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The Art and Science of Hyper-Personalization
In today's cutthroat business climate, personalization is no longer a competitive advantage — it's a customer expectation.
But brands are having a hard time personalizing their customer experiences.
Along with employee skill gaps, lack of a clear strategy and inadequate metrics tracking, mastering personalization remains one of the top challenges for marketing teams worldwide. In a recent Sprinklr-sponsored survey, 38% of respondents mentioned lack of personalization as one of their top three operational challenges within the marketing function.
Source: IDC Global Marketing Survey, August 2024
Slowly but surely, brands have realized that simply knowing their customers isn’t going to be enough to stay relevant in the experience era; predicting their needs and delivering real-time, tailored solutions is what will set them apart.
For instance, Starbucks crafts unique customer experiences through its mobile app. The app’s machine learning algorithms analyze customer preferences and purchasing behavior to recommend drinks or offer special promotions at precisely the right time. By personalizing customer experiences in this manner, the coffee giant has seen a steady rise in customer engagement through digital channels.
Similarly, Netflix’s recommendation engine, powered by machine learning, contributes to 80% of its viewer activity. This level of personalization drives engagement and minimizes churn, helping the streaming giant hold its own in an increasingly competitive streaming market.
Isn’t it cool that brands these days are two steps ahead of their customers? They seem to know what they want and how badly they want it.
Yes, the world of hyper-personalization is fascinating, but the journey to hyper-personalization is both an art and a science, blending cutting-edge technology with data-driven insights and a human-centered approach.
In this blog I’m peeling back the layers of hyper-personalization so you too can bowl your customers over with proactive experiences.
- Collecting and unifying customer data for a ‘heightened customer experience’
- Hyper-personalization: just another fancy word for personalization?
- Applying hyper-personalization across the customer journey
- 5 steps to unlock the value of hyper-personalization during the holiday peak season
- The force multiplier
Collecting and unifying customer data for a ‘heightened customer experience’
Regardless of whether the dreaded “Cookiepocalypse” takes place or not, customer data is and will remain the base upon which you will build your customer experiences. And if yours is a large organization, there’s a good chance there’s data flowing in from various quarters. So, it’s important to connect them to better understand your consumers.
In this video, Sprinklr’s Global Principal Technologist Buddy Waddington talks about different types of data and the need to interlink them for a “heightened customer experience.”
Here are the most common data types you need to be aware of before you roll out your hyper-personalization initiatives.
Transactional data | Behavioral data | Experience data |
Information about the direct interactions between your company and customers, such as purchases, returns or account activities. It provides you with insights into revenue streams, product performance and purchase patterns, giving a clear picture of where, when and how your customers spend. | Track customers' online and offline actions as they interact with your brand, such as page views, clicks, browsing time or even app interactions. It reveals intent and engagement patterns, which can help you shape more personalized and effective marketing efforts. | Gather qualitative insights from customers about their perceptions, feelings and satisfaction with your brand through surveys, reviews or feedback. It gives you a good insight into customer sentiment and loyalty, highlighting areas where experiences can be improved to foster retention. |
Demographic data | Firmographic data | Psychographic data |
Demographic data includes age, gender, income, location, education level and other personal characteristics. Possessing this data helps segment your audience and tailor your marketing strategies to reach them more effectively. | Similar to demographic data but for businesses, firmographic data includes industry, company size, revenue and location. It’s especially valuable in B2B marketing, enabling more targeted engagement by understanding company-specific characteristics. | It comprises customer lifestyle, interests, values and opinions. This data provides deeper insights into why customers make specific decisions, allowing you to align your brand messaging with personal motivations and preferences. |
Geolocation data | Technographic data | Social media data | Attitudinal data |
Real-time or historical data on customer locations which can come in handy for location-based marketing efforts, including personalized local offers, mobile ads and foot traffic analysis for physical stores. | It tracks the technology, software and devices your customers use. Use it to understand customers’ digital preferences and optimize their digital experiences regardless of what mobile devices, apps or social media platforms they use. | Customer interactions on platforms like Facebook, Twitter, Instagram and LinkedIn provide insights into brand sentiment, campaign performance and customer engagement trends on social channels. | It captures customer opinions and attitudes toward various topics and brands, often collected through opinion polls, surveys or focus groups. It’s great for understanding how customers feel about brand values, product preferences and social issues, which can prove quite useful in guiding your brand positioning. |
All of this data together provides actionable insights that can be used to “drive meaning interactions” across channels, so, collecting and leveraging these different data types using AI is important to deliver a “heightened customer experience” that digital-first customers have come to expect.
Hyper-personalization: just another fancy word for personalization?
Personalization customizes content, offers and messaging based on fairly static or broad data points, such as demographics, preferences, purchase history or browsing behavior. Examples include personalized emails addressing customers by name, product recommendations based on previous purchases or targeting segments by location.
So, how does hyper-personalization differ from personalization?
According to Deloitte, “hyper-personalization is the most advanced way brands can tailor their marketing to individual customers. It’s done by creating custom and targeted experiences through the use of data, analytics, AI and automation.”
Seventy-one percent of consumers expect companies to deliver personalized interactions. While 76% get frustrated when this doesn’t happen.
Through hyper-personalization, companies can create a unique and contextual experience for each of their customers, based on their preferences, behavior and demographics. Implementing this strategy not only improves customer engagement but also drives sales, customer retention and brand loyalty.
Applying hyper-personalization across the customer journey
There are no two opinions as to how hyper-personalization can help you better engage with consumers throughout their buyer’s journey, from the initial touchpoint to post-purchase follow-up. And here are some ways brands do it.
Personalized content
Delivering customized content, such as product recommendations, email campaigns or website experiences, based on a customer's interests and behavior.
Segmentation
Segmenting customers based on their behavior, preferences and demographics allows you to create targeted and relevant experiences for different subsets of customers.
Real-time communication
Reaching out to customers through their preferred channels in real time, such as personalized product recommendations with instant discount offers.
Personalized products and pricing
Creating customized products or services based on a customer's preferences. Offering different prices or deals to different customers based on their behavior or demographics.
Predictive modeling
Using predictive modeling, machine learning and AI helps anticipate a customer's needs and preferences and create individualized experiences.
Omni-channel integration
Integrating hyper-personalization across online and offline channels to create a seamless and relevant experience across all touchpoints.
Here are some real-world examples.
Driving traffic and awareness pre-visit: Whole Foods wanted to build brand awareness and drive traffic to its newly opened branches. The company deployed geo-fenced notifications to drive store visits when customers were near either a Whole Foods store or that of a competitor. Roughly 5% of those who engaged with the notification visited a store, which is three times more than the industry average for post-click conversion.
Converting during visit: McDonald’s had identified increasing order size as a core element of its strategy, which led the chain to integrate the physical and digital worlds at its drive-thru outlets. It implemented decisioning-engine technology on the drive-thru menu boards to tailor the menu based on time of day, trending items, location and weather. These efforts made the ordering experience easier, driving stronger conversion and increased basket size, and resulting in customers easily finding the products they wanted and purchasing additional recommended products.
Deepening engagement post-visit: Sephora’s strategy to increase its customer base led the company to channel its efforts toward increasing store visits. It sent location-based notifications to mobile-app users, reminding customers of reasons to visit local stores. These notifications were tailored to customers’ communication preferences or deployed based on a customer’s last visit, to keep the retailer and its products top of mind.
5 steps to unlock the value of hyper-personalization during the holiday peak season
Here are the five things you can do if you’re looking to enhance your brand’s hyper-personalization initiatives this holiday season.
1. Leverage public sentiment data for holiday-specific insights
During the holiday season, customer sentiments and preferences are in full swing across social media and online forums. By using AI to pull “focus group fidelity insights” from these public conversations, you can better understand what holiday trends are emerging, which gift categories are most popular and how your customers are feeling about seasonal topics. As Waddington suggests, tapping into this “social experience data” can reveal ways your brand can authentically participate in these conversations.
Be sure to define and quantify your holiday hyper-personalization goals, focusing on customer-centric metrics like acquisition, satisfaction and retention to make this season your most impactful one yet.
2. Deploy real-time activation capabilities to meet peak season demand
Holiday shoppers are known for browsing, comparing and purchasing on tight timelines. To capture these customers effectively, your brand needs the capability to create and deliver hyper-relevant content in real time. Advanced analytics and AI-driven decision-making allow you to anticipate what customers are likely to buy, which promotions will likely resonate with the majority of shoppers and which products to suggest based on past behaviors.
Seventy-six percent of consumers said that receiving personalized communications was a key factor in prompting their consideration of a brand, and 78% said such content made them more likely to repurchase.
In addition, you need to establish robust tracking processes so that each holiday intervention — whether through email, social media or app notifications — is timely and effective, ultimately enhancing each customer’s journey as they progress from browsing to buying.
3. Focus on targeted martech investments to optimize seasonal objectives
With holiday shopping spikes and unique seasonal demands, it’s crucial to target specific outcomes with fit-for-purpose solutions rather than relying on every available tool. Focus your marketing stack on key holiday objectives, such as increasing cart value or improving gift recommendations, and prioritize investments that will deliver the best customer experience during this high-traffic time.
More importantly, align your resources and build a martech and data roadmap that positions your team to act on holiday trends and data-driven insights quickly.
4. Establish an agile operating model for rapid response during the holiday surge
Brands that succeed during the holiday season are those that can pivot and respond to trends, customer feedback and market shifts in real time. Establishing agile teams that span business functions allows each hub to address specific elements of the holiday personalization journey. From holiday promotions to delivery options, empower each spoke to quickly develop use cases that reflect shifting customer preferences, whether for last-minute shopping or early deals. This flexibility is crucial for navigating the holiday rush.
5. Invest in talent and training to scale hyper-personalization effectively
To fully capitalize on the holiday season, ensure your team is equipped with the skills to support hyper-personalization at scale. This might include expertise in digital marketing, e-commerce, advanced analytics and holiday campaign management. Map these capabilities against your current talent base to identify areas for quick training, upskilling or hiring as needed.
Keep in mind that investing in talent not only boosts your holiday efforts but also lays the foundation for ongoing and future hyper-personalization initiatives.
The force multiplier
Hyper-personalization is a force multiplier — and business necessity — one an overwhelming number of consumers now consider a basic expectation.
Organizations that can build and activate the capability at scale can put customer lifetime value (CLV) on a new trajectory — driving greater revenue growth and richer, more nurturing long-term relationships.
Good luck!
