Basic Concepts of Customer Data Integration

BASIC CONCEPTS OF CUSTOMER DATA INTEGRATION A. Understanding Customer Data Integration In theory, customer data integration (CDI) is the process of collecting, analyzing and managing data from various customer touch points, such as contact details, market research data, social media and customer surveys. The aim is to identify customer behavior, customer preferences and demographics (buyer persona), market demand, and at the same time customer problems (behavioral segmentation concept). Later, customer data will be stored in one place so that the company has a big and broad picture of its customer base. This holistic view of customers who are the company's target market will be useful for many teams in the business environment, starting from the marketing team, sales force, customer service and support, as well as public relations (PR). CDI helps companies ensure that all their business strategies focus on customers (customercentric) and offer customer experiences that are more personal (personalized marketing concept) and satisfying (customer satisfaction). By providing a precise view of the data-based business decision-making process and insight into the customer relationship with the company (customer relationship). That way, company managers can increase business sales productivity and streamline business process management such as billing, shipping, returns, etc. Apart from that, CDI is also useful in things such as: • Minimize time, energy and company resources in collecting data and eliminating duplicate data (data cleansing). • Understand the customer journey cycle (customer lifecycle) in the process of purchasing certain products or services (customer buying cycle) when making sales calls. • Assist the company's marketing team in building the best marketing communications by targeting the most effective marketing strategies and advertising campaigns. With the aim of retaining old customers, reaching or acquiring new prospect customers (prospecting), and overcoming the business's inability to maintain customer loyalty (customer turnover) which causes customer churn. • Assist the production team in collecting customer feedback which will later be useful for developing products and launching products that meet customer desires. • Assist the sales and customer service team in increasing customer conversion rates (conversion rate) or lead generation by focusing on customers (customer-oriented) who are most likely to buy the business's products. Either by upselling or cross-selling, so that the business sales volume will also increase (sales growth). B. Technical Types of Data Integration 1. Data Consolidation Data consolidation refers to the use of ETL (extract, transform, load) technology to aggregate data from multiple sources. Then, the data will be organized and translated into a language format that can be read by humans, then stored in one data storage. The goal is to reduce the number of data storage locations. 2. Data Propagation Data propagation means efforts to duplicate data in its original storage location. This method is the best choice when your company team will only use a few tools and data sources that require data sharing. For example, the marketing team chooses to use Marketo to help process email campaigns more automatically and the sales team uses Salesforce to automate the sales process. So, when carrying out data propagation, both teams will need access to the same customer data. 3. Data Virtualization In short, data virtualization is a data integration method which generally uses certain interfaces to provide unique and real-time data from various sources with various data models. 4. Data Federation Data federation or data ingestion is a form of data virtualization that is carried out by collecting data from various sources. Then make it available in a virtual database for later use by certain business intelligence tools. This type of data integration is almost similar to data consolidation, only data federation does not combine data. 5. Data Warehouse Almost the same as data federation, a data warehouse is a system that combines data from various sources into one single data storage. This data is centralized and consistent to support business analysis, data mining, artificial intelligence (AI), and machine learning. In its application, a data warehouse will store a lot of data from various operational sources within the company into one comprehensive database. Later, the results of data processing will be useful for data analysis. C. Customer Data Integration Strategy Steps After understanding the types of integration techniques above, you can start implementing CDI by following the steps below. 1) Select a valid data source. Starting from social media, surveys, call center transcripts, emails, customer interview results, and so on. 2) Explain the business objectives of integrating data. Create company business targets in integrating customer data by selecting relevant metrics or KPIs. 3) Determine the data category. This means identifying and categorizing anyone in the company's business team who can access customer data. Apart from that, also explain the reasons or roles of team members when accessing the data. 4) Determine policies, procedures, rules and timeline for implementing or using CDI. The timeline here will depend on the type of business intelligence tools related to data integration that the business uses. With good quality technology tools, you can sort out the timeline and type of integration method which determines the sequence of business processes. 5) Data integration manually by the business IT team or automatically using tools supported by Google Analytics. 6) Use a special customer data integration platform that suits business needs and expectations. Call it a customer data platform, which is special software for creating persistent and consistent customer data records.