Transforming Retail for an Omnichannel World
Co-written by Rich Butler, Senior Enterprise Account Executive, and Matt LaRosa, Account Executive, at Pecan.
We loved attending eTail Boston this year. It’s an excellent opportunity to learn from the latest and greatest in retail and e-commerce. We enjoyed thought-provoking conversations with people who live and breathe omnichannel retail every day.
In this time of huge change for the industry, it was especially intriguing to hear about how retailers and e-commerce companies are solving challenges posed by tough economic conditions and constant shifts in omnichannel consumer behavior.
At eTail, we met with both traditional brick-and-mortar retailers who are expanding into e-commerce and working to scale their efforts effectively and efficiently, as well as “born digital” e-comm companies who want to grow their presence for continued success.
Both kinds of retailers share similar challenges: improving customer acquisition and retention, allocating media spend in a changing media environment, and contending with privacy restrictions limiting access to customer-level data.
Driving retail customer acquisition and retention in a challenging economic environment
Consumers may not want to buy the same products and services when every dollar counts, nor may they be able to buy more. Retailers must be exceptionally innovative in developing strategies to reach new customers when household budgets tighten. They need creative solutions to retain their current customers and reinforce their loyalty.
Machine learning is the most powerful capability retailers can develop to inform personal customer relationships. While traditional Business intelligence (BI) includes gathering, storing, and analyzing business data, as well as using that analysis to inform the actions of the business. methods try to find patterns in historical data, the pace of change in retail and e-commerce shows that looking to the past isn’t sufficient. Furthermore, the BI approach isn’t granular enough to anticipate individual consumers’ needs and preferences. Through using predictive modeling, omnichannel retailers strengthen customers’ bonds by personalizing outreach and offers based on advance knowledge of how they’re likely to respond.
Embracing new ways omnichannel retailers can reach consumers
Retailers continue to shift away from traditional advertising methods and move toward social platforms and digital advertising. As we all experienced, this shift accelerated during the pandemic when consumers stuck at home suddenly shifted much of their shopping online.
Digital Commerce 360 estimates that the pandemic added over $102B to U.S. e-commerce in 2020, plus a further $116B in 2021. When shopping in-store became unavailable or undesirable, online shopping boomed. However, some shopping has shifted back to brick-and-mortar stores since the height of the pandemic.
The roller coaster of changing consumer habits has left many omnichannel retailers scrambling to adapt their strategies. They continue to explore the best ways to re-deploy their campaign dollars. That re-allocation reflects their best efforts to foresee the unending transformation of consumer behavior and to determine which customers will likely be most valuable over time.
“In this inflationary age, brands need their media dollars spent effectively. Retail data helps by optimizing spend based on actual sales performance through Predictive analytics uses data, statistics, and machine learning techniques to build mathematical models that can generate predictions about things likely to happen in the future….. While third-party data can be used to create proxy metrics for success, retailer data provides straightforward and accurate campaign attribution.”
— AdExchanger, August 16, 2022
With new advertising platforms and shifting consumer preferences, retailers must continuously adjust their best-laid plans. Agility and responsiveness are critical in rapidly changing market conditions.
Personalizing outreach to retail customers despite new approaches to privacy
Amid all these other changes, retailers are also contending with an evolving privacy ecosystem. The soon-to-be cookieless world and changes in Apple’s iOS have affected retailers’ marketing attribution efforts. It’s also been tougher to understand the return on their media spend.
Without detailed, individual-level information about consumers’ interests and behavior, marketers have been looking for a light in the void. Retailers and CPG companies are finding new ways to gather and work with first-party data. Their strategies include building email, social, sweepstakes, sampling programs, and other direct-to-consumer methods to collect more of their own data. Used effectively — and we’d suggest Predictive analytics uses data, statistics, and machine learning techniques to build mathematical models that can generate predictions about things likely to happen in the future…., naturally! — First-party data is the data that a company collects itself instead of acquiring it from other sources. For example, data on visits to the company’s… is a rich source of knowledge. That information can shape strategies for individualized outreach and effective omnichannel marketing.
“Retail isn’t just about a one-time transaction; it’s about building a relationship that is mutually beneficial and meaningful. This is where first- and zero-party data plays a critical role.”
— Retail TouchPoints, August 15, 2022
Predictive analytics rises to meet omnichannel retail’s challenges
At eTail, we saw a few other vendors who offered different solutions to some of the challenges mentioned above. They made their own promises about the KPI improvements they could provide. But we didn’t see any other vendors doing what Pecan does: putting Data science combines statistics, computer science, scientific methods, and business knowledge to analyze, model, and predict using data. The data science toolkit can be used… directly into the hands of omnichannel retailers’ business teams.
Adding data science staff may be out of reach for retailers during economic uncertainty. However, existing Business intelligence (BI) includes gathering, storing, and analyzing business data, as well as using that analysis to inform the actions of the business. teams can build their own predictive models, for their most common marketing, sales, and merchandising challenges. Importantly, they can accomplish that goal without adding to their strapped data science teams (or hiring them if they don’t have data science at all!), if they’re provided a platform that makes this powerful capability accessible.
In reality, every business team at every retailer needs access to Predictive analytics uses data, statistics, and machine learning techniques to build mathematical models that can generate predictions about things likely to happen in the future….. The predictive approach makes the most of retailers’ rich data. Predictive Analytics is a business practice that uses descriptive and visualization techniques to gain insight into data; those insights can then be used to guide business… allows omnichannel retailers to respond quickly to changes in consumer behavior — as seen in the last couple of years — and to continually optimize their marketing efficiency.
The energy and excitement at eTail showed us that, though it may feel like a daunting time, this is a moment of reinvigoration and reinvention for retail and e-commerce. With predictive analytics, retailers are poised to unlock exciting new opportunities and build lasting loyalty through future-focused, omnichannel approaches that meet customers wherever they are.
Ready to explore how Predictive analytics uses data, statistics, and machine learning techniques to build mathematical models that can generate predictions about things likely to happen in the future…. can support your team’s goals in retail or e-commerce?Get in touch for a quick, easy use-case consultation. We’re here to help you figure out the next steps.