In the ever-evolving landscape of customer experience, personalization has emerged as a crucial factor in driving conversions and return on investment (ROI). According to Gartner, “tailored help,” as they call it, can increase a brand’s chances for intent, purchase, repurchase, and increased cart size by 20%. While personalization is not a new concept, its application continues to evolve, particularly as customers have come to expect some level of personalized experience based on their past behaviors. Studies reveal that 49% of consumers are more likely to become repeat buyers after enjoying a personalized shopping experience.
This article aims to explore strategies and tools that brands can leverage to harness customer data and deliver personalized experiences, to boost conversions and ROI.
Leveraging First-Party Consumer Data
Brands today possess a wealth of first-party consumer data. First-party data is collected with consent directly from interactions with customers on a brand’s channels – such as website browsing activity, past purchases, mobile app interactions, and call center touchpoints – just to name a few. By harnessing the power of customer data analytics, brands can gain valuable understanding of consumer behavior, preferences, and purchase patterns.
However, collecting data is just the first step. To unlock its true potential, brands must tie all of this disparate data together, and analyze this information effectively to come up with patterns that lead to consumer insights. And THEN, they must tie it into their content management/marketing automation systems to customize the messages presented to each customer.
Many companies invest in partnerships with sophisticated “personalization engines” that capture and analyze all of the disparate data generated by daily customer interactions. According to Gartner, “Personalization engines apply context about individual users and their circumstances to select, tailor and deliver messaging such as content, offers, and other interactions through various digital channels in support of marketing, digital commerce, and service/support goals.”
But, if a brand is not ready to invest in a massive personalization tool, one way to begin dipping its toe into a personalization effort is by applying data patterns to groups of customers, or segments – in essence, dividing their customer base into distinct groups based on common attributes or behaviors. With segmentation, brands can create better-targeted marketing campaigns tailored to each segment’s unique needs and preferences, such as what stage of the buying cycle they’re in.
Dynamic Content Personalization
If a brand has invested in a large-scale personalization engine, they can employ it to implement dynamic content personalization, where brands customize website content, promotions, and product displays in real-time based on individual customer data. (Mediatool.com has a great explainer on dynamic content personalization engines.) The integration of AI-driven recommendation engines is a powerful tool for brands to deliver personalized experiences at scale. These engines analyze vast amounts of customer data, including browsing history, purchase behavior, and demographic information, to generate tailored product or content recommendations. The data typically comes from first-party customer sources such as a company’s website, email marketing, social media interactions, buying history, and more; it can even extend to interactions with customer service or in physical stores.
By employing the engines’ sophisticated analyses, brands can create dynamic landing pages and other site features like widgets, which adapt to a customer’s preferences, past behavior, and demographic information to showcase more relevant products and content, increasing cross-selling and upselling opportunities. This approach enhances the customer journey, providing a more engaging and relevant user experience since content/products are based on past behavioral and purchase data.
Taking it a step further, brands can deliver a seamless and consistent experience by implementing omnichannel personalization across every customer touchpoint, including in-store, mobile apps, and social media for example. This approach ensures that customer preferences and data are shared across different channels, allowing brands to provide cohesive and personalized experiences regardless of the platform. By integrating customer data from all possible sources, brands can further present a unified experience that’s been tailored to each customer’s preferences.
Privacy and Transparency
While personalization is a powerful tool, it can also be a double-edged sword. Clearly, it relies heavily on customer data to be effective. However, customers may believe they can only enjoy well-personalized experiences at the cost of their data privacy. Interestingly, Accenture found that 60% of customers are willing to share personal data with their bank or insurer in exchange for lower rates – but, at the same time, 75% stated that protection of personal data is paramount with data breaches coming in second as the biggest concern for consumers in the study.
Given customers’ expectations, brands must prioritize privacy and transparency to maintain their trust. This means brands must communicate their data collection and usage practices clearly and obtain customer consent with language that anyone can understand. Brands should empower customers to maintain control over their data, respecting privacy preferences and, especially implementing robust security measures. By communicating how they prioritize data protection, privacy, and transparency, brands can establish a solid foundation of trust that encourages customers to share their data.
Conclusion
As brands strive to boost conversions and achieve a higher ROI, personalization has emerged as a vital strategy in the customer experience landscape. By harnessing the power of personalization strategies and tools, brands can elevate their customer experiences, boost conversions, and maximize ROI. Indeed, DynamicYield, a personalization platform, outlines several success stories resulting from personalization efforts they’ve implemented for clients, including a 39% average revenue per user (ARPU) lift on home page visits for Jewelry.com, and a 6x return on investment for Sephora.com.
Leveraging first-party consumer data, implementing segmentation, utilizing recommendation engines, embracing dynamic content personalization, and prioritizing privacy and transparency are essential for brands to thrive in the era of personalization and meet customer expectations effectively.
