‘FOCUS’ed SaaS Product Analytics
SaaS (Software-as-a-Service) products revolutionized the way digital products are built and consumed by businesses and consumers. The SaaS industry, where domain experts build cloud-hosted solutions for user needs and problems, transitioned digital product building to an end user-centric approach. With this approach, it is pertinent for product managers to be equipped with a complete understanding of the end user psyche and how the product is used.
The key ingredient for a Successful SaaS product is to keep end user value at the heart of the product strategy and build a robust continuous discovery model for the product. One critical initiative to ensure this is through data-driven product strategy decision making where the data insights are leveraged from prudent product analytics. When it comes to product analytics, “knowing what to track” is more important than the “actual tracking.” Although it is great to track all user-related data, at times reckless tracking can fail to deliver the insights needed. It is pertinent to tie tracked product analytics metrics to the product goals and objectives.
Product Analytics Frameworks
SaaS product analytics need a streamlined approach and can be achieved through a dedicated framework. The “tried and tested” industry product analytics frameworks like AARRR, HEART and North Star help in building a structured way to track user-related product usage metrics for web-based products. AARRR, also known as Pirate Metrics, with a focus on aspects like acquisition, retention, and referral, caters best to B2C digital products. Similarly, Google’s HEART framework aligns itself with Google Analytics capabilities and caters best to B2C web-based digital products. The North Star Framework, on the other hand, relies on identifying a single crucial metric and a few of its contributory metrics. North Star works well along with the other product analytics frameworks. With B2B SaaS products having certain nuances different from B2C SaaS products, one needs to look beyond B2C elements like revenue and referral.
FOCUS Framework
B2B SaaS products require a new framework that keeps the focus narrowed specifically to B2B and ideally helps feed into the product discovery framework. We at Concentrix have ideated the FOCUS framework which is focused on specifically addressing the needs of B2B SaaS products. FOCUS is based upon five aspects of product analytics: Feedback and Analysis, Onboarding and adoption, Customer engagement, Usage and retention and Success of Workflows. Let us broadly discuss each of the above aspects in detail.
Feedback and Analysis:
When it comes to Feedback and Analysis, there are two aspects of data capture and analysis that can add value to the product discovery: in-product surveying to understand end-user feedback, and other sources of feedback capture on how enterprise B2B customers perceive the product. Both of the above aspects are crucial inputs to product discovery and refinement in different ways — end user feedback is predominantly targeted to functional aspects and enterprise B2B customer feedback is targeted at both functional and non-functional aspects in equal stead.
While in-product surveys capture user feedback in the moment of product usage, other sources of feedback such as community pages, product reviews, and support mail capture the feedback post product usage. The feedback value map illustrated in Figure 2 maps all possible avenues of feedback capture and the value generated — through direct quantifiable metrics or derived insights.
Onboarding and Adoption
This component of the framework focuses on understanding the behavior of new users to the product. When new users are onboarded to a B2B SaaS product, there are three key elements of interest for a product manager:
- Registration: When the new user is provisioned access to the product
- First Use: The login experience and exploration of the product by the new user
- Acclimation: The new user getting adapted to the features of the product
For each of the above elements, the corresponding data points can be tracked, and the insights can be fed back into the product refinement of the on-boarding and new user experience. For instance, time to first action is a data point that can be an indicator of how intuitive the product is for the new users to get themselves acclimated to it. There are many such data points of interest in on-boarding and adoption of the end user to the product such as user acquisition rate, time taken for the registration workflow, and first touch feature that can help gain insights into the usage of the SaaS product.
Customer Engagement
In customer engagement, the focus is on understanding how the customer (specifically, the end user) engages with the SaaS product. The key element here is to track the feature-wise usage patterns of the user and the level of usage. This will aid in deducing the value delivered by the feature from the prism of our product feature lifecycle as illustrated in Figure 3.
To build lean products, product managers and business sponsors would rely on the tracking of feature-wise usage trends and time spent on features as a direct metric. Indirect metrics such as feedback as mentioned earlier can also be an input to product feature decisions. As depicted in the product feature lifecycle, at each stage there is a product analytics element that can be leveraged to drive product decisions such as follows:
· Feature Validation: A/B testing, feature flags, beta testing
· Feature Introduction: Time to first touch, time spent on the new feature, frequency of use
· Active Usage: Daily/weekly/monthly hits of the feature, preceding/succeeding user action
With the aid of these product analytics elements, data-driven decisions of the product feature lifecycle — such as feature enhancement, discard feature (post validation), or sunset feature — can be made with higher confidence.
Usage and Retention
While product features deliver specific targeted value to the user, it is also critical to abstract the user perception at the product level and track the user retention/churn elements. Customer engagement focuses on usage from the feature perspective, whereas retention focuses on usage from the end user perspective. To simplify, average time per feature (a metric used in customer
engagement) estimates the usage for a specific feature by users at large and the user retention/churn rate estimates the level of usage and adaptability aspects of the user with respect to product and feature. The principal outcome of understanding usage and retention is to classify the users into specific segments. At Concentrix, we classify the users of our SaaS products broadly into four types.
The four types are newbie, pragmatist, champion and deserter. The criteria for classification varies from product to product, adhering to the traits described in Table 1. The usage data points help to segment users into the buckets and dedicate targeted strategies to achieve the PM goal for the respective segment.
Success of Workflows
Every digital product has built-in workflows targeted to delivering some value to the end user. A workflow is nothing but a functional flow containing several events or user actions along the way to help the end user achieve an objective. Although events or user actions give an insight into the usage, analyzing a workflow helps gain insights into the actual value delivery.
Let us consider a web mail product as an example. One important workflow is for the user to send a new email successfully. There are several events that could be a part of this workflow (sending an email): logging into the app, clicking on the create email option, entering recipient information, entering the email subject, entering the body of the email, adding attachments (if necessary), and clicking on the send button. Tracking each of the events mentioned above as part of the workflow can help to understand the usage of the entire flow and provide insights into enhancements. This way, we can view the workflow as a value delivery mechanism in whole and identify areas where value can be tapped or amplified for the end user.
The two broad analysis aspects we resort to are funnel analysis (primary) and attribution analysis (secondary). Funnel analysis gives a direct metric on the number of hits at every stage (user action) of the workflow defined. Attribution analysis is an advanced analysis that tries to find correlation insights for the success or failure of workflows leveraging other aspects of data such as user cohorts. Attribution helps in identifying root causes for success or failure and serves as an input in product discovery.
With a dedicated SaaS product analytics framework like FOCUS, we can ensure that we are able to identify, track, and drive relevant data points to build lean and high value products.
