Now It’s Personal: AI Redefines Customer Experience

AI is poised to deliver on the larger promise of personalization—going beyond broad demographics to create authentic connections between personal interests, individual behaviors, and buying decisions.  Personalization to date has been a double-edged sword.

Source: Now It’s Personal: AI Redefines Customer Experience

Personalization to date has been a double-edged sword. On the one hand, it’s given customers and products an entirely new and indisputably superior tool for finding each other. On the other hand, in its nascent stages, personalization has been imprecise at best, inaccurate and intrusive at worst. Still, customers are deeply interested in personalization when it works: In a 2020 global Salesforce study, nearly 52% of customers confirmed they expect personalized offers, up from 49% in 2019—and personalized customer experiences play an important role in winning their business.

The first forays into personalization—Personalization 1.0—are typically limited to traditional market segmentation, such as gender, age, and income, and tend to be reactive rather than proactive. Characterized by lags and missed opportunities for tailored experiences and products, Personalization 1.0 often comes up short against ever-evolving customer interests and expectations.

As AI catapults into the mainstream, however, data gathering and analytics are growing increasingly sophisticated, bringing improvements in the breadth, quality, and reliability of data as well as the speed at which data can be collected and converted into meaningful insights. The combination of advances in AI, data, and analytics is driving the next wave of personalization, enabling organizations to more accurately predict customer needs and preferences and provide the positive, tailored experiences that lead to loyalty. Welcome to Personalization 2.0.

Applying AI, Data, and Analytics

In the move to Personalization 2.0, the scope and volume of collected data increases, encompassing touchpoints all along the customer journey that reveal individual preferences and consumption habits. With AI capabilities such as natural language processing, deep learning, and machine learning, diverse types of structured and unstructured data—such as speech, video, text messages, and GPS information—can be integrated into each customer profile. Applying predictive analytics to this 360-degree view of each customer, companies can create a highly personalized omnichannel experience, tailoring each interaction in the customer journey in real time.

AI is at work when a music-streaming app makes tailored recommendations based on past consumption. At every point of interaction during the streaming experience, data is captured to derive insights into an individual’s music preferences and can help a listener discover new musicians or bands. These data points, coupled with third-party information such as location data or digital content with which the listener interacts, can help build a complete view of a customer, further enabling personalized playlist suggestions. As such, linking all aspects of the customer’s experience can build a deeper, more meaningful view of how to shape personalized product offerings, online experiences, pricing strategies, and mobile applications.

3 Pillars of Personalization 2.0

Understanding customers, transparently collecting customer data, and then using that data to power personalized experiences that customers value is central to Personalization 2.0. Three pillars can help guide personalization efforts toward achieving these aspirations and avoiding common problems.

Become a thoughtful steward of customer data. Designing the customer journey with specific decision points and relevant interaction can make it easier to track the omnichannel purchasing habits or needs of each customer while protecting privacy and security. A thoughtful steward of customer data ensures proper mechanisms are in place to use data appropriately and to benefit the customer and the business:

• Design each digital touchpoint to collect and use data in exchange for value.
• Identify which data from one touchpoint can enable personalization in other touchpoints.
• Monitor data use regularly for compliance with data privacy regulations and safeguards for data protection.

Following this model, a company could make real-time recommendations to customers to accompany recent purchases or transactions. For instance, an individual browsing online for a cast-iron skillet might, upon visiting a grocery store later that day, receive a notification on his or her phone recommending skillet-ready recipes and the aisles in which ingredients can be located.

Obsess continually about individual customer needs. Recognize that each customer has unique, evolving needs and preferences. Transitioning from a reactive to a proactive perspective is required to anticipate changes and tailor products or services to meet new needs:

• Establish a 360-degree view of each customer that enables an individualized experience—every time.
• Provide individualized content across channels for an omnichannel experience.
• Access customer data in real time to dynamically calculate intent and cater to it in the moment.

To extend the example above, by aggregating data across multiple customer journeys, a retailer could build a well-rounded view of its customers’ cooking habits and better personalize their online and in-store experiences accordingly, pointing shoppers toward cookware and ingredients likely to suit their palates.

Use data ethically and avoid “creepy” personalization. Customers are increasingly skeptical and hesitant to share insights into buying habits, personal information, and other data required for effective personalization. Demonstrated ethical use of AI and data, along with strong protection against data breaches and leaks, can help build brand trust and mitigate potential risks associated with using customer data for personalization:

• Prohibit AI biases such as algorithmic-based discrimination.
• Identify and minimize excessive influence of AI on human behavior and decisions.
• Prohibit exploitative use of customer data for the sole benefit of the organization.
• Be transparent about how data can be used and give customers the option to opt in or out.

The Next Horizon: Personalization 3.0

Looking ahead, the third wave of personalization will likely be centered on hyper-personalization based on broader customer interactions across organizations, while AI advances based on real-time structured and unstructured data will enable fully virtualized, end-to-end digital customer experiences that more closely simulate in-person interactions. Deep learning will enable companies to better predict customer behavior and tailor recommendations; wider application of biometrics can enable more natural interactions between people and devices.

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Whether it’s today’s Personalization 2.0 or tomorrow’s Personalization 3.0, this trend will continue to be a strong factor in customer decision-making. Applied properly, AI will present powerful new ways to tailor customer experiences and distinguish brands from competitors. Through useful insights about individual preferences and buying habits, companies can anticipate needs and enable brand loyalty and connection to soar.

—by Baris Sarer, principal, Deloitte Consulting LLP; Abhi Arora, senior manager, Monitor Deloitte; and Michael Razzano, manager, Deloitte Consulting LLP[wsj-responsive-related-content id=”0″]

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