Chapter 5 - CRM, Big Data, and Marketing Analytics

6 Key Sources of Big Data

1. Business Systems 2. Social media platforms 3. Internet connected devices 4. Mobile apps 5. Commercial entities 6. Government entities

Marketing planning

2nd phase of CRM process cycle, represents a key use of the output from the first phase.

Unstructured Data

Data that is generated in such a way that it does not possess a specific organizational structure that renders it readily analyzable for knowledge creation. (can include social media posts, emails sent to customer service representatives, call logs from salespeople, videos people make discussing experiences with products, images to express feelings about brands).

Customer retention

Keeping satisfied and loyal profitable customers and channels and thus growing the business profitability over the long run

Organizational learning

The last phase of CRM process that is based on customer response to the firm's implementation strategies and programs

CRM Process Cycle (4 Elements)

1. Knowledge discovery 2. Marketing planning 3. Customer interaction 4. Analysis and refinement

Customer Relationship Management (CRM)

A comprehensive business model for increasing revenues and profits by focusing on customers

Customer loyalty

A customer's commitment to a company and its products and brands for the long run

Individual-level Personalization

Each customer receives an offering customized to his/her specific tastes

Collaborative Filtering

Predicts a customer's preferences for products or services based on the observed preferences of customers who are perceived to be similar.

Attribution

Determining how to give appropriate credit to different elements of the marketing mix through the measurement of their effects.

Customer interaction

Represents the actual implementation of the customer strategies and programs

Customer Satisfaction

The level of liking an individual harbors for an offering

Big Data

Refers to the ever-increasing quantity and complexity of data that is continuously being produced by various technological sources.

Hurdle Rate

The minimum acceptable, expected return on a program at a given level of risk

Knowledge Discovery

The process of analyzing the customer information acquired through various customer touchpoints

Diagnostic Analytics

Utilizes data to explore the relationships between different marketing-relevant factors that influence the organization's performance either directly or indirectly. Statistical methods such as linear regression, which provide a quantifiable relationship between a marketing outcome and specific marketing-related factors believed to influence that outcome, can provide evidence of any relationships that exist, as well as evidence of the relative magnitudes of those identified relationships.

Predictive Analytics

Utilizes data to make predictions about future marketing outcomes of interest. Can be divided into those that use historical measurements of the outcome of interest to determine a pattern that can be extrapolated in the future, and those that make predictions based on the examination of relationships between a set of factors and an outcome of interest that the factors are believed to influence. Can be applied on a wide continuum anywhere between the macro level for the prediction of market level outcomes, and the micro level for predicting customer-level responses.

Descriptive Analytics

Utilizes data to provide summary insights often presented in visual format (ex: histograms, scatter plots, or pie charts); measurements produced from this can include sums (total # of new customers acquired per month), averages (avg. $ amount of purchases made by customer in loyalty program), or measures of changes in variables of interest (% increase or decrease in # of email subscribers to blog for given month vs same month last year).

Four V's

Volume (amount of data produced - bytes), Velocity (frequency data generated over time & speed at which analyzed), Variety (different types of data - text, video, images, audio), Veracity (relates to reliability and validity of all the data), Fifth one is sometimes (Value)

Return on Marketing Investment (ROMI)

What impact an investment in marketing has on firm's success, especially financially = the revenue or the margin generated/the cost of that program at given risk level Originally designed for comparing capital projects.

Customer touchpoints

Where the selling firm touches the customer in some way, thus allowing for information about him or her to be collected

Potential Pitfalls in Marketing Dashboards

- Overreliance on "inside-out" measurement - Too many tactical metrics; not enough strategy insight - Forgetting to market the dashboard internally

4 Key Types of Marketing Analytics (Increasing with Degree of Complexity)

1. Descriptive Analytics 2. Diagnostic Analytics 3. Predictive Analytics 4. Prescriptive Analytics

Return on Customer Investment (ROCI)

A calculation that estimates the projected financial returns from a customer. It is useful strategic tool for deciding which customers deserve what levels of investment of various resources

Data warehouse

A compilation of customer data generated through touchpoints that can be transferred into useful information for marketing management decision making and market planning.

Marketing Dashboard

A comprehensive system providing managers with up-to-the-minute information necessary to run their operation including data on actual sales vs forecast, progress on marketing plan objectives, distribution channel effectiveness, sales force productivity, brand equity evolution, and whatever metrics and info are uniquely relevant. 2 primary goals: diagnostic insight and predictive foresight

Customer mind-set

A person's belief that understanding and satisfying customers, whether internal or external to the organization, is a central to the proper execution of his/her job.

Marketing Analytics

A set of methods facilitated by technology that utilize individual-level and market-level data to identify and communicate meaningful patterns within the data for the purpose of improving marketing-related decisions.

Data Mining

A sophisticated analytical approach to using the massive amounts of data accumulated through a firm's CRM system to develop segments and microsegments of customers for purposes of either market research or development of market segmentation strategies.

Sentiment Analysis

A type of analytic method that identifies the general attitude (ex: positive, negative, or neutral) contained within a message through an analysis of its content.

Marketing Analyst

An individual familiar with different forms of market and customer data and who is trained to conduct different market analyses, as well as the computational costs associated with those analyses.

Content Filtering

Analytic method that identifies which products or services to recommend based on a determination of how similar a product or service seems to be to those that the customer has demonstrated a preference for in the past, or is currently considering.

3 Major Objectives of CRM

Customer Acquisition, Customer Retention, Customer Profitability

Semi-structured Data

Data that contains some elements of structure that make it easier for machines to understand its organization, but still contains parts that do not possess an appropriate level of structure to make them readily analyzable by automated means for knowledge creation. (Ex. Extensible Markup Language - XML)

Structured Data

Data that is generated in such a way that a logical organization is imposed on it during its generation, thus enabling it to be more readily analyzable for knowledge creation. (typically numeric or text limited to a set of input variables - male/female)

Database marketing

Direct marketing involving the utilization of the data generated through CRM practices to create lists of customer prospects who are then contacted individually by various means of marketing communication.

Mass personalization

Everyone receives the same offering

Customer Acquisition

Getting the right customers based on known or learned characteristics that will drive growth and increase margins

Segment-level Personalization

Groups of customers with similar preferences are identified and an offering is developed for each segment.

Customer Lifetime Value (CLV)

Important metric in CRM investment in CRM yields more successful long-term relationships with customers, relationships pay handsomely in cost savings, revenue growth, profits, referrals, etc.

Customer profitability

Increased individual customer margins, while offering the right products at the right time

Prescriptive Analytics

Involves determining the optimal level of marketing-relevant factors for a specified context by considering how adjusting their levels in varying ways will impact different marketing outcomes. Most advanced and most costly Can be used to look at how the level of spending on different marketing communication channels (ex. print ads, tv commercials, online video ads, online banner ads, and social media ads) will impact one or more marketing outcomes related to marketing communications spending (key outcomes might be total sales of products or services, or changes in market share).

Formalization

Means that structure, processes and tools, and managerial knowledge and commitment are formally established in support of the culture.

Recommendation Systems

- Divided into content filtering - Collaborative filtering - Hybrid methods

Marketing Investment Decisions must consider:

- Level of Investment - Returns - Risks - Hurdle Rates