Business Insight
Example:
An executive from an on line retail company was concerned
about customers abandoning shopping cart. He wanted to quantify the opportunity
and understand root cause. He initiated analysis
of logs generated by their shopping cart. They noticed that a good number of
their customers leave their cart at some point without closing their purchases.
Doing further analysis, they realized that items in those carts sums to approximately
18% of their revenue. Further analysis of what their customers talking about
them on social media, an important insight is figured out that customers find
it more cheaper to buy those items from their neighboring local market. However
online retailers on portal are offering reasonable discounts but delivery
charges on such orders make them more expensive compared to local market prices.
Most of such orders are smaller than 750 INR. Portal is offering free delivery for orders
more than 750 INR and 12% out of those 18% uncompleted orders are actually more
than 650 INR.
As a result of this analysis, portal decided to reduce minimum
order value for free delivery charges to 650 INR for a trial period and
realized reduction in abandoned shopping cart during that period.
This is a very simple example of discovering business
insight by analyzing data from various sources.
Business Optimization
Example:
This one is most complex to understand and implement as well.
Since such models need no or minimal manual intervention, accuracy is a key
concern.
One most common and well-known example of such
implementation is automatic filtering of spams in Gmail. Gmail has an automated
system that helps detect spam by identifying viruses and suspicious messages,
finding patterns across messages, and learning from what Gmail users like us
commonly mark as spam.
We will consider another example.
For a Retailer, it is extremely important to manage their out
of stock rate, which means they ideally never want to be out of stock for any
product. But at the same time, they have to manage write-off rate, which means
they do not want to throw away or sell at discounted price for expired or
spoiled products. Both of these indicators are in conflict, over managing one
will distort other.
Existing method at a retail chain with excellent processes might
be as described.
At every midnight during a specified window, all sales
predictions are made using standard prediction mechanism. Based on those
predictions, orders are calculated for each store and sent to store manager.
Store manager then looks at the orders and manually adjusts the order by
reducing/increasing quantities based on his experience and local knowledge. What’s that local knowledge? It is location, season,
whether, price changes, promotions, events etc. These all factors and many more
are evaluated on human experience basis to adjust orders.
We are assuming that all these orders are generated
automatically and are just manually cross checked and adjusted, assume amount
of work it requires if we want to do it seriously. A big retail superstore
would easily have 10K+ products. Cross checking them and adjusting them manually
every day by humans is not simply possible except relying on standard computer
generated orders. These orders have direct relation with out of stock rate
directly impacting sales. Other hand they also have direct relation with
write-off rate directly impacting profit.
Manual ordering is impossible and if order
generating systems has that local
knowledge or access to respective data and speed to infer local knowledge. We
can automate this decision making process and optimize sales and profit.
