🛍️Product: Shopify

Elevator Pitch: Shopify helps businesses to build, customize and scale their online stores seamlessly.

👨🏻‍💼👩🏻‍💻Ideal Customer Profiles:

🚀Basic Customer Journey Map:

Now that we understand who the users are and what they are trying to do, we move on to talking to users and understanding what is important to them. Here is how I did the User calls to identify JTBD.

User calls: I was not able to get anyone from a "Established Enterprise with Diverse Sales Channels", I moved forward with ICP 1 and ICP 2, here is the documentation of the User calls: Link

🙌💼🎯JTBD Analysis:

Using the ICP data, I identified the core Jobs To Be Done (JTBD) for each profile. In this section, I have conducted an in-depth analysis of ICP 1 and ICP 2, outlining the specific jobs these customer profiles are 'hiring' Shopify to perform. Here’s what I discovered:

Based on the above table, we understand what ICP1 and ICP 2 are hiring our product to do, here are a few conclusions we can draw from the above table and go deep into understanding their goals

Conclusions:

• ICP 1: Small and Growing E-commerce Brand
• • Primary goal is affordability and ease of use, as they focus on getting their store up and running with minimal investment.
  • Customization and basic analytics are essential for growth, and they highly value integrations that don’t require technical skills.
  • Budget constraints limit their spending, so they prioritize cost-effective, high-ROI features.
• ICP 2: Mid-Sized Business Scaling Operations
• • Prioritizes scalability, data insights, and multi-channel integration to support growth.
  • Willing to invest in Shopify Plus for premium features and advanced customization, aiming to streamline processes and manage larger sales volumes.
  • Values in-depth analytics and CRM/ERP integrations, indicating a focus on data-driven decisions for optimized operations.

Core JTBD:

I did a teardown of Shopify's user onboarding experience to understand how effectively the platform guides new users from initial engagement to activation. The goal was to assess each step in Shopify’s onboarding journey, identifying key points that encourage users to move forward and areas that might introduce friction or lead to drop-off

📝Approach: I approached this analysis with a user-centric perspective, examining each screen and interaction to evaluate how well Shopify addresses its users' Jobs to Be Done (JTBD) and whether it delivers an intuitive, seamless experience. Specifically, I focused on:

• 👩🏻‍💻User Guidance and Flow: Examining if each step in the onboarding flow is logically structured and easy to follow.
• 🥰Engagement and Retention: Evaluating how each part of the process retains user interest, with a focus on initial actions that encourage users to set up and customize their store.
• 🤩Key Moments of Value ("Aha Moments"): Identifying moments where users are most likely to understand the platform’s core value.
• 💡Reduction of Friction: Checking if any step creates unnecessary friction that could lead to user drop-off.

💯 Evaluation Criteria

To measure the onboarding effectiveness, I set clear criteria based on common onboarding best practices, including:

1. 🎓Relevance to JTBD: Does each screen fulfill a specific need or action that Shopify’s core users are likely looking to accomplish?
2. ✨Clarity and Ease of Navigation: Is the language simple and understandable, and are navigation options clear?
3. 🤝Actionability and Engagement: Does each screen motivate users to take the next step without overwhelming them?
4. 🔄Feedback and Reassurance: Does the system provide feedback, help users feel reassured, and keep them moving forward?

In the below pdf, I have analyzed each screen based on the evaluation criteria, noting key interactions, "Aha moments," and areas for potential improvement. The teardown provides a step-by-step look at how Shopify guides new users through the onboarding process, highlighting the strengths and suggesting enhancements to further optimize user activation and engagement

Shopify - Onboarding Teardown.pdf

Based on the core JTBD and analysis of the onboarding experience, my goal is to identify key activation metrics that signal successful user engagement within Shopify. These metrics will help pinpoint the moments where users experience the product's value, driving continued use and eventual conversion. Here is the step-by-step process I followed to develop activation metric hypotheses:

Step 1: 🗣️Talk to users - Although I didn’t have access to Shopify's actual user data, I simulated this process by reviewing user interviews, online feedback, and surveys available from Shopify’s community. This allowed us to gather insights into common user needs, pain points, and expectations during onboarding.

Step 2: 👁️Observe user behavior - To understand typical actions taken by new users, I reviewed public tutorials, case studies, and onboarding videos for Shopify. This helped us visualize a new user’s journey, including essential actions like store setup and product addition, which indicate deeper engagement with the platform.

Step 3: 🗺️ Map the customer journey backwards - By mapping out the Shopify onboarding flow step-by-step, I identified key actions that likely lead to value realization for users. Actions like adding products, setting up payment methods, and checking the analytics dashboard were recognized as core steps that reflect a user’s commitment to setting up their store.

Step 4: 🤔💭Validate activation metric hypotheses - I used credible sources of public data to validate the relevance and potential impact of our hypothesized metrics. For example, the steady increase in the number of active Shopify stores in India, from 25,000 in 2019 to over 82,000 by 2024, reflects a rising demand for e-commerce solutions among Indian merchants. Additionally, statistics such as the global daily active user base of 4.47 million and the significant year-over-year growth of 56% for Shopify stores in India suggest a highly engaged user community.

🕵Hypotheses for Activation Metrics:

1. Hypothesis 1: Complete Store Setup within 30 Minutes

• 🏃Action (X): Completing initial store setup, including adding products and setting up payment.
• 🕒Time (Y): Within 30 minutes.
• 🧾Explanation: Completing the initial store setup, which includes steps like selecting a theme, adding products, and setting up payment options, within the first 30 minutes after signup.
• 🧠Reasoning: This metric is essential because it directly measures how effectively Shopify’s onboarding guides users through foundational steps that are necessary to make their store operational. Completing setup quickly shows that users are able to navigate the interface without confusion or delays, which is critical for retention. If Shopify makes it easy and efficient for users to get their store ready for sales within a short timeframe, it reinforces Shopify’s value proposition of simplicity and accessibility. This metric also gives insight into how well the platform supports users in setting up a functional store with minimal effort, fostering a sense of accomplishment and motivation to proceed further.

2. Hypothesis 2: First Product Added within 10 Minutes of Signup

• 🏃Action (X): Adding a first product to the store.
• 🕒Time (Y): Within 10 minutes after signup.
• 🧾Explanation: Users add their first product to the store within 10 minutes of signing up on Shopify.
• 🧠Reasoning: Adding a product is a crucial action in the setup process as it enables users to start populating their store with items they intend to sell. This metric is valuable because it shows whether Shopify’s onboarding flow guides users toward this critical action early on, without unnecessary complexity or distractions. Adding a product within 10 minutes reflects a smooth and intuitive setup process, giving users a sense of progress and readiness to sell. This immediate engagement with a core functionality signals a high level of activation, as users are beginning to build out their store, which is central to their journey with Shopify. A high rate of early product addition suggests that users are well-supported in taking their first meaningful steps toward launching their business, which boosts their confidence and likelihood of ongoing engagement.

3. Hypothesis 3: Visits to Analytics Dashboard within First 3 Days

• 🏃Action: User visits their analytics dashboard.
• 🕒Time: Within the first 3 days of setup.
• 🧾Explanation: Users visit the analytics dashboard within the first 3 days of setting up their store.
• 🧠Reasoning: The analytics dashboard provides users with insights into visitor behavior, sales trends, and overall store performance. Visiting this dashboard early on reflects a proactive approach, indicating that users are interested in tracking their store’s progress and understanding its performance from the outset. This metric is crucial as it shows that users are not only setting up their store but are also looking to optimize and manage it effectively. Early engagement with analytics suggests a higher level of commitment to the platform and a deeper interest in making data-driven decisions, which is often associated with successful and long-term users. By tracking how quickly users begin using analytics, Shopify can gauge how well it communicates the importance of these insights as part of the store management process, helping foster a data-informed approach that benefits user success and retention.

To effectively monitor Shopify’s activation and engagement, I identified several key metrics that align with the platform’s business model and user journey. These metrics provide insights into user behavior, retention, and satisfaction, enabling Shopify to optimize onboarding and enhance user success.

🎯Core Metrics:

1. 📈D1, D7, and D30 Retention: These retention metrics track how many users return to Shopify after 1, 7, and 30 days. Tracking retention at these intervals helps Shopify understand the platform’s stickiness and identify where users might drop off. High retention rates indicate successful onboarding and user satisfaction, while dips at specific intervals can point to areas where improvements are needed.
2. 🗓️DAU/MAU Ratio: This ratio (Daily Active Users/Monthly Active Users) shows the frequency with which users return to Shopify. A high DAU/MAU ratio indicates strong engagement and user loyalty, suggesting that Shopify is a vital tool for their daily business operations. This metric helps Shopify gauge how often users rely on the platform, allowing the team to enhance features that boost daily utility.
3. 🎫Subscription Rate vs Retention: By comparing the rate at which users subscribe to Shopify plans with retention rates, Shopify can measure if onboarding efforts lead to sustainable usage. High subscription rates followed by low retention could indicate issues in onboarding or value delivery. Balancing these rates is essential for long-term growth and user satisfaction.
4. ⌛Average Time to Action (TAT): TAT measures how long it takes users to complete key onboarding actions, such as adding a product, selecting a theme, or setting up payments. Fast completion times indicate a seamless onboarding process. Shopify can identify where users experience friction by tracking TAT and make targeted improvements to shorten these steps.
5. 👨‍👨‍👦‍👦User Cohorts: Cohort analysis allows Shopify to group users based on signup date, acquisition source, or behavior. By comparing retention and engagement across cohorts, Shopify can identify which acquisition channels or features lead to the highest success. This enables Shopify to tailor marketing efforts and prioritize improvements for high-value user segments.

Additional Context-Specific Metrics:

1. ⭐Product Reviews and User Feedback: Product reviews provide direct user insights into their satisfaction and challenges. Monitoring trends in feedback helps Shopify identify common pain points or requests, leading to actionable improvements. Positive reviews are also a sign of user satisfaction and trust in the platform.
2. 📩Support and Help Center Interactions: Monitoring the frequency and nature of interactions with support channels indicates where users may struggle during onboarding. For instance, if many users seek help with setting up payments, Shopify could streamline this step. Analyzing help interactions highlights friction points and areas for documentation enhancement.
3. ⚡Feature Adoption Rate: This metric tracks the adoption of specific features, such as analytics, POS integration, or marketing tools. By understanding which features users engage with the most, Shopify can focus resources on refining or promoting high-value features, as well as identifying any underutilized ones that may need better guidance.
4. 💸Revenue Metrics (e.g., GMV, AOV, Conversion Rates): Shopify can track Gross Merchandise Volume (GMV), Average Order Value (AOV), and conversion rates among new stores. High-performing metrics in these areas suggest that Shopify’s tools effectively support merchants in driving sales. Revenue metrics help Shopify evaluate how well users succeed on the platform, impacting their likelihood to remain subscribed.