How to Reduce Customer Complaints by Improving Data Usability

Case Studies

How to Reduce Customer Complaints by Improving Data Usability

Achieve business outcomes with good data

Businesses are being transformed by
they way they use data. As
organizations invest more into newer
initiatives to innovate with data they
struggle to make them pay off. One of
the primary obstacles is that of usable
data

Data quality hampers an organizations ability to make good decisions, reduce costs, increase efficiency, generate growth and innovate. Relevant, timely and trustworthy data is key to success.

Zscore’s Smart Data Platform seeks to enhance data quality and empower business users. Resulting in better business decision making and insights regardless of the volume of data, format, or systems.

With the Smart Data Platform, organizations can:

Enrich data automatically at scale using AI
Map business and data assets enterprise-wide
Fix data quality issues using ML-based automation
Export and share trusted data with applications to drive insights and decisions

Customer data is an important asset that gives you the ability to analyse numerous aspects of your customer relationships and react appropriately.

An important analogy about data quality. Like the “complaint department” days of customer service, many organizations still view data quality as little more than catching and fixing bad contact data. In reality, our experience working with enterprise customers has taught that data quality plays a very strategic role in areas like cost control, marketing reach, decision making and brand reputation in the marketplace.

addresses, mailing information, and phone numbers. This information helps market effectively and use resources efficiently.

Business analytics of customer data is one area where the need for clean data cannot be overemphasised. Good data has a direct impact on Net Customer Rating (NCR) and Net Promoter Score (NPS) scores.

Data Quality in Reporting

Data quality is essential for one main reason: To give customers the best experience when you make decisions using accurate data

Collecting trustworthy data and updating existing records gives you a better understanding of the customers. It also lets companies keep in contact with them using verified email

Data Governance

Data quality and data governance are both indispensable for organisations that want to become data-driven. Both may be separate practices, but they are fundamentally related. To put it simply, you can’t have data quality without good data governance. However, data governance has evolved in the last decade and has is now a hybrid of automation and manual strategies.

Solution

Zscore’s data quality platform can be in the cloud or on-premise or delivered as a managed service. An engagement is kicked off in a couple of weeks of system and data analysis. This was followed up by a two week implementation of all workflows and data recipes.

Data quality transformation is driven with the help of machine learning algorithms. Business workflows and rules are documented in the platform to help identify data patterns and weed dirty data.

Data is cleaned automatically and issues are flagged for manual intervention and creation of new transformations were quarantined.

Zscore’s data health dashboard indicates their DQI (Data Quality Index) and helps monitor progress.

Identify a clear linkage between business processes, key performance indicators (KPIs) and data assets
Identify corrective actions to be taken and provides valuable insights that can be presented to the business to drive ideation on improvement plans
Data assets are acquired from external sources where the DQ rules, authorship and levels of governance are often unknown. Hence, a “trust model” works better than a “truth model.”
Automate Data Quality and increase trust with customers by providing them trustable data with personalisation at near real-time

Data Quality in Reporting

Data quality is essential to deliver the best customer experience.

Accurate and trustworthy data:

Improves understanding of customers
Enables effective communication (addresses, emails, phone numbers)
Enhances marketing effectiveness

Clean data has a direct impact on:

Net Customer Rating (NCR)
Net Promoter Score (NPS)

Data Governance

Data quality and data governance are closely linked.

Data quality cannot exist without good data governance
Modern data governance is a hybrid of automation and manual strategies
Both are essential for becoming a truly data-driven organization

The Challenge

The client faced three core challenges in translating their research into
clinical application:

Complex Signal Processing:

• Required real-time processing of analog EEG/EMG signals (≤70mV) from 8 head/arm sensors into digital data.
• Needed advanced DSP expertise for time-domain filtering and sampling.

Scientific Application Gaps:

• No unified platform for therapists to prescribe exercises or track progress using neuroscience principles.
•Patients lacked guided rehabilitation tools.

Fragmented Data Management:

• Decentralized patient records hindered progress analysis.
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•No secure synchronization for therapy data across clinics.

4 KEY

Data Discovery & Patterns

understand customer data, patterns & relationships

Data Quality Transformation

apply consistent data quality across departments

Error Handling

Automate data quality within the company

Data Recipes

our central repository for all data checks across your business

Key benefits

Improving Reporting Standards

Businesses are hooked onto data right now. Monetising data assets, intelligence, AI and Big Data are key for them to stay innovative. But how do you ensure the dollars being spent on these initiatives are built on a sustainable data practice and good quality data?

The tools we provide let you establish a strong foundation for your data strategy which will allow you to scale your initiatives. Our platform allows organisations to manage their data and ensure the business stands on a strong foundation of good quality usable data to become truly data-driven.

Business analysts get their own automated tools so they can easily manage data quality tasks without dependencies on IT
Data teams can now focus on higher value tasks than manually cleaning data problems
Create a single source of truth to work on trustable data with greater ease of maintenance