In the era of personalisation, fast service and increasing competition, customers have higher expectations than ever. To ensure leads convert to customers and remain loyal, you need to craft tailored experiences that add value to their lives and exceed expectations.

This must happen throughout their relationship with your business, from first impression to purchase and beyond.

Being able to do it accurately means using data. Fortunately, it’s now easier than ever to monitor customer data, with many platforms giving you a wealth of data to learn from.

However, it’s common to get lost in volumes of data, making it hard to draw conclusions and drive change. It is therefore crucial to track specific analytics that best indicate customer behaviour and provide actionable insights.

To make things simpler, we’ve listed the top five types of customer analytics you should be tracking and how to use them to improve performance.

Five types of customer analytics to track_

The customer analytics you should be tracking are split into five distinct categories. Let’s dive into each of them.

Demographical data_

Demographical data relates to information about who your customers are. It could include things like gender, age, location, job title, income, ethnicity, education, family size and so on.

This data helps you to understand your customers better. Using it, you can build out buyer personas and segment customers into groups, based on who your business is relevant to. This makes it easier to target the right people in the right way.

Once you understand customer demographics, you can also anticipate their needs and wants, and meet them with purposely crafted products, services and experiences.

However, demographic data should be taken with a pinch of salt. It’s often based on assumptions (for example, if someone comes from a certain background, we assume what their goals are), which may not necessarily reflect reality. So, you should refer to other data to check your assumptions are true before you get too tied to them.

Engagement data_

Engagement data refers to how customers behave with your business at various points of their journey. It can include things like:

• Website: pages visited, clicks, form submissions, abandoned carts, time spent on page
• Email: opens, click-throughs
• Social media: impressions, views, clicks, comments, shares, likes
• Other: event sign-ups, downloads

Many analytics platforms will provide engagement rates too. Some tools will also provide heatmaps that show where users spend time and complete actions on a website, landing page or email.

Understanding how people interact with your brand can help you envision their journey. You can identify customers who are most likely to close on a deal which supports sales forecasting and opportunity planning.

Engagement data can enable you identify bottlenecks too. For example, if multiple people visit a page and very few click the call-to-action, it might suggest something on that page is preventing them moving forward. You can then review this page and dig deeper into the data. This allows you to optimise journeys and monitor the performance of your marketing activity.

It can also form the basis for any A/B testing you conduct to understand which tactics get better engagement and implement them in future activity.

Experience data_

Customer experience data relates to how a customer is treated as they engage with your brand. It’s covered by metrics like CSAT scores (which may be requested at different stages throughout their journey). It could also include data like complaint and ticket resolutions, response times, SLAs, missed deadlines, effort scores and so on.

Of course, you want your customers to have the best possible experience to gain their spending power and loyalty. If you are witnessing poor CSAT scores, it suggests this isn’t happening.

When you dive into other metrics, like response times, you should be able to find areas that are affecting satisfaction and need to be addresses.

Customer experience data can also be qualitative, such as through complaints. If someone has issues with a specific aspect of your service, it can highlight weaknesses in the experience, especially if common threads are appearing again and again.

By improving your experiences, it will have positive effects for your conversion and loyalty rates.

Customer voice data_

Whereas the data we have listed so far has been largely quantitative, this moves towards the qualitative side.

Interaction data refers to things like social media mentions, comments, reviews and so on. It may also be survey responses asking for feedback.

It covers what people have to say about your brand, both to you and about you to others. By looking into this data, you can discover whether people feel positively or negatively about your business and the specific reasons why. They might share things they love and hate, which can provide helpful feedback.

Customer voice data can also more generally indicate what customers are interested in, which can shape your strategy.

This qualitative feedback can also give context to the quantitative data, allowing you to understand the reasons people might not be engaging with your brand the way you hope.

When there are recurrent themes appearing in the feedback, it can give you actionable insights to take forward and address.

Loyalty data_

Loyalty data shows you how likely it is that a customer will continue using your brand rather than switching to a competitor. It covers things like repeat purchases, retention rates, customer churn, net promoter scores and lifetime value.

These data points will tell you whether a customer keeps coming back to your business, if they’re willing to recommend you and whether they are driving ongoing revenue.

If you face high churn rates or lifetime values, or poor net promoter scores, it indicates that something is preventing their loyalty, which might be price, experience issues or ineffective targeting.

As it’s cheaper to retain customers than target new ones, being able to build a loyal customer base is integral for cost-efficiency. It can also support your growth as a business.

How to analyse the data_

Now we’ve listed the analytics you should be tracking, it’s time to focus on how you can utilise them.

Types of analysis_

Firstly, analysis can fall into four categories, each delivering insight into different ways. These are:

• Descriptive analysis. This gives an insight into the past, describing what happened or what customers have done. It forms the basis for further analysis, allowing businesses to learn lessons and apply new tactics to improve performance.
• Diagnostic analysis. This helps businesses understand the roots of problem by exploring customer behavioural data. It basically comprehends the reasons for customer actions and events. If there is a specific problem you want to solve, you can look at quantitative and qualitative data to theorise the reasons and create solutions to move forward.
• Predictive analysis. This looks at historical and current data to reveal patterns and determine future possibilities of any event. It helps you understand what actions a customer may take, based on past data. Many tools – especially AI – offer predictive analysis, which allows you to set realistic KPIs and plan around future performance.
• Prescriptive analysis. This offers an insight into the best and appropriate action in a particular scenario. Again using past data, it enables you to plot the optimal action that fits any particular circumstance to resolve and prevent challenges.

All the data points we’ve listed can be analysed in each of these ways, allowing you to gain a conclusion regardless of what it is you are trying to achieve. This can include understanding customers better, fixing problems or estimating future performance.

Tips for tracking customer analytics_

1. Devote time to it

It’s very easy for data analysis to get deprioritised in the busy day-to-day of your job. However, you need to make it a regular habit to ensure you are getting the most from data.

Schedule time on a weekly or monthly basis (depending on what you’re looking to find out) to review customer analytics.

By regularly doing it, you will consistently gain intelligence for improved decision making.

2. Have something you want to find out first

Going blindly into data can make it hard to see the wood for the trees. That’s why you should always enter analysis with something you want to find out.

Having a goal to achieve will dictate the data you analyse and make it easier to piece it together. It will also give you a hypothesis you want to prove or disprove, which can make it easier to learn something from your data and turn it into actionable insights.

3. Tell a narrative

Numbers and graphs on their own do not tell you much. You need to build a story around your data that makes it easier to understand and gain insights. This narrative should build the context and place yourself in the shoes of the customer.

By using data storytelling, you can break down complex information in human terms and better engage others with your conclusions.

4. Know where to find data

Being able to analyse data means knowing where to find it. You need to know where specific data points are stored within your business.

Many organisations have several tools for data collection. However, this can mean going in and out of platforms to find it, eating time and causing disconnect.

Aim to find platforms that act as a centralised repository or connect your data together. This will give everyone one location to go to for data analysis, reducing any barriers or miscommunication.

5. Use visualisation tools

Raw data can be difficult to analyse, especially if it just looks like numbers on a screen. Data visualisation tools can bring the numbers to life in the form of charts and graphs.

Power BI is one of the best-known data visualisation tools on the market, providing interactive reports. It makes it much easier to understand your customer data and share it with other people.

6. Leverage AI analysis

AI is incredibly powerful when it comes to data analysis. It can take huge volumes of data and find patterns that may otherwise be missed. Plus, it does it much faster than any human.

Endeavour to utilise AI in your data analysis, with an emphasis on uncovering trends in your data. You can ask it to answer specific questions or give you key takeaways. It can even offer predictive insights.

Tools like Microsoft Copilot can connect directly to your internal data sources, giving it immediate access across your data for easier analysis.

7. Balance quantitive and qualitative data_

Numbers alone can be open to discussion and perspective. However, what customers have to say can give a much-needed context to the figures, making it clear what they’re thinking.

Aim to combine quantitative and qualitative data. This will give you a more thorough insight into your customer analytics and help you to develop accurate stories about their experiences.

How Dynamics 365 helps you with customer analysis_

Dynamics 365 is a powerful suite of business applications that can significantly enhance your ability to track and analyse customer data. Dynamics 365 Customer Insights specifically delivers easy management of customer analytics and intelligent insights.

Here’s how:

1. Comprehensive data collection

Dynamics 365 brings together data from various sources like sales, marketing, customer service and operations into a single platform. As a result, you gain a holistic understanding of customer interactions and preferences.

2. Advanced analytics capabilities

Dynamics 365 offers built-in analytics tools, with features like reporting, dashboards and predictive analytics. It also integrates with Power BI for in-depth data visualisation and exploration. There is also Copilot functionality, so you can leverage artificial intelligence to uncover hidden trends.

3. Customer segmentation and targeting

Within Dynamics 365 Customer Insights, you can group customers based on demographics, behaviour or preferences. This makes it easy to develop targeted marketing strategies for specific segments, leading to enhanced personalisation.

4. Customer journey mapping

You can build customer journeys within Dynamics 365 to better visualise customer interactions, from initial contact to purchase and beyond. This makes it easy to pinpoint areas where customer experience can be improved.

5. Predictive analysis 

Predictive analytics in Dynamics 365 can forecast customer behaviour and predict things like customer churn, purchase likelihood and lifetime value. Using this insight, you can allocate resources effectively based on predictive insights and anticipate customer needs.

6. Customer satisfaction management

Using Dynamics 365’s functionality, you can collect and analyse customer reviews, surveys and social media mentions. You can measure customer satisfaction, net promoter score (NPS) and customer effort score (CES). This feedback can then be used to enhance products, services and customer experiences.

Get started with Dynamics 365 Customer Insights_

Dynamics 365 Customer Insights makes it easier to collect, analyse and get actionable insights from your customer analytics. Covering demographic, engagement, experience, voice and loyalty metrics, you can clearly understand how customers are interacting with your business.

From there, you can find opportunities to optimise the customer journeys, solve issues and better target leads. This enables you to gain increased revenue and build a growing customer base.

It can also connect with your other Microsoft tools, including Outlook, SharePoint and Teams, for access across your existing data sources.

If you’re ready to jump into customer analysis, we can help you build, licence and successfully deploy your Dynamics 365 Customer Insight designation.

As one of the largest independently owned Microsoft Partners in the UK, we hold expertise across Dynamics 365 solutions. We work with you build a platform that is entirely tailored to your business needs and provides the analysis that is going to move you forward.

We also provide long-term support, including training and maintenance, so you get ongoing value.

Find out more about Dynamics 365 Customer Insights and our support here.