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What Is One-to-One Marketing
A one-to-one marketing strategy strives to create an experience that fits the individual customer. Rather than segment customers into groups, one-to-one marketing strategies treat each customer individually. This is similar to how sales representatives remember details about each customer, like date of births, and use them to improve services and increase sales.
In the past, owners of brick and mortar stores would greet each customer with a smile and recommend the products according to their knowledge of the customer. Today, one-to-one marketing is called hyper-personalization. Since the process requires real-time data and dynamic content serving, it is typically performed by artificial intelligence (AI) software.
Examples of One-to-One Marketing
Many companies now strive to provide customers with a hyper-personalized experience. Here are some notable examples:
- Netflix: The streaming platform uses an algorithm to offer personalized content based on previous actions taken by subscribers. By tracking these actions and offering relevant content, the algorithm personalizes the browsing experience of each subscriber. The algorithm also personalizes the visual content, including cover images shown while subscribers are browsing.
- Marie Curie: To increase donations, the charity launched The Great Daffodil Appeal. First, they gathered the geolocation data of each supporter; then, they matched the data with a database of collection sites. They used the information to add a personalized real-time map to their email campaign, where they showed the nearest collection sites. The charity also used modeling to identify a relevant target population. They then used previously collected data to drive persona-driven messaging.
- Cadbury’s: Created their own version of Facebook’s personalized video — rather than showing consumers a look into the past year, Cadbury created personalized videos that matched consumers with a Dairy Milk flavor. Once a user agreed to connect with the brand, they used Facebook profile information, such as interests, location, and age, to automatically generate a personalized video. The campaign ran in Australia and obtained a 65 percent click-through rate (CTR) and a 33.6 percent conversation rate.
- O2: This cellular provider based in the United Kingdom (UK) created personalized ads that informed users about their mobile usage, current plan, information about upgrades, and the location of the nearest store. They generated approximately one thousand versions of the ad, using data from devices and locations. Users responded favorably to the personalized ad, which performed 128 percent better in terms of CTR than other ads previously promoted by the brand.
Types of Big Data Used in Marketing
Marketing professionals typically leverage three types of big data — financial data, operational data, and customer data. Each data type is usually obtained from multiple sources and stored in different locations.
1. Customer Data
Customer data helps you better understand your target audience. Here are common customer data metrics:
- Email addresses
- Web searches
- Purchase histories
Marketers also often collect and leverage data that can indicate audience attitudes and opinions, typically gathered from surveys, online communities, and social media activity.
2. Financial Data
Financial data helps you to measure performance and optimize for greater efficiency. Here are common financial data metrics:
- Sales and marketing statistics
- Costs and margins
- Competitors’ financial data, like pricing
3. Operational Data
Operational data helps you measure and assess business processes. Here are common operational data metrics:
- Customer relationship management (CRM) systems
- Feedback from hardware sensors and various sources
Organizations analyze operational data to improve performance and reduce costs.
How Big Data Enables One-to-One Marketing
The goal of one-to-one marketing is to tailor marketing efforts to fit each individual customer. The process provides a unique personal experience. Typically, this requires achieving high levels of communication with individuals, which translates into a boost in sales.
In addition to increasing sales, one-to-one marketing processes also help create unique relationships between brands and customers. However, before this connection can be achieved, brands need to better understand each individual consumer.
Big data technologies are essential for this process. To serve hyper-personalized content, brands need to create accurate profiles of consumers, including demographic and ethnographic profiles of brand communities as well as individuals. This requires a big data setup that leverages technologies, typically for the purpose of achieving the following key objectives:
1. Capturing All Potential Customer Data: Accurate profiling requires information. The more information the brand collects, the more insights it can generate. This translates into a better user experience. Typically, organizations collect social media profile information, clickstream data, browsing data, geotracking data, feedback, social media behavior, online and offline transactions, and more.
2. Performing Customer Segmentation: Segmentation helps you group people or companies with similar demographic profiles, purchasing patterns, attitudes, buying behaviors, and other attributes. This information can help you better understand customers and provide relevant offerings. Big data sets enable you to create more micro-segments.
3. Create Personalized Marketing Plans: For each customer, based on business objectives and predictive scores — to predict customer responses to personalized content, you can collect campaign results, responses, and feedback. Analyze this data by comparing it with the predicted behavior to further optimize your marketing efforts.
Big data plays a crucial role in creating hyper-personalized content. The more information the brand can collect on each individual consumer, the better they can serve the consumer with the content that suits them. Brands implementing one-to-one marketing report great success and significantly increased CTR and conversion rates. This is all made possible by big data technologies, usually assisted or driven by AI software.