{"id":10733,"date":"2026-01-26T18:48:38","date_gmt":"2026-01-27T00:48:38","guid":{"rendered":"https:\/\/attentionmedia.io\/?p=10733"},"modified":"2026-01-26T18:48:38","modified_gmt":"2026-01-27T00:48:38","slug":"you-no-longer-have-to-choose-between-insight-and-impact","status":"publish","type":"post","link":"https:\/\/attentionmedia.io\/?p=10733","title":{"rendered":"You no longer have to choose between insight and impact"},"content":{"rendered":"
\"Robots<\/div>\n

Marketing has long worked on the assumption that there are two paths to understand people and markets \u2014 often described as \u201cnuance versus numbers.\u201d <\/p>\n

Nuance wants to know how people feel, how brands are conceptualized and understood, what unknown drivers cause human behavior and what drives business success. Numbers want to know exactly how big our markets are, what people buy, what price they pay, what the path they took and what drives business success.<\/p>\n

The difference between these two points of view is most pronounced in Insights and Market Research, where individuals proudly describe themselves as \u201cquant researchers\u201d or \u201cqual researchers.\u201d Now, I do understand the need for specialization, but sometimes we forget that the subjects of the research \u2014 people, products and brands \u2014 are the same.<\/p>\n

Here\u2019s a thought experiment: Imagine you are a brand researcher, and I give you a file of one million tweets. Your job is to extract insight from this cache and use it to move the business forward. I then ask: \u201cAre you doing quant or qual research?\u201d<\/p>\n

Zen koans and the art of inductive\/abductive reasoning<\/h2>\n

The question is a koan<\/a> because both answers are equally right and equally wrong. And, like many good koans, the solution is to un-ask the question. Do that, and you\u2019ll see that the assumption of the question \u2014 the duality of the two types of research \u2014 is what is wrong.<\/p>\n

One reason this thought experiment trips us up is that we associate qual with depth and quant with scale. Yet here the data has both depth (the messiness of random human thoughts) and scale (the million observations).<\/p>\n

Dig deeper: What is predictive analytics?<\/a><\/em><\/strong><\/p>\n

To get nuance, we need depth. We need to interact with individuals and use observations to spot patterns and then form our hypotheses and theories. This inductive reasoning is the core of most qualitative approaches. For it to work, we need depth \u2014 20 questions on a questionnaire won\u2019t cut it. We need to probe and dive. That is expensive to do, and complex and expensive to synthesize. This is why Qual is usually associated with small sample sizes.<\/p>\n

For numbers, we need scale \u2014 the more appropriate the data, the better. We then apply analytics and deductive reasoning. We start with a theory and hypotheses and use (frequentist) statistical methods to confirm them (or not). These statistical techniques require large sample sizes and consistent data, which is why the technologies and platforms to support these have focused on Quant research as an automation and repeatability exercise. That is why Quant research has historically been dominated by the problem of scale.<\/p>\n

The problem with dichotomies<\/h2>\n

You will often see tables like the following offered to summarize the differences:<\/p>\n

\n\n\n\n\n\n\n
Method<\/strong><\/td>\nQualitative<\/strong><\/td>\nQuantitative<\/strong><\/td>\n<\/tr>\n
Outcome<\/strong><\/td>\nNuance<\/td>\nNumbers<\/td>\n<\/tr>\n
Approach<\/strong><\/td>\nSynthesis \/ Inductive Reasoning<\/td>\nAnalysis \/ Deductive Reasoning<\/td>\n<\/tr>\n
Challenge<\/strong><\/td>\nDepth<\/td>\nScale<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n

We accepted this division not because it was ideal, but because it was necessary. That said, many proponents of mixed-mode methods combine the approaches and build workflows that use the best of both.<\/p>\n

The rise of abductive reasoning is changing the quant\/qual dichotomy. In abductive reasoning, we go beyond just looking for patterns and ask, \u201cWhat is the most likely explanation for this (surprising) observation?\u201d In qual, many researchers use what is known as an \u201cabductive loop\u201d where they gather data, notice a surprise, form the best explanation, then gather more data. In a similar way, quant methods now often go beyond frequentist approaches (hypothesis testing) to Bayesian methods, where data is used to successively update our beliefs on what is going on.<\/p>\n

Dig deeper: How to make sense of all that marketing data<\/a><\/em><\/strong><\/p>\n

However, both are still beholden to the depth\/scale dichotomy: Abduction requires deep observations; Bayesian relies on data volume.<\/p>\n

Bringing it all back home<\/h2>\n

Fortunately, new technologies and models allow us to have depth at scale. It wasn\u2019t that qual needed small sample sizes \u2014 it was simply too expensive to collect the observations and harder to synthesize the results if there were too many subjects. Small sample sizes are not a definition of qual. They are just a technological limitation.<\/p>\n

Qual data collection at scale is now completely feasible (either as primary interviews or as mineable data \u2014 such as the tweet example), thanks to newer AI approaches. Specifically, Generative AI\/LLMs can perform computational abduction \u2014 meaning it has the reasoning ability to perform the necessary synthesis.\u00a0\u00a0<\/p>\n

The qual\/quant divide will fade over the coming years as we realize that the old depth vs. scale dichotomy is no longer a limitation. We will see a shift in how data is gathered, collected and how insights are derived \u2014 without any trade-offs between depth and scale made. We will no longer define our research by these binary limitations, but instead derive the truth about our customers, products and brands from simultaneous depth and scale.<\/p>\n

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Fuel up with free marketing insights.<\/p>\n<\/div>\n

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Email:<\/p>\n<\/div>\n

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See terms.<\/a><\/p>\n<\/div>\n<\/div>\n<\/div>\n

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The post You no longer have to choose between insight and impact<\/a> appeared first on MarTech<\/a>.<\/p>","protected":false},"excerpt":{"rendered":"

Marketing has long worked on the assumption that there are two paths to understand people and markets \u2014 often described as \u201cnuance versus numbers.\u201d Nuance wants to know how people feel, how brands are conceptualized and understood, what unknown drivers cause human behavior and what drives business success. Numbers want to know exactly how big … <\/p>\n

Read more “You no longer have to choose between insight and impact”<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-10733","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized"],"featured_media_urls":{"thumbnail":["https:\/\/martech.org\/wp-content\/uploads\/2025\/11\/robot-research-800x450.png",0,0,false],"medium":["https:\/\/martech.org\/wp-content\/uploads\/2025\/11\/robot-research-800x450.png",0,0,false],"medium_large":["https:\/\/martech.org\/wp-content\/uploads\/2025\/11\/robot-research-800x450.png",0,0,false],"large":["https:\/\/martech.org\/wp-content\/uploads\/2025\/11\/robot-research-800x450.png",0,0,false],"1536x1536":["https:\/\/martech.org\/wp-content\/uploads\/2025\/11\/robot-research-800x450.png",0,0,false],"2048x2048":["https:\/\/martech.org\/wp-content\/uploads\/2025\/11\/robot-research-800x450.png",0,0,false],"inspiro-featured-image":["https:\/\/martech.org\/wp-content\/uploads\/2025\/11\/robot-research-800x450.png",0,0,false],"inspiro-loop":["https:\/\/martech.org\/wp-content\/uploads\/2025\/11\/robot-research-800x450.png",0,0,false],"inspiro-loop@2x":["https:\/\/martech.org\/wp-content\/uploads\/2025\/11\/robot-research-800x450.png",0,0,false],"portfolio_item-thumbnail":["https:\/\/martech.org\/wp-content\/uploads\/2025\/11\/robot-research-800x450.png",0,0,false],"portfolio_item-thumbnail@2x":["https:\/\/martech.org\/wp-content\/uploads\/2025\/11\/robot-research-800x450.png",0,0,false],"portfolio_item-masonry":["https:\/\/martech.org\/wp-content\/uploads\/2025\/11\/robot-research-800x450.png",0,0,false],"portfolio_item-masonry@2x":["https:\/\/martech.org\/wp-content\/uploads\/2025\/11\/robot-research-800x450.png",0,0,false],"portfolio_item-thumbnail_cinema":["https:\/\/martech.org\/wp-content\/uploads\/2025\/11\/robot-research-800x450.png",0,0,false],"portfolio_item-thumbnail_portrait":["https:\/\/martech.org\/wp-content\/uploads\/2025\/11\/robot-research-800x450.png",0,0,false],"portfolio_item-thumbnail_portrait@2x":["https:\/\/martech.org\/wp-content\/uploads\/2025\/11\/robot-research-800x450.png",0,0,false],"portfolio_item-thumbnail_square":["https:\/\/martech.org\/wp-content\/uploads\/2025\/11\/robot-research-800x450.png",0,0,false]},"_links":{"self":[{"href":"https:\/\/attentionmedia.io\/index.php?rest_route=\/wp\/v2\/posts\/10733","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/attentionmedia.io\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/attentionmedia.io\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/attentionmedia.io\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/attentionmedia.io\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=10733"}],"version-history":[{"count":0,"href":"https:\/\/attentionmedia.io\/index.php?rest_route=\/wp\/v2\/posts\/10733\/revisions"}],"wp:attachment":[{"href":"https:\/\/attentionmedia.io\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=10733"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/attentionmedia.io\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=10733"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/attentionmedia.io\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=10733"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}