Make your collected data productive.
Most executives know that collecting data is central to making operations efficient. However, the typical data collection systems don’t allow companies to extract the full value from that data.
Companies have to stop thinking that collecting and storing data is enough. Whether structured or unstructured, the purpose of data is to extract insights to support more accurate decision making.
Today, data scientists, trained to decode the meaning within the data, are “must haves” for companies that want to uncover new levels of intelligence. Managers and executives, across departments, who don’t yet see data science as critical to corporate strategy will continue to look back at missed opportunities when it’s too late.
Today’s business “FOMO” (fear-of-missing-out) is a high stakes game—if companies don’t get data science initiatives running quickly, they’ll miss out on reduced operational costs, deeper customer engagement, innovation and most importantly—top line growth.
A LinkedIn study found the number of data scientists has doubled over the past four years. Headcount has increased across all types of industries and departments because data analysis allows managers to look beyond investment risks and base decisions on real trend information from inside the organisation. While data science is becoming critical, it’s not necessary to hire a team of scientists—software is becoming more and more sophisticated to help achieve the same results.
There are mobile and web analytics tools on the market like Mixpanel and Looker which support analysts, product managers and engineers in better understanding the performance of their software and how their users engage with them. Kissmetrics is another tool for digital and content marketers to analyse what’s working and what’s not working, to change the user journey and to prompt visitors to click. But that’s just skimming the surface of how data can be used to drive results. From product development, engineering, finance, marketing and beyond, analytics is taking the guess work out of decision making.
The immediate obstacle is in adoption. Making data science an imperative at a company may be a cultural challenge. Changing the way people think about data or hiring people who see data as intel that shares a richer story about a business, is an operational investment, but one that will eventually yield dividends.
Whether you’re trying to optimise inventory management for ocean freight or to understand a consumer’s decision to purchase one salad dressing over another, data science, at its roots, is really about people. Ultimately, data science strives to understand the human condition and what drives peoples’ behaviour. The best way to understand consumer behaviour is to analyse customer interactions with speech and text analytics. Measuring the sentiment and emotion in the voice of the customer is a company’s most important step toward putting the customer at the centre of corporate strategy.
Sure, every company says they are customer-centric. But it’s not about being “centric.” A company has to be customer conscious, with all of your corporate and digital senses.
As companies undergo digital transformations, data analytics will be the first step toward customer consciousness. As McKinsey noted, companies will be able to “anticipate emerging patterns in the behaviour of customers and tailor relevant interactions with them by quickly and dynamically integrating structured data, such as demographics and purchase history, with unstructured data, such as social media and voice analytics.”
Before the growth of data science and machine learning, we were unable to attain customer consciousness, an organisation’s ability to think and act as the customer using knowledge, experience, and insight—all powered by data. Management teams now have the opportunity to have more holistic perspectives and evidence-based strategies than ever before.
Customer consciousness is the byproduct of an organisation employing data to be more people-centric, and the contact centre is the origin of the customer. Speech and text analytics help to catalyze the contact centre to becoming a nexus of deeper customer engagement, where innovation is sourced through customer recommendations. With stronger insights, companies also learn how to sell more to existing customer bases.
Companies that understand their customers and what their customers need have an edge over their competition, and venture capitalists are accelerating this demand in the enterprise. As reported in VentureWire, “investors poured a record $572.3 million into customer relationship management, the most ever in a single quarter for the category since Dow Jones Venture began keeping track in 1992.”
When a company leverages a data mart that combines analytics from the contact centre, workforce management, CRM systems, website, social media, surveys, product engagement etc., a management team will finally be able to answer some of today’s most complicated questions:
Data science is the most accurate way to invest in and reap the benefits of analysing the customer experience. Companies with the right software and talent can glean insights that not only reduce operational costs, but also inform decisions—heard first from the customer—that alter product development, reaching wider markets and yielding more revenue from existing customers.
The best news yet is that this practise is just beginning, and you’re probably not missing out. Yet.