Transforming Data into Insights, Decisions, and Actions ศาสตร์ของการใช้ตัวเลขและข้อมูล ใน Business Aspect เพื่อขับเคลื่อนองค์กรและกลยุทธ์ทางการตลาด with Case Studies
6. Case studies: Blockbuster
The fallen empire
American-based, home movie, and video game rental services provider
Employed
84,300Peoples
Had
9,094Stores around the globe
At its peak, November 2004…
Got around
$6 Billion
USD (revenue)
$6.92 Billion
At the same year…
7. Case studies: Blockbuster
The fallen empire
Employed
3+peoples
Had only
1store (Oregon, US)
At now (2019)
Got
Bankruptcy
What happen ?
8.
9. How disrupted their opponent
Month-to-month
subscription: DVD
delivered to their door
In 2007, Transition from
physical delivery to
Online movie
streaming provider
Subscriber go from
7.5M in 2007 to 24M
in 2011 Netflix got 150M
subscriber in 2019
Netflix approached
blockbuster for $50M deal.
Blockbuster didn’t
interested
Blockbuster said “We’re
retail-rental company
not tech company”
Blockbuster try to copy
Netflix’s movie streaming
product. But it’s too late.
Blockbuster
10. Under the
Not just ordinary movie month-to-month subscription, not just “AI & Big data powered”
hood
• Buy old TV-shows and
movies license (at a
cheap price)
• Thus, people can order
some movies that can’t find
at the typical local video
store.
• Result: niche market with
high profit
• Already had a large number
of shows and movies
• At the same time, LCD TV
sales started growing
• Personalized content
recommendation: tracking
subscriber’s watching habits
and leverage Big data/AI to
recommended the movies
that suited each behavior
• Monitor habit of users per
each movie
• Try with “House of Cards”,
the first Netflix tv series
• Lots of shows and movies
came afterward. E.g.,
Stranger Things, The Crown
• UX number one: e.g., skip
movie title, released the
entire season at once
Old school strategy Go online Put data analytics Own contents UX
11. “…We’re a retail-rental company, not tech company…”
- Blockbuster, 1985 – 2013 (ceased operation)
13. Analytics(n.): Discovery, interpretation, and communication of meaningful
patterns in data.
Data(n.): facts and statistics collected together for reference or analysis
18. Retail: real-time demand monitoring
Walmart: Biggest retailer in USA
Want to increase Gross Margin Return On
Investment (GMROI)
Solution: Open the partner portal so vendor can
monitor entire sell-through data in real-time
19. Retail: additional services based on the customer data
Alibaba: World’s largest retailer and e-commerce company
Yu'E Bao (Leftover treasure): is an online personal finance product
allowing users to place small savings. And it go to Yu'E Bao fund which
is now the world’s largest money market fund with $267.9 billion in
2018
Got customer purchased behavior: lots of money leftover from
online payment
20. Financial: Payment Information Value-added services (PIVAS)
Lots of banking provide this service to retailers and merchant partners
Behavior
Partners
Shopping with
credit/debit
card
Historical
transaction
E-mail SMS
Social media
Actions
21. Financial: Fraud & Security analytics
Across the globe, also Thailand
Enhance detection & forensic capability, Sunk under big data
Leverage big data & analytics to enhance the traditional solution.
Including
• Security forensic
• Account takeover
• Insider threat
• Anomaly detection
22. CSP: Personalized offers
Every telco
CSP has location-based behavior data from every subscriber
number.
Solution: (Near) real-time Personalized offering from historical
subscriber’s behavior
23. CSP: Traffic management and public safety
Sprint, USA
CSP has location-based behavior data from every subscriber
number.
Solution: traffic management (in corporate with government agencies)
24. CSP: Customer lifecycle management
One Telco in Thailand
New
customer
Existing Retention Win back
Had existing customer’s behavior data.
Solution: Micro-segmentation marketing on new customer campaign
Jeff
Kate
Sam
• Age: 40
• Business man
• Loan seeker
June
• Age: 28
• Office worker
• Love Korean-series
• Age: 20
• Student
• Gamer
• Age: 25
• Freelance
• Online shopping
25. CSP: Customer lifecycle management
One Telco in Thailand
Want to keep the existing customer.
Solution: Customer Experience management. E.g., root-caused
analysis
New
customer
Existing Retention Win back
26. CSP: Customer lifecycle management
One Telco in Thailand
Solution: Churn management with advanced analytics
New
customer
Existing Retention Win back
Dormancy
• Identify which person goes
into early to late dormancy
state
• Leverage statistics or
Machine learning method
Re-activation
• Personalized
recommend the best
campaign to re-activate
the dormant customer
Welcome back
• A warm welcome back
with a personalized
campaign based on old
behavior
27. Transforming business by become its core value
Improve, enhance, and reduce risk
Data-Driven
Cost reductions
Time reductions
Operations
Smart decision making
Management
Customer relationship
building and management
Marketing
New product development
Optimize current offerings
Strategy formation
29. 3 steps
To build the success data and analytics solution to transforming your business
1
2
3
What’s the business problem/opportunity
cleary defined why this problem or opportunity matter
How it should be
What should be done to cease this problem or catch this
opportunity?
Do it as fast as you can, and always
measure the result
Quick win by determining the timeframe of the project
and split to phasing approach. And do it. Do not build the
whole stack in the first phase.
30. Prerequisite
1
2
3
Create the data-driven culture
Every decision should be made from data, The transformation also
Agile way
Do the transformation by yourself or co-develop with vendors. Data
analytics project should do in an agile way
Embrace to adapt and change
Always be ready
