Converting a prospect to a customer requires an understanding of what they’re looking for, why they’re looking, how you can provide it to them, and when that transfer of value can happen. Analysing your customers’ behavioural data across channels will help you discover new segments, uncover granular preferences, deliver proactive customer support, and better target campaigns and sales activities.
Understanding behavioural analytics
Behavioural analytics is a concept in business analytics that reveals insights into the behaviour of customers on your website, e-commerce, mobile app, chat, email, connected product/Internet of Things (IoT), and other digital channels. Each time a user interacts with your digital channels, they provide crucial signals about their needs and wants, including readiness to buy—information you can use to inform your customer profiles.
The purpose of behavioural analytics, a form of digital analytics, is to proactively anticipate customers’ needs by understanding where they are in the customer journey, what information or interaction they need next, and what obstacles stand in the way. While there is a variety of data and analytics to achieve this purpose, behavioural data is unique in that it’s concrete, user-generated data that can inform highly accurate predictions of intent. Moreover, by combining cross-channel behavioural analytics with other types of customer data like past transactions and demographics, you gain richer insights that can drive even more personalised experiences.
That’s why behavioural analytics is crucial in growing your business, helping attract new customers—known and unknown—and retain existing customers based on actual interactions and usage.
Who needs behavioural analytics?
The beauty of behavioural analytics is that once your team starts using it to help inform your customer profiles, anyone and everyone within your organisation—at any level—can benefit from its insights. While members across your organisation can use these types of analytics, there are certain roles who best benefit from them:
Marketers can use behavioural analytics to build cohort data that helps them get the most out of campaigns, optimise customer acquisitions, and maximise retention and conversions. When behavioural data is brought together with transactional and demographic data, it can be used to create richer, multidimensional customer profiles. Insights and predictions about your customers can then inform more relevant, personalised engagements.
Behavioural analytics are where marketers and the sales team connect for a successful strategy. A marketing team using behavioural data to drive successful campaigns helps the sales team to prove a real return on investment (ROI) from those campaigns, while at the same time building a bigger, more qualified funnel. For example, following users’ browsing habits and reactions reveals opportunities to upsell and cross-sell products to the customers most likely to be responsive to those offers—resulting in more sales and at a higher volume.
Using signals learned through behavioural analytics, data analysts help decipher the full customer journey, comparing user intent to actuality. The information can also be used to help identify customers at risk of churn versus those more likely to remain loyal customers. Data analysts can conduct user analyses from complex data, transforming the information into actionable insights. Marketers can then use those insights to make data-driven decisions about streamlining workflows so teams remain focused on activities that create maximum value.
Even after predicting what’s needed, sometimes you miss the mark. Users will let you know through online engagement—including social channels, online chat, or email—that they aren’t receptive to your marketing campaigns. Your customer service team is often on the front line of receiving that information. Behavioural analytics can help frontline teams be ready with the right responses, and important information about customer experiences can be easily relayed back to your sales and marketing teams.
Behavioural analytics vs. business analytics
Sometimes confused with business analytics, behavioural analytics is a subset of business analytics. While the concepts may sound similar, there’s a few key differences. Business analytics, a form of business intelligence, is a process using statistical methods and technologies to analyse past data. Behavioural analytics offers a narrower conclusion by combining two types of technologies: user segmentation and behavioural or event tracking.
Segmentation is based on the traits or data used to bucket customers. Though there are several different types of segment categories, behavioural segmentation defines user actions, such as login frequency, time spent, and a general level of engagement.
While business analytics has a broader focus on who, what, where, and when, behavioural analytics presents a more pinpointed prediction of actions. With behavioural analytics, seemingly unrelated data points from the user journey are used to extrapolate and determine errors and predict future trends, hopefully resulting in a completed customer journey.
Types of user data
Behavioural analytics provides user-level behavioural data about user reactions to and interactions with your digital channels. User data from across multiple digital sources and devices, known as cross-channel analytics, is commonly grouped into three categories. Ideally, all types are used to transform your raw data into valuable information:
Registered data. Data stored in your customer relationship marketing (CRM) or marketing tool
Observed data. A synopsis of the user experience, including interactions with different elements of your website or app
Voice of the consumer. How customers feel, and the methods they choose to express that sentiment online, whether reactively or proactively
Five steps for successful user behavioural analytics
Implementing behavioural analytics data into your business processes can be time-consuming. To ensure that you’re receiving the right type of insights, you must focus on achieving success through technical, analytical, and strategic tasks. The following five steps are needed to start a user behavioural analytics project:
Choose your achievement metrics, KPIs, and goals.
Define the most desirable user journey, which should satisfy both the customer and the business.
Decide which signals you need to track based on user flow, highlighting certain events through a tracking plan and revising as necessary.
Bring together your transactional, demographic, and behavioural data so you can understand your customers and your business by building and enriching customer profiles.
Implement a unified behavioural data analytics experience that allows you to rapidly develop, train, and fine-tune machine learning models. Support innovation with custom AI/ML models that give you the flexibility to consistently update your tracking plan as learn over time.