3. 3
TELECOM CHURN
“Churn of customers is a
particularly severe problem in
the telecom industry.
The challenge is to identify
the propensity of churn up to
a month in advance, even
before a customer moves out,
so that proactive
interventions can begin”
8. PRICE FORECASTING FOR AN
ASIAN AGRICULTURAL ENTERPRISE
Problem Approach Outcome
A Gramener Advanced Analytics Case Study
A leading agricultural
enterprise wanted price
forecasts for their products in
order to plan inventory
release to optimise revenue.
Incorrect timing was leading
either to loss of revenue or
unsold inventory.
Gramener applied a suite of
price forecasting models
based on internal and
external factors.
The models were evaluated
on multiple test datasets to
select one that minimised
median absolute deviation.
The model was able to
forecast the price to an
accuracy of 88%.
Within the first quarter of
deploying the model, the
revenue uplift attributable
directly to pricing was +3.2%.
12. 1212
BEHAVIORAL CLUSTERING
“Delivering targeting media
content to different regions
of the country could improve
reach.
The challenge is to identify
the right clustering of regions
that are similar, but may not
be geographically
contiguous, so that targeted
interventions can begin”
13. 13
SEGMENTING INDIA’S DISTRICTS BASED ON BEHAVIOUR
Previously, the client was treating contiguous regions as a homogenous entity, from a channel content
perspective. To deliver targeted content, we divided India into 6 clusters based on their demographic
behaviour:
14. 14
VISUALIZING THE BEHAVIOURALLY SEGMENTED DISTRICTS
The 6 clusters were created using the three composite
indices based on the economic development lifecycle:
• Education (literacy, higher education) that leads to...
• Skilled jobs (in mfg or services) that leads to...
• Purchasing power (higher income, asset ownership)
Districts were divided (at the average cut-off) by:
Offering targeted content to these clusters will reach a
more homogenous demographic population.
Poor
Rural, uneducated agri
workers. Young population
with low income and asset
ownership. Mostly in Bihar,
Jharkhand, UP, MP.
Breakout
Rural, educated agri workers
poised for skilled labour.
Higher asset ownership. Parts
of UP, Bihar, MP.
Aspirant
Regions with skilled labour
pools but low purchasing
power. Cusp of economic
development. Mostly WB,
Odisha, parts of UP
Owner
Regions with unskilled labour
but high economic prosperity
(landlords, etc.) Mostly AP,
TN, parts of Karnataka,
Gujarat
Business
Lower education but working
in skilled jobs, and
prosperous. Typical of
business communities. Parts
of Gujarat, TN, Urban UP,
Punjab, etc
Rich
Urban educated
population
working in skilled
jobs. All metros,
large cities, parts
of Kerala, TN
Skilled
Poorer Richer
Unskilled Skilled
Uneducated Educated Uneducated Educated
Unskilled
Purchasing power
Skilled jobs
Education
Poor Breakout Aspirant Owner Business RichThe 6 clusters are
18. 18
FINDING PATTERNS
“
Which securities move together?
How should I diversify?
What should I sell to reduce risk?
What’s a reliable predictor of a
security?
SECURITIES
23. 23
ENERGY UTILITY
NEURAL NETWORKS
Inspired by biological
networks, artificial neural
networks are a network of
interconnected nodes that
make up a model, like
humans & animals.
Neural network processes
information by passing it
through layers: one
input layer, 1 or more
hidden layers, and an output
layer.
27. 27
ENERGY UTILITY
RENT OR BUY?
“A constant question with
home decisions is in taking a
call whether to rent or buy.
The challenge is to consider a
variety of factors that could
have long-term implications
and arriving at a sound
financial decision, while also
understanding what drives it”
29. 29
CARGO DELAY SIMULATION
“A global cargo carrier is
struggling to improve
operations by better handling
cargo at the airports.
The challenge is to identify a
combination of the most
important factors that cause
delays, and being able to
simulate turnaround times for
potential interventions”
30. 30
Shift Evening Morning Night
Weekday Fri Mon Sat Sun Thu Tue Wed
Product category FAH N70 RPP TDS ZDH
Part shipment 20-40% 40-60% 60-80% <20% Full
CARGO DELAY
This visualisation measures the cargo price (average
price per unit of capacity), and identifies which factors
most influence the cargo price the most.
It allows automatically detection of
statistically significant flows and
highlights only relevant ones to users.
The system therefore analyses all
possible patterns, but users only see
the insights that matter.
gramener.com/cargo/delay
31. IN SUMMARY…
BLACK-BOX MODELS ARE
INCREASINGLY ACCURATE
ANALYTICAL MODELS NEED
INTERPRETATION (EVEN
MORE)
AS PRACTITIONERS, OUR
RESPONSIBILITY TO SIMPLIFY
AS CONSUMERS, SELF-
EDUCATE & DEMAND
EXPLANATIONS
..AND TOOLS ARE LESS IMPORTANT THAN TECHNIQUE
33. 33
THANK YOU!
B Ganes Kesari
@kesaritweets
@kesari
Talk slides on:
slideshare.net/gramener
INSIGHTS
Extract meaning using
automated patterns
AI & MACHINE
LEARNING
SERVICES
VISUAL
NARRATIVES
STORYTELLING
Creative ThinkingCritical Reasoning
SOFTWARE
THROUGH
SERVWARE: augmenting human
intelligence with technology
We bridge the Data Consumption Gap
by leveraging technology to automate
Analytics, Visuals and Narration
Binding visuals together
into a logical story
GRAMENER IS A DATA SCIENCE COMPANY
Editor's Notes
A decision tree is a visual representation of choices, consequences, probabilities and opportunities. They are visual representations of the average outcome.
Applying the same fundamental to predict the churn handling we were able to calculate the cost per customer and improvements which were done.
On an average, whether an outgoing call was made from the phone. In case of a viable answer, we were able to fix 3 buckets of 0-4 days, 5 to 14 and more than 15 days. If the call has not been made for more than 15 days, there will no recharge voucher applied and the customer may likely leave the network. In cases where the call has been done within 15 days and the simultaneous recharge has been done only once, what has been the recharge amount. If amount is greater than 50, the loop starts from beginning and we establish that the consumer is engaged and spending will not be huge.
Earlier, the telecom operator for whom the design has been done was spending more. The decision tree helped them to save 62% of their costs with only 3.2% of cases on an overall basis.
We were working with the wealth management team of a European bank. They said, “We have a problem. When telling our customers what transactions to make, we base our advice on two very simple principles. First, if you have two securities that behave similarly, you should consolidate. For example, there is no benefit in holding shares of two oil companies. When the price of one rises, the other invariably rises too. So it’s practically like holding the same company’s stock.”
“On the other hand, having consolidated, make sure you have a good hedge. For example, if you hold oil companies, buy a bit of gold. When oil companies drop, gold typically rises. Gold is a reasonably good hedge against oil companies.”
He said, “This is the basis of the bulk of the advice we give clients. But in order to arrive at this advice, our analysts have to go through 150 reports, which is humanly impossible. We know they don’t actually do that. We sometimes pass these reports on to our clients. They clearly never read these. As a result, our transaction volumes are not as high as we would like to be, mainly because people do not understand why they need to make a trade.”
So, what we did was put a variant of this visual together. On the right, you have a series of currencies like the Australian dollar, the Euro, the British pound, etc; some commodities like silver and gold; and some stock indices like Sensex, FTSE, and S&P.
The cells here have a number inside that indicates the pairwise correlation between a pair of securities. For example, the number 68 on the top left indicates a 68% correlation between the Australian dollar and the Euro. To the left of the Euro and just below the dollar (diagonally opposite to the 68), there’s a scatter plot that shows the daily prices of both these currencies. Each dot is one day’s data. The x-axis shows the Australian dollar value. The y-axis shows the Euro value. This helps identify what the pattern of movements of any two currencies is. From this, you can easily see visually that the Australian dollar and the Euro both tend to move together. Or, where there are strong correlations like the FTSE & S&P, the pattern is almost a straight line.
In some cases there are negative correlations. For instance, if you take the Sensex against the Japanese Yen, the correlation is -79%. The cells are coloured based on their correlation values. Greens indicate strong positive correlation. Reds indicate strong negative correlation.
These are also grouped hierarchically. On the left, we have a series of lines indicating clusters. The most similar securities are grouped together. So FTSE and S&P with a 98% correlation are very close. The ones that are less correlated are kept further away based on a tree-structure.
This leads to clustering of securities. For example, there is a green block in the center which has SGD, JPY, XAU, CHF and CNY. All of these are fairly well correlated. When any one currency in this block goes up, all the others go up as well. When any one goes down, all others go down as well.
Similarly, you have another block to its top left: S&P, FTSE, Sensex and to a certain extent, the Pakistani Rupee. These move together as a block as well.
But when this block goes up, all the currencies in the other block go down, as indicated by the red negative correlations between these two blocks.
This can be used very easily for decision making. For example, one client who was trading with Singapore and Japan looked at the strong correlation and decided to consolidate their holdings in Japanese Yen. They then moved up and down this column to find a good hedge. FTSE looked like a good hedge – it was the most negatively correlated with JPY at that time -- and they decided to place a third of their portfolio in FTSE.
A sheet like this improves people’s understanding of relatively complex data, and results in significantly increased trade volumes.
As Analytics Practitioners, please take up the responsibility of enabling consumption of data, interpreting analytical techniques & humanizing intelligence. Think Visualization, Abstraction & User Interactivity!
As Analytics Consumers, please familiarize yourself with this technology, make informed choices and demand explanations, where its not provided