Hyper-Personalization at Scale

Muthumari S • June 06, 2019
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We are living in the era of the ‘Digital Natives’, where consumers increasingly expect a seamless, integrated, consistent and hyper-personalized experience across their digital footprint. “Information” is the key phase in the customer journey for taking informed data-driven decisions.

With more choice, more competition and more information, consumers have “more power”. The fickleness of the ‘Digital Natives’ could be because they are baffled by the huge choice of information and find it difficult to choose from the wide variety of products that keep flowing into the market. Delivering users with precise product recommendations has become the necessary force to drive retention science through quick experiments and continuous measurement.

With modern evolved consumers’ several modes of communication, integrating the consumer’s behaviour across channels to provide an omni-channel hyper-personalized experience through their digital journey is again a challenge starting from data collection across devices to making sense of the different logs to building the right ML models at scale. Customer lifetime value adds a considerable weight to this exercise as it provides us with the power to govern the type of experiences that will click right with market pulse.

Today, recommender algorithms like collaborative filtering are readily available in ML toolboxes, but often these algorithms fall short of creating right recommendations for unique business needs and yields poor results. Algorithms are commoditized, and the differentiation can come in either from the right data or right implementation.

With over billions of photos uploaded every day and visual content being 40 times more likely to be consumed than other forms of content, it’s a no-brainer that Visual Listening is the way forward for providing the right recommendations. But, integrating “Image analytics” use-cases with the recommender systems while attempting to execute them at scale is the biggest challenge. In addition to image analytics, Conversational Computing also holds immense value for enterprises owing to the humungous amount of data captured being of textual nature.

There is humongous amount of data getting created every nanosecond. The expectation of the industry is changing to perform tasks that are quite like human intelligence real-time, that requires cognitive technologies and big data on a huge scale.

We need enterprise-grade solutions to automate the tasks that require human perceptual skills, like face and writing recognition, text mining that involve analysing, reasoning and predicting “at scale”. Data scientists and big data developers/architects are to be cross-skilled and should operate with “growth hacker” mindset.

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