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STB Data by Mind Map: STB Data

1. Examples and standard patterns...

1.1. ...copying standard recipes is very common in the world of analytics, luckily we have a large number of resources that are focussed on the travel, tourism, and hospitality sectors...

1.1.1. Exhibit: Use Cases for Travel - KD Nuggets

1.1.2. Exhibit: Big Data in Travel - Revfine

1.1.3. Exhibit: Big Data for Travel - Travel Compute

1.1.4. Exhibit: Big Data and Tourism - DataHut

1.1.5. Advanced Analytics In Hospitality: - McKinsey

1.1.6. Hospitality Analytics - Skift

1.1.7. ...these combine with even richer resources focussed on customer analytics more generally... Exhibit: Customer Analytics - Deloitte Exhibit: Customer Analytics for Dummies - Wiley Exhibit: Use Cases in the Customer Journey - Datameer

1.1.8. Task: Translate to a Use Case Template Exhibit: Data Science in the Travel Industry - Vertical Leap checklist What is the winning aspiration? Where will we focus our efforts ("play")? How will we achieve results ("win") in this area of focus? What capabilities do we need to achieve these results ("win")? What systems do we need in place?

1.2. ...if we take these resources in overview, common patterns start to emerge and the picture starts to simplify...

1.2.1. Pattern #1 - "Kata" ...I identify a performance metric, set a target, establish a method to monitor it, and then experiment with service or operational design in order to hit the target... Step #1 - identify the performance metric Step #2 - establish a monitoring method Step #3 - experiment and track results Examples

1.2.2. Pattern #2 - "Journey Goals" ...I implement a series of analytics initiatives focussed on helping me to optimise the flow of customers through milestones in the "customer journey"... Step #1 - define the customer journey Step #2 - identify the customer behaviour you want to optimise Step #3 - identify the service and operations design levers you have available to impact the "Journey Goal" Step #4 - select a monitoring solution that can allow us to get feedback on experiments using the "levers" at selected points in the customer journey Examples

1.2.3. Pattern #3: "Personalisation" ...I profile the demographics, behaviours, preferences, and outcomes of my customers and match those profiles to options, bundles, and configurations in the services or customer journey I provide... Step #1 - gather everything we know about each customer Step #2 - identify similar segments in our customer base using cluster models Step #3 - study how each segment responds to components of our existing service, or experimental new services Step #4 - match customer segments to the experiences and services they like best... or ...focus your business on the segments you get the best outcomes from... Examples

1.2.4. Pattern #4: "Prediction" ...I collect data that relates predictors to outcomes, build a predictive model, then use it to guide my actions... Step #1 - collect a "training" dataset Step #2 - train a predictive model Step #3 - use the model to predict future outcomes, and act on those predictions Examples

1.2.5. Pattern #5: "Automation" ...I use data triggers and machine learning to train a bot to perform operational or delivery tasks... Examples

1.2.6. Where to Start? ...a small set of use cases that blend Kata, Journey Goal, and Personalisation patterns can be very powerful in any customer service situation... Task: Translate to Use Cases

2. Questions

2.1. Is there a book to read up on IFTTT?

2.1.1. ...IFTTT is being used to simplify the process of "metrics design", an art and a science... Exhibit: Metrics for Design Decisions Exhibit: Knowns and Unknowns Known Knowns (facts): you use analytics data to check those facts against them. Known Unknowns (hypotheses): can be confirmed or rejected with measurements. Unknown Knowns (our intuitions and prejudices): can be put aside if we trust the data instead. Unknown Unknowns (it can be anything!): are often left behind, but can be the source of great insight. By exploring the data in an open-minded way, we can recognise patterns and hidden behaviour that might point to opportunities. Exhibit: Asking Data Questions Exhibit: Business Problem Statement Root cause problem Impacted stakeholders/product users Impacts of the issues Effects a successful solution must include Purpose ...a piece of knowledge that, if known, would enable a decision to be made that would unlock value...

2.2. Is this storage considered a cube?

2.2.1. ...we were taking about building a simple database that received data from two sources, and combined them in an analytic process... Exhibit: Data Cube

2.3. Please share what is the best way to collect guest satisfaction data on critical touch points in a CX journey?

2.3.1. ...the critical mindset is "instrumentation" - how to generate feedback with minimal intrusion at the point of the experience... Options Exhibit: Embedded Feedback with Mopinion Exhibit: Customer Journey Mapping at BBVA Exhibit: Mystery Shopping at Kantar Exhibit: Usability Tests at Kayak

2.4. How can we leverage on the CRM Data (no interface system) to optimise CX, Revenue and Loyalty?

2.4.1. ...again, the simple answer is "instrumentation", in a case where we cannot access data we are faced with two options... Option #1: Seek compliant data sharing partnership with a quid-pro-quo Option #2: Create an unobtrusive, low cost "shadow system" and infuse it into the part of the journey you own

2.5. How can we run a number data prediction of flights from certain GEO vs Arrivals from that GEO based on historical data? pre and post COVID

2.5.1. ...prediction of flight, traveler, or occupancy numbers would be straight-forward in pre-Covid times because a "training" set of data would be available and robust... Question: What would an ideal training set look like? post-Covid ...when the "training-sets" are in doubt, prediction is problematic, we fall back on forecasts modified by theory...

2.6. What is your view of Revenue Optmisation AI vs Business Skill? I have two groups of leaders which plays there role. But I still find the balance of both are important. What is your view of post COVID of Both Business Skill vs Analytics Skills?

2.6.1. ...models perform best when the system is in a steady state and historic trends are representative of future outcomes, when the "system" is destabilised, machine performance can deteriorate - in these contexts "Human AI" or "Pattern recognition" can come to the fore... Exhibit: Revenue Optimisation Exhibit: Mckinsey on Revenue Management Exhibit: Retail Price Optimisation by Hypersonix Exhibit: RealPage Revenue Optimisation Exhibit: Black Swan Theory

2.7. Any real case study of travel company in SG to showcase to us?

2.7.1. ...forensic case studies of digital transformation are scarce and often resort to "mood music", we can learn from vendor case studies, startups, and "crown jewels"... Exhibit: Digital Upgrade Failed for 3 in 4 Exhibit: The SME Digital Transformation Study by Microsoft Singapore Exhibit: Digital Leap UOB Exhibit: AWS Tourism and Travel Exhibit: Azure Travel ...e.g. Alpha Travel... ...e.g. Melco... Exhibit: Hubspot B2C Exhibit: CleverTap Travel Exhibit: Travel Compositor Exhibit: YCH Exhibit: Far East Organisation Exhibit: AccorHotels Exhibit: SMEs Go Digital Exhibit: Wong Fong Industries Exhibit: Digitalisation of ASEAN MSMEs Exhibit: 6Rs ...due to the limitations of playbooks and instructions, some will think of their transformation in terms of a relationship with the cloud - here they are building a "play to win" strategy around one of six options... recap

3. Leading data-led organisations...

3.1. ...achieving success with a data initiative is about much more than technology choices, the organisation must be capable of using the results...

3.1.1. Exhibit: Ten Red Flags Signaling Your Analytics Program Will Fail - McKinsey 1. The executive team doesn’t have a clear vision for its advanced-analytics programs 2. No one has determined the value that the initial use cases can deliver in the first year 3. There’s no analytics strategy beyond a few use cases 4. Analytics roles - present and future - are poorly defined 5. The organization lacks analytics translators 6. Analytics capabilities are isolated from the business, resulting in an ineffective analytics organization structure 7. Costly data-cleansing efforts are started en masse 8. Analytics platforms aren’t built to purpose 9. Nobody knows the quantitative impact that analytics is providing 10. No one is hyperfocused on identifying potential ethical, social, and regulatory implications of analytics initiatives

3.1.2. Useful Frameworks ...for talent development... Exhibit: Analytics Translator, The New Must-Have Role - McKinsey ...for organisational development... Exhibit: Insight Driven Organisation - Deloitte Exhibit: Building An Analytics-Driven Organization - Accenture ...for connecting data to business... Exhibit: Play to Win ...for designing a data initiative... Exhibit: Achieving Business Impact With Data - McKinsey ...for managing an analytics initiative through its lifecycle... Exhibit: Product Management Framework ...for taking an idea to implementation... Exhibit: Gartner Three Cycles

3.1.3. Good Practice ...strategic, organisational, process... Exhibit: Uber Michelangelo Project

3.1.4. A Personal Note... is natural for us to wonder what skills we need to develop... recap Business Path Technology Path Analytics Path

4. Critical technical considerations...

4.1. Selecting Technology

4.1.1. recap Exhibit: Data Pipelines ...we should not be intimidated by the technology, we simply need to answer how five challenges are overcome...

4.1.2. ...there is no one "right answer" when picking technology solutions, it depends on the profile of your business and how use cases are configured... Scenario #1 - bootstrapping analytics initiatives with spreadsheets... ...using a tool like Google Sheets to perform all of the "Five S" tasks of a data pipeline... Scenario #1.5 - bootstrapping with a data science tool Scenario #2 - investing in a specialised service or operations solution with embedded analytics... ...investing in a solution that specialises in creating value in a specific service or operations domain... Scenario #3 - putting the business on a digital platform, architecting a big data stack from the ground-up... ...designing and building a tailored "Five S" architecture in a cloud platform, typically as part of a broader shift to digitalise the business... Scenario #4 - optimising the business by using a workflow solution or "business in a box" solution, enjoying the native analytics embedded within... ...using solutions that run core business operations out of the box, including the analytics use cases that are built-in... Scenario #5 - instrumenting digital channels with sensors and optimisers... ...a focussed effort to introduce analytics into digital engagement, websites, and apps...

4.2. Farming Data

4.2.1. ...naturally, we need data to power analytics - a "data farming" mindset is useful... Pieces of the Picture ...creating a data resource that is fit for purpose... A Practical Approach ...we can start to tackle the challenges posed by "data farming" by building a data map or model for the business and its use cases... Task: Sketch a Data Map

5. How do we put data to work?

5.1. ...we build data initiatives by formulating "use cases" that bring together business objectives and technical specifications...

5.1.1. Example Use Cases Exhibit: Pointillist Journey Use Cases Exhibit: BCG's AI Use Cases Exhibit: Top Analytics Use Cases - Oracle

5.1.2. Building Use Cases ...we can take use cases "off the shelf", or we can build our own using a template approach... Components A Template

5.1.3. Using Use Cases ...once we have the use cases in the form of "ends-means" statements, we can simply rank and prioritise our options - our analytics strategy simply becomes a stack of modular use cases... Exhibit: McKinsey Prioritisation

6. What can data do?

6.1. ...a data professional will say data can be used to "Describe", "Diagnose", "Predict", or "Prescribe"...

6.1.1. Exhibit: The Analytics Curve "Describe" - what is happening? "Diagnose" - why does it happen? "Predict" - what will happen next? ...predictive analytics is a current focus in the world of customer services... "Prescribe" - what should I do next? ...long read... Exhibit: Analytics Maturity Curve - Intel

6.2. ...viewed against this background, data technologies and techniques are associated with three value propositions...

6.2.1. Automated Reporting ...a data management system collects, organises, and presents business intelligence reports... Exhibit: Performance Dashboards Question: What is the Value?

6.2.2. Design and Decision Support ...a descriptive, diagnostic, predictive, or prescriptive insight enables us to design better services or make a more effective decision about the business and its operations... Exhibit: Customer Lifetime Value Exhibit: Customer Diagnostics Question: What is the Value?

6.2.3. Automated Action ...a data creating system sends a signal to a bot that performs an action in response... Exhibit: IFTTT Question: What is the Value?

7. A quick comment on jargon...

7.1. ...the world of analytics has generated a lot of jargon and hype - we will use three lenses to simplify that picture...

7.1.1. Exhibit: Analytics Translators ...we can use these three lenses to break down interesting uses of data... Lens #1 - Technology - concerned with the engineering of data: "the right data, in the right place, in the right shape, at the right time"... Lens #2 - Analytics - concerned with the creation of actionable insight from data, involves the selection and execution of data science models... Lens #3 - Translation - concerned with formulating the questions, objectives, and challenges from the business side that are necessary for getting value from analytics... Question: Crack the Code

8. Starting points...

8.1. Our Context

8.1.1. Data and analytics are established value-enhancers across a wide range of industries... Exhibit: Deloitte Exhibit: Oliver Wyman

8.1.2. ...this course is about building confidence to make progress in the world of data and analytics applied to the travel, tourism, and hospitality sectors... Exhibit: Advanced Analytics In Hospitality - McKinsey Exhibit: Use Of Data Science To Step Up In Travel Industry - TEW Exhibit: Data Science, Why The Travel Industry Needs It - HospitalityNet Exhibit: Big Data In Tourism, How Big Data Analytics Can Help The Travel And Tourism Industry Grow - Data Hut Exhibit: Big Data, The New Way To Predict Future Tourists - Tourism Review Exhibit: Data In Travel - Eye for Travel

8.2. This Course

8.2.1. Ambition ...use data and data analytics to know more about what customers really think of their experiences, utilising tools for better decision-making, and planning the next move in their business strategies... Objectives

8.3. Tasks

8.3.1. Introductions, then brainstorm the top five questions you have about data and its application in the tourism, travel, and hospitality sectors...

8.3.2. Team Boards Team #1 Team #2 Team #3

9. User Notes

9.1. ...notes on tools used in this session...

9.1.1. Zoom Presentations and Playbacks in Main Room Team Work in Breakouts Chat Board for Questions and Comments

9.1.2. Mindmeister Source of All Course Materials Public Link Stays Public Option: Copy Map

9.1.3. Jamboards For Team Collaboration Content is Wiped After the Session Option: Copy Boards