Welcome to the world of behavioural analytics, where data meets human behaviour…

As businesses continue to collect more and more customer data, behavioural analytics has emerged as a powerful tool to help them understand customer behaviour and preferences. But with great power comes great responsibility, and there are various considerations when using behavioural analytics.

This post is aimed at businesses and individuals interested in using behavioural analytics to improve customer experience, increase revenue, and understand stakeholder sentiment. In this post, we’ll introduce the topic, explore the legal, technical, and commercial perspectives of behavioural analytics and provide practical steps to get you started.

What is behavioural analytics?

Behavioural analytics is a method of analysing the actions and behaviour of individuals or groups, typically for commercial or social purposes.

It involves collecting and analysing data about how people interact with a product, service, or system to understand their behaviour and preferences. You can use this data to gain insights into customer behaviour, identify trends and patterns, and make better business decisions.

Various industries use behavioural analytics, including e-commerce, finance, healthcare, and more. And it often relies on data from multiple sources, including user interactions with digital devices, social media, and other online-and-offline touchpoints.

Typical technologies

You can use different technologies for behavioural analytics. And your decision to do so depends on your organisation’s specific needs and goals.

Some standard technologies used for behavioural analytics include:

  1. Web and mobile analytics tools: These tools collect data on user interactions with websites and mobile apps, such as clicks, page views, and session length. Famous examples include Google Analytics, Adobe Analytics, and Mixpanel.
  2. Customer Relationship Management (CRM) tools: CRM tools collect and store data on customer interactions with a business, such as sales, support inquiries, and feedback. Examples include Salesforce, Hubspot, and Zoho.
  3. Social media monitoring tools: These tools collect data on social media activity, such as likes, shares, and comments. Widespread instances include Hootsuite, Sprout Social, and Brandwatch.
  4. Machine learning and AI tools: These tools are used to process and analyse large volumes of data generated by behavioural analytics, identify patterns and trends, and make predictions about future behaviour. Examples include TensorFlow, Scikit-learn, and Keras.
  5. Heat mapping and session replay tools: These tools visualise user behaviour on a website or app by tracking mouse movements, clicks, and other interactions. Instances include Hotjar and Crazy Egg.
  6. A/B testing tools: These tools allow businesses to test different variations of a website or app to see which design or content is most effective at achieving specific goals. Examples include Optimizely and VWO.
  7. Voice of Customer (VoC) tools: VoC tools allow businesses to collect and analyse customer feedback through surveys, feedback forms, and other channels. Instances include Qualtrics, SurveyMonkey, and Medallia.

Legal perspective

Compliance with privacy regulations

First and foremost, businesses must ensure that they are collecting data in compliance with privacy regulations.

Data protection laws such as the EU’s GDPR, ZAR’s POPIA, US’s CCPA set strict rules for collecting and processing personal data. By implication, you must be transparent about what data you collect, why, and how you’ll use it. You must also give customers the option to opt out of data collection if they so choose.

Avoiding discrimination in data use

Additionally, you must ensure the data you rely on doesn’t discriminate against certain groups of people.

For example, suppose you use behavioural analytics to make decisions about creditworthiness. In that event, you can’t use data that unfairly discriminates against people based on race, gender, or other protected characteristics.

Technical perspective

Accurate and reliable data collection

The data you collect must be accurate and reliable.

Meaning: you must use high-quality data sources and avoid incomplete or biased data. There are various statistical methods to ensure data accuracy and reliability.

Choosing the right tools for the job

Use the right tools to analyse the data.

Many different tools are available for behavioural analytics, from simple dashboards to complex machine learning algorithms. Choosing the right tool for the job would be best based on your organisation’s specific needs and the complexity of the data you are working with.

Protecting data from unauthorised access or use

Protect the data you collect from unauthorised access or use.

This means using encryption and other security measures to protect data from hackers or malicious actors.

Commercial perspective

Improving customer experience

Behavioural analytics can be a powerful tool for businesses to improve customer experience and increase revenue.

By analysing customer behaviour, you can gain insights into what products and services customers are interested in, their pain points, and how they interact with different communication channels.

Practical steps for getting started with behavioural analytics

Getting started with behavioural analytics can seem daunting, but there are several practical steps you can take to get started:

  1. Define your goals: The first step in using behavioural analytics is to define your goals. What do you want to achieve with the data you collect? Are you looking to improve customer satisfaction, increase sales, or reduce churn? Defining your goals will help you choose the right metrics to track and the right tools to use.
  2. Identify your data sources: Next, you’ll need to identify the data sources for your behavioural analytics. This could include web and mobile analytics, CRM, and social media monitoring tools. Ensure you have the necessary permissions and access to the data you need.
  3. Choose your tools: Once you know what data you need and where to get it, you can choose the right tools for the job. Consider factors such as ease of use, cost, and integration with other tools you may already be using.
  4. Set up your tracking and measurement: With your tools in place, you’ll need to set up tracking and measurement to collect the necessary data. This may involve adding tracking codes to your website or mobile app, configuring your CRM tools to capture specific data points, or setting up social media monitoring tools.
  5. Analyse your data: Once you’ve collected the data, you’ll need to analyse it to gain insights into customer behaviour. This may involve using machine learning or AI algorithms to identify patterns and trends or simply reviewing reports generated by your analytics tools.
  6. Act on your insights: The final step is to use the insights gained from your behavioural analytics to make informed business decisions. This could include optimising your website or app design, launching targeted marketing campaigns, or improving customer support processes.

Actions you can take next

  • Prepare your team to use behavioural analytics lawfully by asking us to train them.
  • Manage your customer relationships effectively by asking us to draft your customer agreements.
  • Ensure your privacy and data protection policies and procedures deal with behavioural analytics by asking us to review them.
  • Ensure you comply with applicable laws by asking us to review your behavioural analytics uses.
  • Comply with data protection law by joining our data protection programme.