For years, businesses have collected customer data like gold. Terabytes of clickstreams, CRM records, and survey responses sit in warehouses and pretty dashboards, waiting to “reveal insights.”
Yet, most businesses still struggle with the same question: How do we build a customer analytics strategy that strengthens customer connections and grows business?
This is where a customer analytics strategy comes in. Customer analytics is the process of studying customer data to understand behavior, preferences, and needs.
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A well-designed strategy helps businesses go beyond just looking at numbers. It helps teams answer important questions like: Who are our best customers? Why do people leave? What actions can we take to improve their experience and increase revenue?
Without a clear strategy, companies often end up with lots of data but little action. But with the right approach, customer analytics can help you build stronger relationships with your audience and make smarter decisions that lead to real business growth.
In this guide, we’ll walk through 7 practical steps to build a customer analytics strategy that works for anyone who wants to make sense of customer data and also take action.
1. Set Clear Objectives
Before we even begin collecting any data, it’s important to set clear objectives that can be tied to any business goals. I.e., if you’re collecting something, it should serve the purpose of meeting a business goal, directly or indirectly.
The objective(s) should be able to answer the following questions:
- What do you want to collect/track?
- Why do you want to collect that data?
- When do you want to track the customers? When should these insights be shared with relevant people?
- Who are your target customers? Who will be using the data internally, so it’s accessible and actionable?
- How are you going to meet the above requirements? What tools, sources, and processes can you use to inform your customer analytics strategy?
The example below might give you an idea:
Business goal: Increase overall online revenue by 15% this quarter.
Objective: Improve conversion rate on product pages.
- What: Track clicks on product CTAs, scroll depth, exit rates, and add-to-cart events on key product pages.
- Why: To understand where customers are dropping off and optimize content or UX to increase conversions.
- When: Track during each website session and analyze trends weekly. Share insights with the marketing and product teams bi-weekly to adjust campaigns or page designs and/or run AB tests.
- Who: Target high-intent website visitors (e.g., organic or paid traffic landing on product pages). Internally, the product and UX teams will use this data to make iterative improvements.
- How: Use Google Analytics 4 (GA4) for web analytics data, heatmapping tools (e.g., Hotjar) for qualitative insights, and dashboards to visualize patterns. Set up automated reports and alerts for abnormal drop-offs.
This is just one example, and depending on the objective you’re trying to achieve, the answers to these questions will vary because they will deal with different objectives and goals.
The ‘How’ part might be the same or similar for most of the objectives because you’d be using the same process and tools.
This is the point where you should also start documenting these goals/objectives, so there’s a record of what’s been done and tried.
It will also help you track progress over time, avoid repeating past mistakes, and make more informed decisions in the future.
2. Map Out Customer Journey
Understanding what touchpoints your customers ‘touch to take the desired actions on your site or in the product is crucial to developing an actionable strategy.
By mapping out these touchpoints visually, you can get an idea about what are the places or steps from where they can drop off.
More importantly, what can stop them or encourage them to proceed to the steps that eventually lead to them becoming a customer or taking other actions after they’ve become your customer?
These touchpoints will, understandably, vary for different industries, businesses/types, products, and/or services.
This means, on a larger scale, these journeys are quite unique. Not as unique as our fingerprints, but still unique.
What makes these journeys even more unique is the type of tools or channels we use to communicate or interact with the customers, e.g., website, social media (organic/paid), search engines, blogs, emails, offline events, brick-and-mortar stores, etc.
So, you get the idea. Start by listing down all the touchpoints and then map out the journey for all the different ways a customer can interact with your brand.
Collaborate with relevant teams, if necessary, to understand any nuances that you might not be aware of and keep documentation of these journeys.
Remember, there are two goals here: 1) Understand the touchpoints and their nuances, and 2) Understand the friction points.
By combining the insights gained from these two goals, you’ll be able to create your action plan.
3. Define the Data You Need to Collect
Now that you have a roadmap of the customer journey and the touchpoints, you can define the type of information that needs to be collected.
For instance, what’s more relevant to your objectives, i.e., behavioral, demographic, transactional, etc., based on the touch points, and how will you tie it to your objective and business goals?
This could also inform the sources of data collection, for instance, surveys, customer interviews, heatmaps, session recordings, web and product analytics, etc.
It’s important to stick to what serves your objectives vs trying to collect everything because it’s quite tempting to want to track it all.
4. Choose Your Tools of Trade
To put your strategy into action, you will need tools that can help you collect, organize, and/or visualize your findings so that they can be managed and communicated easily.
Your tool selection will depend on two things: 1) Internal buy-in, i.e., what the relevant teams or the business as a whole are comfortable with and/or approves, and 2) Your data needs, e.g., web analytics, product analytics, surveys, heatmaps, customer data, visualization, etc.
Let’s explore some common and popular tools, assuming you already have some kind of CRM to store customer data.
Google Analytics 4 (GA4)
GA4 is a free web analytics tool that can help you gather important data points, like who the people are on your website without individually identifying them, what their traffic sources are, what pages they land on, what devices they use, what actions they take, and how many of them convert.
This might be a simple way to introduce GA4, but you get the idea. But it can be very useful with its reports like Funnel, Path, and Cohort explorations and advanced segmentation techniques so you can drill down and get key insights.
There is also a paid version known as GA 360 for businesses with huge volumes of data and other requirements, costing approximately $50,000 annually. But for most businesses, the free version will do the job.
Microsoft Clarity
It is one of the best free behavioral analytics tools out there. It can be a great help to understand on-site behavior and, in some cases, the ‘whys’ behind the actions.
The heatmaps it shows are quite detailed and of several types, i.e., click map, Scroll map, and Attention map (average time spent on a section of the site).
Click map can also be further divided to look into specific types of clicks, which are: dead, rage, error, first, and last clicks. The heatmap data is retained for 13 months.
The other good part is the session recordings that it keeps a record of for the past 30 days and 13 months if you label or favorite them.
You can also create funnels in Clarity and then see how those specific users behave on your website.
All of this helps you to understand how users behave on the site and map their flow once they are on the site.
Considering how most tools are now using AI, you will also see that here in action with a summary of heatmaps and session recordings under the ‘Insights’ tab.
You can also integrate with your Google Analytics account and will see a lot of relevant data right in Clarity.
Given it’s free, we don’t have any complaints. However, it would be great if we could also launch surveys from this tool.
Talking about surveys, if you have the budget, Hotjar could be a pretty good alternative as it’s a lot more advanced in terms of heat maps, session recordings, and different types of surveys. However, it can quickly get expensive if the traffic on your site is growing rapidly.
Amplitude
While GA4 can cover a lot of ground when it comes to web analytics, if you want to have product-specific analytics, then Amplitude could be a better alternative.
It does have a free tier, but it is not without its limitations. However, with Amplitude, you get session recordings, web analytics, surveys/feedback, experimentation, and a lot more in one place.
Mixpanel is another similar tool with a focus on product analytics but might not have some features like surveys and experimentation. The free tier can be quite generous, depending on your use case.
Looker Studio
While it’s good to collect the right data, if it’s not being communicated to the people who need it, then it’s not really serving its purpose.
Looker Studio is a great free tool to display the data that you have collected, so the end users are not going to a specific analytics tool every time they want to get answers to simple questions.
You don’t need to create complex, fancy dashboards. Looker’s simple dashboard that gives users information on basic things would be good enough.
For instance, how many users came from paid social media, and what actions did they take? This would show the touch points on the site based on their source.
You could also have information about what type of devices your visitors are on or any other important data for which the team members don’t have to wrestle with the analytics tool.
This helps to encourage them to use the data more as it’s more convenient than having to understand the nuances of the tool itself, which can get overwhelming for some.
Apart from these tools, it also depends on what type of data you want to collect and the sources. For example, if you’re simply mining emails and chats, all you need is to download and add to Google Sheets to start coding into themes and analyzing.
5. Store and Organize Your Data
Before you start collecting from different sources using different tools, it’s a good idea to have a plan for how you are going to store and organize it, i.e., via a database, dashboards, or simply spreadsheets.
But you can also take care of this once you start collecting the data. This step should ensure that the data is clean, consistent, and easily accessible for analysis by anyone on the team.
It’s also important that the tools being stored and/or organized to be used for analysis are something the team can use without much instruction.
But it’s still a good idea to add snippets of notes to explain any anomaly or nuance so that the people using it are aware of those things when using that piece of data.
For instance, if you’re showing data for Total users, New users, and Returning users from GA4, it’s probably a good idea to mention why the new and returning totals don’t sum up to total users.
Another good example is that Users are generally already summed up in GA4 (pre-aggregated), so if you break it down by different dimensions, the same user could be counted in them.
For example, if you’re seeing user data by days, then the user who visited the site on Monday and then Wednesday would be counted twice under both days, even though it’s one user.
This way, you make it easier to interpret the data correctly and get insights that are closer to reality.
6. Analyze, Share, and Take Action
So, the data is being collected, stored, and organized. The next step is looking into how it can be utilized.
This involves analyzing the data, and you should start simply. For instance, look for patterns to see what steps or pages most of the users drop off, what pages or sources convert best, etc.
Compare important metrics, aka KPIs, and how they are faring against your objectives. For instance, do you see what you expected, or is there something new that needs further investigation?
Then, you’ll want to generate some key takeaways that can improve customer experience and/or meet the objectives set in Step 1.
These insights should then be shared with the relevant teams (e.g., marketing, product, etc.) with simple recommendations and clearly defined next steps so they can take action.
It’s also important to have a priority framework where items that are urgent/top priority are actioned right away. You might also want to measure against the action taken, i.e., did it make the expected changes or not?
This could also open up a conversation about conducting AB testing before implementing any major changes so you can test them, which is generally pretty good practice if you want to keep improving!
7. Monitor, Iterate, and Repeat
Check your data regularly to see if your customer analytics strategy is working. For example, are the objectives still relevant or not?
Part of monitoring is also to use any new data/insights to make tweaks or even fix it if something you implemented isn’t working.
Finally, you want to keep repeating this cycle so your analytics strategy stays relevant and delivers value over time.
This final step can help you become flexible as you will be able to quickly adapt to evolving business objectives and/or customer needs, which is quite a competitive edge to have!
Summary
This post covered how we can create an effective customer analytics strategy in 2025 and beyond.
While we understand the importance of having a customer analytics strategy, we briefly discussed how not having one can throw a wrench in the works.
We then moved on to explore 7 steps that we can follow to create our analytics strategy, noting that while the steps are building blocks that come together to make an effective strategy, they are set in sand, not in stone.
That means that they are flexible, and you can adjust them based on your business, the team, and the resources you have access to.
Perhaps what’s very important is to plan for the unknown and have a strategy that takes into account rapid technological changes that are happening around us, i.e., AI/ predictive analytics, privacy concerns, and DToC (Digital Twin of Customer).
A DToC is a digital model of an individual customer or customer segment that simulates their behaviors, preferences, and interactions.
It uses first-party data along with other relevant customer information to recreate and predict how customers engage with a brand in a virtual setting.
You can guess how well it will fare in a world where privacy is becoming an increasingly important factor when it comes to customer data.
Since we mentioned some popular tools, here’s our list of the Top 10 Web Analytics Tools that might be of interest if you’re looking for different tools.
So, how do you formulate your customer analytics strategy? More importantly, how do you make sure it’s adaptable to any changes, especially emerging future technologies?
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