How would you gather requirements for an AI recommendation engine in an ecommerce company?
For an AI recommendation engine, I would first clarify the business goals, such as increasing revenue, improving conversion rates, boosting average order value, or enhancing customer engagement. I would then identify stakeholder needs, required data sources, recommendation use cases, success metrics, and operational constraints to ensure the solution delivers measurable business value.
Full Answer
When gathering requirements for an AI recommendation engine, a Business Analyst should begin by understanding the business objectives behind the initiative. Stakeholders may want to increase sales, improve customer retention, raise average order value, reduce cart abandonment, or create a more personalized shopping experience. Defining these goals early helps determine what the recommendation engine should optimize.
The next step is identifying functional requirements. This includes understanding where recommendations will appear, such as product pages, homepages, search results, shopping carts, checkout pages, emails, or mobile applications. Requirements should also define recommendation types, including frequently bought together, similar products, personalized recommendations, trending products, and recently viewed items.
Data requirements are critical for any AI recommendation solution. The analyst should identify available data sources, including customer profiles, purchase history, browsing behavior, search activity, product catalogs, inventory data, ratings, reviews, and promotional information. Data quality, completeness, privacy requirements, and refresh frequency should also be assessed.
Business metrics must be defined to evaluate success. Common metrics include conversion rate, click-through rate on recommendations, average order value, revenue per visitor, customer retention, repeat purchase rate, and customer lifetime value. Establishing baseline measurements allows the organization to compare performance before and after implementation.
Finally, success measurement should combine technical and business outcomes. The team may use A/B testing to compare recommendation performance against existing experiences while monitoring key business KPIs. Ongoing analysis helps refine recommendation models and ensures the solution continues to deliver value as customer behavior evolves.
Sample Answer
I would start by understanding why the organization wants an AI recommendation engine and what business outcomes it expects to achieve. For example, the goal might be increasing sales, improving customer retention, or boosting average order value. Next, I would gather functional requirements by identifying where recommendations should appear and what types of recommendations are needed. I would work with business stakeholders, marketing teams, product owners, and technical teams to understand their expectations. I would then identify the data required for the solution, such as customer purchase history, browsing behavior, search data, product catalog information, and inventory data. I would also assess data quality and any privacy or compliance requirements. To measure success, I would define business metrics such as conversion rate, click-through rate, average order value, repeat purchases, and revenue generated from recommended products. I would recommend establishing baseline metrics and using A/B testing so the organization can clearly measure the impact of the recommendation engine after implementation.
How This Applies by Industry
An online retailer may use AI recommendations on product pages and during checkout to increase average order value through cross-sell and upsell opportunities.
A SaaS platform could recommend relevant features, integrations, or learning resources based on user behavior to improve adoption and retention.
An enterprise procurement platform may recommend commonly purchased items, preferred suppliers, or contract-based products to streamline purchasing decisions.
Frequently Asked Questions
Want personalized interview coaching?
Work with a CBAP® certified consultant
Vikrant Chauhan has reviewed and coached candidates across 30+ real BA/PM/PO hiring processes in healthcare, SaaS, and fintech.