From Insights to Decisions: How Research Builds Strategic Confidence

How Research and Analytics Build Strategic Confidence Before Big Decisions

Leaders today have more data, reporting, and analytics than ever. Yet when conversations move from understanding performance to committing to a strategic decision, confidence often softens. The issue is rarely a lack of information. It is that most organizations are optimized to explain what already happened, while the decisions that matter most depend on what must be true going forward.

This is where research-driven decision making creates real value. At its best, research and analytics do not describe outcomes. They reduce uncertainty, surface hidden risk, and clarify the conditions required for growth bets to succeed. Used this way, research becomes a strategic tool. It helps leaders commit with intention, justify shifts internally, and move before early signals harden into performance problems.

It should answer strategic questions such as:

• How do we better understand how customers make buying decisions?

• What are the pain points and unmet needs in the market today and how can we capitalize on these to drive our growth?

• How can we most effectively reach new customers and penetrate new markets to expand our audiences, offerings and footprint?

• What customer segments offer us the greatest near-term and lifetime value?

• How should we position ourselves for competitive advantage to drive our strategic goals?

• What messaging and identity will crystalize our story and drive resonance and affinity with our key audiences?

• How do we ensure our employees understand our strategy and are inspired and supported to deliver it?

This piece shares lessons on how organizations:

• de-risk high-stakes strategic bets

• justify strategic shifts internally

• strengthen the CMO and CEO relationship through a shared insight lens, and

• spot blind spots earlier than dashboards can

It is written for leaders who want research and analytics to influence decisions, not simply describe outcomes.

1. How organizations de-risk high-stakes strategic bets

The companies that manage big growth bets well tend to follow a simple discipline:
Before they scale, they prove the few conditions that carry the most risk.

Microsoft applied this approach as it pushed deeper into large enterprise cloud accounts with Azure. The leadership team did not start by asking whether demand existed. Demand was already clear. The harder question was what had to be true for global enterprises to standardize on Azure across business units.

Targeted enterprise research surfaced three non-negotiables:

• Procurement credibility and enterprise-grade contracting

• Seamless integration with existing IT environments

• Clear executive accountability and long-term roadmap confidence

Feature advantage mattered far less than operational trust.

Microsoft invested first in these proof points by strengthening enterprise sales governance, improving integration tooling and formalizing C-suite engagement models for large accounts. As a result, Azure enterprise adoption accelerated and multi-year contracts increased because the largest risks were resolved before full-scale rollout.

The bet became safer because the right uncertainties were reduced early.

A similar pattern shows up in hospitality.

At brands like Marriott International, portfolio tiering has been used to reach more traveler segments without breaking the core brand promise. The question was never whether guests valued more choice. It was whether owners, guests, and partners would experience that expansion as additive or dilutive.

The insight work centered on three tensions:

• Trust. Would loyalty still mean something across tiers?
• Economics. Would growth strengthen owner returns or pressure them?
• Experience integrity. Where could standards flex and where could they not?

Progress came once the team understood where expansion would hold and where clear guardrails were required.

What these examples share is a discipline many organizations skip:

• They surface the assumptions that could derail the bet early, rather than discovering them at scale.

• They use targeted research to resolve those uncertainties quickly.

• They commit and scale with speed, because they are no longer guessing.

This balances rigor with speed.

Practical checklist to de-risking a big bet before committing to a strategic move:

1. List what must be true for success.

2. Rank those items by vulnerability, not excitement.

3. Design research to pressure test the top two or three.

4. Treat everything else as secondary until those are resolved.

This alone prevents a disproportionate share of painful misreads.

2. How research helps justify a strategic shift internally

Shifting strategy is rarely difficult because people disagree with the logic. It is difficult because it redistributes certainty, autonomy, status, and resources, often before performance forces the issue.

The organizations that navigate this well do not use research as persuasion. They use it as a shared explanation of what is changing and why staying the course is riskier than moving.

A well-known financial services firm did this when pivoting its growth focus from product volume to relationship depth. The research did not highlight dissatisfaction. Research-driven customer insights coupled with account-level analytics revealed a gradual erosion in perceived value among high-value clients who still looked loyal on paper.

The story did not attack performance. It revealed the cost of inaction visible before revenue decline made it undeniable.

The shift was accepted because the evidence removed politics from the conversation and replaced it with clarity.

A technology company facing platform expansion took a similar approach. Instead of arguing for consolidation on efficiency grounds, insight work demonstrated how fragmentation weakened trust with senior decision makers in key accounts. The conversation changed. The shift became less about internal optimization and more about credibility in the market.

When research is used this way, it does three things well:

• It quantifies the real tradeoffs.

• It shows why “do nothing” carries its own risk.

• It gives leaders a way to change course without creating internal winners and losers.

Practical tool: the two-story test

Write two short narratives:

• the future you are actively choosing by proceeding as planned

• the future you are implicitly accepting by not shifting

Then ask one question: Which future would be harder to explain to the board in two years?

3. How a research-led strategy strengthens the CMO and CEO relationship

The strongest CMO and CEO relationships we see do not treat research as a marketing artifact. They use it as a shared operating lens for enterprise decisions.

In a global services organization, the turning point came when insight work stopped reporting campaign results and instead focused on where growth inside major accounts was silently capped. That reframed growth from “marketing performance” to “customer belief and organizational credibility.”

The CEO and CMO began making choices together because they were now solving the same problem, using the same evidence.

In a large hospitality brand, research helped bridge the gap between brand strategy and owner economics. The conversation shifted from messaging to value creation across stakeholders. Trust increased. Decisions moved faster. Internal alignment improved.

A research-led strategy strengthens the relationship because it:

• creates a common language for risk and opportunity

• clarifies what the business is really betting on

• reduces the gap between instinct and evidence

Practical discipline: shared questions, not separate briefs

Agree upfront on the question the research must answer for both leaders, not just for one function.

Not “what will this tell marketing,” but “what must this clarify for the business.”

When that happens, research stops living in a deck and starts living in decisions.

4. Where blind spots most often hide

Across industries, blind spots tend to show up in the same places:

• Accounts that look stable on paper, but where momentum has clearly slowed

• Segments that remain profitable, yet no longer feel like engines of future growth

• Adoption metrics that stay high while buying decisions quietly become more cautious

These signals rarely appear in top-line metrics. They emerge when research and analytics are used to look beyond performance and into decision behavior.

They show up in:

• Longer decision cycles despite similar deals or renewals

• More stakeholders involved in approvals, with fewer clear champions

• Renewals without expansion, or expansion that requires deeper discounting

This is often seen in large enterprise SaaS platforms, including Salesforce.

In mature enterprise accounts, performance indicators can remain strong while momentum quietly softens. Usage stays high. Renewals continue. Yet other signals begin to shift:

• Decision cycles lengthen, even for routine renewals
• Buying groups expand, adding complexity and slowing approvals
• Expansion becomes harder, often requiring more concessions despite stable core usage

In these situations, customers are not rejecting the platform’s value. They are responding to perceived complexity, integration effort, and the internal cost of expanding usage across the organization.

Industry commentary and company actions suggest that Salesforce has addressed this dynamic by simplifying packaging, sharpening its enterprise platform narrative, and adjusting how expansion is sequenced within large accounts.

The risk in moments like this is not immediate churn. It is gradual stagnation that does not trigger traditional performance alarms.

This is where research and analytics matter most. They help leaders connect what customers say with how buying behavior is changing, before friction hardens into a performance problem and strategic options narrow.

Practical prompt: where does it feel slightly harder than it used to

Ask your teams:

• Which accounts now require more explanation, reassurance, or internal alignment than they did a year ago, even if revenue is stable?

• Where are we seeing strong usage or renewal, but slower expansion, tougher negotiations, or longer approvals?

Then use targeted research alongside account-level analytics such as decision-cycle time, expansion velocity, approval depth, and discounting patterns to understand why.

Study those first. Early friction is often the first true signal.

5. How AI fits into this way of working

AI has made it easier to synthesize feedback, analyze signals across large datasets, and surface patterns quickly. That speed creates real advantage in complex decision environments.

The value is highest when AI is used to accelerate learning against clearly defined uncertainties, not to reinforce familiar narratives faster.

Unilever uses AI this way in innovation and portfolio decisions. Rather than asking AI to predict winners, teams use it to scan large volumes of qualitative feedback, customer service data, and performance signals to detect early friction points. Where adoption slows. Where confidence softens. Where trial does not turn into repeat.

AI surfaces the pattern. Leaders decide what it means.

In practice, this allows teams to test assumptions sooner, focus research where risk is highest, and adjust propositions before scale locks in the wrong choices.

In every example above, judgment still belonged to leaders. AI amplified insight. It did not replace the responsibility of deciding what the organization should risk, protect, or change.

6. What leaders can take forward

Across industries the pattern is consistent.

Organizations that make better strategic choices do one thing differently.

They do not start with “what do we know.”
They start with “what must we understand before we commit.”

They use research to:

• de-risk the assumptions that matter most

• justify shifts without creating internal winners and losers

• build a shared confidence between the CEO and CMO

• surface vulnerability early rather than late

The goal is not to eliminate risk. It is to understand it clearly enough that when you choose, you are choosing on purpose.

That is when research and analytics begin to serve strategy in a way that is genuinely valuable.

‍