Every time I use Spotify, I’m reminded that there are multiple product teams working to make my Spotify experience better — making it easier for me to play music and discover new artists, genres, and podcasts.
But people alone cannot provide personalized recommendations to millions of Spotify users, each with a unique set of tastes and preferences. And this is where behavioral data comes into play.
In this guide, I want to give you an overview of how the best companies use behavioral data to improve their products, optimize critical KPIs, and drive growth.
Google Analytics: The OG of behavioral data
Google Analytics (GA) was the first major mainstream behavioral analytics tool. The world of behavioral data has been mostly limited to digital products: web apps, mobile apps, and ecommerce sites, and while those will be the focus of this post, the field is evolving to include IoT and physical devices.
I mentioned Google Analytics because almost everyone who works with data has seen it or used it. GA even has a tab called “Behavior,” where they show you what your users are doing within your website or app — you’re might also be familiar with behavioral metrics like Time on Page and Bounce Rate.
In the last 15 years since GA was released to the public, the way behavioral data is tracked has improved significantly. Now there are dedicated tools out there for any use case: onboarding, retention, stickiness, etc but the principles are still the same — you want to understand your users’ behaviors to design better experiences for them and increase overall revenue or growth.
What can you do with behavioral data?
Let’s jump into practical case studies and examples by looking at 3 of the most popular areas that benefit from accurate behavioral data: user acquisition, activation, and retention.
Behavioral data, when tracked accurately, can help measure the impact of your acquisition campaigns and understand how relevant is the traffic generated from those campaigns. For SaaS companies running online ads, the conversion event for a campaign could be someone creating an account, scheduling a demo, or even downloading an ebook (by providing their name and email address).
Similarly, for an app like Spotify, the conversion event is generally downloading the app and creating an account. However, this doesn’t tell you how these users will behave over the long term. They may sign up but then promptly drop off before onboarding successfully.
You could use behavioral data to focus on bottom-of-the-funnel conversions. What if you could optimize your campaigns for users who are more likely to be retained six months after they sign up? Or optimize for users who complete the onboarding? That’s the magic of behavioral data. You might end up with literal new conversion events inside Facebook Ads or with access to data that can help you refine your paid ad targeting.
After acquiring users, the next step is to focus on activation which essentially takes place once a user has performed the desired actions to derive the core value of your product. However, new users don’t magically become activated — you need to devise your onboarding process in a manner that reduces the time it takes for a new user to get to the activation event.
For a project management app, creating the third task in a list could be considered as the activation event whereas for Spotify, it could be the third song being played.
Behavioral data, when analyzed in a product analytics tool via a funnel report, allows you to visualize how users move through your onboarding process, allowing you to identify points of friction.
The funnel report shows you exactly where users are dropping off in your onboarding and how you could fix it. You can also segment the data using data points like user device, country, marketing campaign source, etc.
Finally, behavioral data can help figure out how to retain your customers, which is not only applicable to SaaS businesses or consumer apps (repeat usage), but also to ecommerce stores (repeat purchases).
Ecommerce companies generally focus on optimizing the first purchase but what about repeat purchases? Would you prefer customers who purchase once or those who become loyal customers?
Retention reports powered by behavioral data allow you to track how often customers come back to use your product or make a purchase from your store.
Retention Analysis shows you how users are being retained over time. As you run different promotions or experiments, you can see how they impact retention over weeks or months.
The tools and technologies you need
To make behavioral analytics work, you need the right set of tools. There are several options for any given category and sorting through them can feel frustrating. Let me help you by showing you the most important categories or questions you should be thinking about.
Category 1: Data Collection and Storage
You want to have a way of easily collecting data from all the relevant data sources. You might have a website, paid marketing channels like Facebook and Google, offline data sources, a CRM, an email marketing tool, and others. I would recommend that you explore the world of CDPs (Customer Data Platform) and ELTs (Extract, Load, Transform).
CDPs are hot right now, and you have players like Segment, mParticle, and MetaRouter. They give you one API to track data from your first-party sources — website, web app, and mobile apps. CDPs also connect to third-party sources, allowing you to pull data from SaaS tools into a data warehouse as well as sync the data back to other SaaS tools.
ELTs, on the other hand, are purpose-built to ingest data from third-party sources as well as from production databases into the data warehouse.
ELTs are fast replacing ETLs (Extract, Transform, Load) that have been around for years. Popular ELT vendors include Stitch, Fivetran, and Matillion, along with open-source alternatives like Airbyte and Meltano.
Category 2: Attribution
You also want a tool that can handle marketing attribution for you. This simply means that you know what campaigns are driving the best customers for your business. While Google Analytics can handle basic attribution, solutions like Wicked Reports and Rockerbox are purpose-built attribution solutions worth exploring.
Category 3: Product Analytics
The bulk of what we are talking about in this guide will be done by a Product Analytics tool like Mixpanel, Amplitude, Heap, Indicative, or Rakam, to name a few popular ones. These tools will make it easy to create funnel reports, cohort analysis tables and dive deeper into your data.
You could also build many of these reports in a Business Intelligence (BI) tool, but Product Analytics tools tend to be easier to use as they are purpose-built to work with behavioral data.
Category 4: Business Intelligence
You will eventually run into queries that are too complex for any single tool. Perhaps you want to see the entire customer journey, which requires data from multiple locations. The easiest way to do that is to store everything in a data warehouse and then visualize it using a BI tool like Looker, Mode, SiSence, Tableau, or Domo, to name a few popular ones. Also worth mentioning are open-source alternatives like Metabase, SuperSet, and Redash.
Category 5: Qualitative Analysis
You also want to have a way of capturing qualitative data that comes from surveys, heatmaps, and session recordings. Tools like Hotjar, FullStory, Mouseflow, and CrazyEgg are all popular options to collect qualitative data.
The key to making data actionable: clean and accurate data
Regardless of what technology you choose, you need clean and accurate data to make sense of it and act upon it. Lack of trust in data is one of the most pervasive issues that I see companies struggle with and to avoid this problem, you need to start with the right foundation — a tracking plan.
A tracking plan is merely a document that outlines what data you want to track. You’re interested in 3 things: events, event properties, and user properties.
- Events are actions such as signing up or making a purchase.
- Event properties provide more information about the event, such as the product that was a purchase or the user’s email address.
- User properties specify things about the user: their name, email address, or country.
You’ll find countless tracking plan templates out there but instead of sifting through them, you can get started right away using the template from Data-led Academy. And you now also have tools like Avo and Iteratively that enable you to create smart, collaborative tracking plans, make this process easier to manage over time.
Lastly, if you’re using a data warehouse, check out dbt that makes it easy to transform data into the correct format before it’s analyzed.
Dealing with privacy and sensitive data
We can’t finish this guide without briefly touching upon privacy. There is a growing trend towards building more privacy around how companies manage data and every company should be working on designing privacy-friendly strategies. The rules and regulations are rapidly changing, but here are 3 ideas that will be relevant for years to come.
1. Understand precisely what you’re tracking and from where. You do this by maintaining a process document outlining what data is being ingested by your company and by whom. Having this documentation makes it easier than tracking down risky data such as personally identifiable information or PII.
2. Make it easy to delete customer data across your entire company. You do this by having central storage of data or linking up with all your relevant systems.
3. Tell users what you’re collecting, how you will use it, and how they can opt-out. Design clear privacy policies that anyone could understand and get consent from users.
It’s easier than ever to build products that people love and behavioral data makes it easier to learn and iterate rapidly. Knowing exactly what data you want to track makes it easier to figure out the tools and technologies you need.
Finally, keeping the privacy element of data top of mind will enable you to adhere to the growing number of privacy laws around the world and more importantly, gain the trust and respect of your customers.