SKIMspiration blog

Consumer behavior is usually embedded in daily habits; sometimes consumers make more deliberate decisions. COVID-19 triggered a dramatic change in the context of consumer decision making: Many daily habits have come to an abrupt halt; more decisions are now deliberate.

Your company is probably feeling the immediate impact of these behavioral shifts and you may be charged with adapting short-term marketing strategies accordingly.

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?

FMCG revenue professionals are challenged with creating a win-win-win situation: Provide consumers with the right product at the right price, create value with retail customers in challenging times, all while delivering top and bottom line growth. And all of this while working within legal limits in countries that prohibit resale price maintenance.

Leading consumer goods companies are increasingly adopting a net revenue management (NRM) approach to tackle this challenge. By applying a structured approach to analytics and encouraging open-mindedness, companies like Unilever are maximizing their net revenue and profits.

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 a virtual shelf approach to launching a premium brand

In 2018, Nestlé signed a $7.2 billion deal to market, sell and distribute Starbucks’ packaged products outside of the company’s cafes, providing Starbucks at home. With high brand recognition, Starbucks would clearly make an impact at the coffee shelf. However, one of Nestlé’s European insights team saw an opportunity to rethink the crowded grocery store shelf to drive even more growth – for Nestlé and its customers.

Albert van Meeteren, Nestlé’s Head of Consumer and Shopper Insights and Analytics, wanted to see how they could best launch Starbucks in a “new and innovative” way in Dutch supermarkets by focusing on in-store execution.

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.

From clicks to cart: making smarter use of product images

In today’s crowded online marketplace, we all face the same challenge: how do we attract and convert shoppers? While increased media spend is an almost guaranteed way of attracting more people to an online platform, getting them to actually buy is a whole different ball game. Many online retail giants aren’t forthcoming with behavioral data, so knowing how visitors think and behave from the time they land on the platform until they check out, is a blind spot for many of us.

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?