A data scientist is leading a project to enhance an e-commerce platform's product recommendation system. The primary business objective is to maximize the return on investment (ROI) within a two-year timeframe. After initial research, the team has analyzed three potential solutions with the following projections based on a total annual revenue of $100M:
Solution A: Advanced Collaborative Filtering
Initial Development & Implementation Cost: $200,000
Annual Maintenance Cost: $50,000
Projected Annual Revenue Lift: 3%
Notes: Deploys on existing infrastructure.
Solution B: Hybrid Deep Learning Model
Initial Development & Implementation Cost: $750,000 (includes new GPU hardware)
Annual Maintenance Cost: $150,000
Projected Annual Revenue Lift: 6%
Notes: High technical complexity; requires hiring specialized talent.
Solution C: Third-Party SaaS Solution
Annual Subscription Cost: $400,000
Initial Integration Cost: $50,000
Projected Annual Revenue Lift: 4%
Notes: Limited customization; involves third-party data sharing.
Based on a cost-benefit analysis focused on maximizing ROI over two years, which of the following is the most compelling recommendation for the data scientist to present to business stakeholders?
Recommend the Hybrid Deep Learning model because it generates the largest absolute net gain of $10.95M over two years, delivering the greatest overall value despite a lower ROI.
Recommend initiating a limited-time pilot for both Solution A and Solution B to gather empirical performance data before committing to a full-scale implementation.
Recommend the Third-Party SaaS solution because it provides a competitive revenue lift with predictable subscription costs and avoids the risks associated with complex in-house development.
Recommend the Advanced Collaborative Filtering model because it offers the highest ROI of 1900%, leveraging existing infrastructure and providing a substantial net gain of $5.7M with the lowest upfront investment.
The correct answer is to recommend the Advanced Collaborative Filtering model. A cost-benefit analysis requires calculating the return on investment (ROI) for each option over the specified two-year period. The ROI formula is: ROI = (Net Gain / Total Cost) * 100.
Since the primary business objective is to maximize ROI, Solution A is the clear choice with an ROI of 1900%. While Solution B generates a higher absolute net gain, its ROI is significantly lower, making it a less efficient investment according to the stated goal. Solution C has the lowest ROI and introduces data sharing risks. Recommending a pilot program would defer making a decision based on the available analysis and is not the most direct response to the prompt.
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Why might the Hybrid Deep Learning model be less favorable despite its high net gain?