only used last-click attribution, the sale would be credited entirely to the PR article. This ignores the role of social ads and SEO content in building awareness and driving interest; which means the business might underinvest in the channels that actually generated demand.
Advanced marketing analytics looks at the full journey, evaluating how each touchpoint contributes to the final sale. This helps businesses understand which channels are worth scaling, which need optimisation, and how AI-driven campaign adjustments influence overall performance.
Common challenges with marketing data analytics
Despite access to powerful tools, many businesses struggle with:
- Inconsistent or incomplete tracking
- Disconnected data across platforms
- Metrics that look positive but don’t align with revenue
- Limited internal resources to interpret insights
These challenges often lead to uncertainty and underperforming campaigns.
How we help businesses make sense of marketing data
This is often where businesses benefit most from working with an experienced digital marketing agency.
When we run marketing campaigns, analytics and insight are built into everything we do. You will have 24/7 access to clear, actionable insights, helping you understand performance at any point in time, not just at reporting milestones.
Our approach ensures that data supports day-to-day decision-making, not just retrospective analysis. We help businesses:
- Understand performance across channels, rather than viewing each in isolation
- Spot changes in acquisition or efficiency early, before they impact results
- Make sense of AI-driven optimisation, particularly within paid and organic campaigns
- Turn complex data into clear recommendations that support growth
If you’d like to find out more about how we can help your business then be sure to get in touch.
Conclusion: Making data analytics work in an AI-driven world
Data analytics for marketing provides clarity in an increasingly automated and AI-powered landscape. For consumer-focused brands, it enables better acquisition, improved efficiency, and more confident investment in growth. When analytics is unclear or unreliable, even well-funded campaigns can struggle to deliver results.
If your data feels difficult to trust or interpret, the issue may not be your marketing, but the analytics supporting it.
Data Analytics: Frequently Asked Questions
Q: How does data analytics improve marketing ROI?
By showing which channels, campaigns, and tactics drive real results, data analytics helps businesses allocate budgets more efficiently, reduce wasted spend, and optimise campaigns for better performance.
Q: What skills are needed for marketing data analytics?
Key skills include analytical thinking, familiarity with analytics platforms, basic statistics, and an understanding of marketing strategy.
Q: Is data analytics only for large companies?
No. Businesses of all sizes can benefit. Even small and medium-sized businesses can use analytics to improve targeting, optimise campaigns, and understand customer behaviour.
Q: What tools can businesses use to track marketing performance?
There are many platforms available to help track and analyse marketing data. Popular options include:
- Google Analytics / GA4: website traffic, conversion tracking, user behaviour
- CRM platforms (HubSpot, Salesforce, etc.): manage customer data and track sales influenced by marketing
- Social media analytics: Insights on engagement, reach, and audience demographics
- Marketing automation platforms: Track email campaigns, lead nurturing, and performance
- Data visualisation dashboards (Google Data Studio, Tableau, etc.): Turn raw data into clear charts and reports
While these tools provide valuable data, interpreting them correctly and connecting insights to real business outcomes, especially in AI-powered campaigns, is where many businesses benefit from expert guidance.
Q: How does AI affect marketing attribution?
AI-powered platforms optimise campaigns dynamically, meaning traditional last-click attribution can underestimate the impact of earlier touchpoints. Advanced analytics helps businesses understand how all channels contribute together, not just the final interaction.
Q: Can AI optimise campaigns without human oversight?
AI can improve efficiency, but human oversight is essential. Teams need to ensure AI optimises toward meaningful business outcomes, not just clicks, impressions, or short-term engagement.
