How Predictive Marketing Can Guide Brand Recovery in a Post-COVID World

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The business world is beginning what is expected to be a slow and potentially bumpy recovery from the impact of COVID-19 in early 2020. While we are far from done with this pandemic, companies across industries need actionable plans to adjust to long-term changes. In this regard, the need for real-time data and predictive marketing has never been greater. The powerful cross-section of these tools can provide a roadmap for tackling new marketplace challenges while keeping audiences engaged and businesses competitive.

To take a quick step back, for the purposes of this article, predictive marketing refers to the well-established practice of leveraging data, analytics and technology to make better future marketing decisions. Given the new set of variables that COVID-19 has brought to the table, predictive marketing will be more essential than ever to adapt to the changing environment—but its application is going to look different in 2020 and beyond. Let’s look at how marketers should set themselves up during recovery planning to thrive.

Leverage new data sources to fill in gaps

Amid market and societal upheavals, it’s natural for gaps to emerge within customer data sets. As the pandemic prompted business closures and widespread lockdowns, even the most loyal customers curbed purchases with their favorite brands, and many shifted their buying behaviors to alternate channels, including e-commerce and curbside pickup. Customer buying patterns that were well-established and well-understood were upended, and brands are now tasked with identifying gaps in their customer knowledge, filling them instead, with available data resources.

Your customers shifted their behavior. Even if they weren’t buying from you during the lockdown, that doesn’t mean they weren’t spending. Where and how they were purchased will tell you a lot about how customers and prospects will want to engage with your brand going forward. To this end, brands need to leverage their purchase data and blend it with transactional third-party data to reestablish their understanding of consumers’ new habits in this new world.

Establish an adjustable communications plan

Flexibility has always been under-prioritized by brands, and it should become a new key requirement to ensure both short- and long-term success. This applies not just to being able to adapt to changing customer behaviors, but also to new realities of conducting business in turbulent times.

Customers have established new business engagement patterns across in-store, online, pick-up, takeout and other options, and this variety will continue into the new reality. However, brands must be prepared to meet their customers wherever they choose to engage. For example, if a loyal brick-and-mortar retail customer moved to online purchasing during the lockdown, that retailer should follow that customer into online channels and continue to promote e-commerce options going forward. However, that customer might also be delighted to hear that her favorite store is reopening, and the retailer needs to also capitalize on the opportunity to celebrate that moment with its loyalists, all while seamlessly acknowledging the customer’s product preferences and purchase history across both in-person and online channels.

But the need for flexibility doesn’t end there. As the pandemic has demonstrated, business disruptions can also prompt the need for nimble, real-time communications strategies that segment customer communications for maximum relevance.

For example, a candy company had to shut down operations for the first time in 99 years just three weeks before Easter, which is a critical season for their online sales. The brand pivoted and partnered with another e-commerce storefront to offer a limited Easter assortment to its loyal customers, many of whom value the candy company’s products as a part of their family traditions. By analyzing historical purchase and engagement data, the candy brand used customer intelligence to identify target segments most likely to respond. To manage customer experience, the campaign was deployed in stages over the course of two days, and the brand applied key learnings from each previous send until their Easter assortment was sold out. The brand was then able to leverage these insights when they reopened their own online store. In doing so, the candy company quickly recaptured sales lost over the prior month.

Overhaul your acquisition and retention efforts

Looking to the future, brands must adapt their acquisition and retention efforts according to competitive shifts in the market. Not all of a brand’s competitors will emerge unscathed in the post-COVID business world, and companies should be prepared to adjust their strategies to uncover and capitalize on opportunities opened by marketplace consolidation by feeding third-party business data into their analytics.

Likewise, client bases are in a dramatic state of flux as well. Consider the banking industry, for example, where business clients are facing new challenges related to insolvency, furloughs, suspension of operations, bankruptcies and closures. Bankers need to be able to assess what their existing clients look like now and what they need most. They also need to be able to prioritize acquisition targets in this new marketplace based on the freshest risk and credit information. By leveraging predictive marketing techniques in concert with real-time data sources, bankers can profile their existing client portfolios, analyze portfolio risk, and smartly prioritize their up-sell and new business opportunities. Predictive marketing can help them manage the customer experience, ensuring they reach clients at home or in the office with relevant service offerings or suggested modifications based on what’s happened to their businesses or personal finances in recent months.

Refine personalization strategies

Even brands that had mastered personalization in a pre-COVID reality will have to scrap and rebuild previous best practices to accommodate new business engagement patterns and competitive shifts. We’ve seen this play out in recent months in the insurance industry. For example, given the dramatic shift in the employment landscape following the pandemic, a top health insurance company knew they had the opportunity to reach recently unemployed individuals with appropriate health insurance offerings. To do this, though, they needed to understand this audience, their values and their mindsets and use this information to develop segments and target each with appropriate offerings based on their real-time situations. The provider tapped into business, consumer and intent data sets and used internal predictive models to identify recently unemployed individuals. The insurance provider then reached out via digital channels to offer less expensive, more relevant individual family plans, Medicaid and Medicare in lieu of Cobra, thereby meeting consumers at a time of great need with the right solutions.

Done right with expanded data assets, predictive marketing builds toward more personalized customer experiences and interactions that continue to improve over time, even as consumers’ worlds shift around them. Predictive marketing is about meeting consumers on their terms, where and how they prefer—and being there for them when they are ready.

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