Use Case Time: Building vs. Buying: Navigating E-commerce Predictive Search Decisions
How Pets Are Us chose the right path for enhancing their search experience.
In the e-commerce landscape, one would imagine that searching for a product is straightforward. Yet, as any pet lover shopping on Pets Are Us knows, misspelling a brand name or a particular type of food can lead to a wild goose chase. As e-commerce solutions evolve, predictive search promises a more user-friendly experience. So, the dilemma arose for Pets Are Us - should they build this feature in-house or opt for an off-the-shelf solution?
Build vs. Buy Considerations:
5 Things to Consider When Building or Buying Off-the-Shelf:
Resource Allocation: Even if your development team has bandwidth, are other teams (like UX,QA,Analytics) equipped to handle the extra workload? Ensure a balanced distribution of resources across all involved units.
Expertise: Does your product management team possess the expertise to pioneer a solution from scratch, especially if it's unfamiliar terrain?
Cost Analysis: Building in-house might seem cost-effective, but consider the costs of maintenance, updates, and potential overruns. Often, third-party solutions offer regular updates and dedicated support. Get a sense of cost from 3rd parties and build out a cost analysis for an in-house build out + future support/maintenance.
Time to Market: Building a feature from scratch is time-intensive. Purchasing an off-the-shelf solution can significantly accelerate the implementation timeline. As you consider the ROI, how would it be impacted if you were able to pull the feature release months ahead of schedule?
Future-Proofing: While bespoke solutions offer customization, third-party options might offer scalability, integration with emerging technologies, and adaptability to future market needs.
Let’s use the pitch deck and E3 Framework to dive into this use case.
Envision
Connect with Customers Discovery:
As a Product Manager, this step involves reaching out to customers, conducting interviews, and using surveys to understand their pain points and needs. By directly interacting with users, PMs can gain insights into how they use the product and what challenges they face.Pitch Deck w/ ROI:
Problem Statement: Our end customers struggle with misspelled brand names and food types, leading to unsatisfactory search results.
Observations: Quantitative data reveals a drop in 'search to add-to-cart' conversion rates. Competitor benchmarks highlight the efficiency of predictive search features.
Proposed Solutions: Implementing a predictive search feature would likely lead to better search term accuracy and improved search results.
Proposed Test: Design an A/B test splitting traffic, exposing half to the current search and half to the new predictive search feature.
Expected Outcomes: Increase 'search submit to add to cart' conversion by a projected 15%. This will also enhance user satisfaction rates.
Measurement Plan: Focus on A/B testing, closely monitoring the primary KPI – 'search to add-to-cart conversion'. Secondary metrics include time spent on the site and user feedback.Stakeholder Perspectives:
Engage with department leads, sales, marketing, and even finance to get a comprehensive understanding of how a feature might impact the broader business.Prioritize Roadmap:
Using the insights gathered from the customer discovery and stakeholder perspectives, design a strategic roadmap outlining which features or improvements should be tackled first.Formal Kickoff:
A meeting to formally announce the start of the project, ensuring all teams are aligned, resources are allocated, and everyone understands the vision and objectives.
Envision
Connect with Customers Discovery:
As a Product Manager, this step involves reaching out to customers, conducting interviews, and using surveys to understand their pain points and needs. By directly interacting with users, PMs can gain insights into how they use the product and what challenges they face.Pitch Deck w/ ROI:
Problem Statement: Our end customers struggle with misspelled brand names and food types, leading to unsatisfactory search results.
Observations: Quantitative data reveals a drop in 'search to add-to-cart' conversion rates. Competitor benchmarks highlight the efficiency of predictive search features.
Proposed Solutions: Implementing a predictive search feature would likely lead to better search term accuracy and improved search results.
Proposed Test: Design an A/B test splitting traffic, exposing half to the current search and half to the new predictive search feature.
Expected Outcomes: Increase 'search submit to add to cart' conversion by a projected 15%. This will also enhance user satisfaction rates.
Measurement Plan: Focus on A/B testing, closely monitoring the primary KPI – 'search to add-to-cart conversion'. Secondary metrics include time spent on the site and user feedback.
Stakeholder Perspectives:
Engage with department leads, sales, marketing, and even finance to get a comprehensive understanding of how a feature might impact the broader business.Prioritize Roadmap:
Using the insights gathered from the customer discovery and stakeholder perspectives, design a strategic roadmap outlining which features or improvements should be tackled first.Formal Kickoff:
A meeting to formally announce the start of the project, ensuring all teams are aligned, resources are allocated, and everyone understands the vision and objectives.
Empower
Epic & User Stories:
Break down the predictive search feature into a pitch deck, high level assumptions, high level scope, and open questions. Each line of scope becomes one to many user stories that provide the scenarios to be handled.Architect Review / Refinement:
Ensure that the technical architecture aligns with the product vision. Work closely with the lead technical team to review feasibility and understand potential challenges.Stand-Ups:
Regular check-ins with the the UX, QA, and Development team to gauge progress, address blockers, and keep everyone aligned. Remove all hurdles.Design, Code, QA, Analytics:
Oversee the design phase, ensuring it aligns with user feedback. Monitor the coding process, work with QA to identify and rectify issues, and integrate analytics to measure the feature's success. Remove all hurdles.Review & UAT:
Engage in a final review and User Acceptance Testing to ensure that the product aligns with the initial vision and meets quality standards.
Elevate
Go/No-Go:
Decision-making point on whether to launch the feature or not based on all the accumulated data, tests, and quality.Stakeholder Updates:
Keep all invested parties informed about the project's progress, any changes, and the expected outcomes post-launch.Customer Engagement & Feedback:
After launch, actively seek feedback from customers on the new feature. Understand how it's being used and any challenges faced. Utilize SaaS features like FullStory to view customers engaging with predictive search. Provide a feedback form experience for users that utilize the feature.Value Updates:
Track the value delivered by the feature. This could be in terms of increased conversion, user satisfaction, or any other relevant KPIs.Optimization Roadmap Updates:
Based on the feedback and value metrics, iterate on the feature. Update the roadmap to reflect new insights and potential improvements.
The decision-making journey of Pets Are Us highlights the importance of taking a holistic view of business needs, team capacities, and long-term goals. While building in-house can offer customization, purchasing a predictive search feature off-the-shelf promises quick implementation, expertise, and scalability. As Pets Are Us has demonstrated, sometimes the best way forward is leveraging the expertise of those who have tread the path before. Remember, in the fast-paced world of e-commerce, delivering a seamless user experience is key, and the path you choose to achieve it can make all the difference.
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