Blog articles about Market Research Trends

The role of empathy and surprise in consumer interactions

Market changes are constant and inevitable. Your competition isn’t slowing down, and you must react quickly and strategically. When information and innovation travel fast, the costs of a late response are high.

Like many marketers today, you’re forced to make fast decisions and quickly adapt brand strategies.

How can you ensure your communications, products, and services deliver on your brand promise – surprising and delighting consumers?

Mistakes may be inevitable when it comes to consumer experiences, but how can you best rectify them?

We recently conducted research to answer these questions and explore the role of emotions in consumer experiences and decision-making.

Why do people choose one product or service over another? What needs or objectives are tapped into when you consider buying or recommending one brand over another?

Whether your brand targets consumers or professionals, understanding how decision behavior works and systematically applying its principles can help you build more effective strategies to drive customer acquisition, brand loyalty and continued growth.

We recommend you tap into “habitual” behavior to reinforce positive habits or break the ones that don’t benefit your brand and analyze “deliberate” behavior to assess how, where and when your brand can intervene.

Here we explain how decision-making works and the behavioral steps that are involved in making choices. Read on to learn how leveraging these insights to reinforce or disrupt habits can help your brand grow.

The COVID-19 crisis and the Corona crash are shaking the context for consumer decision behavior. As a result, the context for net revenue management (NRM) has also been disturbed for consumer goods companies.

Companies must adapt revenue strategies to the new situation, at the same time as consumers are adapting to “the new normal.” To be successful, you need to anticipate and influence consumer decisions in a changing environment. This holds true in times of normalcy, but especially during the recession to come.

Although the COVID-19 crisis is not yet over, learnings from previous crises and knowledge of consumer behavioral frameworks mean we can map out consequences for net revenue management strategies.

So, what should you consider in planning your next moves?

COVID-19 has radically changed the context in which we make decisions, disrupting many habits. No one can predict if the behavioral shifts will last or what the recovery period will look like.

Online shopping and media consumption will undoubtedly continue to grow (as it was pre-COVID-19) … but to what degree? Will brand-loyal consumers who switched brands due to limited stock eventually return?

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 voice analytics in new product development research with Johnson & Johnson

Have you ever conducted early-stage innovation research and found yourself in a situation where you don’t entirely trust what consumer feedback is telling you? Many of us have had to deal with overstated interest and the need to dig deeper into unmet needs.

Uncovering both rational and emotional needs is vital for new product development (NPD) strategies – to accurately size the unmet need or opportunity for innovations. However, what is the best insights approach?

Exploring a data-fusion approach for holistic pricing decisions

Whether you’re introducing a new SKU or reacting to a market change, managing your pricing strategy can often feel like a complicated balancing act. You know solid revenue decisions should be grounded in sound data, but that input often comes from a variety of sources and stakeholders.

Understanding decision-making and choice overload in crowded markets

In today’s highly competitive telecommunications market, consumers face an abundance of choices online. To thrive in this environment, your product portfolio strategy should be optimized based on how decision-making is changing. You need to know how customers identify the best carrier and plan for their needs. And that’s where the most accurate customer and market insights can help.

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?

How to unlock the numbers in niche indications by using Multi-Criteria Decision Analysis

If you’re in a highly specialized area, such as MedTech or rare diseases, you’ll be only too familiar with the challenges of working with small sample sizes. Qualitative insights are important but when go/no go decisions are at stake, you may need a more data-driven approach. But, how can you obtain robust market data for decision-making when your target population and treating physicians are limited?