Cohort Analysis for Startup Growth - Data-Driven Strategies

Understanding Customer Behavior with Cohort Analysis

Cohort analysis represents a valuable method for business evaluation. It involves segmenting customers into distinct groups – known as cohorts – and then tracking their behaviors over a defined period. A frequently employed technique is monthly cohort analysis.

This approach groups customers based on their signup month, enabling a comparison of users who joined in, for example, November versus those who registered the preceding month.

A Forward-Looking Business Perspective

Cohort analysis provides a dynamic and predictive view of your business performance. This contrasts with simpler, static metrics like averages or total numbers.

Illustrative Example: Bluetooth Coffee Company

Consider the scenario of being the Chief Marketing Officer (CMO) for “Bluetooth Coffee Company.” This company markets a technologically advanced “coffee composer” that brews coffee, monitors usage, and automatically reorders supplies when needed.

Customer retention is crucial, as longer subscriptions translate to increased revenue. Recently, a Black Friday promotion was launched on a popular deals platform, and the company is now assessing whether to repeat this campaign.

Initial Marketing Performance Assessment

The following chart illustrates a basic analysis to evaluate marketing effectiveness. It displays the total number of customers acquired each month, revealing a noticeable surge in November following the Black Friday promotion.

Initially, the results appear positive – November saw more than double the customer acquisition compared to October.

Digging Deeper: Customer Value Evaluation

However, before reinvesting in the promotion, it’s essential to determine if these new Black Friday customers possess the same value as other acquisitions. Comparing monthly customer percentages offers a means to assess this.

Monthly Cohort Analysis: September 2020 – February 2021

The chart below presents a monthly cohort analysis of new customers from September 2020 to February 2021. Similar to the previous chart, it lists the monthly cohort size. Additionally, it includes the customer engagement rate.

This rate is calculated by dividing daily active users (DAU) by monthly active users (MAU) for each month (M1 represents month 1, M2 represents month 2, and so on).

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Analyzing Cohort Engagement

This analysis allows for a direct comparison of customer engagement across different monthly cohorts.

The data indicates that most cohorts exhibit a customer engagement rate of 42%-46% in their first month (M1), signifying that this percentage of new customers use the coffee composer daily.

However, the November cohort demonstrates significantly lower engagement (M1, 30%) and continues to underperform in subsequent months (M2, 26%) and (M3, 27%). Notably, the customer engagement rate declines solely within the November cohort, returning to typical levels with the December cohort (M1, 45%).

Key Takeaway: Engagement and Long-Term Value

This engagement analysis reveals that, despite attracting a larger customer base, the users acquired through the Black Friday promotion are consuming less coffee and may ultimately prove less valuable over the long term.

Customer Retention and Valuable Cohort Analysis

Cohort analysis offers adaptability, enabling the examination of diverse performance indicators such as revenue streams, acquisition expenses, and customer churn rates. We’ve already established that the November 2020 Black Friday cohort exhibits diminished engagement. Let's investigate how this impacts both customer retention and churn.

The data reveals that the November cohort demonstrates comparable customer retention during the initial month (M1, 94%) when contrasted with October (M1, 98%). However, retention declines significantly to just 19% by M8, while customers acquired in October maintain a 48% retention rate. The reduced engagement observed within the Black Friday cohort appears to have directly contributed to decreased customer retention and increased churn.

A frequently employed cohort analysis focuses on average revenue per user (ARPU). This allows businesses to gain a clearer understanding of customer lifetime value and whether revenue generation increases or decreases over time. Let’s examine the effect on ARPU for Bluetooth Coffee’s Black Friday cohort.

As illustrated, the November cohort initially shows a substantial ARPU increase linked to the Black Friday promotion (M1, $5.5) compared to regular October customers (M1, $3.1). However, the projected lifetime value (LTV) of the November cohort diminishes considerably with each subsequent month’s ARPU.

Indeed, the LTV of the Black Friday cohort falls below that of regular cohorts as early as M3, totaling $8.2 ($5.5 + $1.5 + $1.2) versus $9.6 ($3.1 + $3.2 + $3.3) for October customers. By M8, the difference is even more pronounced. It is evident that, beyond lower engagement and retention, the Black Friday cohort yielded customers who ultimately generate significantly less revenue.

Evaluating Customer Acquisition: Paid vs. Organic Strategies

Experienced marketers can refine their strategies by utilizing cohort analysis to identify and eliminate potential biases present in average metrics. A valuable application of this technique involves segmenting Average Revenue Per User (ARPU) by both paid and organic acquisition channels. This segmentation provides insight into the long-term viability of customer growth.

Consider the ARPU example from Bluebooth. By dividing the data based on how customers were acquired – through paid marketing efforts like Facebook or Groupon, versus organic methods such as word-of-mouth – a clearer picture emerges.

Remembering that the February 2021 cohort consisted of 300 customers with an ARPU of (M1, $3.6), it’s important to recognize this figure represents a blend of both acquisition types. Let's now dissect this cohort further.

As illustrated, the February cohort comprised 255 customers obtained through paid channels, generating an ARPU of (M1, $3.0).

Furthermore, this same cohort included 45 customers acquired organically, demonstrating a considerably higher ARPU of (M1, $7.3).

This detailed channel-based cohort analysis reveals that the majority of Bluebooth’s customer base originates from paid channels (255 out of 300 total). However, these customers contribute significantly less revenue (M1, $3.0) when compared to those acquired organically (M1, $7.3).

Consequently, a strategic shift towards increasing the proportion of organically acquired customers would substantially enhance Bluebooth’s overall unit economics.

Concluding Remarks

Cohort analysis proves to be a valuable method for tracking the evolution of customer actions throughout their lifecycle. Our investigation revealed that Bluetooth Coffee’s Black Friday campaign resulted in an initial surge of new users, however, these customers demonstrated a greater propensity to discontinue service and contributed less revenue in the long run.

Furthermore, a significant portion of Bluetooth Coffee’s customer base originates from paid advertising, which typically delivers a lower ARPU (Average Revenue Per User) and LTV (Lifetime Value) when contrasted with organically acquired customers.

To optimize business expansion, the Chief Marketing Officer of Bluetooth Coffee – mirroring the strategies of all growth-focused marketers – should prioritize marketing initiatives that attract customers with higher LTV, rather than those solely focused on short-term acquisition with lower revenue potential.

Each organization possesses unique characteristics and necessitates tailored analysis to accurately assess its performance. It is crucial for marketing teams to collaborate proactively with their finance departments to utilize cohort analysis effectively, leading to more informed marketing investments and sustainable growth.

Key Takeaways

• Black Friday promotions, while boosting initial customer numbers, can attract less valuable customers.
• Paid marketing channels often yield customers with lower long-term value.
• Focusing on maximizing LTV is essential for sustainable business growth.
• Collaboration between marketing and finance is vital for effective analysis.