Category Deep Dive: Product Analytics
Growth has ushered in an era where the science of big data is crucial to offer the best customer experience. From awareness to onboarding and re-engagement across web, app, and physical locations, each brand touchpoint attempts to create a unique user experience captured by data and optimized by the interpretation of it.
In the previous two chapters, we highlighted the importance of mobile attribution and marketing automation to optimize the journey through user acquisition, retargeting, and customer engagement. Although both of these technologies provide in-app analytics that facilitate user understanding, they were not designed to optimize the core product experience itself.
It is for this reason that product analytics has become the bedrock of high-performance product, marketing, and growth teams.
Product analytics is the study and understanding of your product and business through the analysis of your users, their journey through your product experience and their success across various outcomes.
Traditional, non-digital businesses often lack the benefit of massive data teams, top talent, cutting-edge machine learning models, and the people, organization, and processes to effectively manage user understanding, experimentation, and growth. That type of exercise can take years even among the most polished business environments. Realizing this, companies have turned to self-serve product analytics platforms like Mixpanel to solve complex digital-first needs, from behavioral analysis to user segmentation, and activation across web and mobile.
The product analytics category didn’t develop in a vacuum. Rather, it evolved as legacy, all-in-one technologies failed to meet the needs of digital-first companies, while at the same time the activation energy to DIY was too much to overcome.
The Power of Self-Serve Product Analytics
Product analytics tools bring marketers, product managers, engineers, and data scientists together under a single platform designed to test, understand, and optimize the product experience across devices.
They provide numerous self-serve use cases, including funnel flow optimization, cohort analysis, user segmentation, and integrations with other stack partners. More advanced product analytics platforms like Mixpanel also provide targeted engagement functionalities that intersect with marketing automation—including push, email, SMS, and in-app messaging.
The core emphasis of modern product analytics tools is self-serve (i.e. no dependency on a business analyst) because, without this, you quickly find yourself wading through BI tooling warefare.
Product analytics facilitates data democratization, enabling cross-functional teams to answer complex behavioral queries without the overhead of a data scientist writing SQL. This allows core business units such as marketing and product to move fast, be flexible, and answer the majority of their data and user behavior questions in minutes, rather than weeks. Likewise, it protects some of your most important and expensive resources – data scientists – from answering basic KPI questions, creating basic dashboards, verifying data, and instead allows them to focus much more on strategic, high value, and extremely complex problems.
Ultimately, product analytics overcomes three crucial hurdles in the growth cycle:
- Understanding user engagement, the customer experience, and the complete lifecycle through your product – from upstream marketing touchpoints (e.g. clicks, attribution) to downstream user data (e.g. events).
- Improving the customer experience through deep KPI analysis and robust digital analytics capabilities.
- Driving positive business outcomes by leveraging this understanding to activate in-platform messaging capabilities or pre-built partner integrations.
With a generalized understanding of product analytics, let’s explore a few core use cases within the broader framework of a marketing tech stack.
Example #1: Understanding Users with Behavioral Analytics
Take a common example of a Fortune 500 retail app, where the client – a user acquisition marketer – is trying to understand the user journey by analyzing conversion rates from paid advertising to install to lower-funnel metrics that drive bottom-line revenue. UA marketers are interested in a few things. First, they want to understand the rate at which users convert to their core metric – for a retail app, this is purchasing products. Second, they want to understand how they got there and what drove their attribution.
Attribution can be broken into objective and subjective measures…
Let’s talk about objective measures first.
The acquisition funnel starts with the user clicking on an ad shown from one of the client’s media sources, such as Facebook, Google, or Apple Search Ads. Through deep linking, the user navigates from the ad to the website, the app if they have it, or the app store to install the app. Once the user installs and opens the app, the attribution provider (e.g. AppsFlyer) attributes that user to the right media source so the client can objectively measure which media sources and creative variants drove the most efficient conversions.
After the installation is where subjective measures of attribution come into play.
Ultimately the marketer will be interested in understanding their return on ad spend (ROAS). This is a function of the user’s experience with both the acquisition source (the ad itself + deep linking UX), as well as the onboarding experience within the app or website. Following that initial onboarding experience, additional factors can either help or hinder the flow that ultimately leads the user to check out and purchase.
While the UA marketer can draw valuable conclusions from attribution metrics, the ability to dig in, analyze that experience, and map the flow of the user across a multitude of events such as login, category selection, add to cart – that is what makes product analytics so essential.
Within Mixpanel, one tool to understand this journey is our Flows product (above). This allows marketers and product managers to map out the most common flows the user might take and their respective conversions – and identify any roadblocks to onboarding, or poor user experiences that prevent the marketer’s hard-earned dollars from contributing to successful conversion outcomes.
While Flows provide a top-line view, the bread and butter of product analytics tools is in segmentation, funnel, and retention analysis and cohort grouping. Each of these standalone features facilitates a deeper understanding of user behavior.
For example, Mixpanel’s Funnels report allows you to see how your users are converting along a defined event pathway. This means you can identify opportunities to increase conversion rates by nailing down when and where users drop off (or fail to convert) in your product cycle or in your marketing campaigns.
Likewise, the Retention analysis product allows teams to see if new users who try your product each day, week or month are coming back after you add, modify or improve features or flows.
Mixpanel’s self-serve dashboards can save teams thousands of hours. A product analytics toolset unlocks the data dabbler in everyone—including C-suite executives
From these analytics capabilities, teams can take action of their own, whether it be reporting this data to their key stakeholders and company decision-makers, deciding which paid media platforms to invest in, or choosing which product flows to modify with help from engineering and product.
Example #2: Optimizing Messages and Flows with Cohort Analysis
For even deeper analysis and improvement, let us take another example, this time of a subscription-based video streaming company that wants to understand how to best serve its users with relevant content.
Example KPI structure for a subscription-based video streaming product
Once the user is acquired and attributed to the right source from an attribution provider, it’s important for the video streaming company to know how much time the user is spending and on what content they are spending more time on.
Frequency and repeat consumption are core metrics for their success.
If the product team is working on a new AI-based content suggestion interface, it’s important for them to analyze how many hours of particular content the user is watching. Thus, they could create cohorts based on the type of content and hours watched.
The product team might create these cohorts and feed the data in a raw data format to their AI and ML models. Or they could export these cohorts back to the marketing team’s attribution tool, like AppsFlyer. The marketing team could also create its own cohorts for comparison, examining retention rates vs. drop-off rates from specific content or within a certain time duration.
The possibilities for activation are limitless. Product and marketing teams can work together to customize onboarding flow experiments by combining attribution data (media source, campaign, ad type, etc.) with product analytics cohorts. In Mixpanel, cohorts can be further activated with our built-in messaging capabilities or our integrations with marketing automation partners. Ultimately, the only bound is the team’s creativity and motivation to experiment and iterate.
Example #3: Predictive Analytics for Data Science
In addition to customizable reporting and segmentation, some product analytics platforms, including Mixpanel, have additional AI applications to unlock the data scientist in marketers and product managers alike.
For example, Mixpanel’s predictive analytics model uses past behaviors to surface which users are likely (or unlikely) to perform an action. This allows you to target potential converters or at-risk users with timely messages and special offers through marketing automation, as well as paid retargeting and incrementality testing via audience integrations with attribution providers such as AppsFlyer.
Left: Mixpanel’s predictive analytics model gives insight into more relevant communications.
Right: Know which user segments are causing metric spikes with automatic segmentation.
These insights can be further amplified by real-time triggers such as automatic segmentation and anomaly detection. Anomaly detection notifies you when important metrics spike or dip unexpectedly for a particular segment, along with the users causing the change.
These automatic insights can be applied to product UX tests, messaging experiments, and even paid media allocation.
In each case, product analytics enables down-the-line contributors to take control of their business outcomes and better serve their customers. This empowers teams to be more efficient, propelling growth at every level of an organization and within every individual contributor.
To learn more about best practices for product analytics, check out Mixpanel’s Guide to Product Metrics.