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Analysis derived from the Greek “Analusis” refers to the breaking up in to parts of a composite to better understand the whole. The derivative “Analytics” then is a method of discovering hidden trends or patterns and gaining insights in to what is under investigation. Customer Analytics could therefore be interpreted as analyzing a customer in all the individual’s dimensions to gain deeper insight and to arrive at a complete understanding of the person. This complete 3600 degree view of the customer and a deep understanding of all dimensions describing the individual is critical in today’s competitive landscape to establish lasting bonds and strong relationships between a consumer and products, services, organizations and even between other consumers as well.
Principal among the dimensions a customer can be analyzed are the individual’s Demographics, Psychographics, Sentiment, Relationships with other customers, Product Associations derived from Purchases, and the Value contributed to an organization.
Demographics are attributes that describe characteristics that are more or less fixed and provide an insight in to who an individual is. Psychographics on the other hand analyses the Personality and the belief system of an individual and is key to understanding what motivates a customer to do what he does and behave the way he does. The sentiments expressed by a customer through the various organizational touch points in relation to interactions with the products and services of the company provides valuable feedback to an organization in near real time. The Relational aspect is studied through Social Network Analysis that characterizes and quantifies the relationships an individual has with other individual’s and also in some instances the relationship between the individual and the products of his choosing. The fifth principal dimension attempts to model the consumption behavior of the Individual by analyzing the basket of goods purchased by a customer and the associations between products that provides insight not only in to the combinations of products that “go together” but also allows the retailer to demographically profile individual customers who prefer certain valuable or unusual combinations of products to understand the purchases better. The value of a customer to a business is commonly measured via the Life Time Value of a customer and is a measure of customer profitability that provides an assessment of the contribution of an individual customer to the profits of the business over the life time of the customers association with the business.
Analytical techniques exist to model each dimensional aspect of the customer using algorithms derived from the Physical Sciences to model behaviors and statistical techniques to evaluate and quantify performance. Analytics is also very an inter-disciplinary field combining knowledge and techniques from the Social Sciences as well as Mathematics, Engineering and Computer Science. Research methods carried out by means of surveys and questionnaires originated mainly from the Social Sciences while Network Theory, Time value of Money and Probabilistic models used to analyze Customer Networks, their value and lifetime association with a product or organization respectively are derived from the Physical Sciences.
In any analysis, access to customer data is key. In this regard Retailers have the Demographical information which they obtain at the Loyalty Card Signup and product purchase information each time a customer shops from which they can carry out association rule analysis and profile individual customers participating in interesting and valuable product purchase combinations. This also allows them carry out store wide shelf optimizations where certain closely associated products are stocked together, bundled discounts offered and decisions taken on which product categories and specific items to carry. The Retailers also possess the Mobile telephone number of the Customer but they do not have the Customer to Customer or Person to Person Social Network Relational information that the Mobile Network Operator possess.
The next principal source of customer data is the Relationship information the Mobile Telecommunications Service Provider has due to the patterns of communication between members of its subscriber base. The mobile Service Provider (MSP) also plays a key role in the understanding of the customer by providing insight in to the hierarchical relationships that exist in most kinds of socially related organizations that are then exhibited in their mobile calling patterns. Thus it is possible for the MSP to identify important persons from within their customer base such as Leaders who have Communities of Followers around them and Gate Keepers who link otherwise disparate communities together. The MSP also then has the ability to observe how a product idea diffuses through a Social Network by word of mouth communication if it has access to the product purchase information. The main goal of the identification of influential members in a social network is due to their ability to efficiently pass a message to a larger audience who comprise their followers. Since the Leader has established trust among his followers, generally through a process of being highly connected with his followers who are also themselves well connected he wields influence over his subordinates in the social hierarchy.
Recent research has also shown that an MSP has the ability to estimate the Personality type of its customers via the way each individual interacts with different mobile services, other callers, his movements and by his preference for certain types of mobile service products.
Every customer touch point is a potential source of feedback to an organization on its products and services in the form of customer feedback comments, complaints, suggestions and problem reports. These touch points may be take the form of call centers, Facebook pages and web sites, Twitter feeds, and retail outlets. Mining this customer feedback data for the emotional content expressed by customers about products and service levels is sometimes the only way that certain quality issues are identified. This type of feedback also provides an organization firsthand information on customer tastes and preferences which is useful in making strategic choices regarding which product lines to pursue, features to add, promotional strategies to implement and a whole host of internal decisions regarding channeling investments correctly. Many approaches exist to analyze and rate sentiment, chief among them are word matching algorithms which are generally unsupervised and supervised techniques based on Bayesian methods.
Personalization and extremely individualized targeting is possible via the Mobile Telecommunication Network as a carefully crafted message tailored to the specific tastes of the Customer can be sent to each individual customer through the mobile network via SMS and similar means. Thus the Retailer and the MSP form a portent nexus if their data sources and analytical capabilities are combined. In such an event both the relational and the product preferences together with demographics can be combined to arrive at a detailed picture of the customer and his behavior. Advertisement Diffusion through the Social Network model provided by the MSP’s combined with the product preferences and associations derived from Retail data will allow for extremely well targeted campaigns directed at the very specific tastes of individual consumers.
In conclusion Analytics opens the door to a host of possibilities which were not previously available until the development of quantitative methods to accurately describe and model consumer behavior in all its dimensions.
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