kevin gray festival of newmr 2016

An Analytics ToolkitKevin Gray, Japan, Festival of NewMR 2016

An Analytics Toolkit

Kevin GrayCannon 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 GrayCannon Gray

Sue YorkThe Handbook of

Mobile MarketResearch

#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 .

http://cannongray.com/philosophy

http://cannongray.com/methods

http://cannongray.com/news