Explore more innovation ideas before choosing what to research next

An innovation workshop or hackathon might produce dozens of ideas. Some build on existing products. Others explore new technologies, services or business models.

Each may involve different customers, industries, markets and problems.

But a promising idea on its own isn’t enough to define a research opportunity.

Consider an AI-based planning service. It might address maintenance coordination in a manufacturing company, resource scheduling in logistics or service delivery in an industrial services business.

The technology could be similar, while the customer problems, responsibilities and operating constraints differ substantially.

That’s why a useful starting point is the combination of an idea and a specific customer context, not the idea in isolation.

Instead of asking which proposal deserves the highest score, investigate what would need to be true for each idea to create meaningful customer value.

A shared discovery framework helps teams investigate different ideas without forcing them into the same product assumptions.

For each idea and customer context, explore six questions.

Learning area · What you need to understand

• Customer and situation: Who experiences the problem, in which organization and under what circumstances?
• Problem and consequences: What becomes difficult, costly or frustrating? What happens if nothing changes?
• Current alternatives: How is the work handled today? What already works well enough?
• Responsibilities and constraints: Who owns the problem? What dependencies, risks or organizational requirements matter?
• Critical assumptions: What must be true for the idea to make sense? What could prove the team’s assumption wrong?
• Next decision: What decision is approaching, and which missing customer evidence could change it?

Keep the underlying questions consistent, but adapt the conversations to each customer’s actual work.

You aren’t trying to make every initiative look equally attractive. You’re creating a common way to expose uncertainty.

An idea with many unanswered questions isn’t necessarily worse than one with fewer. Its questions may simply be more important to investigate before committing resources.

Virtual Customer lets you create virtual customers based on role, industry, geography and company size.

For portfolio discovery, use those dimensions to investigate several relevant business situations.

A practical approach is:

1. Select a manageable group of ideas. Start with initiatives facing an upcoming investment, development or research decision.
2. Define the customer context for each. Identify the relevant industry, market, company size and customer role. If one idea has several plausible customer contexts, treat them as separate exploration cases.
3. Run structured conversations. Explore the customer’s work and problems before introducing the idea. Then investigate relevance, alternatives, responsibilities, constraints and possible objections.
4. Capture the unresolved assumptions. Record possible customer needs, competing explanations, unanswered questions and what could challenge the original hypothesis.
5. Compare research needs. Bring the findings together using the same framework, while keeping simulated suggestions separate from verified evidence.

When different customer roles are involved, explore their perspectives separately. Keep the relevant company and market context consistent when comparing roles within one situation.

The result is a collection of structured learning hypotheses that your team can review across initiatives.

It isn’t an automatically generated portfolio ranking or a prediction of commercial success.

Illustrative scenario. These ideas, contexts and research questions are fictional, not findings from actual Virtual Customer sessions.

Imagine a corporate innovation team reviewing three proposals from an internal innovation program.

The team has limited capacity for real customer interviews and needs to decide what to investigate next.

Idea and customer context · Consequential customer assumption · Potential next research

• Equipment monitoring service for maintenance managers in German mid-sized manufacturers: Consequential customer assumption: Earlier equipment warnings would help maintenance teams take useful action rather than simply create more alerts.; Potential next research: Reconstruct recent equipment incidents with actual maintenance managers and investigate when warnings could have changed decisions.
• Supplier information service for procurement teams in large UK manufacturers: Consequential customer assumption: Collecting and updating supplier documentation is an important unresolved problem for procurement.; Potential next research: First establish who owns the difficulties and where they occur before committing to a larger study.
• Service quotation assistant for commercial managers in US industrial service companies: Consequential customer assumption: Preparing quotations takes significant effort and is an important constraint on the commercial process.; Potential next research: Investigate recent quotations to distinguish document preparation from approval, pricing and information-gathering delays.

All three initiatives use the same discovery questions, but their most useful next research activities differ.

The equipment-monitoring idea may need detailed workflow evidence. The supplier-information idea may need a clearer problem owner. The quotation idea may need to establish which part of the process actually causes difficulty.

These are examples of possible research directions, not recommendations based on observed customer behavior.

The important outcome is that each initiative becomes more specific about what it does not know and how that uncertainty could be resolved.

After exploring the ideas, review what the team has learned and what remains uncertain.

A useful portfolio review can distinguish three next steps.

Research now. An important assumption remains unresolved, a consequential decision is approaching, and real customer evidence could help settle the question.

Reframe first. The problem, customer role or intended situation is still too vague. Clarify the hypothesis before recruiting participants or commissioning a larger study.

Hold for later. The question may be interesting, but resolving it would not change an important near-term decision, or other evidence is needed first.

These are research-planning choices, not judgments that an idea will succeed or fail.

For each proposed study, record the assumption, what evidence already exists, the real customers or observations needed, and the decision the research is intended to inform.

You can then allocate research capacity according to the importance of the unresolved decision, the cost of being wrong and the evidence needed to reduce uncertainty.

Virtual Customer can help you prepare that work. It cannot replace the real customer evidence required for investment, product or portfolio decisions.

Can we compare very different ideas using one method?

Yes, provided you’re comparing their research questions rather than treating simulated reactions as directly comparable market measurements.

The customer contexts may differ. A shared framework helps you identify what is known, assumed and still missing for each initiative.

Does this replace innovation portfolio prioritization?

No. Strategy, commercial potential, technical feasibility, resources and investment decisions remain separate responsibilities.

Virtual Customer supports the customer-learning part of that process by helping teams investigate assumptions and prepare relevant real-world research.

What if an idea has several possible customers?

Explore the most plausible customer contexts separately. The same idea may address different problems or face different constraints depending on role, industry, geography and company size.

That distinction can be valuable before deciding which customer group warrants deeper investigation.