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Behavioral Data

Behavioral data is data generated by, or in response to, a customer’s engagement with a business. This can include things like page views, email sign-ups, or other important user actions. Common sources of behavioral data include websites, mobile apps, CRM systems, marketing automation systems, call centers, help desks, and billing systems. Customers can either be consumers, businesses, or individuals within a business, but behavioral data can always be tied back to a single end-user. It’s important to note that this user can be a known individual (logged-in) or anonymous (not logged in). This type of data is typically created and stored in the form of an “event,” meaning an action that was taken, with “properties,” meaning metadata used to describe the event. For example, an event could be “site visit” and a property for that event could be “device type.” It may help to think of events as the “what” and the properties as the “who, when, and where.”[1]


Behavioral Data Types[2]

Behavioral data, data that describes the observed actions of users or customers, gives you real insights into how people are or will potentially use your product. It is one thing to hear what people say they want, but to see how they actually behave is even better. Below is a list of all the types of behavioral data that are available to product teams. The list is roughly ordered from most common to least common.

  1. Website Analytics Data: You are probably most familiar with this behavioral data source: page views, clicks, browser choices, device choice, etc. Website analytics are a form of behavioral data, showing how users interacted (views/clicks) with your website, mobile app or web app and the choices they made related to web browsing (device/browser/resolution). Common tools for this type of data are Google Analytics and the more enterprisey Adobe Experience Cloud.
  2. App Analytics Data: If you are building a product you will be familiar with this behavioral data as well: button clicks, active usage and other user events from your applications. You can even include application and error logs here. Common tools for this type of data are Mixpanel, Amplitude and KISSmetrics. They help you define custom events to track what your users perform. What better way to understand behavior than to see what your users actually interact with and how they interact with it (or don’t)? The journeys a user might take and when they stop using are also useful data points to look at.
  3. Search Data: Use search volume data from Google, Microsoft and others to understand behavior by seeing what they are searching for. The act of searching is a behavioral data point itself and it can also provide insight into another behavior the user or customer is undertaking. You can get search data from Google’s Keyword Planner.
  4. Ad Clicks, Competition and Impressions Data: Running ads on LinkedIn, Google, Facebook or anywhere else online provides behavioral data. You can see what messages people actually respond to versus what they say might appeal to them. You can also test out which segments will respond best to what. You can do all this almost instantly. For example, to understand how best to sell a product to a bank one could target Facebook and LinkedIn ads at bank managers over 24–48 hours and observe which phrases and variations of the ad/pitch work best. At the very least, there will be some initial, data-backed insights based on observed actual behavior versus stated behavior. This is pretty revolutionary. No more guessing about which words to use in your marketing.
  5. Product Reviews/Feedback: Seeing how people actually experienced a product, the challenges they had or the things they enjoyed most is a useful insight. You don’t need to limit this to your product; you can see competitors, complements and comparables by checking out websites. Amazon provides a great trove of information for different physical goods. When analyzing product reviews, keep in mind that not all product reviews are real.
  6. Customer Support Queries: The feedback from your users and customers — requests, bugs, problems — is another type of behavioral data. The fact that someone has gone out of their way to interact with you, especially in the world of digital and software-as-a-service, must carry some weight. If you have enough volume there is also analysis you can do to understand this. Keep in mind the bias or context that the customer is coming from. That is, the way you market your product, how you describe a feature, the journey someone went through will all impact the type of feedback you receive.
  7. Social Media: The likes, comments, hearts and shares of social media is another behavioral data source. You can even take the analysis further and analyze the content/text of the comments themselves or the content of images. Compiling this information lets you see what people are actually saying, sharing or liking versus what they say they might actually interact with. This can give you insight into past or future trends. When analyzing social media in order to understand behavior, consider the ease with which a user can like/share and think about the motivations behind what someone might like or share. In many instances, many people haven’t actually read the content but saw a buzzword or title that sounded agreeable so they shared it.
  8. Cursor Tracking: Monitoring where someone’s cursor moves over your site or app is another type of behavioral data. You can gain an understanding of what they are focusing on. Hotjar is an example of a tool to track cursors. Keep in mind that where the cursor is, is not an exact representation of what they are focusing on. It could very well be that while you are reading this point of the article, your cursor is at the top of the screen while you are taking notes elsewhere.
  9. Eye tracking: Having access to right facilities and technology, behavioral data can be taken to the next level and track where people are focusing by tracking what they are looking at. This is helpful with digital products — web apps, mobile apps, digital material — ads, web sites, other content — as well as non-digital products like supermarket shelves and billboards.
  10. Physical Interactions: Computer vision has improved to allow tracking of what people touched on a supermarket shelf. This can be extended to what they touch in any environment like what playground equipment, condiments or seating they used at a McDonalds.
  11. Facial Expression Analysis: Computer vision can also help understand behavior by reading someone’s expression while they interact with your product or your product’s marketing. As far as behavioral data goes, you would want to carefully consider how much weight you place on aggregate analysis of facial expression recognition. For example, if we had believed what Amazon’s AI suite told us about one of our team members felt about something then we would have thought it always invoked a negative, unhappy response. However, Amazon just could not interpret his moustache and beard properly, especially when he was smiling. You would also need to think about whether the expression they show (the emotion it infers) matters — just because someone is stern looking when they sail on a boat, doesn’t mean that they are not enjoying it.
  12. Purchase History and Transaction Data: Seeing what someone bought is another form of behavioral data. A purchase is generally a strong indication of need or want. Some organizations have this on hand in limited forms — purchases made of their products or services. Other organizations have access to all or many of the purchases made by individuals. You can also get access to anonymized purchase histories. These anonymized sources, though, are a bit too general to be applicable to a specific product. And sometimes the data set is not sufficient to make it easy to analyze in any meaningful time frame. For example, transaction data might show $124 spent at a supermarket but not what was bought - $124 of gourmet cheese is quite different to $124 of baby food.


Behavioral Data Use Cases[3]

Behavioral data powers an enormous number of use cases. A single high-quality data asset continuously delivers values like:

  • Attribution: Assign credit to each marketing touchpoint that influences high value user behavior; bespoke to your product and user journeys.
  • Personalization: Understand what drive user engagement and personalize the experience in real time to drive acquisition and retention.
  • Product Analytics: Develop a strong understanding of user behavior to inform product strategy and optimize the product experience.
  • Churn Reductions: Identify trends in user interaction to isolate behaviors predictive of retention and churn for better forecasting and interventions.
  • Data products: Put great behavioral data at the heart of your products to deliver compelling and unique value propositions to your customers.


References

  1. Definition - What Does Behavioral Data Mean? Indicative
  2. 12 Behavioural Data Types for Product Management Product Coalition
  3. Uses Cases powered by Behavioral Data SnowPlow Analytics