introduction to marketing analytics

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Chapter 01
Introduction to Marketing
Analytics
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Introduction to Marketing Analytics
How does Expedia,
Orbitz, or Hotels.com
determine the price to
quote when you are
shopping for a hotel
room?
How does Spotify know
what songs to suggest for
you?
How does Stitch Fix
achieve the highest-ever
rate of purchased items
per “Fix” for its female
customers?
In this chapter, we describe and explain an analytics framework, relevant
marketing analytics concepts, and industry best practices.
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Marketing Analytics Defined
Marketing analytics
uses data, statistics,
mathematics, and
technology to solve
marketing business
problems.
Modeling and
software drive
marketing decisions.
The fastest growing
field of analytics
applications.
Increasingly applied,
and the impact and
benefits are evident.
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Source: Google Trends. 3

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Analytics Level and Their Impact on Competitive Advantage
As organizations adopt more advanced techniques, higher data
management and analysis maturity are required to achieve a competitive
advantage.
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Source: Adapted from SAS. 4

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Analytic Levels
Descriptive analytics are techniques used to explain or quantify the
past.
• Data queries, visual reports, descriptive statistics.
Predictive analytics is used to build models based on the past to explain
the future.
• For example, historic sales can predict future sales.
Prescriptive analytics identifies the optimal course of action or decision.
• UPS route optimization, Amazon’s price optimization.
Artificial Intelligence (AI) and cognitive analytics are designed to
mimic human-like intelligence for certain tasks, like discovering patterns.
• This type of analytics uses machine learning to understand new data.
• Hitachi uses AI to discover patterns typically undetected by humans.
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Defining the Right Business Problems
Understanding requires deep
knowledge of the customer’s path.
• How they search.
• Where they purchase.
• Their satisfaction.
Problem identification uncovers
strategic business opportunities.
• To improve market share.
• To establish a better customer
relationship.
• To position the company to
take advantage of innovation.
How do you arrive at the right
business problem?
• Understand the intent behind
the question.
• Include stakeholder input
through discovery methods.
• Discovery begins with: what,
who, where, when, why, and
how.
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SMART Principles
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The SMART
principles can be
a goal-setting
technique.
Equally as important as following the SMART
principles is examining:
• The potential success of the analytics
project.
• Whether it makes a valuable impact.
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Data Sources
Data consists of both primary and
secondary data.
• Primary data is collected for a
specific purpose.
• Secondary data relies on
existing data collected for
another purpose.
Sources of secondary data
include:
• Public datasets.
• Online sites.
• Mobile data.
• Channel partners.
• Commercial brokers.
• Corporate information.
• Government sources.
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Types of Data
Structured data is made up of
records organized in rows and
columns.
• Can be stored in a database or
in a spreadsheet formula.
• Includes numbers, dates, and
text strings.
• Easy to access and analyze.
Unstructured data includes text,
images, videos, and sensor data.
• No defined structure.
• Content does not fit into a table
format.
• Requires advance analytics to
prepare and analyze.
• Technology has advanced to
support manipulation and
exploration of this data.
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Data Measurement
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Numerical data can be discrete
(integer) or continuous.
• Discrete data is measured in
whole numbers.
• Continuous data can include
values with decimals.
Categorical data exists when values
are selected from categories.
• Binary can have two values.
• Nominal has no meaningful order.
• Ordinal data has meaningful values.
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Metric Measurement Scales
Metric scales can be measured as intervals or ratios.
• Both scales possess meaningful, constant units of measure.
• The distance between each point of the scale are equal.
However, there is a difference between these scales.
• Interval variables do not include an absolute zero.
• Ratio scales have an absolute zero point and can be discussed in
terms of multiples.
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Predictors versus Target Variable
Variables are
characteristics or
features that pertain
to a person, place, or
object.
Does weather impact ice cream sales?
• Weather conditions are the independent
variable.
• It influences or drives the dependent,
target, or outcome variable which is ice
cream sales.
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Modeling Types: Supervised versus Unsupervised Learning
Supervised learning suggests
the target variable is known.
• A training dataset helps
“learn” the relationship.
• A validation dataset assesses
the algorithm’s accuracy.
• A testing dataset evaluates
the final selected algorithm.
• The algorithm is applied to
new, unlabeled data.
If the target variable is continuous,
results are a prediction.
If categorical, supervised learning
is called a classification.
Unsupervised learning has no
previously defined target variable.
• The goal is to model data to
discover and confirm patterns.
This technique may include:
• Association analysis such as
offering product suggestions
based on past purchases.
• Cluster analysis which groups
customers based on key
variables.
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Exhibit 1-9 Supervised Learning Steps
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Exhibit 1-11 The 7-Step Marketing Analytics Process
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• The 7-step marketing analytics process is iterative and continuously
evolves to develop and manage improvements in the modeling cycle.
• Each step plays an important role in achieving a successful outcome.
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Step 1: Business Problem Understanding
Most marketing analytics models are
developed when a business identifies a
problem.
• The idea is to develop a model
using analytics to better understand
the problem and design a solution.
• A key element is to question
whether the problem is the correct
problem.
Exactly what are you trying to
understand and solve?
How will the stakeholder(s)
use the results?
Who will be affected by the
results?
Is this a single, short-term
problem or an ongoing
situation?
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Step 2: Data Understanding and Collection
The analyst must identify where data is stored, its format, and how it can
be combined to understand the question.
• Examine databases, interview stakeholders, and observe processes to
confirm the identified problem is the actual problem.
Once the problem is understood, the analyst samples data from
databases for records to analyze.
• For example, examining past purchases and customer returns.
Marketing analysts must have a good understanding of the types and
sources of data.
• The data’s origin may directly affect the decision.
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Step 3: Data Preparation and Feature Selection
Data in different formats is combined in this step.
• Identify the unit of analysis; the target and the predictor variables.
• Examine target and predictor data columns both visually and
statistically.
• Clean the data – deal with missing values, data errors, and outliers.
• Merge data from different sources so data is measured consistently
and then used to develop the models.
Other features are further refined in this step.
• Adjusting date formats.
• Increase accuracy by including predictors with a strong target variable
relationship – some may be eliminated or transformed.
• Understanding the meaning of each variable and its unit of analysis is
essential in this step.
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Step 4: Modeling Development
In this step, the analyst selects the method to use.
• Choice depends on the target variable and problem.
• Options – classification, prediction, clustering, or association.
If the problem is unsupervised, the analyst partitions data into datasets.
• Training, validation, and testing.
The analyst should decide on appropriate modeling techniques.
Different models should be tried to find the one providing accuracy,
speed, and quality.
• The chosen model should be simple, practical, and useful.
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Step 5: Model Evaluation and Interpretation
The model is evaluated to identify the algorithm providing
the best solution.
The algorithm is initially run on the validation dataset.
If the validation shows high accuracy, the model can be
thought to predict new cases and address the problem.
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Step 6: Model and Results Communication
It is key for the analyst to present the model in a way other people can
understand, particularly management.
• A good approach is to collaborate with key stakeholders early-on.
• A full understanding of the model is important.
• Whether simple or complex, the model should be explainable in
straightforward terms with appropriate visualizations.
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Step 7: Model Deployment
The model is not finished until it has been implemented and running on
real-time records to offer decisions or actions.
• This step involves other key stakeholders who need trained to
implement the system.
A key consideration throughout the 7-step marketing analytics modeling
process is to evaluate the ethical dimensions of the analysis.
• Are the privacy and anonymity of the subjects being protected?
• Does a bias exist in the data that could impact the analytics results?
• Are the model results accurate?
• The model may be correct, but the objective is unfair to some subjects
or unrealistic in its predictions.
• Another issue is that the data, features, data cleaning, and the model
are determined by analysts – ethics is imperative.
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