1-to-1 Personalization describes the practice of delivering a unique, optimal digital experience for each customer using all available data from 1st and 3rd party sources. In order to take action in real time to deliver a customized experience to every visitor across channels, 1-to-1 personalization requires rapid data aggregation and analysis, cross-channel deployment, and machine learning optimization. The term 1-to-1 personalization is derived from the more general term personalization and is interchangeable with individualization.
Key Components to 1-to-1 Personalization
With consumer expectations at an all-time high, boilerplate web experiences that fail to appeal to users on an individual basis can hurt sales and revenue. Never before has it been so important to recognize users that have visited your website before and what their behavior has been. At scale, an engaging, personalized user experience (UX) can lead to better customer retention, recurring sales, and referrals.
Product recommendations dynamically show products based on user data like gender, size, colors and style preference, and other product variants that the user has chosen in past web shopping. You’ve likely seen prompts and recommended products on retail websites before. “You Might Like…” and “Others Also Purchased…” are common prompts on ecommerce websites that signal a product recommendations engine at work. While their impact on your shopping habits may seem subliminal, retailers have found that implementing recommendations can consistently increase average order values and revenue by keeping customers engaged and helping them find the right products.
Customer context, including everything from recent browsing history to local weather, plays a big role in the success of your product recommendations. Kibo’s Monetate Intelligent Recommendations solution combines real-time contextual customer data with priority sets and rules set by your marketing team to determine the best recommendations for each visitor in the moment, driving higher engagement and revenue.
Omnichannel personalization creates an individualized experience for users across devices, channels, and in shopping experiences beyond the web. Using real-time data aggregation and analysis, individual user behavior from every channel automatically informs 1-to-1 personalized customer experiences across brand touchpoints including websites, apps, emails, in a retail setting, and even in the call center.
How Kibo Creates 1-to-1 Personalization Experiences
Kibo’s Monetate Personalization Engine collects and analyzes user data from multiple 1st and 3rd party sources, combining information that includes browsing history, location, shopping preferences, psychographic data, demographic information, and behavioral data. This user data then filters into Monetate’s machine learning algorithms, which use artificial intelligence to determine and display the most personally relevant dynamic content for each user based on the collected data and complex algorithmic decisions.
Office Depot, a leader in the 13 billion US office supply market, saw an increase of nearly $6.9M in revenue as a direct result of 1-to-1 personalization implemented by the Monetate Intelligent Personalization Engine.
As Office Depot and other organizations have discovered by using our platform, displaying precisely targeted information at the right time to the right user decreases bounce rate, maximizes conversion rates, and encourages recurring sales and visits.