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Editorial

How to Leverage Data Science to Capture the Fickle Consumer

7 minute read
Richard Cook avatar
Loyalty is complicated — and when it comes to the relationship between brands and consumers, it has become increasingly challenging to attain.

Loyalty is complicated — and when it comes to the relationship between brands and consumers, it has become increasingly challenging to attain. But for data savvy brands, instead of a problem, this evolving consumer dynamic poses immense opportunity.

For well-established consumer packaged goods (CPG) brands, loyalty that took years to build is being reconsidered. For newer brands, loyalty is challenging to earn, yet exceedingly easy to give away. Today’s US consumers embrace experimentation more than ever, with a massive 32% of Americans claiming they are more product-disloyal today than they were five years ago, according to a recent Nielsen survey. To continue to grow in this era of experimentation, brand owners would do better to find the levers to change consumers’ minds — particularly those who have yet to try their products.

Digital discovery and data provide consumers the power to test the waters — and those very tools can power brand owners to adapt their strategies accordingly.

Navigate the Dichotomy Between Digital Discoverers and Long-Term Loyalists

CPG players trying to sift through the data from today’s retail landscape have multiple consumer dynamics they must understand. On the one hand, the fluidity of digital discovery is driving consumers to explore, encouraged by artificial intelligence algorithms to experiment with brands that have been “chosen especially for you.” And whilst brand choice online continues to expand — the number of available products has grown by more than 9,000 in the last 10 years — the average retail store is almost 7,500 square feet smaller today than it was 10 years ago, limiting the offline shelf space and assortment offered.

Loyalty does still manifest itself. Some categories continue to have high levels of loyalty that sometimes run generations deep, with purchasing behavior deep-rooted: toothpaste and tissues are good examples of this. Other categories, such as infant formula milk or cat food, achieve high levels of brand loyalty despite the shoppers never consuming the items themselves, basing their decisions on the perceived acceptability by a loved one and an associated fear of switching and rejection.

So we have an interesting dynamic between consumers who don’t feel tied down to any brand, and those who opt for what they know — and sometimes these can be the same people, shifting habits from one category to another, even from one retailer to another. Consider the shopper who goes to a dollar store and cares little for the brand they pick up, yet continues to shop at the supermarket or specialty store for the brands they will not do without.

For brands and retailers, with consumer data so readily available, it becomes a question of data science investment to ensure the right consumer understanding and the optimal brand investments. How does one proceed?

Related Article: Positive Memories: The Shortcut to Customer Loyalty

Focus on Winning the Researchers and Explorers

Retailers and product marketers are still spending billions of dollars every year to either retain loyal customers, or obtain loyalty with new customers. Yet more than half of the top grocery retailers in the United States have also lost share of wallet over the past five years due to consumers who are willing to explore and experiment. So why not change the strategy?

The message to industry players is this: flip the traditional loyalty model and embrace the explorers. To be clear, this does not mean that loyal customers are not highly valuable — quite the contrary, they will contribute disproportionately to brand profits — but for this consumer segment, the ROI from marketing activities will be limited. The challenge is to find the right plan to attract category-loyal but brand-disloyal consumers in a way that drives growth and profits.

Any business decision should be based at least in part on data, and consumer attraction is no different. Strengthening one’s data science ecosystem, powered by predictive analytics and the speed and horsepower of artificial intelligence, can help brands and retailers better understand and segment their target consumers and the ROI those segments offer. The prime target audience is no longer a mass of loyal consumers. It is instead individual consumers dissatisfied with other brands, or otherwise willing to experiment.

Data science can guide companies toward the levers that entice potential customers to try something new. Consumers have hundreds of touchpoints with media and brands every day. The opportunity here is to learn as much as you can about them. The more you know, the easier it will be to understand and connect those touchpoints with the marketing levers that are impacting one’s business the most.

First, it’s important to have a clear business purpose and marketing focus. If you’re trying to win on price, highlight that as the competitive differentiator — and use data science to better understand who your target consumers are, and where they are shopping (inclusive of physical and digital channels).

You could also focus on convenience: nearly half of consumers view shopping as a chore. Busy lives and lengthening commute times mean time and patience is limited. Brands and retailers will need to get on board with increasingly frictionless shopping — from pop-up shops with cashier-less payment to simply more user-friendly online experiences — as consumers seek zero resistance from discovery to payment to fulfillment.

Learning Opportunities

And even in the US where the economy is performing strongly, many people aren’t feeling the effect. So the marriage of price and value remains essential. Understanding the tipping point for price and value is imperative, as is knowing the targeted marketing actions that attract the curious shopper — profitably.

Knowledge is power, and the right data science tools help organizations acquire both. Data science is a necessity to ensure brands get the right assortment in the right channels, then understand how in-store placement impacts consumer attention, and finally, deploy a price and promotion mix that maximizes conversion. And alongside this, brand owners will need to execute successful tactics for the increasing pool of consumers who prefer to shop on their phones or computers.

Product developers, marketers and sales teams cannot go blindly into the new era. Every brand or product needs a reason to exist in a consumer’s life, tapping into consumer needs and values. Think healthy ice cream, hard seltzer and plant-based meat alternatives — innovations all born out of consumer data. Not every organization is positioned to hop onto these trends, but a foundation in predictive analytics and data science tools gives them the insight to activate their strategies with laser-guided precision.

Related Article: Why Are Companies Investing in Customer Retention?

For Bigger Brands, Make it Easy for Long-Term Loyalists

Activating the levers that can shift consumer interest and purchasing habits should be the priority for organizations, but the bigger name brands have an opportunity to make long-term loyalty as seamless as possible.

Once again, emerging technology provides the tools. A recent global Nielsen survey indicates that while 20% of surveyed consumers globally are currently using recurring “auto-replenish” orders, an additional 42% said they will in the future. Signing up for these auto-replenishment product orders can occur through mobile and desktop shopping, but even more easily through smart speakers that can take vocal orders while allowing consumers to avoid browsing. 

Opportunistic organizations should understand their base of loyal consumers and ensure they can take advantage with a strong presence on voice-assisted platforms and easier subscription service offerings. The long-term loyalists make the barrier for entry difficult for new products, but tried-and-true brands and offerings can capitalize.

No matter the strategies that marketing and sales teams are adopting, quality and robust data needs to be at the core of every strategy. Data that is merely "good enough" can steer an organization toward a flawed understanding of the consumers they are hoping to reach, and the tactics they need to do so. Organizations need data science to point to what has encouraged consumers to broaden the pool of brands to which they’re giving their business — and thereby gain insight on how they can become their own long-term loyalists. Loyalty and trust are tough to come by, and with the right data-driven strategy, one brand’s loss can be another brand’s gain.

Related Article: 'Good Enough' Data Will Never Be Good Enough

About the author

Richard Cook

Richard Cook is the senior vice president of data science, Nielsen Connect Global at Nielsen. He has served in various roles at Nielsen across his more than 25 years at the company, across marketing, product, business process improvement and now data science.

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