Kevin Gray Festival of NewMR 2016

An Analytics Toolkit
Kevin Gray, Japan, Festival of NewMR 2016
Kevin Gray
Cannon Gray
2 February, 2016
#NewMR 2016 Sponsors
Media Partner GreenBook

What Is ‘Analytics’?
Analytics is the discovery and communication of
meaningful patterns in data. It makes use of information
technology, statistics and mathematical algorithms to
develop knowledge, to quantify performance or to make
predictions. It uses the insights gained from this process
to recommend action or to guide decision making.
Analytics is best thought of as a research procedure for
decision making, not simply as isolated tools or steps in a
process.

What Is ‘Analytics’?
Basic components:
1. Defining Objectives
2. Data Collection
3. Data Preparation and Cleaning
4. Model Building
5. Model Evaluation
6. Interpretation
7. Scoring New Data or Simulations Using the Model
8. Communication of Results and Implications to Decision Makers
9. Implementation and Monitoring Effectiveness

• Descriptive and Exploratory Analysis
– frequencies, means, bar charts
• Models that Predict
– predicting consumption frequency of new
customers
• Causal Models
– identifying brand choice drivers

• Analysis of Cross-Sectional Data
– data collected at one period in time
• Analysis of Longitudinal or Time-Series Data
– data collected at several periods in time
• Models with Quantitative Dependent Variables
– monthly spend

• Models with Categorical Dependent Variables
– product user/non user
• Time-to-Event Models
– customer churn analysis
• Methods that Group Variables
– factor analysis of attribute ratings

• Methods that Group Cases
– cluster analysis of consumers
• Text Mining
– analysis of social media conversations
• Simulations and Forecasts
– sales forecasts under various marketing mix
scenarios

Important Considerations
• Big Data?
• Prediction versus Interpretation
• Statistics versus Machine Learning

‘Macro’ Categories
• Supervised methods are used when there is a
dependent variable (‘label’)
– e.g., regression, logistic regression
• Unsupervised methods are used when there is
no dependent variable
– e.g., cluster analysis, factor analysis

Popular Supervised Methods
• Generalized Linear Models (GLM) are a large family of
statistical techniques
• Extremely versatile and can be used when dependent
variable is:
– Continuous (OLS regression)
– Categorical (binary logistic and multinomial logistic regression)
– Ordinal (ordered logistic regression)
– Count (Poisson regression)
– Repeated over time (longitudinal analysis)
– Clustered (e.g., departments within divisions of a company)

Popular Supervised Methods
• Methods widely-used in Data Science but less
familiar to marketing researchers include:
– K-nearest neighbors
– Artificial Neural Networks (‘neural nets’)
– Support Vector Machines
– Boosting (e.g. AdaBoost)
– Bagging (e.g. Random Forests)
– MARS
– GAM

Popular Unsupervised Methods
• Principal Components Analysis
• Factor Analysis
• Correspondence Analysis
• Biplots
• MDPREF
• Canonical Correlation

Popular Unsupervised Methods
• K-means Cluster Analysis
• Agglomerative Hierarchical Clustering (‘AHC’)
• Partitioning Around Medoids (‘PAM’)
• Self-Organizing Maps (‘Kohonen networks’)
• Mixture Modelling and Latent Class
• Frequent Pattern Mining (e.g., Apriori, FP-
Growth)

Structural Equation Modelling (SEM)
• SEM is arguably the most multitalented of the
bunch...It is certainly the most general
• It unites GLM and Factor Analysis
• A Mixture Modelling variant (SEMM) adds
Cluster Analysis
• It can also be used with longitudinal and
clustered data

Time-Series Analysis
• A very large group of methods that originated in
Engineering, Operations Research, Econometrics,
Statistics and other disciplines
• Used when data have been collected at many
points in time, (e.g., weekly sales)
• In MR used most often for sales forecasting and
Marketing Mix Modelling (aka Market Response
Modelling)

Time-Series Analysis
• Exponential Smoothing
• ARIMA
• ARMAX
• VAR, VEC
• State-Space Modelling
• Dynamic Factor Models
• GARCH

Time-To-Event Modelling
• Used for analyzing the expected time until one or
more events happen
• Also known as Survival, Duration or Event History
Analysis
• MR examples include:
– What factors cause customer churn?
– Predicting how long a customer will remain (‘survive’
as a) customer
– Analysis of purchase behavior and website usage

Time-To-Event Modelling
• Quite complex…heavily used by Medical
researchers and also by Economists, Engineers
and in Operations Research
• Kaplan-Meier, Cox regression and parametric
models are the main methods
• Modern variations can include a segmentation
component rather than assuming all customers,
patients, etc. are the same

Why Use Advanced Analytics?
• Advanced analytics adds value to data - it can
help data speak to us!
– For example, we can take respondent scale usage
patterns and background characteristics into
account
– This provides us with a deeper and more accurate
understanding of attitudes and behaviours and
how they connect

• In any kind of research totals and crosstabs
only show us the surface - they’re just the first
steps in exploratory data analysis
• Moreover, running lots of crosstabs to find
something interesting increases the risk of
fluke ‘findings’...and clients can make bad
decisions based these chance results!

• Looking at variables two-at-a-time can also be
very misleading
– older consumers may seem to be heavier users of
a particular category but…
– after taking gender, income and other
characteristics into account, we may find that
category usage actually declines with age!

Key Points
• What I’ve just shown are only some of the tools a
Marketing Scientist may wish to include in their
toolbox
• More than the tools, though, it is how they are
used that’s most important
• Making advanced analytics work involves much
more than math and programming
• You need to put the patient before the cure!

Future Directions
• IoT, Artificial Intelligence, Quantum Computing and
unforeseen innovations will likely have a profound impact
on our lives in the future
• This means they will also impact MR and analytics!
• Some kinds of analytics will be largely automated in the
not-so-distant future, but human judgement will remain
essential
• Further ahead, Marketing and MR may be extensively
automated...but in that sort of world will they still be
necessary?

Thank You!

Q & A
Kevin Gray
Cannon Gray
Sue York
The Handbook of
Mobile Market
Research
#NewMR 2016 Sponsors
Media Partner GreenBook

Cannon Gray LLC is a marketing science and analytics company that partners with marketing research agencies,
consultants, ad agencies and clients located in many regions of the world. Most work is done remote by
phone/Skype or email. Advanced Analytics and consultation are provided for a broad range of quantitative
marketing research. The focus is on developing knowledge and insights for decision making, not on number
crunching. Each project is tailored to address specific marketing issues and to the country or countries being
researched.
Cannon Gray was established in 2008 by Kevin Gray, a marketing scientist who has been in marketing research
for more than 25 years. Previously Kevin had worked for The Nielsen Company's Consumer Research division,
Kantar Research International and McCann Erickson as well as on the client side. His background covers dozens of
product and service categories and over 50 countries. A member of the American Marketing Association and the
American Statistical Association, he is always keen to learn about innovations in marketing research and to borrow
ideas from other disciplines.
For more information on Cannon Gray’s approach to MR see http://cannongray.com/philosophy and about
methods used see http://cannongray.com/methods . Cannon Gray articles about MR can be found here:
http://cannongray.com/news .

Kevin Gray Festival of NewMR 2016

Recommended

Recommended

More Related Content

Similar to Kevin Gray Festival of NewMR 2016

Similar to Kevin Gray Festival of NewMR 2016 (20)

More from Ray Poynter

More from Ray Poynter (20)

Recently uploaded

Recently uploaded (20)

Kevin Gray Festival of NewMR 2016