In today’s world where organizations are moving towards complete digital transformation, data has taken the center stage in the journey, and has been one of the most vital components of consistent growth.
Data is the King. Organizations that have more data about their customers have a better chance of conversion, engagement, and retention. More data points to analyze the customer means more trigger points for engagement. Customized offerings can only be ‘truly’ customized if organizations know their customer in and out. Knowing the customer well can help organizations to stay ahead of their competitors.
But where to find the data? And how to read it?
This is the biggest dilemma that organizations face today. There is an ocean of data out there that is still not touched. Using means and tools to gather structured data and performing analytics on top to arrive at decisions is the easier part. Structured data is just the tip of the iceberg. The challenge is to delve into the unstructured data and gather intelligence to deliver personalized offerings and experiences to the customer.
Organizations are spending a lot of time and marketing dollars in misjudged targeting or umbrella customer engagement with generic offerings and messaging that leads to a lower conversion rate. This can be easily optimized by understanding what the customer wants and deciding whether they are the right audience for the campaign.
‘Unstructured Data’ is data that does not have a recognizable structure and can’t fit into a structured sheet. Emails, webpages, social media profiles, open-ended surveys, voice commands are some of the examples of unstructured data. Trying to comprehend it is difficult but achievable with the right tools. Advancement in AI and ML has offered us new ways in which this data can be extracted, cleaned, and simplified, and visually displayed in ways organizations can comprehend. With the help of this data along with the structured data that’s already there, organizations can create ‘truly’ customized offerings that will enable better conversion.
For instance, a bank can sieve through social media feed of their customers and identify their purchase patterns, spending behaviour, travel patterns etc. to create a customized loan or card offering that might attract the customer for their future trip or a purchase.
To fully realize the potential of unstructured data, organizations must combine and assimilate data silos and create a scalable data lake. With systems to store and analyze data from a variety of sources and share it with decision-makers to act on, organizations can finally leverage it and derive enormous business value.
Three Steps to achieve Total Business Intelligence using Unstructured Data
- Identify sources of data
Identify data points around the customer that are a must for product development and marketing. Gather only relevant data and filter out the rest. Try to optimize and reduce the data to save data storage costs. Data sources can include information from online reviews, customer feedback forms, as well as information from the web and devices such as smartphones, apps, browser, etc.
- Design a concrete end-objective
The amount of data out there is colossal. Starting without knowing what to do with the data will only lead to more confusion. Data extraction has a cost and hence must be considered only after devising a clear road map on why, what, and how? Objectives can be as easy as understanding public reaction post a marketing campaign or how a brand is perceived among the target audience or to curate tailor-made offerings for the customer. Knowing an end-objective will significantly help in carrying out data extraction and analytics.
- Create data models that complement the end-objective
Once the data is gathered, it must be cleaned and simplified so that business intelligence tools can structure it and create reports that can facilitate business decisions. Analytics teams must create a data extraction architecture and data consumption process that works like clockwork. This streamlining can help them to optimize time and create results faster. Use of AI & ML will add to an upfront investment but will generate far superior returns with its faster data modelling and pattern recognition abilities.
