The document provides information about business analytics in different industries including business analytics, automotive analytics, FMCG analytics, and e-commerce analytics. It discusses key components of business analytics including data aggregation, data mining, association/sequence identification, and forecasting. For automotive analytics, it outlines use cases for predictive analytics, data from sensors for traffic and insurance, and cost/financial tracking. Top FMCG analytics uses cases include inventory optimization, forecast optimization, and price/promotion analytics. E-commerce analytics focuses on functions like supply chain management, merchant analytics, product analytics, online marketing, and user experience analytics.

1. Analytics of Different industries
Presented by:
SEJAL PRAJAPATI(2048)
HETVI PARMAR(2054)
DRASHTI SOLANKI(2061)
JAINI KANSARA(2066)

2. Business Analytics
•Business Analytics is the process by which businesses use statistical methods and technologies for
analyzing historical data in order to gain new insight and improve strategic decision-making.
The main components of a typical business analytics dashboard include:
• Data Aggregation: prior to analysis, data must first be gathered, organized, and filtered, either
through volunteered data or transactional records
• Data Mining: data mining for business analytics sorts through large datasets using databases,
statistics, and machine learning to identify trends and establish relationships
• Association and Sequence Identification: the identification of predictable actions that are
performed in association with other actions or sequentially
• Text Mining: explores and organizes large, unstructured text datasets for the purpose of
qualitative and quantitative analysis
• Forecasting: analyzes historical data from a specific period in order to make informed estimates
that are predictive in determining future events or behaviors

3. Top 3 Automotive Analytics Use Cases
 The automotive sector has witnessed a sea change over the last decade, disrupting the
conventional ecosystem of automotive players. Car connectivity growth is predicted
by Gartner to reach one billion in 2020, which is expected to have some major impact on
product features like safety, infotainment, mobility and vehicle management that will enable
new revenue streams and business models for automotive OEMs.

4. Let’s Look At Some Of The Crucial Automotive Analytics Use Cases

5. 1. Predictive & Advanced Analytics: Product Quality, Recall &
Customer Satisfaction
• The quality management team has to look out for a whole gamut of aspects before and
after any launch ranging from customer satisfaction, regulatory requirements to cost
control. The growing number for automakers are deploying advanced analytics solutions
for quicker and proactive response to such events.
• With predictive analytics, the quality management teams can process large set
information (historical) to reveal the underlying cause. It allows for early detection of
issues and minimises the probability of its occurrence in the future.
• For example, a transmission system may be performing below the expected level,
indicating the need for early repair work (and consequently refuting the need for a costly
replacement job).

6. 2. Data From Sensors: Traffic Congestion For Smart Cities And
Setting Insurance Premiums
• Cars contain about 50 or more sensors accumulating data like speed, emissions, fuel
consumption, and security. Leading automotive players are using predictive analytics and
collaborate with the government to predict and identify high traffic congestion zones based on
data collected from automobiles for town planning and building smart cities.
• Telematics is enabling automotive manufacturers, sellers, and insurers to gather information
from GPS, satellites, and cell phone data and usage patterns. OEMs (Original Equipment
Manufacturers) collect their customers’ data even after a sale is complete.
• The data generated by sensor-driven cars about driving behaviour, speed, braking habits,
turning styles, acceleration, and abidance with the traffic rules in a country is then used to
create a driver profile. Insurance companies vary premiums basis the driver profiles.

7. 3. Cost And Financials Tracking For Automakers
• There are several moving parts across a plant in the form of goods from suppliers, inventories,
semi-finished and finished goods, cost of labours and machinery involved on the shop floor. The
information of the cost associated with each is spread across files, logs and support systems.
• A financial analytics system can help decision-makers to understand the full picture of their
company’s costs and revenues beyond accounting information. These systems can easily connect
to any number of cloud or on-premise sources and databases, combine tables from different
sources and deliver answers to any ad-hoc questions quickly. The governance teams can set
permissions at the database, dashboard and user level, ensuring every user is only exposed to
the figures that they require.

8. Conclusion(Automotive)
• Modern automobiles manufacturers are tracking their products and machinery using
RFID, or radio-frequency identification tags proximity and heat sensors. From the above
use cases we can see that it becomes pivotal to harness the data from the supply chain
of the automotive industry to unlock benefits like increased margins, lesser downtime
risks and a lean supply chain. Good automotive analytics practise can go a long way in
attaining a sustainable competitive advantage.

9. Top 3 Analytics Use Cases In FMCG Industry

10. Why Is There A Need For Data Analytics In The FMCG
Industry?
• According to Subrata Dey, Global CIO at Godrej Consumer Products Limited (GCPL), in
today’s disruptive and competitive environment, every business has the challenge to grow
their top line. With Godrej being no exception, the company is trying to ramp up its top line by
leveraging data analytics in the FMCG industry.
• Presently, FMCG organizations have an opportunity to revamp their marketing and
operations. Having data analytics techniques in place, FMCG companies can move beyond
simple reactive operations and take proactive decisions.
• Numerous factors such as - (marketing, inventory, seasonal changes, returns, out-of-stock,
raw material availability, localized pricing, and so on) drive the FMCG industry. In these
unstable times, the FMCG industry can depend on data analytics to identify trends, gaps, and
opportunities in customer behavior and supply chains.

11. 1 Inventory Optimization
Numerous organizations are struggling to find the right balance between on-shelf availability
and inventory levels. The “rising bar” of customer expectations and business objectives as well
as the increasing complexity of FMCG supply chains is driving organizations to ask more complex
questions about their inventory management. FMCG Analytics can reveal insights about crucial
performance drivers such as service levels, inventory and asset utilization.
Inventory optimization Analytics outcomes embrace:
# Escalate on-shelf availability.
# Enhance efficiency and workforce effectiveness
# Rebalance inventory between raw materials VS work in progress, VS finished product.

12. 2 Forecast Optimization
Organizations need to forecast sales to trickle-down effect across departments. The process of
generating a forecast needs combining FMCG analytics with business and product knowledge, as
well as a continuous focus on improving results to keep up as the business evolves. With leading
analytical capacities in place, companies can approach each problem from the different angles
required—from the product perspective, customer perspective, retail structure and complexity,
and supply chain interdependencies.
Analytics-driven outcomes include:
# Build knowledge of product group behaviors based on historical tendencies.
# Understand the effect of product forecast
# Understand the forecasting accuracy leading to reductions in excess inventory, better
manpower utilization, lower expedite costs, and reduced stock-outs.

13. 3 Price and promotion analytics
With such large investments in trade promotion processes, FMCG companies find it challenging
to make informed decisions that trigger appropriate actions and equip them to win in both
emerging and developed markets. In such scenarios, FMCG Analytics can help manufacturers
become more sophisticated in managing pricing across the value chain. This would include shelf-
based pricing, price to distributor and price to the retailer as well as optimization of promotional
spend-a massive expenditure for CPG companies.
Analytics-led outcomes include:
#Balance the sales and marketing investment mix to increase sales.
#Enable control and visibility on trade spend investment.
#Improve sales and demand forecast accuracy

14. Conclusion(FMCG)
So, to maintain a competitive edge in a fast-growing marketplace, it is becoming increasingly
necessary for FMCG companies to look for proactive methods of harnessing new and extensive
data sources in unique ways. Analytics can help the FMCG companies achieve a deeper
understanding of their customer data and can offer insights to transform a market laggard into a
leader.

15. Analytics in E-Commerce industry
E-Commerce is a very dynamically evolving industry and this is primarily because of its underlying
ever-changing technology. Companies like Amazon, E-bay are capable of building predictive
algorithms being executed in real time on big data environment.
I just spoke 3 big words, which when combined delivers something unmatchable and uniquely
executed by E-Commerce industry:
1. First, e-commerce players have evolved significantly in their decision making over time. In early
days simple basket analysis was used to make recommendation, today we have customer specific
predictive algorithms being executed.
2. Second, recommendation systems and other technology which used to take days to execute is
now executed in seconds, which make them even more effective.
3. Third, every thing has now moved to a big data environment. We no more see tabular format data
stored in CSV formats. To churn millions of activities of billions of customers, we need
parallelization of processes. Everything is done seamlessly by E-Commerce industry and customer
doesn’t even notice.

16. Role of Analytics
in E-Commerce
If you have worked in financial industry, you
will probably be aware of analytics playing a
crucial role into risk and marketing strategy.
However, E-Commerce industry goes beyond
these two pillars. The primary job of E-
Commerce industry is to make user
experience on their website is delightful.
Other than that they are simply a platform
between sellers and buyers. With such focus
on user experience, analytics itself becomes
a product instead of just being business
enabler. For instance, Recommender Engines
you see on Amazon sidebar is a classic
product.

17. Functions supported by Analytics in E-Commerce
Industry
1. Supply Chain Management :
This includes managing data for products right
from warehouse to the customer. E-Commerce
industries use analytics extensively to manage
Inventory. Also a significant portion of work is
into optimising transportation and pricing of
delivery.

18. 2. Merchant Analytics :
Merchants form the core of E-Commerce
industry. If the merchant grows, E-commerce
provider also grows. So E-Commerce players do
extensive analysis for Merchants to get into new
markets or set the right price for their goods. For
instance, Amazon can recommend a Cricket Bat
vendor to keep Hockey sticks because of a
growing demand in his locality. Such decisions
would have been much more expensive for the
vendor, had they not partnered with E-
Commerce players.

19. 3. Product specific analytics :
These teams generally work on product
specific details for example – Satisfaction
rate of customers for a product, forecast
of sales for a product etc. Their work cut
across verticals and are specific for a
family of product or a single product.

20. 4. Online Marketing Analytics :
As E-Commerce provides you a virtual
environment to buy stuff, they have to
market on the virtual environment
extensively. The online marketing team
generally works on bidding for ads on
Google or other websites. They analyse the
funnel of new prospect customers and
maximize the likelihood of a customer
clicking an ad .

21. 5. User Experience Analytics :
This probably is the biggest task for
analytics in E-Commerce industry. It’s all
about customer centricity because of the
ease to shift from Amazon to Flipkart. This
team primarily works on creating the right
architecture of the website. This will include
how is product searched across portfolio,
what decides the rank ordering of products
for a particular search, what is the best
landing page of a custosmer coming from
Facebook etc. They also test what type of
layout is better for what type of customers.

22. Thank You Have A Nice Day Ahead