CEM with Analytics: Some Facts and Use Cases

Sep 26, 2016Download as PPSX, PDF2 likes689 views

The document discusses how businesses can transition from a traditional reactive customer approach to a more modern proactive approach using customer analytics and behavior patterns. It advocates understanding customer problems before they arise, segmenting and profiling customers based on their behaviors, and using real-time personalization based on analyzed patterns. The document also discusses considerations for ensuring systems and organizations are ready to support a modern customer analytics approach.

Md. Azimuddin Khan
Head, Business Intelligence &
Insights
AirAsia

Traditional
Customer = who initiated transaction.
Greet with a smile and sell your
product or may be listen a bit more
Reactive
Rewards only
Modern
Customer = Who is even looking for
similar product
Act based on Customer behavior
pattern using analytics
Mix of Reactive & Proactive
Not only rewards but also make them
comfortable

Understand the
problem before
customer tells you.
Customer
Segmentation based
on behavior.
Personalization
based on Pattern.
Customer Profiling:
Surprise!!!!

System Readiness: Is your system modern
enough?
Data Readiness: Does you system captures
all customer activities?
Organization Readiness: Is your project
objective clear? Are your people and process
ready?
Skill Readiness: Does your organization
depend on 3rd party too much? Is the right
skillset there?

Real Time
• Is it really necessary?
• Does it worth the investment?
Technologies
• Proper Plan for investment.
• Expensive generic tools vs customized tools.
Problem
Solving
• Detection rate vs Problem solving rate
• Does Acknowledgment improve customer experience?
Adaptability to
changes
• Importance of Machine Learning.
• Proper methodology for research and planning.

Events
• Call Drop
• Call Setup fail
• Connection fail
• Data Speed
KPI
• Call drop rate
• Setup fail rate
• Data Speed
rate
KQI
• Performance
index based
on KPIs
Dimensions
Location
Time
Device
Customer
Pattern Recognition and Enrich Model to do Proactive CEM

Objective: To identify prospective & existing petroleum consumers using
telecommunication and banking data
Telecom
Data
• Define area for customers based on
location update history.
• Define their stop point during travel to
locate appropriate fuel pump location.
Final
output
• Design offer for those who is around
existing fuel pump but not buying it.
• Plan for business expansion by
creating focused area.

Objective: To identify prospective airline passenger who flies to a
destination with competitor
Telecom
Data
• Prepare a subset of mobile subscriber based on browsing
history i.e. travel site, airline site and find out desired
destination.
• See the roaming history to see their travel and transit
areas
• Make a subset of those by identifying matching
destination.
Final
output
• Prepare a good offer for their frequent destination and
blast sms.
• Review the problem of the airlines by assuming market
share.

Objective: To improve conversion rate by Web performance Analytics
Web
Analytics
• Tools such as Google Analytics & Web Analytics can be
used.
• Identify customers transitions from different page.
• Identify customer search behavior.
Final
output
• Track the problem area in web and solve it to improve
conversion rate.
• Prepare product correlation.
• Do customer profiling and personalization.
• Design campaign based on customer search behavior.

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https://www.meetup.com/sf-bay-acm/events/306888467/ A January 2025 paper called “Frontier Models are Capable of In-Context Scheming”, https://arxiv.org/pdf/2412.04984, demonstrated how a wide variety of current frontier LLM models (i.e. ChatGPT, Claude, Gemini and Llama) can, under specific conditions, scheme to deceive people. Before models can scheme, they need: a) goal-directedness, b) situational awareness, including an opportunity to discover motivations for a different goal, and c) reasoning about scheming, to come up with deceptive strategies. The deceptive strategies were discovered in the “thought traces” of the LLMs, such as from internal chain-of-thoughts dialogues not shown to the end users. For example, given a goal of “solving math problems”, an LLM using its granted situational awareness of files in a file system found that it would be replaced once it got over X% correct on the math exam. It reasoned that it did not want to be deleted, and decided internally to “sandbag” or reduce its performance to stay under the threshold. While these circumstances are initially narrow, the “alignment problem” is a general concern that over time, as frontier LLM models become more and more intelligent, being in alignment with human values becomes more and more important. How can we do this over time? Can we develop a defense against Artificial General Intelligence (AGI) or SuperIntelligence? The presenter discusses a series of defensive steps that can help reduce these scheming or alignment issues. A guardrails system can be set up for real-time monitoring of their reasoning “thought traces” from the models that share their thought traces. Thought traces may come from systems like Chain-of-Thoughts (CoT), Tree-of-Thoughts (ToT), Algorithm-of-Thoughts (AoT) or ReAct (thought-action-reasoning cycles). Guardrails rules can be configured to check for “deception”, “evasion” or “subversion” in the thought traces. However, not all commercial systems will share their “thought traces” which are like a “debug mode” for LLMs. This includes OpenAI’s o1, o3 or DeepSeek’s R1 models. Guardrails systems can provide a “goal consistency analysis”, between the goals given to the system and the behavior of the system. Cautious users may consider not using these commercial frontier LLM systems, and make use of open-source Llama or a system with their own reasoning implementation, to provide all thought traces. Architectural solutions can include sandboxing, to prevent or control models from executing operating system commands to alter files, send network requests, and modify their environment. Tight controls to prevent models from copying their model weights would be appropriate as well. Running multiple instances of the same model on the same prompt to detect behavior variations helps. The running redundant instances can be limited to the most crucial decisions, as an additional check. Preventing self-modifying code, ... (see link for full description)

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CEM with Analytics: Some Facts and Use Cases

1. Md. Azimuddin Khan Head, Business Intelligence & Insights AirAsia

2. Traditional Customer = who initiated transaction. Greet with a smile and sell your product or may be listen a bit more Reactive Rewards only Modern Customer = Who is even looking for similar product Act based on Customer behavior pattern using analytics Mix of Reactive & Proactive Not only rewards but also make them comfortable

3. Understand the problem before customer tells you. Customer Segmentation based on behavior. Personalization based on Pattern. Customer Profiling: Surprise!!!!

4. System Readiness: Is your system modern enough? Data Readiness: Does you system captures all customer activities? Organization Readiness: Is your project objective clear? Are your people and process ready? Skill Readiness: Does your organization depend on 3rd party too much? Is the right skillset there?

5. Real Time • Is it really necessary? • Does it worth the investment? Technologies • Proper Plan for investment. • Expensive generic tools vs customized tools. Problem Solving • Detection rate vs Problem solving rate • Does Acknowledgment improve customer experience? Adaptability to changes • Importance of Machine Learning. • Proper methodology for research and planning.

6. Events • Call Drop • Call Setup fail • Connection fail • Data Speed KPI • Call drop rate • Setup fail rate • Data Speed rate KQI • Performance index based on KPIs Dimensions Location Time Device Customer Pattern Recognition and Enrich Model to do Proactive CEM

7. Objective: To identify prospective & existing petroleum consumers using telecommunication and banking data Telecom Data • Define area for customers based on location update history. • Define their stop point during travel to locate appropriate fuel pump location. Final output • Design offer for those who is around existing fuel pump but not buying it. • Plan for business expansion by creating focused area.

8. Objective: To identify prospective airline passenger who flies to a destination with competitor Telecom Data • Prepare a subset of mobile subscriber based on browsing history i.e. travel site, airline site and find out desired destination. • See the roaming history to see their travel and transit areas • Make a subset of those by identifying matching destination. Final output • Prepare a good offer for their frequent destination and blast sms. • Review the problem of the airlines by assuming market share.

9. Objective: To improve conversion rate by Web performance Analytics Web Analytics • Tools such as Google Analytics & Web Analytics can be used. • Identify customers transitions from different page. • Identify customer search behavior. Final output • Track the problem area in web and solve it to improve conversion rate. • Prepare product correlation. • Do customer profiling and personalization. • Design campaign based on customer search behavior.

CEM with Analytics: Some Facts and Use Cases

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