Archive: data science

Learn how Market Research Analysts, Managers, and Consumer Insights Analysts drive business growth by decoding trends, aligning goals, and fostering i...

Delve into the implications of synthetic data in marketing. Learn why relying solely on quantitative data may overlook human insights that drive succe...

Syndicated data provides a broad overview of trends, whereas custom research delivers tailored insights that drive specific business decisions.

Discover the impact of AI Washing on business and society. Learn about responsible AI applications and the importance of human input in this insightfu...

Discover how Bill Valle, Senior Director of Innovation at Quid, revolutionizes market research with Generative AI, delivering actionable insights effi...

Learn how to engage and captivate your audience with storytelling techniques. Explore examples and tips for incorporating storytelling into data analy...

Explore the use of synthetic data to bridge the gap between sales and ad exposure data. Learn how it can enhance targeting and validate ad effectivene...

Explore how Ryan Zemel, a respected figure in data-centric market research, empowers organizations to make strategic decisions using consumer data ana...

Elevate your data storytelling game by harnessing the power of conflict to create compelling narratives that captivate audiences and drive meaningful ...

What do market research, data science, user experience, and customer experience all have in common? When insight experts are asked this question, they...

Demand for insights about customer experience is more urgent than ever. Whether it’s in-depth strategic research led by insights professionals or cust...

Data Science is making way for more complex meta-analyses.

Why your customer experience must align with your brand promises.

Some marketing data science directly competes with traditional marketing research areas and many marketing researchers may wonder what the future hold...

How delineate has been able to thrive by offering clients what bigger firms can’t.

I often wonder whether, in research, we spend so much time navigating the complexities of gathering the data that we neglect the all-important field o...

The crucial balance between man and machine in analyzing insights.

Joel Rubinson details the great risks if we abandon the use of stat testing of marketing data.

Why statistical modeling will stay its ground in market research.

Jack Miles shares his most valuable learnings from the recent Nudgestock event.

A rebuttal by Steve Needel defending the validity of significance testing.

Defining algorithm basics of supervised and unsupervised machine learning.

Statistical and conceptual models are inherently limited. MR needs to rely on true experiments to understand causality.

Data Science is clearly the skill most in-demand right now, followed by strong storytelling and critical thinking abilities.

Combining insights from market research and data science can lead to deeper understanding of marketing issues

Clear thinking about causation is increasingly important in MR, especially as we move into a world of bigger, more diverse data sets.

How statistical analysis can be used to determine actionable insights for clients.

Demystifying AI, machine learning and data science. Offering ideas for taking action.

Interview of data scientist Jingjing Cannon on Verizon’s use of consumer data for product launches.

How to become a data analyst and its opportunities for the future.

Demystifying AI and how can it be applied to marketing and research.

Marketing scientist Kevin Gray asks Dr. Randy Bartlett of Blue Sigma Analytics what Data Science really is and how it can help decision-makers.

Interested in becoming a data scientist? Learn the basics about this trending field in Kevin Gray’s interview with Jennifer Priestley.

Ray Poynter summarizes results from an August NewMR survey on statistics and statistical tools used by market researchers.

Marketing scientist Kevin Gray asks Professor Frank Harrell about some important things we often get wrong about statistics.

Tim Bock reveals one of his biggest time saving techniques and tips for highlighting significant results on a table.

Jeffrey Henning details the 10 most retweeted links shared using #mrx over the last two weeks.

Data science software maker Dataiku explores the types of data problems facing retail and the steps to take to become more data driven.

Our industry pace of change is not one that can be evaluated in individual years. Rather, it should be measured in decades.

Marketing science is continuously evolving and in 20 years time will probably look quite different from the way it does now.

Much marketing research involves ideas about causation, for example that a new product failed because it didn’t meet certain consumer needs.

CoderRank is to text analytics what Google’s PageRank has been to search.

This article explores analytics, big data, and their impact in and outside of sports; and why the Browns are exploiting the power of data.

Seth Grimes interviews Preriit Souda on merging network and content analysis approaches to driver deeper, more impactful insights.

You see research findings and statistics quoted by the media all the time. Here’s why you’d better be careful before you actually use them.

The term data science has entered business vernacular with a bang…but what exactly is it? And what does it mean for market research?