Data is a priceless asset for businesses. It has always been around, but perhaps people realized its overwhelming significance in the last decade. Researchers, data analysts, and scientists — in fact, anybody who works with data unanimously agrees that procuring and using accurate data is not easy. But that does not deter people from using it.
In every aspect of our lives, we give and take a lot of information. We work with data when going about our duties, performing tasks, or doing any activity. We may not realize it, but a lot of information exchange occurs. Human beings work with data on a macro as well as micro-level. It is no surprise that data is referred to as the oil of the 21st century. Companies that do not use data and analytics will not be able to survive.
Every organization, big or small, strategizes and makes decisions based on the solid bedrock of information provided by data and analytics. This information is precious to organizations as it shows them the way forward. Obtaining it isn’t an easy process.
To get the necessary information, data is cleaned, processed, and managed systematically. Once this process is complete, the information obtained from the raw data is deemed fit for use. The enormity of the process makes information gathering seem difficult, but the result obtained is worth all the effort.
The proliferation of the Internet and accelerated digitalization ushered in a new age of multichannel communication. It has made consumers all-powerful. Today’s consumers are tech-savvy, intelligent, and not easily convinced. Consumers believe that their trust should be earned by any organization that wants to earn money from them. Businesses have evolved — from an impersonal, transactional relationship to an activity that is personal and holds significant emotional value for consumers.
Producers (manufacturers, sellers, service providers, etc.) pivoted to the digital age by using insights obtained through data and analytics. Insights help organizations take a closer look at consumers and better understand what they want. Insights obtained from data and analytics help organizations bring their understanding of customer requirements on par with reality. It helps businesses get inside the mind of consumers.
Getting to know what customers are thinking is a breakthrough. It helps companies evaluate their performance and take corrective actions before it gets too late. Let’s dive into the details.
Most businesses face the pressing problem of losing customers for whatever reason. Changes in customer behavior can leave business owners flummoxed. Everything seems fine one day, and suddenly, the customer decides to leave one company for another—for reasons best known to them. This lowers morale, causes loss, and can spell doom for the company if it goes unchecked.
The reason for customer churn could be caused due to a single reason or a combination of many issues. The problems need not be related to one another to create a repelling effect on customers. If customers are unhappy with a brand, they will move to its competitor. People simply want products and services that are their money’s worth and a brand journey devoid of friction. Nobody likes to face unpleasant experiences. Bad experiences discourage people from wanting to repeat the experience — that is why consumers choose to go with a competing brand.
Insights from sources like touchpoints, surveys, and feedback regarding the product and support tell us about the customers’ journey. Data of past inconveniences faced by consumers gives brands the chance to correct what is wrong before it's too late and the customer crosses the point of no return.
Before launching a new product, brands carefully study their target customer demographics and try to understand what people are looking for in a new product. When pain points are studied, and creases in the customer journey are smoothed out, it improves customer satisfaction. Happy customers imply the business is sustainable and profitable.
Data and analytics are not just for firms that need corrective action in their business strategies. It is equally important for organizations that are doing well. Insights help organizations continue doing their good work and prevent brands from stagnating. Customers quickly spot the slightest dip in product and service quality. This is why, every organization should inculcate a healthy culture that encourages good data collection, processing, and storage.
Also Read: What Are Customer Touchpoints – And Why They Matter
The main problem faced by organizations and individuals who work with data is its sheer vastness. Data governance teams (responsible for collecting, processing, researching, and storing) process a lot of information from multiple sources to create valuable reports that serve as beacons for decision-makers. Creating a dedicated research and analysis team within the company (or outsourcing this work to research firms) is the first step towards creating an environment that relies on hard facts before making crucial decisions.
Think of it as completing a giant jigsaw puzzle with hundreds or thousands of pieces. Working with data and analytics requires careful thought, patience, and a result-oriented vision for the future. Insights should help the business and endear it to customers. However, putting data to work is easier said than done.
The evolution of customers — aided significantly by digital and information technology (IT), compelled businesses to rework their priorities, keeping customers at the focal point. However, the paucity of time presents new challenges for data-driven organizations.
Truth be told — there isn’t enough time to gather all the necessary information to attract consumers, engage them by making their journey personalized and enjoyable and retain them. Organizations try to do their best by consistently providing the best product, support, and service to engage consumers, besides dedicating resources for continuous innovation.
Each of these business functions requires quality insights to produce the desired results. Despite advancements in technology and science, no solution/tool can readily pull out reports and trends from raw data. Data needs analysis and refinement before it can be of any use. These findings serve as the single source of truth or authorized information for the organization. Business and process-critical decisions are taken based on these insights.
To usher in a positive transformation that elevates consumers’ overall experience (CX), organizations should focus on building a data-driven, customer-centric culture that considers the customer’s complete lifecycle value. Decisions made by the organization for the benefit of consumers should be backed by empirical research and evidence.
Most businesses understand the need to be consumer-centric but lack awareness about new systems and applications. Hesitancy to adapt to modern technologies, the cost of replacing legacy systems with more modern, sophisticated software, blindly aping others without researching individual needs, and believing in a one-size-fits-all approach with regard to choosing software and technology to carry out business functions are just some of the reasons why a venture fails to take off, despite having the consumers’ best interests at heart.
Don’t let bulky legacy software drag you down. Break free of unreasonable limitations imposed by aging software that is not capable of supporting modern channels of communication or integrating different applications used to service consumers. It is imperative for service and support delivery teams to choose a modern AI-powered helpdesk software that’s intuitive, reliable, and facilitates easy exchange of information between different teams so that tickets can be resolved in the fastest possible time, without any stress.
Also Read: Proven Methods To Empower Customer Support Teams
So far, we have seen how good and clean data can help a business grow. Now let us take a look at how analytics help enterprises. Analytics looks at raw data in great detail to find similarities, differences, patterns, etc., that humans may overlook.
Analytics uses automation, algorithms, and programming to pull out valuable insights that help enterprises grow. Information, when used at the right time and at the right place, can give wonderful results. Analytics Continuum helps enterprises achieve that.
Gartner introduced the Analytics Continuum to represent the various stages of data analysis in an organization at any time. It helps enterprises get a clear picture of their position and the scenario that is likely to unfold in the future.
The insights get richer as the analytics process matures over time. Each subsequent step in the analytics continuum has a set of predetermined objectives. The method of examining data and results too varies from one stage to another. Most organizations worldwide base their analytics on the framework provided by the analytics continuum. Retail giants like Walmart, BestBuy, and Costco are just a few examples of organizations that have used an analytics continuum to take stock of things and pivoted at crucial junctures over time to stay relevant to consumers and grow with them.
Stage 1: Descriptive analytics
An enterprise takes its first step in analytics with Descriptive Analytics. As the name suggests, descriptive analytics gives the organization situational awareness. It brings clarity and allows business owners to observe all that is happening within the organization. This stage serves as the foundation for entrepreneurs and prepares them for what is to follow.
Stage 2: Diagnostic Analytics
The second stage of analytics maturity in an organization aims to answer why. The why behind everything that happens in a company is responded to with diagnostic analytics. It draws insight from past data to create an informed present and tells what has transpired until now within the organization.
Stage 3: Predictive Analytics
As the name suggests, the third stage of analytics analyzes information and gives information that will come in handy at a later stage in the organization’s life. The insights received here go beyond the obvious. Here, the goal is to go beyond the present and delve into understanding what the future has in store for the company. Predictive analytics uses Machine Learning (ML) to communicate what is to come and gives enterprises a chance to correct/modify/alter their process/functioning to ensure smooth sailing and a desirable outcome.
Stage 4: Prescriptive Analytics
In the early stages, business owners were introduced to exciting insights related to the present and were made aware/forewarned of what they were likely to face not too far into the future. With prescriptive analytics, insights provide business owners with information about what to expect and trigger the recommended action when the situation arrives. A combination of technologies like automation and ML ensures the activity is carried out without any intervention.
Do not waste precious time churning data and creating reports. Get instant insights into your customer's journey with Discover's pre-configured reports in a simple, easy-to-configure dashboard. Discover helps you to monitor and organize your customer conversations by topics. OwlDesk's built-in AI drills down into individual support conversations and creates topics from it. ThinkOwl self-learns how a process works from live agents by observing them.
You also gain meaningful predictive insights on how to handle cases. ThinkOwl remembers what worked effectively last time, suggests the next move, and provides predictive insights. Such insights work best for both agents and customers.
The Discover module conducts predictive analysis on every customer interaction on the helpdesk. Drawing contextual information from every customer case helps estimate demands even before the customer raises a query and allows your support team to improve efficiency and quality.
Discover also helps you make sense of data to understand your contact center's KPIs better. How? With Discover, you get to know what's going on between your customers and quick support. The tool lets you understand customer behaviors so you get the right balance between customer and agent experience.
A robust data and analytics team cushions enterprises from any unpleasant shocks that may arrive unannounced. Valuable insights obtained after processing voluminous data help organizations prepare for any unforeseen circumstance or event. Prescriptive analytics proactively encourages human-machine collaboration by taking proactive action to protect people's interests.
Data and analytics help organizations remain steadfast toward their objectives. With the advent of technologies, data analytics helps organizations go the extra mile to deliver memorable consumer experiences. It shows the value of customers' journey with the brand and creates reasonable expectations among customers — of receiving stellar service that makes every interaction something to remember.
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