To start, this massive amount of information made available is referred to as “big data.” But what is big data? The definition continues to morph as businesses grapple with what to do with the overwhelming amount of data they collect. With over 2.5 quintillion bytes of data collected every day, a recent Voucher Cloud infographic seems most appropriate in defining big data as the collection of large data sets such that it is difficult to capture, process, store, search and analyze using convention systems. But big data is available everywhere, what’s so complicated about big data?
Brandon Purcell from Forrester Research wrote in The Forrester Wave™: Customer Analytics Service Providers, Q3 2017, “According to Forrester Data’s Global Business Technographics® Data And Analytics Survey, 2017, only 25% of business and technology decision makers report seeing increased revenue from their implementation of big data solutions. This means a vast majority of companies are not effectively harnessing the insights in this data to win, serve, and retain their customers.”
Most businesses do not use big data effectively because it is challenging to understand what is relevant to them. Data that is relevant to one brand may not be available or relevant to another brand. IBM estimated that poor quality data cost the US economy $3.1 trillion in 2016, and nearly one third of business leaders were unsure of how much of their data was inaccurate. A Harvard Business Review article claims “the reason bad data costs so much is that decision makers, managers, knowledge workers, data scientists, and others must accommodate it in their everyday work. However, doing so is both time-consuming and expensive.” So identifying quality relevant data is key to reducing costs and providing meaningful insights.
What businesses are now realizing is they must identify relevant data within all of the big data they have collected, and become efficient in identifying meaningful useful quality data. They are learning to supplement zero and first party data with the right second and third party data to complete the holistic view of the customer, and use it to enable personalized experiences for their customers.
Four Steps to Deliver Exceptional Customer Experience
Delivering exceptional customer experience has four crucial steps:
A data-centric approach enables companies to succeed in today’s fast-paced marketplace and deliver a seamless customer experience throughout the customer journey on a richer and more personalized basis.
The first step in developing these insights is to gain a full understanding of your customer population. A customer profile analysis provides a rich, descriptive summary that you can use to ensure your target market does indeed match your existing customer personas. This process also allows companies to identify relevant and quality data. Additional key insights may be gained by applying a comprehensive set of third party data attributes. This addition of external data will help obtain a deep understanding of active customers’ characteristics and behaviors in terms of demographics, socio-economics, lifestyle/interests, ethnicity and geography. Brands can use the outcome of this analysis to refine or redefine their marketing efforts and create relevant messaging. A customer profile analysis is foundational to any targeting using predictive modeling or advanced segmentation if performed in the future.
What you learn from this customer profile might surprise you, will certainly help identify your relevant data, and may lead you to new target audiences. This will allow you to reduce your data costs and provide the best experience for your current and future customers.
Start identifying the relevant data that truly matters - your customers and Executive Team will reward you!