How can IPG improve its data analytics tools to provide more actionable insights for advertisers?

Introduction

I would first update the company and product context. Omnicom completed its acquisition of Interpublic on November 26, 2025. On January 7, 2026, it announced the new Omni, integrating Acxiom, Interact, Flywheel Commerce Cloud, and Omni's expanded agentic AI framework into one marketing-intelligence operating system. The prompt's IPG/Interact framing is therefore historical; Acxiom and the inherited Interact capabilities now sit within Omni's current foundation. (Omnicom acquisition completion; new Omni announcement)

Acxiom's current official site presents identity, customer-data management, data clean rooms, consumer data, analytics, and martech as distinct products and services—not one generic “IPG analytics tool.” (Acxiom) For this interview scenario, I would audit the actual Omni analytics and decision workflow first, then improve a verified gap rather than assume a separate layer is missing.

“Actionable” should mean more than a dashboard or an AI-written summary. A useful insight identifies what changed, the supporting evidence and freshness, plausible drivers, uncertainty, the recommended decision, who owns it, and how its effect will be measured. This is especially important because IAB's 2026 research points to fragmented data, signal loss, privacy constraints, and inconsistent cross-channel measurement. (IAB State of Data 2026)

Step 1

Clarifying Questions

• **Which product and decision are in scope?** Is the user planning audiences, diagnosing campaign delivery, reallocating budget, measuring outcomes, or preparing an executive report?

Why it matters: each decision needs different evidence, latency, and controls.

• **Who acts on the insight?** An advertiser analyst, media lead, agency strategist, or activation operator?

Why it matters: the reader, decision owner, and executor may be different people.

• **What business outcome and measurement method are authoritative?** Which KPI, attribution window, experiment, incrementality method, or marketing-mix model is used?

Why it matters: a recommendation cannot be more trustworthy than its outcome definition.

• **Which data is available, and under what permissions?** What are its source, owner, granularity, freshness, identity coverage, and privacy constraints?

Why it matters: missing or delayed data should lower confidence, not disappear from the interface.

• **What is failing today?** Time to answer, inconsistent numbers, low recommendation adoption, weak explanation, or inability to execute? What is the baseline?

Why it matters: the right intervention may be governance or workflow rather than another model.