Understanding what customers like and what they want is important for companies during this time of digital upheaval. However, Customer 360 programs offer an integrated understanding of clients’ behaviour by combining data from multiple sources. Such success stories have largely depended on Data Engineering processes. 

This article explores how data engineering can improve Customer 360 initiatives for AWS data engineering, big data engineering, and data analytics companies.

What Are Customer 360 Initiatives? 

Customer 360 initiatives are designed to bring together relevant information about individual consumers from different touch points, including but not limited to sales, marketing, customer service, and social media platforms. Doing so enables companies to personalise their offerings, enhancing client satisfaction and helping make more informed decision-making processes. 

How Data Engineering Enhances Customer 360 Initiatives

1. Data Consolidation Description: Data engineering allows information from different sources, such as CRM applications, social media platforms, online shopping sites, and customer support exchanges, to be integrated into one view. 

Example: Amazon 

Implementation: Amazon employs integration of information interfaced by its online shopping platform, Alexa conversations, and usage of Prime Video service, among others. 

Tools Used: AWS glue for data integration and transformation.

2. Data Cleansing and Quality Control Description: Data engineers generate procedures that cleanse and validate information, guaranteeing precision and uniformity in all customer databases. 

Example: Netflix 

Implementation: To guarantee correct viewer data, such as watching habits, Netflix uses various techniques to clean its data. 

Tools Used: Apache Spark for processing and cleaning data.  

3. Efficient Data Storage Description: In data engineering, scalability is everything, meaning efficient storage systems tailored for large volumes of consumer information must be developed. 

Example: Spotify 

Implementation: Spotify saves and processes information on user listening habits, playlists, and previous interactions.

Tools Used: Amazon S3 is used for reliable scalability, whereas AWS Redshift is an example of a cloud-based storage system.

4. Real-Time Data Processing Overview: Data engineering is invaluable for real-time data processing, which enables organisations to collect immediate feedback and provide individuals with a personalised experience. 

Example: Uber

Implementation: To match riders with drivers almost instantaneously, Uber processes real-time data about ride requests, driver locations in real-time, and rider locations as well.

Tooling Used: Apache Kafka is used for real-time streaming and processing of real-time data.

Benefits  of Data Engineering in Customer 360 

• Enhanced data quality: High-quality data matters greatly when collecting customer insights. Data engineers use various processes to clean and validate the data, reducing errors and inconsistencies. According to Gartner, the average annual losses associated with poor-quality data are $12.9 million per organisation. 
• Improved customer insights: Integrating data from different sources will give businesses deeper knowledge about customer behaviour and preferences. Consequently, better decision-making will occur alongside improved marketing strategies. 
• Customised customer experiences: When businesses have an all-encompassing view of their customer information, they can personalise their interactions according to each individual’s needs, making them feel more satisfied. According to McKinsey, spending on analytics is to gain competitive intelligence on future market conditions, target customers more successfully, and increase operating profits in the 6 per cent range.
• Efficient data management: Data engineering guarantees effective storage and processing of the required information so that it is quickly accessible. According to a Forrester research report, data teams spend 70% of their time on new data sets, prepping them for analysis and data plumbing. 
• Personalised Financial Services: Financial services are offered by analysing customers’ financial history, spending tendencies, and life events to recommend suitable products. They also offer fluctuating or changing lending rates on loans or credit cards that correlate with the customer’s creditworthiness and financial habits. Using Customer 360, marketers apply segmentation to present highly specific attributes of a particular category of clients depending on their actions and tastes. This is how services are divided respectively. If, for example, one travels a lot, then the bank can provide travel-based finance like travel assurance or worldwide transaction support.

According to McKinsey, fintech companies using Customer 360 data to personalise their offerings have increased customer satisfaction by 15-20% while improving customer retention by about 10-15%.

A Visionary Approach to Customer Engagement

Forward-thinking companies can be at the forefront of customer interaction and thus redefine customer relationships, using Customer 360 to achieve this exceptional experience.

Customer 360 goes beyond improving customer engagement. It transforms businesses from being product-oriented to being customer-centric, where every decision is driven by deep insights into customers’ behaviour that are actionable.

These tools help businesses develop a comprehensive view of their customers by gathering data from different sources, including purchase history and social media interactions. This approach helps organisations link individual transactions together and better understand customers’ experiences. Instead of waiting for customers to voice their needs, firms can predict them.

For instance, when a person is reaching retirement age, a bank can identify this and, therefore, offer such an individual personalised retirement planning services or even predict the next purchase on an online marketplace before it happens, thereby providing appropriate suggestions at the right time.

How TransOrg Helped a Luxury Hospitality Company Enhance Customer Data Management

A high-end hospitality firm managing luxuries like hotels, resorts, safaris, palaces, spas and airline catering had to transfer its client and commercial data to the cloud to enable sophisticated analyses. TransOrg automated the data migration process to Azure Cloud and generated a data lake framework and a Customer DataMart, which would facilitate advanced analytics. 

Challenges 

The customer faced challenges from exploding volumes of information, which caused integration headaches, job failures, and SLA delays. 

Solution

TransOrg built a flexible structure in Azure Cloud due to its accessibility, scalability, and integration features. The main steps involved were: 

• Data Ingestion: Moving information into Azure Blob Storage and keeping it raw in Azure Synapse. 
• Data Transformation: Using Azure Synapse ETL pipelines for cleansing and transforming data. 
• Data Storage: Keeping altered data within Azure Synapse’s enrichment layer. 
• Data Visualization: TransOrg used Power BI to generate live dashboards from enriched information. 
• Data Engineering and Customer DataMart: TransOrg used 14 sources of information to create the data lake framework and the Customer Data Mart, which consisted of data Cleansing, fixing errors, tracking missing items, and standardising views across columns.

Key Impacts

The solution from TransOrg enhanced data integration, increased processing efficiencies, and allowed the hotel firm to use sophisticated analysis for improved client management and more effective decision-making.

• Reduced redundancy: 45% lessened in identical customer profiles. 
• Faster Processing: Azure made data processing considerably quicker. 
• Advanced analytics: Customer segmentation and demand forecasting were the supported use cases. 
• Better Decision Making: Customer 360 dashboards for customers provided insights and improved decision-making. Data integration, increased processing efficiencies, and advanced analytical tools were part of TransOrg’s solution to make better choices and manage clients for hotel firms.

Conclusion

Relying upon data engineering has become crucial for an effective Customer 360 project today amidst the digital millennium. Moreover, using these systems makes it easier for them to have personal interactions with their clients or customers. One can safely say that technology improves efficiency and enables mass storage of all types of data without altering its meaning. Any changed information would consequently be useless or could lead to loss.