Unlocking the Mystery: A Deep Dive into Root Cause Analysis

Root Cause Analysis (RCA) stands as a crucial methodology in the world of product and analytics. It's the time when you get into the boots of Sherlock Holmes, diving deep into the layers of data to uncover the reasons behind a problem.

Example Question:

A popular e-commerce platform has experienced a sudden drop in conversion rates during the past month. The conversion rate has decreased by 15% compared to the previous month.

Initial Queries:

Before diving headfirst into RCA, it's essential to ask the correct questions. Here are some initial queries that can help you to set the stage for RCA:

1.     Product and Metric Definition: 

• What exactly is the product, and what metric are we calculating? Understanding the product's nature and the metric in question is crucial for starting the analysis.

The product is an e-commerce platform, and the metric being analysed is the conversion rate. After testing it was found that it was specifically observed for mobile users. The metric, conversion rate measures the percentage of users who purchase after visiting the platform.

2.      Trend Analysis: 

• Was there stability or growth in the metric until a certain point, followed by a decline?

Examining trends helps pinpoint when the problem emerged and its potential causes.

Until a month ago, the conversion rate for mobile users was stable or experiencing growth. However, in the past month, there has been a decline of 15% compared to the previous month.

3.     Seasonality: 

• Is the dip in metrics influenced by seasonal factors?

Recognising seasonal patterns aids in distinguishing between usual and abnormal behaviour.

The team investigated whether the observed dip in conversion rates is influenced by seasonal factors, such as holidays. However, initial analysis suggests that there are no significant seasonal patterns associated with the decline.

4.     Platform Specificity: 

• Is the issue specific to a particular platform, such as mobile or desktop?

• If so, which operating systems or browsers are affected?

Understanding platform-specific nuances guides targeted investigations.

The issue appears to be specific to mobile users. Further investigation reveals that the problem is not limited to a particular operating system (iOS/Android) but affects both platforms. 
Additionally, explored whether the decline is associated with specific mobile browsers like Chrome, Safari, or Edge, but no conclusive evidence is found to attribute the issue to a particular browser.

Initial Framework:

Post these initial queries, we can structure our RCA process into four main components:

1)    Internal Analysis: 

This involves questioning internal factors, such as 

o   Metric/Tool Functionality: 

The team examined whether there have been any updates or changes to the platform's mobile app or website that could have affected the calculation of the conversion rate. They verified the integrity of the data being collected to ensure accuracy.

o   Data Input: 

The team reviewed the data input processes to confirm if the user interactions and transactions were being recorded correctly.

o   Team Changes: 

Any recent changes made by other teams, such as marketing campaigns or product updates, were evaluated to see if they correlated with the decline in conversions.

o   Design Changes: 

The team assessed whether recent changes in the platform's design or user interface could have impacted user experience and conversion rates.

2)    Funnel Approach: 

Adopting a funnel perspective, we trace the customer journey from acquisition to conversion. Identifying drop-off points within the funnel highlights areas of friction, whether it's marketing channels, user onboarding, or payment processes.

The team maps out the customer journey from landing on the platform to completing a purchase. They identify potential drop-off points at each stage of the funnel.

Top of Funnel: Investigate if there are issues with user acquisition, such as changes in marketing channels or issues in app accessibility.

Middle of Funnel: Examine if users encounter obstacles in navigating the platform or understanding product offerings.

Bottom of Funnel: Specific issues like payment failures or coupon redemption problems are checked to pinpoint potential barriers to conversion.

3)    Demographic Insights: 

Analyzing demographic data offers insights into whether specific segments, such as age groups or geographic regions, are disproportionately affected.  Understanding the preferences and behaviors of different demographics aids in tailoring solutions.

The team analyzed the demographic data to determine if the drop in conversions is specific to certain user segments, such as age groups or geographic regions.

Also, consider other factors like price sensitivity or competitor actions that may influence user behaviour and purchasing decisions.

4)    External Factors: 

o   Macroeconomic changes

The team explored whether external factors such as changes in economic conditions or consumer spending habits could have impacted purchasing behaviour

o   Competitor actions

Any recent moves by competitors, such as pricing adjustments or promotional campaigns, were examined for potential effects on the platform's performance.

o   Regulatory shifts

Societal events like the COVID-19 pandemic or any new government guidelines can significantly impact metrics.

o   App Install/Uninstall Trends 

Trends in app install rates, uninstall rates, and user reviews were monitored for insights into user sentiment and satisfaction

Implementation Strategies:

• Once potential root causes have been identified, it's essential to prioritize them based on their value versus the effort required.

• Implementing solutions through A/B testing allows for experimentation to validate hypotheses.

After conducting a comprehensive RCA, the team identified several potential factors contributing to the decline in conversion rates for mobile users. They discovered that a recent update to the mobile app's checkout process introduced a bug, causing payment failures for a subset of users. Additionally, changes in marketing strategies resulted in a decrease in new user acquisitions from certain channels.

Armed with these insights, the team formulated targeted solutions, including bug fixes for the payment process and adjustments to the marketing approach to diversify user acquisition channels. By addressing these root causes, they restored the platform's conversion rates and enhanced the overall mobile user experience.

Conclusion:

RCA acts as a guiding star in the world of data and analytics. It shows the way to effective problem-solving by asking the right questions, structuring an analysis framework, and implementing targeted solutions. Companies can confidently tackle challenges by doing this.

Every number has a tale to tell – and with RCA we have got the key that opens its closet.