Driving Personalization with Digital Footprints at Scale

Lisa Zizas

Lisa Zizas

October 20, 2023

A coupon for a free dinner at your favorite restaurant on your birthday, an email to remind you that the next season of your favorite show is dropping soon, a message beckoning you to return to your cart and complete a purchase – what is the common thread running through these messages?

You matter!

Over the past few years, personalization has been an important marketing strategy harnessed by companies to cater to the individual buyer's needs and preferences—the result being an enhanced level of customer retention and sustained consumer engagement across channels. Personalization is a win-win strategy in today's retail landscape with B2B and B2C marketers worldwide. In the report "Customer experience personalization and optimization software and services revenue worldwide from 2020 to 2026," Statista projects global revenue touching $11.6 billion by 2026, with many companies already upping their marketing spend on this category to consume half their budgets.

Using digital footprint to drive personalization

Personalization is a sum of many parts, with AI now playing a leading role in this script. This one-to-one approach prioritizes qualitative messaging using real-time data over the quantity-based approach that treats customers as an indistinguishable group with homogenous needs.

In a 2023 report on Personalization Marketing, McKinsey cited statistics that make a commendable case for personalization to increase the ROI on marketing by 10-30% and decrease customer acquisition costs by half while improving revenues by 5-15%.

Every time a shopper shares data over e-commerce apps, these retail preferences or digital footprints are used to construct personalized messaging. In a 2022 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, and 39% said they trusted AI to optimize email send time. AI is built to convert consumer data into customized email messages ranging from cart abandonment reminders to sale announcements and personalized coupon codes.

Recommendation Engines are another example of this preference-based marketing and facilitate upselling and cross-selling across omnichannel marketing campaigns. The system uses machine learning tools to identify customers' digital footprints to offer items to suit their patterns and preferences. Product recommendation, content recommendation, e-commerce product recommendation, and streaming recommendation engines together are set to corner a market of $12.03 billion by 2025, with a CAGR of 32.39% during 2020-2025 per the report - "Recommendation Engine Market Report – Forecast (2020-2025)", by IndustryARC. 31% of e-commerce site revenue is generated from customized recommendations, and almost 35% of Amazon's revenue is generated by its recommendation engine.

Points to ponder with personalization

As personalization brings a sense of exclusivity and attention to the customer, it also comes with a set of challenges. The primary challenge is related to data. The protection of sensitive data weighs on consumers' minds. While an interactive Accenture report shows 83% of consumers are willing to share their data to build a personalized experience, they want to be sure businesses offer transparency about how they are using data and that the end -customers have control over it. 64% were wary that brands had access to information about the consumer that they didn't share knowingly or directly.

Secondly, while well-intentioned, not all personalization is created equal. Consumers have reportedly found it intrusive and unnerving when they receive a text or mobile notification from a brand as they walk by a physical store or get ads on social media for items they have browsed on a brand website.

Thirdly, facilitating a two-way communication channel between the consumer and creator helps build trust and offers a sense of agency and ownership. Living profiles go beyond the what of a customer's digital footprint to the 'why' behind the choices. For example, in book recommendations that go beyond genre preferences to curate lists based on fashion, a living profile would include style, fabric, size, and sustainability metrics to show how a customer's style has evolved with changing times.

As brands look to offer personalized messaging across platforms to engage with customers across the Customer Lifecycle, they must provide privacy and allow agency while harnessing the power of AI and sophisticated data analytics to keep the 'person' at the heart of personalization.