Turning Customer Insights Into a Personalization Strategy

The evolution of customer expectations toward personalized experiences has radically altered the way companies approach UI/UX design and digital strategy. Major consumer brands like Amazon and Walmart have reset consumer expectations by offering highly personalized experiences that reshape how consumers interact with and expect other brands to operate.

As smaller enterprises try to catch up, they often look to these leading brands for inspiration. Our clients — both large and small — commonly reference not only Amazon but also companies like Spotify as benchmarks for this form of experiential marketing.

Amazon led the way in this field since 2013 with its product curation and recommendation algorithm, while Spotify developed an algorithm to determine a user’s “taste profile” based on listening behavior.

Meanwhile, at Walmart, personalization is only one of a three-part AI overhaul that also includes associate operations and supply chain optimization.

It’s fair to say many consumers — and perhaps even some business owners — don’t realize the extent of personalization happening behind the scenes, but increasingly, big companies use their access to data to create highly targeted experiences.

These major players have set a high standard for personalized experiences, leading others to explore similar strategies to stay competitive. According to VentureBeat, the vast majority of companies use AI-driven personalization in some way to drive growth.

Let’s assess some of the benefits of the primary strategies for personalization.

Benefits of Personalization Strategies

Personalization strategies deliver several key benefits, particularly in terms of customer loyalty and revenue growth. By tailoring experiences to individual preferences, companies create a more engaging and rewarding environment for their customers.

Here are a few of the different applications of personalization:

• Loyalty and Rewards: Programs that offer personalized rewards based on customer preferences can foster loyalty and encourage repeat business. Especially relevant for commodity businesses (such as grocers) that compete on loyalty, targeted experiences and exclusive offers provide customers with tangible reasons to stay engaged.
• Targeted Experiences: Using data to personalize ads and product recommendations offers users a more relevant and contextually appropriate experience. This approach can lead to higher conversion rates and customer satisfaction.
• Communication and Messaging: Personalization also extends to communication and messaging strategies. By crafting messages tailored to individual preferences and needs, companies can better connect with buyers and build stronger relationships with their customers.
• Customer Support: Empowering customer support teams with embedded personalization tools enhances customer service, allowing employees to provide customers with more personalized interactions and solutions.
• Operational Efficiency: For your internal customers (your employees), personalization can improve operational efficiency with customized tools, dashboards, etc. for individual roles within an organization, facilitating a smoother workflow.

Challenges in Developing Personalization Strategies

Despite the benefits, companies face significant hurdles when gathering customer insights for personalization strategies. One of the primary challenges is the temptation to prioritize technological solutions over addressing the real-world problems or struggles customers face. This approach often leads to privacy concerns and a sense of violation among users, not to mention tools with poor ROI and a lack of connection with validated customer pain points.

Another common hurdle is moving too quickly, especially when jumping into predictive personalization strategies. To avoid this, companies can practice “progressive disclosure,” building trust over time by providing clear value in exchange for personal information.

In other words, give your customers a reason for collecting their data, emphasizing the value they receive in return. When they then share their information, use it meaningfully, creating an exchange that benefits both parties. Storing customer data without a clear reason or value proposition for the customer is ultimately a liability (which is why we urge discretion around data strategies and even data minimization practices).

I’ve written previously about the paradoxes of marketing, especially when it comes to Gen Z. The high-wire tension between personalization and privacy, avoidance and annoyance, remains as precarious as ever.

However, this study from the Journal of Consumer Behaviour delivers a welcome surprise, revealing a substantial uptick in demand for AI-curated web services and apps among Gen Z. With 75% of surveyed Gen Zers saying they’re likely to reject brands that don’t offer AI-curated-and-customized offerings (compared to 66% for all consumers), the future seems primed for personalization.

Successful Use Cases of Personalization

Several standout use cases exemplify the successful integration of customer insights into digital strategies.

Spotify’s Discover Weekly playlist is one example of successful personalization. By leveraging first-party data about users’ listening habits, Spotify seems to have cracked the algorithmic code for creating unique playlists that keep users coming back for more.

Sephora’s Beauty Insider program effectively uses customer data to offer personalized product recommendations, exclusive offers, and even birthday gifts. The program’s clear value proposition has contributed to Sephora’s loyal customer base.

But these are just the tip of the iceberg when it comes to the ubiquity of personalization in the commercial sector:

• Gatorade tracks users’ sweat for a personalized sweat profile with a special patch that calibrates precisely how much sodium and fluid is lost during exercise.
• Whole Foods’ app keeps each customer’s purchase history, driving new product recommendations and targeted notifications.
• Nike implemented its 3D sneaker customization platform, through which customers can build custom-designed kicks and get benefits with a personalized NikePlus loyalty program.
• Tesla has pushed for seamless integration of personalization technology, including driver profiles that “remember” preferences for seat, steering wheel, mirror, and radio presets, even logging the telemetry of the vehicles’ suspension, brakes, and individual driving styles.
• Covergirl combined experiential marketing, augmented reality, and personalization to create “AR glam stations” tended by human beauty associates. These in-store demonstrations helped recommend beauty products to customers based on their skin tone, facial features, and even emotions. (A great example of not only personalization but also of the symbiosis and collaboration between humans and AI in a commercial environment.)

These use cases will likely come to represent the Wild West days of AI personalization, but for now, they demonstrate how brands can deliver personalized experiences with meaningful value propositions for consumers and end users.

Practical Steps for Implementing Personalization Strategies

For companies looking to refine or kickstart their personalization strategy, a few practical steps and considerations can guide their approach, especially in the context of technology and engineering. The following steps offer a roadmap for successful implementation:

1. Start With a Clear Problem Statement: Understand the problems or struggles your customers face before diving into solutions. This ensures your personalization efforts address real needs.
2. Implement Progressive Disclosure: Build trust by disclosing data collection practices gradually and providing clear benefits for customers who share their information. Give people “what they need when they need it.”
3. Customize Personalization Approaches: Personalization isn’t one-size-fits-all. Explore the different approaches — customization, segmentation, and individualization (more on these below) — to find the right fit for your brand and customer base.
4. Ensure Data Strategy / Organizational Alignment: Guarantee you’ll have the information you need when you need it with questions like: How is our data quality? Do we have data modeling? Is there system integration between content, communications, and data? Do we have analytics and feedback loops to facilitate continuous learning?
5. Emphasize Privacy and Control: Transparency is crucial. Give customers control over their data and respect their privacy choices. This approach builds trust and fosters long-term relationships.
6. Remember Operational Efficiency: Personalization isn’t just for customers; it can also improve internal operations. Tailor internal tools and processes to individual roles to enhance efficiency.

3 Personalization Approaches

The three personalization approaches mentioned above — customization, segmentation, and individualization — are the primary stages of the maturity curve of personalization strategies. While many companies deploy all three together in various configurations, understanding each on its own terms helps to broaden your view.

Personalized Experiences: Customizing Experiences According to Individual Preferences

Customization is the most straightforward approach to personalization, tailoring experiences to individual preferences and providing a more humanized experience. It gives consumers control over specific elements of a product or service, such as alerts, and offers a sense of ownership, autonomy, and personal connection. For example, ecommerce platforms often allow users to customize their shopping preferences.

Customization ranges from changing user interfaces to selecting preferred content. In a broader context, companies may offer customizable product configurations or allow users to create personalized playlists (see Spotify) or dashboards (Google Analytics).

Even something as simple as addressing users by their first name fosters a stronger relationship with a brand, but reactive and predictive adjustments, such as sending communications timed to move the consumer along their journey, provide a convenient customer experience while improving business outcomes.

Targeting and Segmentation: Grouping Consumers Based on Shared Characteristics

Segmentation involves dividing your consumer base into distinct groups based on shared characteristics or behaviors. This siloed approach is driven by decision engines and often relies on rule-based frameworks.

Marketing teams use segmentation to define groups based on a combination of demographics, psychographics, and/or behaviors, allowing companies to deliver the most relevant content and advertisements to each customer group. As it matures, this strategy moves companies from broad messaging for general audiences to tailored messaging for segmented groups to hyper-targeted messaging for individuals.

Intelligence and Approach: Individualization Powered by AI

Individualization is the most advanced form of personalization. Unlike segmentation, which groups consumers based on predetermined common traits, individualization tools learn over time, adapting to each user’s behavior and preferences to deliver a unique, one-to-one experience.

In the past, individualization was driven by simple if-then triggers or channel-centric rules. Today, companies like Amazon and Netflix have conducted veritable clinics on using the latest in generative-AI-recommendation algorithms to learn from user interactions.

It’s not an overstatement to say that individualization has become a cornerstone of the digital economy, helping companies maintain a competitive edge by offering highly customized experiences that keep consumers engaged.

The Future of Personalization in Digital Experiences

The future of personalization in digital experiences is moving toward a more data-sophisticated approach. Companies will have to balance maintaining a qualitative human experience with the increasing need for data collection to power personalized services.

Identifying and understanding “struggling moments” around products and gathering data in a privacy-forward manner will be crucial for maintaining customer trust. Presently, less than half of consumers trust brands to keep their personal data secure and use it responsibly. This highlights the critical need for transparency around AI and data privacy, as well as the importance of using personalization responsibly.

As we move into an era of greater personalization, companies that stay mindful of privacy concerns and ensure their personalization strategies align with customer expectations will enjoy substantial benefits in both customer satisfaction and operational efficiency.