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The Trade-Off Between Real-Time & Relevance

By
Gokul
• 
3 min
Real-time recommendations

From the dotcom bubble to COVID-19, the Internet has evolved with companies changing the way they do businesses online leading to increased modular digital experiences by creating varied lifestyle changes for consumers, especially in shopping and media consumption.

76% of consumers expect companies to understand their needs and expectations

Personalization for consumers is one such Gold-Standard experience in Digital Commerce and Media verticals and a proven approach to consumer retention, up-sell and cross-sell that result in quick ROI and high profitability.

What is Hyper-Personalization?

Hyper-personalization is the art of delivering 1:1 personalized experiences to consumers regardless of the number of consumers, products, content, and complexities in the data. The crux of hyper-personalization lies in the ability of the company to accurately predict consumer behavior whenever it changes, in real-time.

Real-time

Let’s say a company has a personalization-pit-stop time to understand consumer behavior and ease their decision making. But, unfortunately, personalization-pit-stop time for companies ranges from months to years.

Relevance

An AI-powered prediction system can enable Enterprises to drive engagement and improve conversion rates significantly provided the accuracies of predictions are in 90% range. But, unfortunately, that is just a dream for most Enterprises!

Real-time Relevance

The question here is how to fit all the apt variables around everchanging consumer behavior, without compromising on relevance and executing at lightning speed, making the personalization-pit-stop time minimal or say, close to real-time?

Technical challenges in predicting consumer behavior accurately at scale

There are two big challenges though in predicting consumer behavior accurately at scale

  1. Poor data quality: Data wrangling becomes extremely difficult when the incoming consumer behavioral data streams are in high volume and velocity, coupled with complicated data quality issues like unstructured data format or no data at all.
  2. Manual engineering: The end-to-end process of converting raw data into truly meaningful business insights is scattered and fragile often leading to poor results and never taken-off proof-of-concept.

The only solution for these challenges is to optimize and automate these key processes

  1. Data Curation — Automation of 80% of the manual and repetitive tasks can reduce the overall data ingest timeframe from months to hours. Through AI, companies can automate tasks like data cleansing, protecting sensitive information, and removing duplicate data. A self-serve console can be utilized for pipeline generation, management, and scheduling.
  2. Autonomous AI — Machine learning models digest arrays of data called “feature sets” to process and produce results. Increasingly datasets are large and complex. It is important for companies to addresses this problem using the same type of deep-learning AI that powers image recognition and autonomous vehicles. By applying unsupervised learning to detect patterns in complex data sets and grouping these into feature sets, one can dramatically improve predictive accuracy in their platform.

AI is used in various applications ranging from Autonomous cars to Genetic Engineering. AI can also be used to build AI. The complex data quality issues and manual engineering efforts can be eliminated by using advanced machine learning techniques like deep-learning and thereby predicting consumer behavior at scale accurately.

Hyper-personalization at scale is not aloof anymore. By using the right technology at the right time companies can truly provide a differentiated consumer experience and stay on a competitive edge.

Argoid enables Digital Commerce and Streaming Media Enterprises to boost customer retention KPIs significantly by providing 1:1 personalized Search & Recommendations to Consumers in real-time. Argoid uses AI-enhanced auto data curation and Autonomous AI in predicting consumer behavior with unparalleled accuracies at scale.

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