This document provides an overview of deep learning including its definition, use cases in marketing, and comparisons to traditional analytics methods. It discusses how deep learning models can be used for segmentation, brand optimization, and media strategy. The document also outlines experiments comparing the predictive accuracy of deep learning models to traditional regression for credit card choices, finding deep learning models achieved up to 73% accuracy compared to 70.5% for regression.

1. CONTENTS
• Abstract
• Definition of Deep Learning
• Introduction
• Example
• Three use cases
• Technical Note For Analysts
• Implications for Companies
• Accuracy rate
• Costs and Benefits
• Advantages
• Applications
• Future and Conclusion
• References

2. ABSTRACT
 •In the future, deep learning is likely to substantially change both
marketing strategies and customer behaviors.
 •Building from not only extant research but also extensive
interactions with practice, the authors propose a multidimensional
framework for understanding the impact.

3. DEFINITION OF DEEP
LEARNING
Deep learning is an AI function that mimics the
workings of the human brain in processing data
for use in detecting objects, recognizing speech,
translating languages, and making decisions.

4. INTRODUCTION
Deep learning is delivering impressive results in AI
applications.
The technology that underpins deep learning is becoming
increasingly capable of analyzing big databases for patterns and
insights.
It isn’t difficult to imagine a day when companies will be able
to integrate a wide array of databases to discern what
consumers want with greater sophistication and analytic power
and then leverage that information for market advantage.

5. EXAMPLE

6. THREE USE CASES
 Segmentation
 Brand optimization
 Media strategy

7. SEGMENTATION
 Marketers take on segmentation projects to more
efficiently target customers and refine and optimize
marketing strategies.
 AI-driven market research can assist by making it
simple to build dynamic micro-segmentation models,
create archetype segmentation for messaging design
purposes, develop attitudinal segmentation to
understand the customer mindset and optimize global
marketing investment with country clustering.

8. BRAND
OPTIMIZATION
 AI-driven market research tools can deliver a brand
measurement framework centered on the customer,
accessing diverse information sources to provide a
comprehensive picture of brand health.
 This gives marketers insight about brand equity and
the actions they can take to capitalize on it most
effectively.
 Practical applications can include a brand health
dashboard and identification of key brand loyalty
drivers.

9. MEDIA
STRATEGY
 The right AI-driven market research tool can assist
with media planning and execution by defining
campaign success and identifying impact.
 It can bring diverse data together, including survey
information, social media and in-house sales data to
provide recommendations that improve customer
interactions.
 AI-driven research can also measure ROI from media
spend and promotions and recommend channels to
optimize reach.

10. TECHNICAL
NOTE FOR
ANALYSTS
The first section is used to “train” the model using
algorithms that minimize the losses and that measure how
well the model fits the data. This is manipulating the data
to describe past choices. To avoid overfitting, analysts can
configure models so they are less sensitive to noise.
The second section of the data is used to determine the
model’s overall architecture (the number of hidden
layers and the type of nonlinear transformations
between them).
The final section is used to test the predictive accuracy.

11. IMPLICATIONS FOR COMPANIES
Develop Develop plans for experimentation.
Build
Build capability through training
and new hires.
Create Create rich databases.
Be Be alert to the future opportunities.

12. What We Found
 Based on our analysis, the two models that drew on
deep learning were able to predict credit card choices
with more precision than the traditional approach.
(See “How Deep Learning Can Outperform
Traditional Marketing Analytics.”) But the
improvements were not as large as we thought they
would be.

13. EXPERIMENT
 To compare deep learning with traditional methods
for marketing analytics, we studied a large database of
click-streams, demographics, and ad exposures
relating to the credit card market from NerdWallet, a
large online vendor of credit cards, based in San
Francisco.
 We wanted to see if a multilevel deep learning model
could predict credit card choices more accurately than
traditional models.

14. The first model was a straightforward linear regression
of choice as a function of the individual user
demographics and card attributes.
 Each variable had a simple direct effect on choice
through one equation, with no interactions among
variables.

15. How Deep Learning
Can Out perform
Traditional
Marketing Analytics
 Estimating sales response to marketing inputs can be a
relatively straightforward analytics exercise.
 Many organizations use a variety of classic statistical
methods and tools to simulate the effects of things
like price changes and shifts in advertising,
promotions, and distribution.
 It’s common for brand managers to track their
progress with simple models and online dashboards.
 In some cases, they use A/B testing: If you have a
statistical sense of how customers are likely to react to
a marketing offer, you can look for ways to adjust the
variables to improve the outcomes.

16. ACCURACY RATE
 The simple regression model had an accuracy rate of
70.5%, meaning that in 70.5% of the cases, we were
able to predict correctly which card a particular
consumer would apply for and which ones he or she
would not apply for.
 The simple deep learning model was slightly more
accurate, at 71.7%, and the more sophisticated model
had an accuracy rating of 73%.

17. COSTSAND
BENEFITS
 Costs to acquire deep learning technology, staff
required to implement it, and additional data costs that
may need to be incurred.
 Finally, even with today’s fast computers, deep
learning models need huge amounts of computer
power and may experience long run times.
 This becomes a real limitation if real-time
implementation is required.

18. ADVANTAGES
Advantage is tied to the technical
criteria used for estimating
success. Traditional statistical
methods use the ability to
describe historical relationships
as the criteria for success.
This improves the model of
consumer response and the
probability that predictions will
actually occur.

19. •Amajor drawback of deep learning is that it's difficult to identify which variables drive
the biggest response.
DISADVANTAGES

20. APPLICATIONS
 Deep learning might also be used to design
products to meet consumers’ personal needs,
which could then be produced and delivered
through automated 3D printing systems.

21. THOUGHTS ABOUT
THE FUTUREAND
CONCLUSION
Improve estimates of market response to help
maximize profit and marketing ROI,
Improve
Reveal new opportunities for product
development, and
Reveal
Allow for more targeted product design,
distribution, promotion, and media optimization.
Allow

22. REFERENCES
 P. Dhillon and S. Aral, “Modelling Dynamic
User Interests: A Neural Network Approach,”
working paper, MIT Sloan School of
Management, Cambridge, Massachusetts,
2019.
 https://www.kaggle.com