The need of big data testing for digital customer experience

In today’s world of digitalization, the top motive for enterprises is to ensure a robust digital customer experience. However, tracking this experience has been a challenge for eons. 

The advent of emerging technologies such as Artificial Intelligence (AI)/Machine Learning (ML), 5G, Internet of Things (IoT), and Big Data & Analytics, has made measuring customer experience possible now.  

According to Ed Thompson, distinguished VP analyst, Gartner, “The field of customer experience (CX) management is on the rise and there’s no sign of it stopping. More than 5,000 organizations worldwide now have a dedicated CX leader, nearly half of whom report to the CEO. This increasing level of CEO oversight shows the importance of CX to the bottom line, hence the need for measurement.” 

While AI/ML, 5G, and IoT can indirectly help gauge the customer experience, Big Data & Analytics is going to play a big role in measuring the profundity of customer experience. 

Measuring Customer Experience 

Gartner in its recent study emphasized on 5 types of metrics such as Customer satisfaction (CSAT), customer loyalty/retention/churn, advocacy/reputation/brand, Quality/operations, and employee engagement to measure customer experience. 

While all these metrics quantify the measurement of customer experience, it is imperative to understand that only those metrics need to be taken into consideration that are relevant to the business. 

Nate Jones, Director of Customer Support at SimpleNexus says that” No matter which metrics you choose to use, make sure they are predictive of the outcome you are trying to avoid or promote. Too many businesses gather the metrics because they feel like they are supposed to and don’t understand how to get the value out of them.” 

It is learnt that more enterprises are better off with one customer experience metric and one related behavioral metric. Keeping it simple without any complexity adds value and helps enterprises focus on things that truly matter. 

Recent studies by industry experts suggest that it is only the correct analysis, meaningful use of data collected, and holistic approach that can help improve the digital customer experience. 

Improving digital customer experience with Big Data Analytics 

Big Data Analytics has immense potential to empower customer experience in today’s rapidly developing digital economy.  

Customer insights are collected from various valid touchpoints. Brands accrue an incredible amount of data such as purchase history to social media annotation from its customers.  

Big Data processes this data using a set of techniques or programming models and subsequently extracts useful information for supporting and providing decisions. 

Few ways to improve digital customer experience are:  

• Collect customer experience data across the digital life cycle. 
• Understand customer sentiments to connect on an emotional level. 
• Analyze and evaluate experiences through an open-ended framework. 
• Embrace your customers for what they do, rather than what you think they’re doing. 
• Identify the metrics that have scope for improvement. 
• Enhance targeted marketing practices and improve customer communications. 
• Focus on the quality of the data rather than the quantity. 

Online businesses must really comprehend the behavior and experience of the customer in order to succeed or survive in this pandemic era. it is imperative that they easily tap into Big Data sources and leverage that data to gain critical insight.  

While it is evident that Big Data & Analytics play a pivotal role in enhancing the customer experience, it is prudent to verify the sanctity of the data being processed. This is where Big Data Testing comes into the radar. 

Big Data Testing for better data quality 

According to Evans Data Corporation, “19.2% of big data app developers say quality of data is the biggest problem they consistently face”. 

Data quality is a vital characteristic that determines the trustworthiness of decision-making. It helps you manage and cleanse data while making it available across the enterprise. 

With Big Data comes bad data. So, it is imperative that the processed data adheres to the highest standards. 

Big Data testing is completely different when compared to the typical testing around traditional data warehouses or databases that revolve around structured data and use Structured Query Language (SQL) to accomplish the testing. 

Big Data deals with not only structured data, but also semi-structured and unstructured data and typically relies on Hibernate Query Language (HQL). 

Data Validation, Process Validation, and Outcome Validation are the key components of Big Data Testing. 

While validating the data, the collected data is ensured to be accurate and not corrupted. This collected data is sent to Hadoop Distributed File System (HDFS) for validation where it is partitioned and thoroughly checked. 

The processed data is then validated and considered complete only after the tester verifies the process and the key-value pair generation.  

The data is then down streamed to check for distortion and the output files are moved to Enterprise Datawarehouse (EDW) which checks for data corruption. 

The final processed data is ensured to be corrupt free and this quality data will act as a catalyst to fuel superior customer experience. 

Accurate data analysis ensures superior customer experience 

By parsing massive volumes of data into bite-size pieces for analysis, data analytics tools can deliver sales and service teams with:  

• in-depth insights into consumer behavior. 
• predicting future outcomes of campaigns. 
• helping reps manage their personal pipelines. 
• showing clients up-to-date data.  

With these factors, it becomes easy for the sales and service teams to identify what is required to the customer and subsequently help attain superior customer experience. 

According to Gartner, “With the right approach, business intelligence can be a leading source of competitive advantage. Organizations have an opportunity to use enterprise analytics to drive digital transformation and redefine the customer experience. To accomplish this, data and analytics leaders must create a data-driven culture focused on delivering business outcomes.” 

Rather than attempting to control or own business analytics, successful IT leaders train and partner with business led teams to help them effectively exploit Big Data analytics for enhanced digital customer experience. 

Closing Thoughts 

It is imperative to identify core business priorities first to ensure that business opportunities are driving your analytics investments. While many firms become more data-driven with business models that depend on data, deprived quality data will gradually become a systemic problem. 

Cigniti’s Big Data Testing framework helps clients measure and improve digital customer experience using Big Data Analytics. 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 such as BI application Usability Testing. 

Our customer experience solutions include –  

1. Quantifying digital CX and close visibility gaps 

• Holistically (pre, run-time, post) quantify how digital users interact with your services. 
• Close the visibility gap by ingesting device, sentiment, analytics, and infrastructure data into a single AI&ML powered analytics platform. 

2. Real-time digital CX knowledge 

• Outside-in digital CX approach by focusing on real user monitoring and device end point analysis. 
• Identify any symptoms that contribute to performance issues, downtime, or other disruptions to the digital CX in real-time. 
• Assess the root causes and recommends solutions to support, development, and infrastructure teams. 

3. Composite digital CX Score, predictability of ratings and correlation to business outcomes 

• Summarize end-to-end digital experience in a configurable composite business KPI – digital customer experience score. 
• Analyze digital CX insights early enough for proactive digital service improvements by predicting ratings and story board sentiment. 
• Deliver AI/ML-based business outcome correlation by providing business metrics – customer churn prediction and digital CX benchmarking. 

Need help? Talk to our Big Data Testing and Customer Experience experts to know more about Big Data Testing for superior digital customer experience.