How can Morning Consult improve its Brand Intelligence platform to provide more actionable insights for clients?

Introduction

Morning Consult should improve actionability by connecting each supported brand signal to a specific client decision, its evidence, its uncertainty, an owner, and a follow-up test. Before building anything, audit the current product and customer workflow. The current platform already advertises dashboards, custom charts, comparisons, exports, automated reports, AI Research Agents, API integrations, and AI connectors, so proposing those as missing features would be inaccurate.

Verified context and interview assumptions

• Morning Consult describes Intelligence as an always-on consumer intelligence platform spanning Global Context, Industry Trends, Brand Intelligence, and Audience Intelligence. Its Brand Intelligence measures include awareness, favorability, purchase consideration, trust, NPS, reputation, usage frequency, value, employer admiration, and buzz for thousands of brands.
• Morning Consult’s current data and methodology overview says its survey engine uses representative sampling, continuous collection, quality controls, and external validation. Those are company claims; a product team should expose the applicable method and sample details with each client result.
• AAPOR’s survey-research standards and best practices explain that transparent reporting includes the population, sample construction and size, question wording, mode, weighting, and precision where applicable. This is a useful trust standard for turning a chart into a decision artifact.
• The prompt supplies no customer research, usage data, entitlement details, retention issue, or verified product gap. Treat all proposed pain points and solutions as hypotheses.

Step 1

Clarifying Questions (5 minutes)

• **Which decision is currently hard to make?** Is the client deciding whether to respond to a reputation event, change positioning, reallocate research, select an audience, or brief leadership?

Why it matters: “More actionable” has no single meaning. A monitoring decision needs different evidence and speed than a strategic brand decision. Ask for: Recent decision examples, intended user, recipient, decision deadline, available actions, and cost of a wrong decision.

• **Where does the current workflow fail?** Is the problem finding the right signal, interpreting it, trusting it, sharing it, assigning action, or measuring what happened next?

Why it matters: The right fix may be onboarding, configuration, methodology disclosure, or workflow design rather than a new analytic feature. Ask for: Task recordings, search and dashboard paths, exports, agent usage, support tickets, abandoned workflows, and client interviews.

• **Which existing capabilities and entitlements are in use?** Do affected clients have access to dashboards, scheduled reports, AI Research Agents, API or export access, and connectors, and are these configured for their brand and market?

Why it matters: A feature can exist but remain undiscovered, untrusted, unavailable under a contract, or poorly fitted to the job. Ask for: Account entitlements, setup completion, feature discovery, repeat use, output acceptance, and support interventions.

• **How will we know an insight is actionable and correct?**

Why it matters: More clicks, exports, or generated reports can rise while decision quality falls. Ask for: A decision-quality rubric, evidence requirements, current task completion, time to supported decision, error rate, and downstream outcome ownership.