Summary
You should not be doing research for the sake of doing research. Research takes time and needs to be well throughout. More importantly, you need to determine if your findings are actually meaningful to the organization. In this session we will look at the idea of statistical significance and meaningfulness when reporting research findings.
Key Insights
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Research must be linked to clear business imperatives and KPIs to avoid meaningless data collection.
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Statistical significance alone does not determine if a finding is meaningful to business strategy.
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Sample size and data variability significantly impact whether differences in scores are statistically significant.
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Qualitative research provides crucial context and meaning that pure quantitative methods often miss.
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Competitive benchmarking should include industries outside your own to meet evolving customer experience expectations.
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Significant changes parallel to industry trends may not represent meaningful progress for a company.
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Driver modeling helps prioritize research efforts by identifying which perceptions most influence key outcomes like NPS or loyalty.
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Frequent measurement without implementing changes dilutes insights and wastes resources.
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Researchers should tailor findings to the priorities of different stakeholders to increase buy-in and understanding.
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A mixed-methods approach combining qualitative and quantitative data yields the most actionable and trustworthy insights.
Notable Quotes
"Research is not easy to do and it’s not something we should waste time in; it needs to be well thought out."
"A number like an NPS score means different things to different people depending on their role."
"Sample size has a direct impact on whether a change in score is statistically significant or just noise."
"Just because a change is statistically significant doesn’t mean it’s meaningful enough to change business strategy."
"When the pandemic hit, loyalty was shattered across industries; companies had to earn it back."
"If you measure without making changes, you shouldn’t be measuring more often — that’s a big no-no."
"If you had an infinite sample size, every change would be significant, which defeats the purpose."
"Driver modeling tells you which initiatives matter most so you know where to focus your research efforts."
"Attach your findings to what executives care about — increasing revenue, decreasing costs, innovating faster."
"I always do mixed methods because one alone doesn’t give you everything to truly understand what’s going on."
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