tech enablers - mobile, social media, analytics

Question 1

for new tech, is a mobile app needed?

Answer 1

depends
- eg, CDC vouches through website link sufficient

Question 2

mobile app or mobile website?

Answer 2

depends
- eg. shopping apps generate higher conversion rates

Question 3

5 step framework to launching a mobile app people want (pre reading)

Answer 3

1. make sure there’s a market for your app
2. create a marketing plan
3. design an easy-to-navigate interface
4. build an audience before you launch
5. measure analytics and feedback

Question 4

mobile strategy (7)

Answer 4

a) Availability: Be There For Your Customers Every Time
b) Marketing: Develop A Direct Marketing Channel
c) Priority: Give Value To Your Customers
d) Visibility: Build Your Brand and Recognition
e) Interaction: Enhance Your Customer Engagement
f) Uniqueness: Offer Something Out-of-the-Box
g) Loyalty: Be True to Your Customers

• would these be advantages to your app? do you need an app?

Question 5

case study - starbucks
(personalise experience)
- objective?
- implementation?
- results?

Answer 5

obj: gather customer information for better service

implementation:
- offers loyalty programs and special deals on app
- delivers relevant messages and notifications
- place order and pay using app

results:
- 11 mil users
- 21% of transactions through app
- collect data for personalised audience engagement

Question 6

case study - janssen (psoriasis 360)
(empowering patients digitally)
- objective?
- implementation?
- results?

Answer 6

obj: help psoriasis patients understand health condition

implementation:
- app to track seriousness of condition
- app index to allow doctors to assess the condition
- facebook page to share stories

results:
- facebook page received more than 30,000 posts
- right therapy to the right patient during critical time

Question 7

case study - Kopitiam card vs fairprice app ( prereading)
- benefits
- helping with transition?

Answer 7

benefits:
- streamline loyalty programme
- convenient
- eco-friendly
- cost saving : reinvested into other aspects of their operations

transition:
- elderly can flash their physical Pioneer Generation or Merdeka Generation identifier cards to get the discount

Question 8

trends shaping future mobile app dev & usage (7)

Answer 8

1. Voice-Enabled technology - hands free app use
2. 5G technology - data-sharing speeds (real-time data more accessible)
3. IoT - monitor IoT devices on mobile
4. Mobile Commerce - cashless, cardless payments
5. Augmented and virtual reality
6. motion sensing and location service
7. wearable device

Question 9

Social Media platforms and marketing

Answer 9

find the social networks that help you connect and engage customers

Question 10

Social Media and its power

Answer 10

• 95% online adults aged 18-34 are most likely to follow a brand via social networking
• 71% of consumers who have had a good social media service experience with a brand are likely to recommend it to others
• 79% of B2B marketers use LinkedIn as an effective source for generating leads
• ukranians used social media to humiliate russians and rally the world

Question 11

shift of social media use

Answer 11

create new opportunities
- rising trust in social commerce
- social media as a search engine
- bringing “offline” online

Question 12

the rise of social commerce

Answer 12

• e-sales exceed $735 billion by 2030
• 81% of consumers search for products on Instagram and Facebook
• shopping experience on social media: no longer just a channel to increase traffic to traditional sales

Question 13

the power of community and crowd

Answer 13

• no longer sufficient to just rely on ads/shopfront
• era of experience and word of moth is more powerful

Question 14

what is analytics

Answer 14

turning data into value

Question 15

CRISP-DM framework

Answer 15

CRoss-Industry Standard Process for Data Mining
- identify data sources
- clean data
- transform data

Question 16

data type and pre-processing

Answer 16

• structured data: defined length and format
• unstructured data: need to be processed

Question 17

data preparation

Answer 17

1. data cleaning
2. data fairness
3. data standardisation - adjust for differences in scale

Question 18

algorithm VS model

Answer 18

An algorithm is a method or a procedure we follow to get something done or solve a problem.
A model is a computation, or a formula formed as a result of an algorithm that takes some values as input and produces some value as output.

Question 19

when to use what algorithm

Answer 19

• output is discrete (categorical) variable→ Logistic Regression algorithm (handles classification problem)
• output is continuous (value) variable → Linear Regression algorithm (handles regression problems)

Question 20

difference between logistic and linear regression

Answer 20

logistic regression handles classification problems (predicting a label); provides a discrete output
linear regression handles regression problems (predicting a quantity); provides continuous output

Question 21

similarities between logistic and linear regression

Answer 21

• both used for predicting an output (a label or qty)
• both are supervised ML algos (labelled data)

Question 22

slide 50 - 60 :

Answer 22

analytics illustrations, model evaluation

Question 23

data challenges (3)

Answer 23

• missing value
• mixed categorical and numerical variables
• imbalanced data

Question 24

how to handle categorical data

Answer 24

• label encoding
• one-hot encoding