Why big data is important in a customer-facing business

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Market research and analysis has always been an integral component of the strategy-making process. Since the time when businesses’ focus shifted from what-we-want-to-sell to what-customers-actually-need, data started gaining prominence. However, data was not as easily available as it is now. Data collection used to be an extremely tedious process and involved several man-hours of surveying and feedback obtainment. 

As digital presence of customers started existing parallel to their physical presence, as the world started getting more and more inter-connected, the data procurement process became easier. Today customers are producing heaps of data, which can prove to be the ultimate gold mines for businesses. With such an exorbitant amount of data available and with so much hype about data being the oil, one would expect that businesses would be utilizing it to the fullest. A McKinsey survey reported the contrary. Against the expectations of the available data being leveraged to take strategical decisions, businesses are exhibiting a rather feeble interest. This is mainly because of the growing gap between the leaders and the laggards. 

The McKinsey report indicated a clear growth in revenues and earning for the companies who chose to use data and analytics for developing a customer-focused plan. Doing so is especially critical for the consumer-facing organizations in this time of rapidly-changing trends and business scenarios. PayPal co-founder, Max Levchin aptly said, “The world is awash in data and we can see consumers in a lot clearer ways.” 

The amount of data being generated in this interconnected world can be truly overwhelming. Big data encompasses massive volumes of structured and unstructured data sets. All these data sets are valuable only when they are mined to extract relevant information that can contribute towards improving a process, product, or service. 21% of the respondent of the McKinsey survey rated data and analytics as their number-one key to success. Although there are a lot of factors at play that determine the viability of a customer-focused strategy, big data analytics significantly increase the probability of a favorable outcome. 

Today, 60% of the business decision-makers believe that it is better to lose half of their revenue over losing half of their data. This distinctly emphasizes the value of data in the present business landscape. Let us try to understand how exactly big data and analytics contribute to the growth of businesses today. 

Understand your customers 

In the world of cut-throat competition, the ability to deliver quality at speed is the only differentiator that can keep a business ahead in the race. While speed is a non-negotiable factor, quality is subjective that depends on what customers expect from a business and how the business was able to fulfill those expectations.  

For meeting the customer expectations, it is essential to know and understand the customers. By applying analytic observation on the past user experiences, purchase pattern, behavior, and feedback, businesses can identify the gap areas in their services. When the ‘areas of improvement’ are determined, the strategy can be customized to address them and optimize the service as per the customers’ needs.  

Based on the purchase history and buying pattern, the customer experience can be personalized using the information provided by big data analytics. The touch-points and POS can be customized in such a way that together create a seamless buying experience for the customers.  

By combining quality delivery with a personalized experience, businesses can significantly enhance the customer retention rate. 

Derive actionable insights to make better decisions 

Big data analytics supports a business to determine which strategy, product, or service is resonating with the customers, and which is bouncing back off. This information allows them to tweak and improve their existing customer strategy in such a way that all the ‘bouncing’ parts are eliminated and only the ‘working’ parts remain, thus, generating an immaculate plan for satisfying the end users. 

Big data analytics empower smart decision-making. Using the power of automation and AI-driven analytics it becomes possible to identify the potential red flags and steer the business away from risky outcomes. Additionally, by enabling decision-making, big data increases operational efficiency and business revenues. 

The fundamental objective of big data is to ensure that businesses make better decisions. As Carly Fiorina, former executive, president, and chair of Hewlett-Packard Co. puts it, “The goal is to turn data into information, and information into insight.” 

Big data is the ultimate determinant of all that is happening in the digital as well as physical world. By putting analytics on the collected and stored data, businesses can establish end-to-end transparency while segregating good ideas from bad ones. 

Ensure compliance, build trust 

In this digital age, privacy has taken the top-most priority. As businesses scavenge for the digital footprints of their customers, they must also adhere to the laws and regulations that protect the privacy of users. 

Big data analytics can easily narrow down multiple options to a couple of practical choices, so that the probability of success increases. While doing so, the businesses must ensure that they take all the measures to safeguard the confidentiality of the sensitive user information. The growing cyberthreats and data breaches make it essential that necessary measures are deployed to secure all the vulnerabilities and security gaps. By protecting user data, businesses can establish trust among the customers. 

With robust data architecture, fraudulent activities can be detected and controlled in real-time. This allows businesses to nip any potential threat in its bud itself, thus, building a breach-proof, trustworthy, data storage ecosystem. 

Big data testing for assured quality 

Big data testing operates on three levels – information integration, data collection and deployment, and scalability. By taking care of volume, variety, and velocity of data, big data testing provides assured value for businesses by enabling superior customer experiences. 

Testing Big Data applications requires a specific mindset, skillset and deep understanding of the technologies, and pragmatic approaches to data science. Cigniti leverages its experience of having tested large scale data warehousing and business intelligence applications to offer a host of Big Data Testing services and solutions. Cigniti Testlets offer point solutions for all the problems that a new age Big Data Application would have to be go through before being certified with QA levels that match industry standards. Connect with our big data testing experts here.