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Anna Papachristos | July 9, 2014

Data-Driven Individualization's "Relevant" Revolution


477605239.jpgConsumers currently share more personal data than ever before. However, in many instances, the companies that collect said information fail to integrate this insight in ways that improve the customer experience. Though they actively apply consumer preferences to boost personalization, they neglect to move beyond the general everyman, thereby focusing on the average customer, not the individual.

But, as brands continue to wade through these unknown waters, individualization continues to prove more beneficial than personalization alone due to its granular, targeted nature. Here, we speak with Waqar Hasan, senior vice president of Big Data at Apigee, to explore data-driven individualization's powerful role in maintaining customer satisfaction, loyalty, and retention, as well as its increasingly important impact on relevant messaging:

1to1 Media: What are the primary differences between personalization and individualization? How does individualization make up for what personalization lacks?

Waqar Hasan: Marketers have long talked about personalization, but most have really only been engaging in mass profiling and coarse segmentation. For example, let's consider ad targeting, which is often based on easily deceptive segments--not every 30-something female living in the suburbs wants coupons for diapers. When enterprises talk about personalization, they are talking about classifying you into one of a handful of segments and then marketing to the "average" consumer persona in that segment.

But the vast amounts of data available today can help breathe new life into personalized customer interactions, moving to a new brand of individualization. Individualization is about marketing to your needs and wants as opposed to the segment average. Jane, the 30-something suburban female may be in-market for a big screen TV and a racecar. Personalization misses that people are multi-faceted by focusing only on attributes that are most important on the average. Individualization uses Big Data to give consumers the fastest possible path to what they want. Unlike personalization, this is meaningful value for which consumers are willing to share data.

1to1: How can companies go about pulling together the necessary data from various channels in order to deliver individualization? What tools and strategies must they have in place?

WH: From a strategy perspective, companies must design compelling experiences such that customers see incremental value as they agree to share more data. Examples today include sharing your location with an app in exchange for recommendations on nearby restaurants or sharing your identity with Google in exchange for more relevant search results. It is important to use data in a manner that retains customer trust and it's helpful to take the viewpoint that the customer fundamentally owns data. The Big Data transformation is making available a number of tools and technologies to use data for individualization. One of the key technologies enterprises can tap into for individualization is machine learning.

Machine learning is modern technology for predictive analytics that has been refined over the last decade at top Internet companies like Google, Yahoo! and Amazon. Unlike traditional statistical software, it's able to use signals in Big Data, such as the real-time location of mobile devices, comments on social networks, and fine-grained behavior data from websites, email, and store visits. It permits computers to adaptively learn from Big Data without requiring programming. The main benefit is that predictive models developed using machine learning are more precise than those developed using traditional statistical technologies. Increased precision of predictions drives relevance, which is essential for individualization.

1to1: What are the benefits of individualization vs. personalization? How does individualization impact customer experience, satisfaction, loyalty, and retention?

WH: The main benefit of individualization is relevance. Relevance is the difference between delight and annoyance for customers. Today, customers are inundated by personalized offers and experiences across every touchpoint. There's fierce competition out there for customer attention. The lack of relevance is causing information overload and customers increasingly simply ignore irrelevant communication. This is why individualization is an imperative, both for capturing customer attention and maintaining it.

Used wisely to predict customer needs, digital data can transform how people use their time. Leveraging data to make an immediate and accurate guess about what consumers want the moment they visit a website, start an app, or interact with an enterprise business process means that humans could get 90 percent of their time back--time that is normally spent searching and navigating. In addition to time saved, individualization can better answer this question: "What does the customer want?" or even, "what will this customer want?" Every new dimension of data uncovered enables enterprises to better decipher human desires and in turn, tailor interactions to enhance their experience in a meaningful way.

One example of this: In the case of healthcare, a wearable device offered to patients to more accurately track compliance with their prescribed exercise routines, ensuring better outcomes for cardiac patients. The opportunities for providing consumers with more profound value through this new breed of data-driven individualization are endless. By offering consumers this increased personal value, enterprises boost customer satisfaction, enhance the customer experience, and win customer loyalty.


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