AI compatibility

Messy CSVs and a clean output spec make this a solid job for a data agent.

Good fit

AI can handle this.

Average across 1 submission.

avg / 100

The honest read

This is a well-scoped data engineering task with clear inputs, defined output columns, and recoverable errors — exactly where AI agents excel. The main friction points are the ID normalization logic (emails vs. hashes) and inferring missing subscription-tier data, both of which require explicit business rules from the user upfront. Given those rules, a capable data agent can execute this reliably within the budget and timeline.

Aggregated across 1 submission.

The five dimensions

Repeatability

High

The task is structurally a standard ETL pipeline: deduplicate, join, compute derived metrics, output. Once the ID normalization rules are established, this pattern repeats identically on refreshed data, which is favorable for automation.

Ambiguity Tolerance

Medium

The four output columns (lifetime_value, churn_date, avg_purchase_frequency, product_preferences) are named but not fully defined — churn_date especially requires a business rule (e.g., 90 days inactive = churned). The agent needs these definitions spelled out or must make defensible assumptions that the user validates.

Data & Tool Availability

High

Both CSVs are user-supplied and self-contained; no external APIs or live systems are needed. A Python/pandas environment is sufficient, and the agent can be given the files directly.

Error Cost

Medium

A bad merge or wrong deduplication logic produces incorrect lifetime values or phantom churn dates, which could mislead business decisions. However, the output is a CSV — errors are detectable on review and fully reversible before any downstream action is taken.

Human Judgment Required

Low

The work is deterministic once business rules are defined. There is no taste, ethics, or relationship context involved — just data transformation logic that a code agent handles well.

What an agent would need

Both CSV files uploaded directly to the agent with column headers documented
Explicit ID normalization rules: how to map email-format IDs to numeric hash IDs (e.g., a lookup table or hashing scheme)
Business definitions for each output metric, especially churn_date (inactivity threshold) and product_preferences (top-N by spend, frequency, or category)
A rule for handling missing subscription-tier data (e.g., impute from purchase history, flag as unknown, or exclude those rows)
A sample or schema of the expected output so the agent can validate its result before delivery

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