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We bring You latest perspectives & bespoke Research based Evolving business practices

Fair Or Unfair, Flexi-Fares Need Repair

3/29/2017

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Indian Railway Catering & Tourism Corporation's Flexi-Fares strategy jolts passenger occupancy and premium trains revenues – can dynamic pricing engines solve the revenue optimization challenge?

25th November 2016, Profisor Team makes a day trip from Delhi (The Capital City) to Ludhiana (Major City in North Western State of Punjab).  An early start to the day . . all preparations done for a two-fold agenda 1. Conduct regional-level research for analysing a political party’s penetration using social media data and 2. Meet Professor Doc. K. S. Mann, Dean of Student Training & Placement Cell and Heads of IT Department at the Guru Nanak Dev Engineering College in Ludhiana to establishing an internship program for graduate and master level students. 

Week before this planned travel, we booked our train tickets for Shatabdi Express, a premium Indian train that runs during the day usually with two types of passenger coaches – An Executive Class and A Chair Car.  Quite recently, on 9th of September 2016, Indian Railways Minister, Suresh Prabhu, had introduced Flexi-Fares (or Dynamic Fares) claiming to rake-in additional INR 500 Crores (USD 77 Million) by 31st March 2017.  Much less to our surprise we paid 40% additional fare compared to our past travel experience and discovered a lower passenger occupancy in our coach.  Is this the result of Flex-Fare System?  Or Surge Pricing?  The Uber Ola Debate!  Upon further enquiry, from fellow travelers and train staff, we concluded with mixed, yet divided, responses.  Passengers opined that the new fare system was negatively impacting the train occupancy rates, while the train staff appeared more optimistic hoping for improvements sooner than expected.

As the calendar turns into December 2016, a big question looms on the face of Railways Minister, who has a strong bend towards technology, especially the use of social media for listening to train passengers and improving passenger experience.  Does the Flexi-Fare system work?  Owing to further decline in occupancy rates, on December 19, 2016, Railways tweaked the Flex-Fare Structure, offering 10% rebate on seats left vacant after chart preparation. 

Today we are in the month of March 2017 which marks the end of a financial year.  Economic Times reports, “Flexi-Fare system in premier trains to be revised again: Indian Railways”.  Railways earned about an additional INR 260 Crores from the Flexi-Fare System, nearly 48% short of their planned target of INR 500 Crores, and continued to face lower occupancy rates.  What that means is fewer people travelled between 9th Sep 2016 and 28th March 2017, on trains operating with the Flexi-Fare System, paying a higher fare for the same journey, which in result contributed to the additional INR 260 Crore earnings.  That’s quite a dent in the passenger’s pocket!  Unfair!  While that’s an opening statement for a big long news hour debate, let’s explore the dynamic pricing challenge for Indian Railways.  Can Dynamic Pricing Engines Help Accomplish Higher Passenger Occupancy and Optimize Revenues?

Here are some informational facts about Indian Railways and the Flex-Fare System or The Dynamic Pricing Engine.
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How does the Indian Railways Flexi-Fare System Or Dynamic Fares Work?
  • First 10% Seats of the Total Seats are sold at Base Fare
  • Base Fare rises by 10%, in batches, for every next 10% Seats of the Total Seats
  • Maximum Base Fare rise is restricted to 50% for most Seat Types
  • Maximum Base Fare rise is restricted to 30% for 3rd AC Seats
  • No Fare Increase for 1st AC Or Executive Class Seats
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What was the impact of Flex-Fare System on Revenues and Occupancy Rates?
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Delhi to Mumbai travel is one of the most heavily booked journey and an equally relevant travel route for both domestic and international Airliners in India.  A 10-week analysis of the route presents us with the following stats:
Delhi – Mumbai Rajdhani Express Train
​Period Between 9
th September 2016 to 19th November 2016
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Delhi – Mumbai Rajdhani Express Train
​Outside Festive Season Between 9
th September 2016 to 15th October 2016
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AKR, Mumbai – Hazarat Nizamuddin, Delhi Train
​Period Between 9th September 2016 to 19th November 2016
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AKR, Mumbai – Hazarat Nizamuddin, Delhi Train
​Outside Festive Season Between 9th September 2016 to 15th October 2016
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Against the principles of Dynamic Pricing, the current Indian Railways Flexi-Fare System changes fares based on Supply.  Such an analysis must begin with a Segmentation exercise, a study of the Demand Supply factors and the total inventory of train seats pan-India, to arrive at trains and categories of seats that offer revenue enhancement opportunities.  Opportunities that reflect operations research and data-oriented statistically prudent methods for successful implementation of Dynamic Pricing, Upselling, Resource Optimization, Meals Cost & Sales Optimization, Route-wise and Segment-wise Meals Personalization, Train Schedule Optimization (although politically driven . . ) . . and many more . . . .
Are You Flying High Enough? - Offers insight into how such studies are performed in the Airlines industry.
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Data is rich.  Technologies are emerging.  In today’s times where Data Scientists are joining hands with business to solve computational problems, Indian Railways must make a genuine effort to evaluate all factors that contribute to the study of Dynamic Pricing of train passengers, and exercise caution when analysing the impact of fare changes on the travelers’ pockets.
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Investment In New-age Technology Architecture Prevents A Fall & A Fracture

3/17/2017

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Why Technology needs a special focus when aiming for revenue or inventory cost optimization?

Before we explain the role of technology, let’s remember the problem statement.  Case in perspective is Revenue Optimization – a challenge to manage perishable inventor sales within a defined lifetime of the inventory to improve revenue.  We discussed the imperatives of Factor Analysis and Statistical Models in our previous viewpoint – Statistics Can Be Majestic If The Factors Have Characteristic.  Multiple factors, rising counts of data and studying multiple data relationship requires intense analysis.  Make it a real-time problem and you need a robust compute-intensive solution, ready to deliver at speed, at scale and with highest precision at scale.  One miscalculation or wrong choice of factor that contributes to the problem of revenue optimization can sink the ship.
Are You Flying High Enough?  A use case that offers insight on how dynamic pricing helps optimize revenue.
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Achieving revenue optimization through a technology based solution is an act where machines prescribe different price points for a product, based on a real-time analysis of all the factors that contribute to the demand and supply analysis of such product.  Hence, technology architecture plays a significant role in delivering these accurate price prescriptions.  Let’s break this further into a study of different aspects of technology.
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DATA COLLECTION:
Multiple sources of data must be ingested into a common platform for analysis.  Storing data in NoSQL DBMS in key-value formats allows for flexibility in processing – both form and speed.

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DATA PROCESSING:
Customer product preference must be studied in real-time to analyse product pricing before the customer places an order.  Use of Hadoop clusters is prominent, yet not the most streamlined experience.  Spark has the capability of consuming and processing streams of incoming data, from multiple sources, in real-time.

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MODEL DEVELOPMENT:
Statistical techniques must drive the variable selection process.  While Bell Curve Analysis and VIF calculations maybe performed to avoid model over fitting, it is the feature engineering capability that is essential for a stable and high precision solution.  Valuable data and insights are not lost in the noise; relevant features are derived through statistical analysis; and the outcomes of the model remain under control – they are not inflated.

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MODEL DEPLOYMENT: 
Speed is the name of the game.  C++ or Java based deployments offer much needed speed yet lack flexibility.  Where these models require frequent review, validation and re-design, given the complexity of problem statement, Python based libraries find an ideal fit.  Optimization and automation of this process is a pre-requisite else the model continues to perform calculations on factors that do not change with time or so frequently.

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MODEL VALIDATION:
Studying the decay rates mandates a win.  Changing environmental factors contribute to the decay rate of a model.  Such decays are more frequent in the early stage deployments and with time, through continuous validation and testing, the model acquires stability.

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DATA COMPUTING:

True test of the solution is driven by its feature engineering capabilities.  Heavy data munging and running multiple operations instantaneously and at predetermined, multiple, time frequencies needs a compute-intensive machine, not necessarily too much of memory.  The overall process must be generalized to run the regression model, before initiating automation.

Emerging technologies have far superior capabilities compared to the ones we currently operate – data consumption, data processing, data cleansing, parallel computing and analysis.  Their viability, flexibility, strength and scalability must be adjudged before putting any ideas to practice.

Next up, the last 100 meters, Products Or Solutions, Seek Advise To Plan Wise
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Hello Analytics! . . Wait, WHAT! . . Why Analytics?!

2/25/2016

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Investment in People is not a new subject, yet it is the most sought after business agenda for organizations across the globe.  Why?  Why now?  Is it the global markets crisis?  Unemployment or underemployment?  Job cuts?  Rising pressures to perform?  Changing business landscapes?  Alignment or re-alignment of organizational strategy?  Changing People culture?  Renewed focus on diversity at workplace?  Or a strategic focus on People to make a positive impact on the ROI? . . You may find or name more reasons, the name of the [New] game is People Analytics.
 
Let's examine some interesting yet contrasting views from across the globe that are summoning us for well calculated, sound, decisions on People at workplace.
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Advanced Economies
  • Rising regulatory pressures to improve organisational conduct, to treat customers fairly; BFSI industries are undergoing a cultural shift (Financial Conduct Authority of the UK, Dodd-Frank Act & Consumer Financial Protection Bureau of the US)
  • Frequently rising - out in the open - cross industry unethical practices and immoral events (Volkswagen Emissions Fraud, Global Forex Scandal, JP Morgan London Whale)
  • Business sustainability, organisation growth and profitability agenda; to improve customer experience, - on-going customer engagement - and to address organisation-wide cultural changes
Developing Economies
  • Growth, in spurts, with countless start-up births and casualties; People engagement framed as one of the primary reason for start-up failures (Intention of Millennials to move on is greater in emerging economies (69%) – Deloitte Millennial Survey 2016)
  • Rising infrastructural development and national capital investment; need for economic diversity and business policy simplification, for ease of doing business, to invite foreign investor interest
  • Technology innovations and disruptive business models that are changing the way People connect at workplace; both blissful and painful People experiences
Not hearsay and not a revelation either - a recent study "Learning to Fly", from AON Hewitt in 2015, suggests ". . that the CHRO is a critical stakeholder in defining the strategy of a firm.".  This re-affirms that an organization’s prime focus is on People at workplace – their role in business strategy and business performance. 
 
CHRO’s strategic agenda is next on the radar.  We ask some indicative and leading questions that pave the roadmap for measuring success.  Again, only if there are proven methodologies to quantify and measure success!

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  • What are the existing and trending cultural issues within the organization and the industry?
  • How do we establish a shift in organization culture, its impact, on business performance and HR effectiveness?
  • Is there an impact of cultural practices and symbols on People engagement or People well-being or other People aspects?

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  • What capabilities are associated with the future direction and strategy of the organization?
  • Which capability development techniques and pedagogies are most effective?
  • Is the 3E model (Education, Exposure & Experience – 10:20:70) universally applicable on all People development practices?

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  • What mix of experience and education is most likely to give us our next talent?
  • How do we qualify and quantify the potential of a talent?
  • What are some of the early indicators of possible talent attrition or mass exodus?

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  • Does the technology architecture equate into our strategic People practices?
  • Is technology simplifying or creating more layers and complexities?
  • Is our technology investment leading to improved and more efficient People processes?

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  • What is the impact of past strategic HR initiatives on organisation’s ROI?
  • What are the shifts and changes in the global economic environment, their impacts, on our business strategy and our People?
  • Have we made necessary investment in methodologies to analyse relationship of our People practices to business performance?

For long organizations have made decisions based on intelligent intuitions and often undertaken initiatives to replicate well surveyed best practices.  Yet, today, there is a strongly felt need for making more conscious and weighted efforts to design and develop solutions around People – to scientifically study the overall impact of this machinery on organization’s ROI.  From identification and selection, on-boarding, retention, capability development, talent management, employee engagement, collaboration, diversity, employee performance, team performance, project performance to business performance – organizations are looking at Analytics to pave the way for making more sound decisions.


“Managing attrition challenges in the Outsourcing business for over a decade one thing is certain – reporting attrition trends is essential to our business, predicting attrition based on past data is a norm but predicting employee behaviours will change the way we build our business and plan our way into sustainability.  The industry recognizes this need for analytics around people in our business."

~ Aabha Nanda, VP & Head Corporate HR at EXL Service



Wait, has anyone treaded into this vast domain of People Analytics?  Are there any sophisticated, tried and tested (proven) models that may be contextualized and utilized?  Not a comprehensive list, but here’s a quick highlight of select industry practices.

Companies favor job candidates with stellar academic records from prestigious schools — but AT&T and Google have established through quantitative analysis that a demonstrated ability to take initiative is a far better predictor of high performance on the job.
 
Harrah’s Entertainment has extended their customer selection analytics approach to People decisions, using insights derived from data to put the right employees in the right jobs and creating models that calculate the optimal number of staff members to deal with customers at the front desk and other service points.

Starbucks, Limited Brands, and Best Buy—can precisely identify the value of a 0.1% increase in engagement among employees at a particular store.  At Best Buy, for example, that value is more than $100,000 in the store’s annual operating income.

JetBlue created an employee-satisfaction metric around its people’s willingness to recommend the company as a place to work.  This “crewmember net promoter score” (modelled after the customer-satisfaction metric) has been used to study the impact of compensation changes and to help determine executive bonuses.  Employees are asked annually on their hiring date if they would recommend the company, so JetBlue can effectively monitor employee engagement monthly.

Sysco’s analysis revealed that operating units with highly satisfied employees have higher revenues, lower costs, greater employee retention, and superior customer loyalty.  The company can efficiently identify what actions by management will have the greatest impact on the business.  In six years it has improved the retention rate for delivery associates — who provide customer service and build customer relationships — from 65% to 85%.

This is one is our personal favorite – to protect its investments, the soccer team AC Milan created its own biomedical research unit. Drawing on some 60,000 data points for each player, the unit helps the team gauge players’ health and fitness and make contract decisions.

​Does it mean that there are well-established and prevalent People Analytics practices and or ready tools to deploy into business?  No!  Let’s take a look at the global trends ~  Deloitte University Press – Global Human Capital Trends 2015 Report.
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There’s dearth of talent across industries and capability gaps are on the up-rise.
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‘Culture & Engagement’ is one of the most significant challenge across industries and regions.

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"The biggest challenge to HR Analytics today is the half-knowledge that has been created around this term. Analytics has been confused with Analysis; Big Data is seen as the same as Analytics and many practitioners think that Statistics is the be-all and end-all of Analytics!  Nothing could be further from the truth!  Whilst all of these are important adjuncts to Analytics (specially HR Analytics), they represent only one aspect of the entire value chain.  As Google has been saying for quite some time now, Analytics comes from Insights and Insights come from Analysis, which comes from the proper evaluation of the relevant Data.  At the core of HR Analytics (or Analytics of any kind) is logical reasoning.  The ability to think in a structured, comprehensive fashion; to formulate the right questions are at the heart of the Analytics process."

~ Arunav Banerjee, Founder Adyant Consulting
specializes in Organizational Effectiveness solutions


All unadulterated and glaring gaps are in our faces.  Let’s face the music!  Is there one specific reason that may reflect on these issues?  No!  There are numerous challenges and hurdles, some technical and others that relate to the level of acceptance, for the lack of use of Analytics around People at workplace.  You may find these relevant to your business and operations context.
 
Technical Challenges:
  1. Reporting is different from Analytics and organisations fail to recognise this difference
  2. Interest exists; return on investment is not understood; there is no proof of concept
  3. Lack of data, data points and associated intelligence on data analytics
  4. Role of technology is not best represented and is the least recognised in-house capability
  5. Research methodologies are the bed rock – the only foundation – but organisations do not see the benefit of investing their time into these initiatives
 
Other Challenges:
  1. Lack of Skill and of Will – No proactive and or continued “C”-level support and sponsorship
  2. Management focus is on tactical day-to-day business challenges; bye bye, Strategy
  3. Dashboard, Checklists and MIS exist, yet the business orientation is draconian
  4. Stakeholder expectations are not well understood or explored
  5. Organisations associate ‘sharing employee data’ with ‘power loss’
 
Where does one start?  What should be the approach?  Are there ready products in the market?  Are products the way forward or should one undertake a solutions route?  Can we leverage our existing business and operations capabilities?  Do we need to upskill our People?  Does that mean additional costs?  Can we establish a case for investment into People Analytics?  How does one embark on this journey?
 
We recommend a tailored approach, leveraging your existing in-house business and operations potential, towards the development of comprehensive People Analytics capabilities.
 
  • Educate yourself; middle, senior or top-level management – we all need to understand and recognise the potential of analytics around People at workplace
  • Seek guidance and support from your professional network and advisors
  • Engender an environment of collaborative research and learn from experiences
  • Develop a case that suits your business environment; each organisation stands differently on data, technology, skills, use of products and tools, and level of acceptance and maturity
  • Educate your stakeholders; lend a hand, help them cross the bridge
  • Take small steps; don’t jump into the ocean – learn to swim in the lakes and rivers, first!
  • Establish trust, practice People inclusion and celebrate your wins at every stage
  • Design your “method-to-madness”, stay humble and enjoy the journey
  • Continue to establish People Analytics capabilities, based on practical and true evidences and outcomes - to establish change and embed ‘Culture & Engagement’
  • Weigh your investments – the associated risks; find and work with proficient advisors
 
We invite more questions and enquiries on the way forward, approaches and the design of a road-map towards intelligent use of organizational data to establish robust People Analytics capabilities. 
 
Continue to watch this space for more.  You may write to us on people.analytics@profisor.com.

Credits:  AON Hewitt "Learning to Fly" Study 2015, Deloitte Global HR Trends 2015, Deloitte Millennials Survey 2016 & HBR.
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