In today's customer-centric world, marketers must get to know their customers as well as possible. The better you understand your client's motivations and behaviors, the more engaging interaction with your brand you can provide.
Collecting and processing behavioral data is key to understanding your customer and creating a personalized experience for them.
What is behavioral data?
Behavioral data describes how customers interact with your website, app, or any other platform that connects your company to a user. This includes page views, newsletter sign-ups, browsing activity, in-app purchases, and other events which depict the customer journey. This kind of data is always connected to one end-user whether they are identified or anonymous and helps to see a clear and comprehensive picture of the customer journey.
Behavioral data gives you a full view of a customer’s journey from their first interaction with your brand to making a purchase.
Behavioral marketing implies using data on customers’ behavior, intentions, preferences, geolocation, and other metrics to target audiences, adjust content for them and create a personalized experience for each consumer.
Behavioral marketing is the main tool that helps online businesses create truly personalized experiences for their customers — create custom feeds based on their browsing history, display ads and special offers based on their purchasing behavior, and adjust the whole business strategy based on deeply detailed data.
Behavioral analytics is a method of tracking, collecting and analyzing users’ actions that help you better understand their expectations from your company. Looking at behavioral data, like pageviews, sign-ups, and drop-offs, marketers get a better view of how people interact with your product, and what influences and triggers users. Having that information is crucial if you want to improve your product and create a better-selling personalized experience for your customers.
Behavioral analytics is event-based. It refers to events like creating an account, purchasing a subscription, browsing a product, or abandoning a cart. These actions are tracked to learn more about the customers and their preferences, find ways to improve a digital platform and add more value to it.
How behavioral data boosts your marketing
Behavioral data allows for creating a better experience for customers, tracking and evaluating the performance of marketing campaigns, and, finally, adjusting the product in accordance with the requests of its target audience.
Here are a couple of examples of how using insights from behavioral analytics can benefit your marketing and product management:
comparing the performance of marketing campaigns and finding marketing tools that bring the most customers;
identifying shared behaviors of the most frequent customers to increase customer lifetime value;
finding the most and least engaging spots in the customer life cycle to maximize retention and reduce churn;
seeing customers’ full journey with full context to be able to analyze weak spots and adjust the product making data-driven decisions.
Besides marketing, behavioral data is used in product management and data analysis. It’s an important part of the modern analytics stack. High-quality data sets can be used for advanced analytics or BI to find behavioral patterns. Aside from that, behavioral data is used in AI and ML to train the algorithms. For instance, with the help of behavioral data-powered AI, you can predict customer value cost and focus on the most value-generating clients.
How behavioral data benefit different teams
Information on consumers’ behaviors benefits marketers, product managers, and, naturally, data analysts. Here are some examples of how different teams can use customer behavioral data to improve their performance.
|Product teams||Marketing teams||Data Analysis teams|
|Build the product funnel||Segment the audience in marketing platform||Build product recommendation systems|
|Identify the weak spots in the sales funnel||Create lookalike audiences to target users with the same characteristics and increase customer acquisition||Predict customer retention or churn rates|
|Improve the customer journey||Create personalised experience on the website, as well as in emails and other platforms you use to connect with customers||Make data-driven decisions|
|Conduct segmented A/B tests|
Sources of behavioral data
Behavioral data comes from your first-party data source — your website or app — and third-party data sources — tools used by product and marketing teams. Here are some common examples of where you can get behavioral data.
difference events such as sign-up, subscribes, purchases, and search queries;
email marketing stats;
Facebook Ads stats;
call center and help desk inquiries.
First-party data always comes in raw format. Data from third-party sources is usually aggregated. Here’s the difference between these two types of data.
Types of behavioral data
Depending on your data-collecting workflow, you can get your behavioral data in one of these two conditions: raw or aggregated.
Raw data is non-processed data. It’s a new way of gathering data that allows tailoring data for each metric and structuring it the way you need. Because it’s not processed by a third-party, raw data from different sources can be unified and combined to be used together. This provides a fuller picture of both the audience as a whole and each individual user.
Aggregated data is the one that’s already been processed by the third party that has collected it. For instance, when you look at stats from Facebook Ads, Mailchimp, or Google Analytics, you look at aggregated data.
Each of the third-party platforms processes data with its own logic and structure. Therefore, aggregated data cannot be compiled with data from other sources. This means that the ways you can use this type of information are very limited.
With aggregated data, it’s very difficult to see a full picture of a user’s journey to your platform.
How does behavioral data look like
There are three main components to data that help you structure it:
Main entity — a subject that caused an event. This usually refers to a user.
Event — the action that the customer completed. For example, subscribed to a newsletter or clicked on a button.
Properties — additional information on the event to understand the context. For example, the location or time of the event.
Thus, each event refers to an end user and describes what kind of action they performed and what characteristics, aka properties, that action had.
Challenges when gathering behavioral data
Behavioral data provides a lot of information on customers. That doesn’t come without a price. Let’s see what is the main challenge when gathering behavioral data.
Systems like Intelligent tracking prevention in Safari and ad blockers recognize cookies requested from these apps as third-party, which means that the cookies can be blocked or manipulated. Moreover, data on some of the users can be completely lost. For instance, the actions of people who use Safari exclusively may not be tracked at all. Incomplete data like this leads to poor decision-making in marketing and product management.
With this tool, you can set up streaming of your behavioral data to any data warehouse and any business tool you need. You build your workflow once — and then collection, processing, and activation of data happen without any effort from your side.
Behavioral data helps marketing and product management teams build their end-to-end customer analytics solutions and, as a result, improve their strategies and create a better-personalized experience for their customers. Collecting behavioral data can be tricky, but with the right tools, you can reach extraordinary results.
Read a case study on how to integrate customer behavioral data with the Facebook Conversion API.