In this fast-paced business environment, understanding your customers is paramount, and that's where data analysis comes into play for CRM Marketing.

Data analysis is a versatile tool that can be used to address various challenges or tasks encountered in the business landscape. Here's how it can be applied:

• Segmentation and Customer Grouping: divide your customer base into meaningful segments to tailor your approach;

• Understanding Customers' Purchasing Behavior: gain insights into what drives your customers' buying decisions;

• Analysing Communication Activity and Engagement Levels: track customer interactions across different channels to optimise engagement. (CRM channels, website activity etc.);

• Tracking and Evaluating Satisfaction Levels: monitor metrics like NET score, CSAT, CLV, Churn rate, and Retention rate to evaluate customer satisfaction;

• Predicting Future Behavioral Patterns and Demand: Anticipate future trends and demands to stay ahead of the curve;

• And many more!

Before moving to the Data collection step it's crucial to have a clear question or objective in mind that you seek to address through data analysis and visualisation. 

What are the steps of Data analysis in CRM 

1. Data collection, preparation, configuration

Data collection 

This stage hinges largely on collaboration, whether you're flying solo or working in tandem with analysts and developers. Here are the key questions to consider:

• Which data do you currently have or require for analysis? Think contacts, leads, SDRs, website activity logs, CRM campaign activity logs, payment activity, and purchase items etc.

• Where is this data stored, and how can you access it?

• Whom better approach to find or reach this data? 

If you're going solo and have access to the necessary servers or databases, the next step involves writing queries to extract the desired data. This results in a comprehensive set of tables or lists containing valuable customer data.

Data preparation

Now that you've got your hands on the data, it's time to clean it up and prep it for analysis. 

This entails:

Examples of focus areas at this step: 

• Eliminating duplicates

• Addressing missing values and errors

• Standardising data formats

• Rectifying numerical inconsistencies

• Identifying and addressing significant data outliers etc.

Check out this article for advanced techniques of data cleaning  — 'Top ten ways to clean your data'.

Data configuration

With your cleaned data in hand, the next step is to establish connections between tables and enrich them with additional fields to enhance future analysis. Think of it as adding layers of insight to your data for a deeper understanding.

Adding extra fields with categories will help you to both aggregate and customly filter the data once you create your visualisation or dashboard. 

Examples of additional fields for CRM data could be: 

• A content category (blog article/ webinar/ offline event etc)

• A campaign type (nurture/ churn/ onboarding) 

If you decide to work on data configuration steps inside your CRM system/ CDP and do the whole data analysis there, you will need to create and assign additional ‘values’ by creating new data properties through automation — for example, by workflows in Hubspot or by programs in Marketo. 

2. Analysing and manipulating the data

As I mentioned earlier you should always have a clear question or several hypotheses to be answered by the analysis. 

Depending on your objectives, you can choose a combination of analysis techniques:

• Descriptive or explanatory analysis
  Data explains information from the past (conversion rates/ customer retention);

• Predictive analysis
  Analysis shows insights that are likely to happen. E.g.: Forecast, customer churn, revenue growth;

• Prescriptive analysis (Segmentation and grouping).

As soon as you decide on the analysis techniques and have your first results, you should prioritise the main focus for data visualisation — what to show in the first place, what not to visualise at all. 

In the upcoming article, I'll delve into various techniques of data analysis tailored specifically for CRM. Make sure to subscribe to stay tuned for more insights and updates!

3. Visualise your data

*This step description is inspired by and supported by the information from ‘Data Visualisation for Storytelling’ course on Domestica from Stefanie Posavec.

Now comes the fun part—bringing your data to life through visualisation. Whether it's histograms, scatter plots, or line graphs, the goal is to transform raw data into actionable insights.

Parameters to consider for the ideal visualisation of your CRM data: 

• Category (distributions like histogram or box-plot/ comparisons like bar chart or radar chart)

• Hierarchy (pie chart/ stacked bar chart)

• Relationships (correlations like scatter plot or bubble plot/ connections like network diagram)

• Time (line graph/ stream graph)

• Space (cartogram, heat map)
  
  

Resources like Data Viz Project offer inspiration for crafting visually compelling dashboards.

If you have frustration on how to build and combine your data inside a dashboard, you can try a paper sketching technique to organise your thoughts and streamline the visualisation process.

4. Final Steps and Sharing Insights

Data is organised, paper layout for the dashboard is ready. It’s time to visualise your data!

Before unveiling your masterpiece, it's essential to fine-tune the details:

1. Decide on visual variables: what data properties to display on the dashboard, what not to visualise at all)

2. Build the layout: filters for categorisation, prioritisation of dashboard screens etc.

3. Write a dataviz dictionary: explain the dashboard layout, include metrics description for your close collaborators and stakeholders

And finally share your first version of the visualisation and analysis insights with the team and collect necessary feedback to keep data analysis accuracy and reliability. 

While I've only scratched the surface of data analysis and data visualisation for CRM, stay connected for deeper insights and updates in our forthcoming releases!