Blog articles about Advanced Modeling
Why consumer health brands need a more advanced analytics approach to decision journey research
The consumer health shopper journey is a particularly complex one. As with traditional FMCG products, the shopper journey for consumer health products is a labyrinth of channels and touchpoints. It’s never a linear decision-making process.
However, as a consumer health marketer, you face additional challenges when trying to untangle messy decision journeys: understanding the unique role of healthcare providers and their influence on the path to purchase.
A real-world market research case study with Big Data
The market research industry is no stranger to consumer data. However, the scale of “Big Data” generated through online behavior brings a host of challenges and opportunities for insights professionals and marketers alike. As consumers leave an endless supply of digital breadcrumbs online, how can we most effectively analyze and act on this behavior at the individual level?
Such was the challenge ArchDaily.com faced after amassing 20 terabytes of Big Data over the past three years. As the leading architectural website worldwide, interpreting this data was much more complicated than it had anticipated. With 150-200 million-page views a month, the company could see behavior volumes. However, ArchDaily.com didn’t know what was driving user behavior. It wanted to better predict architectural trends for and identify key drivers to optimize its online content strategy.
Exploring expert views on AI and the role of ‘stats’ in market research
Machine Learning (ML) is everywhere, from social media and virtual assistants to financial services and data security. For sales and marketing professionals, machine learning offers unprecedented analysis of big data. It holds the potential to decode increasingly layered buyer journeys. However, does machine learning truly have a role in market research, a field where we rely on analytical methods to understand nuanced and complex decision-making? Or, is statistical thinking still the essential analytical element behind the insights we deliver today?
Explore a driver analysis solution for analyzing stated and unstated factors
Tracking studies provide extensive information on brand perceptions over time. They play an important role in understanding what influences consumers and professionals to make decisions. However, as these groups become more empowered and have more choices to consider, your approach to brand trackers needs to evolve as well.
As telecom and technology markets are continually disrupted, our approach to their specific market research challenges needs to evolve with them.
What are you to do when you’re facing intense price competition, losing volume to the competition or launching new product innovations? Today’s digital consumers have more telecom and technology product options than ever before. For marketers in these industries, historical data will only take you so far when optimizing your product portfolio.
And the challenge only becomes more complex when you consider how today’s empowered consumers make purchase decisions differently than they did just a short time ago.
Have you come across the situation where many items (descriptions, statements or concepts) had to be put in preference order? Ranking and rating type questions are often used in these situations, although the last few years MaxDiff has been gaining more attention as a good alternative.