This document proposes a system called JediForceX that uses consumer data like finances, health, and preferences to provide analytics and recommendations to empower consumers when choosing food products. It aims to place analytics in the hands of consumers to help them make informed decisions. The system would analyze advertisement attributes and provide rankings and categorizations to help consumers identify options that meet their needs. A 3-phase approach is outlined to first provide reactive analytics and then shift to more proactive and interactive analytics over time to positively influence consumer behaviors around food purchases and finances. The goal is to help consumers meet needs and wants rather than just buy products. Experiments are proposed to test the impact on consumer spending and savings habits.

1. Democratization of Analytics
Presented by: Vaibhav, Anupam , Ed, Prajakta , Vlady

2. The Consumer
The Invisible Hand
Need Vs Want

3. JediForceX - Analytics of the people, by the people, for the people
JediForceX
Consumer
Food Marketing
Industry
By placing Analytics in the hands of a consumer we want to empower them with the knowledge to make the right
decisions.
JediForceX uses consumer finance , health , lifestyle ,preference data to provide ranking , categorization ,scoring on
Ads, Email , search results generated by food industry for the consumer.

4. Old process flow of Food Marketing
Need ?
Want ? Buy ?

5. Introducing – The Product
This looks like good option for you
based on the price and nutrition

6. New process flow of Food Marketing
Need ?
Want ? Buy ?
JediForceX
Consumer
Food Marketing Industry

7. New Process flow diagramJediForceX
Analytical
Enhancement

8. Data Source
Data
Category
Customer Financial
Food Product/Service
specification
Customer
Preference
Customer 0Health and
life style
Advertisement / Promotion
email Attributes
Data
Source
Access to Accounts
and Transaction
Online data from Yelp ,
OpenTable etc
Customer
profile on
JediForceX
Wearable devices and
Customer Profile
Online Ads and emails
delivered to customer
Attributes
Account Balance Rating
Vegan/Non
Vegan
Allergy
Source
Credit debt, limit,
score Reviews Cuisine
Medical condition
Product
Average Monthly food
expense Nutrition details
Diet type (low
fat)
Average number of
hrs. sleep per day Deal
Transaction data
attributes Per Unit cost
Average number of
steps per month Price

9. Analytical Improvement & Phase 1-Reactive Finance and Health Management
Phase 1 – Reactive Finance
and Health Management
Phase 2- Proactive Finance
and Health Management
Phase 3 – Interactive Finance
and Health Management
Phase 1 –
 Finance and health tracking.
 Out of box reviews from Yelp.
 Identifying data sources,
feature vector and target
vector.
 Collect Data.
 Normalize the data.
The simple answer – Neural Network

10. Phase 2- Proactive Finance and Health Management
This can be represented on a graph
of P(x) v/s f(x) and takes the shape
of a sigmoid curve.
Official – Logistic Regression
Note – The idea is to not help people buy food products but to help them know what
they want and buy right.
 The categorization of Ads as Good, Bad will help the task of deciding whether a
consumer wants a product or should buy a product. The value of target variable
Buy (1/0) will be represented as Good (1) and Bad (0).
 The feature vector X corresponds to a particular scenario new data item.(ad
appears on screen)
 f(x) is the model’s estimation of the log-odds such that x belongs to the positive
class.
 Class probability p+(x) = 1 / (1 + e - f (x)) .
 However, in real life scenario this might not be the case and hence we will choose
a threshold of p(x) above which the value of target variable will be considered as
one.
 Initially the threshold can be the mean of p(x) for product (previous entries).

11. Phase 3 - Interactive Finance and Health Management
Experiments – To extract the right behavior patterns and use them to
influence bad behavior patterns.
Use similarity matching to selectively choose neighbors with good behavior
patterns and use weighted values to identify a target variable for the person.
Use Clustering to identify clusters showing similar behavior and then use
specific model to improve them.
Interactive assistance to consumers.
Example - If the consumer goes for lunch every day between 12-12:30 near
work, it will display healthier and reasonably priced food choices without making
repetitions.

12. Experiments
We do not want customer to simply buy .We want them to meet their needs,
identify their wants and make the right choice when they buy.
The Idea is to take good behavior patterns from data and use them to
influence bad behavior patterns.
• Custom model
generated from
Financially
stable
consumer only
Financially
Stable
• Custom model
generated from
Financially
unstable
consumer only
Financially
Unstable
• Standard Model
C -Random
set of Both
Hypothesis – Using the model will improve the spending habits of consumers in the 2nd Target set .
Measure improvement in spending and savings on food on all three models.

13. Matrices
Number of suggestions (categorization, ranking results) used by customer.
Average saving per customer per view or adoption or both (Ad classification,
ranking).
Average consumption of nutrition per customer (Protein).
Average expenditure per mg nutrient ($1 per mg on Protein).
Average income per recommendation for JediForceX.
Number of advertisements, mails and searches which are consistently classified.
% Improvement in accepting the given recommendation (Ad classification,
ranking).

14. Future Prospective
Personalized analytics can be
extended in below areas:
Housing
Transportation
Health Care
Entertainment
Apparels and services

15. Conclusion
This is not exactly counter analytics ..we are simply democratizing.
Create ideal Customer to Business scenario.
Mass marketing negatively affects the customer.
Use the power of personalized analytics for common man.
May the Analytical force be with You….
https://brorlandi.github.io/StarWarsIntroCreator/#!/AKTC4CglFfCBx6yIif24

16. References
Slide 2 –Images from - https://www.youtube.com/watch?v=ulyVXa-u4wE And http://awesomenator.com/content/2012/03/want-need-apple.jpg
Slide 3 –Images from http://vignette4.wikia.nocookie.net/starwars/images/c/c3/Yoda_TPM_RotS.png/revision/latest?cb=20130810185858
https://s-media-cache-ak0.pinimg.com/736x/c9/18/36/c918361ec6445af7a09419120c44a45d.jpg
Slide 4 – Images from Adapted from - https://s-media-cache ak0.pinimg.com/736x/c9/18/36/c918361ec6445af7a09419120c44a45d.jpg AND
http://vision.cloudera.com/wp-content/uploads/2016/01/360_view_customer_R2.png And
http://blogs.lse.ac.uk/impactofsocialsciences/files/2014/03/data-mining.png
Slide 5 – Personal Mail , Facebook and Google search
Slide 14 –Image from http://www.inflatablestartup.com/images/infographics/consumer-spending-2009.jpg
Slide 15 – Images from http://www.inflatablestartup.com/images/infographics/consumer-spending-2009.jpg
The idea of Invisible hand failed the consumer . In the absence of the force of analytics the consumer awakens, the sinister Mass Marketing has risen to the power of Demi gods and will not rest
until JediForceX empowers the consumer with Analytics .
With the support of the consumer financial ,health life style data JediForceX leads a brave RESISTANCE. Consumer is desperate to have the right information to make the right choice .Its time to
restore financial and health justice to the consumers in the galaxy.
Group 9 has taken the first step in the evolution of the Idea of a Balanced C to B ecosystem....