CARE – a digital analytics framework.

Mikko Piippo Updated 20.8.2024

Does your organisation struggle to provide a return on investments in digital analytics?

Many organisations collect data and report it. Unfortunately, they often fail to derive insights and take action based on those insights. This results in missed opportunities and a need for tangible business impact.

It’s crucial to approach digital analytics with a structured framework to overcome this. My CARE framework is such a framework designed to derive value from data.

Collect data
Analyse data
Recommend actions
Experiment and execute

Collect Data

Collecting data is an essential step in the digital analytics process. In digital analytics, this involves setting up tracking and reporting.

In this phase, ensuring that your data is accurate, relevant and compliant is crucial.

Relevance means alignment with business goals. For example, your business goal might be to increase online sales. In this case, metrics like conversion rate, average order value, and revenue per session are good candidates for KPIs.

Your data should be as accurate as possible. For this, I recommend automating quality assurance for tracking data. Automated tests also help in identifying issues related to consent management.

And you should understand the biases caused by, e.g. users not giving their consent to tracking. Of course, you should also understand the differences between your data sources.

These help you to identify opportunities to optimise your website and marketing campaigns.

Analyse Data

Collecting data is only the beginning. You need to analyse it.

For example:

If your conversion rate has decreased, you must find potential causes.
If the average order value rises, you should find drivers for this.
Try to identify products with better and lower-than-average add-to-cart ratios.

The key is to segment the data by different dimensions, such as traffic source and device type. This will allow you to find interesting patterns in your data.

The analysis phase involves using different tools and approaches.

Data visualisation
Statistical analysis

Data visualisation is an essential step in the analysis. This can involve creating dashboards in tools like Looker Studio or Power BI to track KPIs. Often, data visualisation allows us to identify patterns and insights in the data.

The same is true for statistical analysis tools like R or Python. With them, we can dig deeper into the data. In the future, machine learning SaaS tools might partially replace statistical programming.

But right now, you need to do the work yourself. Analyze the data and provide insights.

Recommend Actions

Once you’ve identified insights in the data, it is time to move on. Recommending actions is critical for your analytics process.

This involves, for example:

making changes to your website and marketing campaigns
adjusting your targeting or messaging
exploring new opportunities based on your findings.

Some ideas for e-commerce:

If many users drop off at a certain point in the checkout process, recommend optimising that step.
If some marketing channels perform better than average, recommend increasing its budget.
If site search doesn’t find products, recommend testing a new search algorithm.

As an analyst, you need help making decisions. You won’t be the only decision-maker, so you need to sell your ideas to other stakeholders.

Experiment and Execute

The final step in the CARE framework is to experiment and execute.

This involves testing your recommendations through different methods. For example:

A/B testing
sequential testing.

In this phase, the analyst should cooperate with other teams. Depending on the organisation, the growth, PPC, and CRO teams are usual stakeholders.

In e-commerce, you can, for example:

experiment with the checkout process to improve the conversion rate
experiment with the product pages to improve the add-to-cart rate
and experiment with messaging to improve the conversion rate.

Analysts should also be involved in assessing the experiment’s results. This helps understand whether their recommendations are effective and improve the CARE process.

(For example, you might identify data collection issues also at this stage.)

CARE provides more value

The CARE framework provides a structured, strategic approach to digital analytics. It helps ensure that you collect

accurate data
create meaningful insights
provide actionable recommendations
experiment and assess the experiments.

With CARE, you can improve the customer experience, marketing and website. It also helps you improve your internal processes and show the value of analytics to key stakeholders.

How to CARE more?

The CARE framework is central to improving the user experience, conversion rate and marketing.

Using CARE is an ongoing process, never a project. Because of this, organisations need a dedicated analyst (either internal or external) to support decision-making in the organisation. It is necessary to include analytics and analysts in, for example,