Driving Customer Engagement Through Effective Personalization

4/30/2024 Lisa Zizas

A coupon for a free dinner at your favorite restaurant on your birthday. An email reminding you that the next season of your favorite show will drop tomorrow. A message prodding you to return to your cart and complete a purchase. What’s the common thread running through these messages? You matter. 

Over the past few years, personalization has evolved into a critically important marketing strategy, harnessed by companies to cater to their buyers' needs and preferences. The result: an enhanced level of customer retention and sustained consumer engagement across multiple channels.

In today’s retail landscape, personalization is a win-win strategy among B2B and B2C marketers worldwide. Recent Statista research projects global revenue of the customer experience personalization and optimization software and services industry reaching $11.6 billion by 2026. According to the report, many companies are already upping their marketing spend on this category to consume half their budgets.

AI and machine learning lead the way

Personalization is a sum of many parts, with artificial intelligence (AI) now playing a leading role. AI can be utilized to convert consumer data into a variety of customized email messages, ranging from cart-abandonment reminders to sale announcements and personalized coupon codes. 

This one-to-one approach prioritizes qualitative messaging using real-time data over a quantity-based approach that treats customers as an indistinguishable group with similar needs. According to a 2023 McKinsey report, personalization increases the ROI on marketing by 10-30% and decreases customer acquisition costs by half — all while improving revenues by 5-15%.

Every time a shopper shares data over e-commerce apps, these retail preferences or digital footprints can be used to construct personalized messaging. In a Statista report based on market data sourced from professionals in the US, UK, India, and Canada:

• 47% of respondents trusted AI to target ads
• 42% trusted AI to personalize offers in real-time 
• 39% said they trusted AI to optimize email send time

Recommendation engines, another example of preference-based marketing, serve to facilitate upselling and cross-selling across omnichannel marketing campaigns. Here, the system uses machine learning tools to identify customers' digital footprints and then offer items to suit their patterns and preferences. 

Product recommendation, content recommendation, e-commerce product recommendation, and streaming recommendation engines are set to corner a market of $12 billion by 2025, with a CAGR of 32.4% during 2020-2025 according to a report by IndustryARC. The report further notes that more than 30% of e-commerce site revenue is generated from customized recommendations, and nearly 35% of Amazon's revenue is generated by its recommendation engine.

Personalization’s challenges

While personalization brings a sense of exclusivity and attention to the customer, it also comes with its share of challenges. 

The primary challenge relates to data: The protection of sensitive data weighs heavily on consumers' minds. An Accenture report shows that while 83% of consumers are willing to share their data in order to build a personalized experience, they also want to be sure businesses offer transparency about how they’re using data — and that end customers have control over it. The report suggests that more than 60% of consumers are wary that brands have access to information about the consumer that they didn't share knowingly or directly.

A second challenge is that not all personalization is created equal, even if well-intentioned. For example, consumers have reportedly found it unnerving when they receive a text or mobile notification from a brand as they walk by a physical store. Others have indicated that they find it intrusive if they receive an ad on social media for items they have browsed on a brand’s website. 

Establishing a two-way communication channel between the consumer and message creator can help build trust. Living profiles — aka self-service preference centers — go beyond the “what” of a customer's digital footprint to the “why” behind the choices. For example, in recommendations for lists based on fashion, a living profile would include style, fabric, and size, as well as sustainability metrics to show how a customer's style has evolved with changing times.

The heart of the matter

As brands look to offer customized messaging across platforms, they must provide privacy and allow for control in order to build trust. While harnessing the power of AI and sophisticated data analytics to engage with customers, however, it’s essential that brands remember to keep the “person” at the heart of personalization. 

Lisa Zizas is the Vice President of Business Development and Marketing Partnerships for RRD's Precision Dialogue