Actian Blog / Embracing Customer Profiling and Data Management

Embracing Customer Profiling and Data Management

Buyer Persona Vector Illustration. Customer Profile Representati

Defining good service

All organizations want to provide good service to their customers. There are many definitions of what good service is from a giving and receiving perspective. Good and excellent service can mean the same thing in many cases. Then there is exceptional service; this is the type of service many organizations try to avoid because it’s an exception to good or excellent service and will raise the expectations of the customer beyond the other services that they may deliver. When they experience another service from the same organization that is not exceptional but is good or excellent, it becomes a disappointment to the customer, which affects the overall perspective of the company.  Some companies have avoided giving an exceptional service until they can raise all services to the same level or have the consumer pay extra for exceptional services.

To give a good service, we have to know what experience the customer is expecting. There have been many good companies with great products that have failed because the customer experience was terrible. The need to understand customer expectations and needs to deliver a good service becomes crucial to the organization’s survival.

Understanding the customer

There are two types of customers – the current customer and the prospect. The prospect you may not think of as a customer yet, but they have started the journey. They may experience zero moments of truth about your organization before actually visiting your company online or in person. The customer experience can begin with social interaction or before the purchase of a product or service. The outside data that the customer consumes is important and should not be taken for granted.

To understand the customer, we have to communicate with the customer with personal data requests or with technology data exchanges. This helps us to understand the customer experience and value of the product or service purchased. Personal data requests are usually made in the form of a general survey or a specific customer satisfaction survey. Technology-based data exchanges are based on the usage of technology and devices. Experiences data collected by devices without formally asking how was the service or product perceived is very important for understanding your customer.

Profiling and managing data

To understand the customer needs before or after a purchase, we have to collect data. The data has to be modeled to be customer and service-oriented. This requires knowledge of marketing data, transactional data, mobile data, and organizational service data. Marketing data may include customer name, demographic information, age, economic attributes, online histories, etc.  The transactional data can give insights into buying patterns and habits.  Mobile data enabled by sensors in devices can tell you customer location, proximity, light, accelerometer, heart rate, and many more things about the wearer. The marketing, transactional and mobile data need to be integrated with the organizational service data. The service data includes service, applications, infrastructure, platform, capabilities, etc. The data has to be collected from all sources possible such as cloud, on-premises, IoT devices, etc., including partner sources. Data is everywhere and needs to be collected from everywhere.

Most of the things we do in life are habitual. Psychologists say that between 45 to 95 percent of the things we do are habits. We also have beliefs built by society and our associations that influence buying habits, services, and products that we use. These habits are now being collected with the enablement of technology in our lives. Customer beliefs can also be collected and used for profiling and understanding the habits of a demographic.

Understanding habits create the ability to use analytics to predict. Predictive behaviors based on historical data analysis can help us make decisions in profiling needed services and products related to various market segments such as health, finance, consumer, and many other markets. These decisions can be used to influence and create trust with the consumer.

Enhancing data and decision support

To enhance data for profiling decision support, first, we have to collect the data from as many sources as possible. Across all clouds, on-premises, and any other data sources. Enterprise-wide data collection capabilities are a must-have to completely understand the customer and profile them for your services or products.

The data must be collected as fast as possible to be used just in time to engage your customer. Everyone is mobile, so as data is collected, you have to be able to communicate and influence your customer as quickly as possible before the competition does. You may also need to provide just-in-time service to deliver a good service, enhance your customer’s life, and influence the customer’s decisions.

The more you understand your collected customer-related data, the better you can use automation, machine learning, artificial intelligence, and other emerging technologies effectively. These emerging capabilities all begin with having an Enterprise-Wide data strategy, the technology to support the strategy, and the enterprise’s daily data collection operations. Discover how Actian can empower your data-driven enterprise with its data management and data integration solutions. Actian Avalanche is a hybrid cloud data warehouse that delivers unparalleled analytics performance for the data-driven enterprise, no matter where your data resides, and  Actian DataConnect connects and orchestrates data movement across all your applications.

About Pradeep Bhanot

Product Marketing professional, author, father and photographer. Born in Kenya. Lived in England through disco, punk and new romance eras. Moved to California just in time for grunge. Worked with Oracle databases at Oracle Corporation for 13 years. Database Administration for mainframe IBM DB2 and its predecessor SQL/DS at British Telecom and Watson Wyatt. Worked with IBM VSAM at CA Technologies and Serena Software. Microsoft SQL Server powered solutions from 1E and BDNA.

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