What is Customer Analytics

In this chaper we will introduce what is Customer Analytics.

Origins of Customer Analytics

Data Dispersion Data Organization Data Ownership Data Collaboration
Evolution Phase Early 90s. Independent solutions: SFA, Call Center,… Late 90s. CRM is understood as marketing, sales and support 2000 - 2010. CRM as a global strategy 2010 – 2020. Beginning of Social CRM
Characteristics Technical solutions are developed for customer interactions Customer Data is used in the organizacion Customer Data is a critical asset. Loyalty programs are developed Use of external data y data analytics
Focus Technical Solution Tactical and operational solution Corporate culture and estrategy Value co-creation and customer experience
Objective Improve sales and support efficiency Improve customer retention and cost savings per customer interaction Cost savings and Revenue Growth. Predict customer behaviour Value creation based on customer. Customer Centricity

CRM as the origins of Customer Analytics. Authors: Hannu Saarijärvi, Heikki Karjaluoto y Hannu Kuusela

The new consumer

A new breed of customers has emerged. This new consumer is:

  • Informed: Information about products and services is available to consumers.
  • Instrumented: They have devices and channels to accees information.
  • Connected: They have pervesive access to information. Anywhere, anytime.
  • Less Loyal: They are open to try new services and products.
  • More demanding: they expect more value, honesty and transparency from companies.
  • ROBO (Research Online, Buy Offline/Online) or ROPO (Research Online, Purchase Offline/Online): They have different and non-linear purchasing patterns.

Definition

We need a definition. We will use the one from (Wharton Customer Analytics Initiative):

Customer Analytics refers to the collection, management, analysis and strategic leverage of a firm’s granular data about the behavior(s) of its customers

But need to remember that:

Essentially, all models are wrong, but some are useful (George Box).

Characteristics

Customer Analytics can be characterized as:

  • INHERENTLY GRANULAR: must be individual-level
  • FORWARD-LOOKING: orientation towards prediction not just description
  • MULTI-PLATFORM: combining behaviors from multiple measurement systems
  • BROADLY APPLICABLE: consumers, donors, physicians, clients, brokers, etc.
  • MULTIDISCIPLINARY: marketing, statistics, computer science, information systems, operations research, etc.
  • RAPIDLY EMERGING: starting to take on its own unique identity as a “standalone” area of analysis and decision making
  • BEHAVIORAL: customer analytics’ primary focus is on observed behavioral patterns
  • LONGITUDINAL: It’s ALL about how these behaviors manifest themselves over time

What does it mean to generate value?

  • Customer Lifetime Value (CLV) is a prediction of the net profit attributed to the entire future relationship with a customer..
  • Customer equity is the total of lifetime values of all your current and future customers – the sum total of all the value you'll ever realize from customers.
  • Value Proposition is a business or marketing statement that summarizes why a consumer should buy a product or use a service. This statement should convince a potential consumer that one particular product or service will add more value or better solve a problem than other similar offerings.
  • Customer Satisfaction is a marketing term that measures how products or services supplied by a company meet or surpass a customer's expectation.
  • Customer delight/sacrifice is the value that is added or subtracted to the customer value proposition as a surprise.
  • Switching Cost are the negative charges associated with the change of supplier, brand or product by a customer..
  • Customer Loyalty is the result of consistently positive emotional experience, physical attribute-based satisfaction and perceived value of an experience, which includes the product or services.

Types of strategies

  • Customer Acquisition: the organization wants to increase the number of customers.
  • Customer Development: the organization wants to increase customer profitabity and/or loyalty.
  • Customer Retention: The organization aims to prevent customers leaving the service either because they stop consuming/buying or because they are switching to other suppliers.
  • Acquisition-Retention Optimization: The organization aims to balance customer acquisition and retention strategies and to avoid side effects such as customer churn propensity.

Methodology

Methodology

  • [BU] Business Understanding
    • Determine business objectives
    • Assess situation
    • Determine data mining goals
    • Produce project plan
  • [DU] Data Understanding
    • Collect initial data
    • Describe data
    • Explore data
    • Verify data quality
  • [DP] Data Preparation
    • Select data
    • Clean data
    • Construct data
    • Integrate data
  • [M] Modeling
    • Select modeling technique
    • Generate test design
    • Build model
    • Assess model
  • [E] Evaluation
    • Evaluate results
    • Review process
    • Determine next steps
  • [D] Deployment
    • Plan deployment
    • Plan monitoring and maintenance
    • Produce final report
    • Review project

It must be noted that:

We must find the balance between analytical results, the business needs and operational constraints.

Without taking this into account, Customer Analytics initiatives will fail. This is true for any analytical initiative.

How to obtain customer data

In order to understand the customer we need data. Companies use several resources:

  • Internal resources: data from information systems such as CRM, ERP, Call Center, e-commerce, etc.
  • External resources: Cookies; Super Cookies -HTTP Strict Transport Security (HSTS)-; mobile devices ID (iOS Identifiers for Advertisers or Android Advertising ID); beacons; HTML5 storage; geolocalization and wifi (IP); Fingerprinting; Adobe Flash, Applets Java and ActiveX Controllers; Plug-ins, toolbars and spyware; Etags, Google Data; Whatsapp and Facebook; Windows 10 Telemetry; Deep Packet Inspection (DPI); third parties data; security breaches.

Increasing the analytical maturity of the organization

  • Descriptive analysis: the organization is able to understand what happened in every customer interaction.
  • Diagnostic Analysis: the organization is able to understand the reasons why interactions with customers happen.
  • Predictive analysis: the organization is able to predict certain customer interactions.
  • Prescriptive analysis: the organization is able to make decisions related to customer interactions based on scenarios.
  • Preventive Analysis: the organization is able to act in advance of customer needs.

References

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