Why do you need to invest in Unstructured data analytics for improving customer acquisition and retention? — Saxonglobal

Each minute generates ∼500,000 tweets, ∼53,000 Uber trips, ∼300 hours of YouTube videos uploaded every minute, and more. Hold your breath and now look at this: in 1964, 1TB of memory would have cost about $3.5 billion. Today? $47 . . . and it fits into your back pocket.

Added to this, consider the impact of technologies such as blockchain and the Internet of Things (IoT) on the velocity of data currently being collected. Data collection rates are moving at a rocket speed(if they aren’t already there).

In the Analytics Age, data (the majority of it is unstructured) is the currency, and decisions are made in real-time or near-real-time. Unstructured data is the lift, shift, rift, or cliff for any business today. The growing importance of unstructured data is recognized both by industry practitioners and academicians, which has resulted in tools and techniques for handling unstructured data. This new era is a playground for artificial intelligence, machine learning, and deep learning.

Previously, data intelligence and applied analytics were mostly living in research labs. In marketing, analytics climaxed with the emergence of the internet, the influx of voice search data, and online behavior.

Marketers earlier were praised for their intuition and creativity in developing and executing campaigns. Today the marketers begin by their campaigns asking questions such as:

• Which customers should we retain?
• Which prospects should we acquire?
• Which customers should we win back?
• Which customers should we upsell?
• To whom should we market?

Along with these, we see four new drivers that will influence the rise of UDA (Unstructured Data Analytics), which are:

2. Voice of the consumer/customer. We are leveraging the voice of consumers/customer to increase intimacy, satisfaction, up-sell, and profitability.

3. Scramble for semantic. Coupling text analytics with business intelligence to create self-learning artificial intelligence. Using a human-like approach to leverage context and concept when searching for a candidate’s resume from a resume database, answering user questions on the weather or on directions, responding to patients’ questions regarding their health. Recognizing images, videos, the concept, and the context of the words in any unstructured text or document.

4. Integration of text analytics in predictive analytics. Coupled with predictive analytics, text analytics transforms unstructured text, such as customer e-mails, chatbots voicemail transcripts, and social media activities, into actionable intelligence to address the most imperative business problems companies could face in the future.

This requires tools, techniques, and resources that sift through all customer interactions to understand their state of mind. Understanding the VoC goes beyond analyzing customer purchasing and usage behaviors. It involves listening to what they say and connecting it to what they do. If you need to gain a comprehensive view and understand your customer behavior, it is necessary to combine unstructured data from the VoC with structured data. UDA helps companies to fully understand their customers’ pain points while enhancing the accuracy and effectiveness of predictive models. UDA and predictive models can be embedded into business operations to create actionable value for your business.

Originally published at https://saxonglobal.com on April 23, 2021.