Applying Big Data and Big Analytics to Customer Engagement
Featured in The Cloud Computing Journal, authored by Dan Smith.
Customer engagement has long benefited from data and analytics. Knowing more about each of your customers, their attributes, preferences, behaviors and patterns, is essential to fostering meaningful engagement with them. As technologies advance, and more of people's lives are lived online, more and more data about customers is captured and made available. At face value, this is good; more data means better analytics, which means better understanding of customers and therefore more meaningful engagement. However, volumes of data measured in terabytes, petabytes, and beyond are so big they have spawned the terms "Big Data" and "Big Analytics." At this scale, there are practical considerations that must be understood to successfully reap the benefits for customer engagement. This article will explore some of these considerations and provide some suggestions on how to address them.
Customer Data Management (CDM), also known as Customer Data Integration (CDI), is foundational for a Customer Intelligence (CI) or Customer Engagement (CE) system. CDM is rooted in the principles of Master Data Management (MDM), which includes the following:
- Acquisition and ingestion of multiple, disparate sources, both online and offline, of customer and prospect data
- Change Data Capture (CDC)
- Data cleansing, parsing, and standardization
- Entity Modeling
- Entity relationship and hierarchy management
- Entity matching, identity resolution, and persistent key management for key individual, household, company/institution/location entities
- Rules-based attribute mastering, "Survivorship" or "Build the Best Record"
- Data lineage, version history, audit, aging, and expiration
It's useful to first make the distinction between attributive and behavioral data. Attributive data, often referred to as profile data, is discrete fields that describe an entity such as an individual's name, address, age, eye color, and income. Behavioral data is a series of events that describe an entity's behavior over time, such as phone calls, web page visits, and financial transactions. Admittedly, there is a slippery slope between the two; a customer's current account balance can be either an attribute or an aggregation of behavioral transactions.
MDM typically focuses on attributive data. Being based on MDM, the same is true for CDM. Personally Identifying Information (PII) such as name, email, address, phone, and username are the primary drivers behind identity resolution. Other attributes such as income, number of children, or gender are attributes that are commonly "mastered" for each of the resolved entities (individual, household, company).
Enter Big Data. As more devices are developed - and adopted - that capture and store data, huge quantities of data are generated. Big Data, by definition, is almost always event-oriented and temporal, and the subset of Big Data that is relevant to a CE system is almost always behavioral in nature (clicks, calls, downloads, purchases, emails, texts, tweets, Facebook posts). Behavioral data is critical to understanding customers (and prospects). And, understanding customers is critical for establishing meaningful and welcome engagement with them. Therefore, Big Data is, or should be, viewed as an invaluable asset to any CE system.
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