Entertain me…

According to World Travel & Tourism Council (WTTC) the Entertainment and Hospitality Industry is a $8 Trillion industry today and will grow to $12 Trillion by 2020. It is also a very accurate indicator of the global economy – the canary in the coal mine. If global economy is down due to any reason, E&H falls steeper and deeper and almost real time – a true barometer of the global economy.


It’s typically broken into 3 sub-segments:

  1. In Travel & Leisure parks and resorts, cruise lines, car rentals, airlines and mediators in between
  2. In Lodging vacation clubs, hotel chains, etc.
  3. In Media & Entertainment Content producers, Content aggregators, Digital right management companies, TV distributors, etc.


All of them are changing really fast – with the onslaught of Social Media and a truly Digital Lifestyle, this industry is seeing some paradigm shifts. Every customer touch point is evolving so fast in this changing world. Let’s look at some examples of this

  • You look at a poster of Bahamas. Right there is a QR reader for you to see the top 3 hotels, car services and sea excursions.
  • A website like TripIt – once your itinerary is up, they will send you emails 1 day before to checkin. If there is a volcanic eruption in Iceland, they will show you 2 hours before flight that it is cancelled. Options for another flight right there. No running to gates with 300 other passengers and trying to use your Elite status to get another seat home.
  • They offer Travel Diary or “Traviary’ -  for before, during and  after pictures/comments
  • When you land after a certain flight – Hertz sends you a message/ email that your car is in a certain lane.
  • No looking at online behavior itself, 80% business can be lost in less than 5-7 seconds. If your mobile or website waits more than 5 seconds, typically people hit BACK button. After 2 more attempts you have typically lost the business.


So all these customer touch points are dictated by only one word – EXPERIENCE. That is what the process and technology platforms have to deliver and enable the human interacting with the customer. And that EXPERIENCE starts much BEFORE traveler walks into the property.



cust exp



As an example take Cruise Lines customer touch points, looking at this from a customer’s glasses:







Segmenting the customers – Seniors, Boomers, Families, Couples, Singles, etc. – is key and the unique experience they seek. E.g. Singles can find information on print media – men’s magazines (GQ, Cosmo, etc.), Travel Mags (CondeNast, etc.).



The consumer is

  1. more knowledgeable with internet and mobile accessibility, online consumer-created content and social networks. Everyone who books a vacation, looks at TripAdvisor, Expedia, etc. for ideas, feedback and reviews. Price and convenience are essential differentiators for most consumers.
  2. more empowered and demands unique requirements and they want more self-service and multi-channel options. You go online the moment your friend tells you something. This speed, volume, and transparency associated with internet travel distribution has put new pressure on most companies to re-calibrate their value chains.
  3. more diverse since there is a lot of global and local influence and the consumer is very individualistic and demanding more convenience. Everyone looks for specific personalization and destination flexibility – such data-driven is key for companies to compete.



Hospitality 3


Courtesy: http://blog.friendlyplanet.com/about-dave-blazek.html


Sentimental Customer – Analyze This!

I was talking to an executive of a famous cruise ship company. He was flying from S. Florida to Seattle and was of course dreading the flight as it is. On top of that his flight was cancelled, and he got bumped down to a middle seat on to a much later flight. At that point he started tweeting some of his friends about his horrible experience. In some time the stewardess came up to him and apologized and offered a free ticket for another trip. He naturally appreciated this gesture and that set him thinking if it was his tweets that were being tracked. He is now doing a whole new CRM effort within his own company  - he knows the cruise customer is there for  4-7 days and can offer more upselling opportunities if they can listen and act better.



Sentiment Analyses 1






At the core of any sales and marketing effort, there only really 3 pieces:

  • Customer acquisition – Increase revenue and market share
  • Customer Insight & Innovation - Create, improve and differentiate products and services
  • Customer Experience & Service - Improve customer loyalty and reduce or avoid costs


In today’s world, with the advent of connected consumers, the Customer Insight discipline is so important, especially in a B2C world. This is done through focus groups, buzz monitoring, community tracking, sentiment analyses and market research or pure competitive intelligence.



The social media phenomenon has surely led to a new way of business engagement is revolutionary set of constituent interaction channels. This technology platform (Twitter, Facebook, etc.) gives everyone expanded ‘voice of the customer’ ability to exercise influence on the interaction with companies and peers.

  • User-generated content is often triggered by emotion
  • This can be amplified via the “viral effect”
  • Impact cannot be stopped or undone
  • It can forces companies to act in shorter cycle times
  • The scale can be fast and truly global


Earlier this is what happened:

  • You talked to your customers, told them what to do
  • 95% happy customers is good
  • You wanted them to come to you


Now this is what happens and companies need to adjust ASAP:

  • Consumer are talking among themselves, it’s time to listen
  • 5% unhappy customers is bad
  • You go to where they hang out


It’s like the sewing circle of colleagues or clique of friends that you tell about a good doctor or a great sale has expanded to be truly global with platforms like Twitter, Facebook, etc.



1.      LISTEN


Companies need to be listening on platforms like Blogs, Open Micro-Blog (Twitter), Closed Micro-Blogs (Yammer), Open Social Network (MySpace, WhatsApp), Closed Social Network (Facebook), Commentable User Generated Content (YouTube), Discussion Forums (Ning, TripAdvisor), etc.



The listening part has many technologies like claraBridge, Attensity, Radian 6, Overtone, SAS, etc. Some companies are using Natural Language Processing (NLP) to understand syntax & context. As an example please see sentence below posted somewhere:



Sentiment Analyses 3



One can use advanced linguistics to understand topics and sentiment:

“The EFTPS enrollment for my tax return was easy,

but it took too long to get the confirmation package

So the Category Sentiment is:


= Positive for “enrollment”


= Negative for “timeliness”


One customer verbatim from the customer can result in multiple categories within multiple taxonomies


E.g. “After being a retired Army General and a FSCO member for 38 years, I am quite upset that service representative would treat me so rudely over the phone when I called in to complain about the fact that you guys unexpectedly raised my auto premiums at the same time Geico is calling me offering me a major discount.”


  • Core Values > Service
  • Core Values > Loyalty
  • Core Competency >  Deliver Exceptional Customer Experiences



  • Eligibility > Retiree
  • Rank > Officer > General
  • Service > Army
  • Tenure> Greater than 30 years



  • Premium Increase



  • Products > P&C > Auto-Insurance


Customer Experience-Related

  • Moments of Truth > Rate Increase Notification



  • P&C/Auto Insurance / Geico



2.      ACT


The tough part of all this is to separate the signal from the noise and do something when its really important. The prioritization criteria for such an action is typically based on:


  • # of customers impacted
  • Severity of impact to customer experience (i.e. moment of truth)
  • Severity of potential impact (e.g. legal liability)
  • Level of control to resolve




Sentiment Analyses 2

Show me the Money – Business Value Realization

When it comes to Information Technology (IT) everyone knows the different questions the executives are interested in:




  • How can I better use IT to deliver shareholder value?
  • How can I use IT to deliver on our business strategy?
  • Am I spending the right amount on IT?



  • How can IT reduce the costs of operation?
  • Are there better ways to use technology to become more effective?
  • Where should I focus my attention for new capabilities?



  • How do we get a return on our invested IT capital?
  • How do I measure the value and results from IT?
  • What IT assets do I need to own?


The term “Value” is used so much in business and never completely understood. What is value anyway. Value at the highest level has 3 components:

  • How much cash will the company generate? How much cash injection will it need?
  • How certain is the cash generation and realization of the investment ?
  • When will cash be generated? When will value be harvested??

Show me the Mullah 1



Measuring and communicating the business value of IT remains one of the biggest challenges for CIOs. The major challenges in assessing business value of IT are:


  1. Difficult to separate value achieved from other improvements (organizational, process redesign, etc.)
  2. Measuring value is often subjective
  3. Practices for monitoring/inspecting and communicating value are seldom underappreciated



To really understand what a project’s value is to the firm and then did it ever deliver that value, there is some focus needed on:


  • Value Identification – Identifies what the sources of value are from IT, that can be used to deliver business benefit
  • Value Capturing – Identifies to what extent new IT initiatives are explicitly tracked and benefit realization for the initiatives
  • Value Delivery – Formalizes the expectations set between the business and IT, both in terms of value and relationship
  • Value Measurement – This capability defines how the success of an IT organization will be measured


Value should be measured against business capability measures, those that can be directly quantified into financial measures. Measures should be developed jointly by the business and IT, then aligned with business capabilities. For business cases, benefits should be quantified and aligned with the capability measures for the impacted capabilities.Value Management is a journey and becomes the generation of Change Management. The components to do this are:


  1. A business case framework to define and quantify benefits captured from investments (Templates/ Model)
  2. A methodology to measure benefit realization against management expectations for investments and report performance (Process)
  3. A sustainable discipline to enable better decisions in terms of future project prioritization and investments (Governance)



Show me the Mullah 2







Some of the best practices:


  • Think of the Shareholder Value Tree for your project- always try to get a helicopter view of the situation
  • Value should be measured against business capability measures, those that can be directly quantified into financial measures. For business cases, benefits should be quantified and aligned with the capability measures for the impacted capabilities
  • Incorporate Benefits Realization as a work stream. Include checkpoints in project lifecycle for monitoring value during implementation
  • Partner with the business to discuss value in the language that stakeholders want and then drive business results
  • Define the right metrics you need to track for
    • Behavior Modifier — Aligns employees with the IT organization’s goals and objectives in a manner that motivates employees and influences desired behaviors
    • Accountability for Results — Holds managers accountable for results and forces them to direct value to the business
    • Performance Orientation — Shows what the performance of the IT organization “has been” and “where it is headed to” – and where to focus management attention. Shifts focus from reactive to proactive management`
    • Vision Connected — Quantifiable statement of the IT organization’s “to-be” state or vision




Have you ever sold an old belonging – basics of Revenue Management

Have you ever sold anything? Old books, your bike car or even your house?? Whether you did that over a garage sale, on eBay or even more advanced technology like Zillow, etc. what did you feel right after the sale? – “I could have raised the price another 10% and the dude would have still bought it”. “Dang! Why did I throw in my comics for free with the sale”. Or “oh my god I didn’t sell this bike just because I didn’t reduce my price by $10. It’d have been good riddance”.


Well, that’s just the nature of sales and pricing. Now imagine the plethora of players in the value chain of any industry like Entertainment & Hospitality

  • Distributors like Kayak, Expedient, etc.
  • GDS’s like Worldspan, Sabre, Amadeus
  • Operators like Contiki, Pleasant Holidays, etc.
  • Consolidators like Pegasus, Trust, etc.


The consumer is willing to pay only so much depending on the product, the season, her expectations, different bundling of products. But all these players lead to so many types of rates for the same room: Published, Negotiated, Packaged (Expedia), Opaque (e.g. LastMinute, PriceLine), and  Restricted. Of course like any pricing strategy there is a stark dependency on so variables: demand patterns, channels, competition, etc. Imagine a hotel or vacation club that offers rooms – different destinations, different experiences (Jacuzzi, etc.); Services (jet ski tour thrown in), Security, additional room types, etc.



The basic problem is as depicted below:


REv Mngt 5



The only options management has are:

  • reject demand
  • inventory excess demand (queueing)
  • modulate capacity (add facilities, scheduling, resource allocation)
  • modulate demand (pricing, yield management)


In the Hospitality industry there is a detailed science behind this pricing and demand fulfillment. This is to really be able to maximize the revenue. It’s called Revenue Management or Yield Management.


The idea is two-fold: Have a market segmentation strategy (capture consumer surplus) and match price to demand (peak-load pricing). So airlines sell First class, Business class, economy class etc. Hotels create suites, single rooms, double rooms, etc. and price the products differently. Intelligently allocate fixed capacity to products. Also then allocate more capacity to low price points if demand

is weak; allocate more capacity to high price points if demand is strong. Ever wondered why flights are always over booked and over sold.


Hotels, travel agencies, airlines, vacation clubs, car rentals, theatres, sporting venues, and other industry players are even unlocking the power of Big Data to enhance revenue management. That’s why this is a cycle of these activities and combines a lot of science with the art of pricing. Providers recognize that data analytics are helpful in establishing the optimal price for their products – the right price to the right customer at the right time. The basic algorithms are as below:



1. Segmentation/product design – discriminate (sort) customers based on their actual willingness-to-pay (reservation price). Since the willingness to pay is tough to find for all customers, they try to find a variable that is correlated with willingness-to-pay (a “sorting mechanism”). Advance Purchases are encouraged.



2. Forecasting – factor in seasonality, trends, truncation (reservations accepted vs. potential demand), special events, promotions, etc.

  • perfect forecasts (deterministic)
  • uncertain forecasts (stochastic)



3. Capacity Allocation – evaluate the opportunity cost (displacement cost/bid prices) of using resources required to meet current demand. So accept current request if Revenue is greater than Displacement Cost. Mostly companies have to rank demand from highest revenue to lowest



4. Control – Try to control demand and prices by ad hoc negotiation, “posting” remaining availability (reservation system booking limits), open/closed status indicators, bid prices/hurdle rates, etc. Ultimately, it boils down to an accept/deny decision for each service request.


This is where statistical tools and big data help. An example of a tree function for marginal revenue analyses for overbooking a flight for example is as below:


 REv Mngt 4





C          capacity of flight


p          probability that the reservation shows up


r           revenue from booking seat


s          cost (free flight, goodwill) of denied boarding









Rev Mngt 2



In a nutshell the business can be described as:


Demand Creation is           …     Marketing


Demand Capture is            …     Sales


Demand Management is   …    Revenue Management  


CRM basics from Caribbean street performers

Happy New Year to all my readers!


As all northern folks who look to get a winter break in the warm waters of the Caribbean, we just returned after savoring our piece of heaven. There were hordes and hordes of tourists – from cruise ships, flocking from airports, from all parts of the world, etc.


On one of the beaches they had street performers doing all sorts of things – swallowing swords, eating fire, other pyrotechnics, etc. The way some of the performers conducted themselves to get the spectators to throw in money into their TIPS jar was nothing short of CRM basics 101.


One of the street performer – a man juggling balling pins on a unicycle, also balance some pyrotechnic with his mouth – was amazing. He focused on the Core CRM processes:

  1. Generate demand – He stood adjacent to the arrival of cruise locations and had flames lit up in the sunset-laden beach area to get everyone to be curious enough to swing by
  2. Engage Customer – When he started his juggling act with bowling pins, he invited a front row standing  kid to come up and throw to him one by one the bowling pin while he balanced on a unicycle> he talked to the kid quite a bit and complemented him on his throws.
  3. Acquire new customers – He would yell at oncoming folks and passersby. He chose a location which was conducive to folks stop by as they enjoyed their ice creams or lemonades.
  4. Service customers – He brought stickers for kids and lulled them to watch his flame and juggling
  5. Deal with Globalization – He spoke a few Mandarin words to the Chinese travelers – “Ni hao”. Asked a kid and heard he was Italian and mentioned greetings in Italian.
  6. Embrace the Viral Effect of Social CRM –  He yelled at people making his video and taking pix to send to fried through Facebook or even YouTube.



As someone said, “Customer service is not a department, it’s an attitude”. The science behind itthe framework for such capabilities has three main components:


1.     Insight-driven Marketing



Esther Dyson summed it up in the magazine strategy+business (December 2009): “People spend a

lot of time online not looking for something, or at least not for something that can be bought or sold. Marketers need to understand that the Web is not about them; it’s about us. Marketers and media sites keep thinking, ‘Well, if we can only tweak our banner ads right, we can get the same success rate as Google.’ But they can’t, because a banner ad is usually shown to someone who is not looking for the item advertised.”


cr0 3



2.     Customer Segmentation and Targeting



This the ability to classify or cluster customers / prospects based on certain business rules or inherent customer data behaviors and buying patterns. This is made possible through customer insight, data mining, segmentation, and prognosis. The key to creating customer segmentation and to targeting the right customers is to have adequate insight and to drive interactions with customers as per that insight.








3.     Customer Touch Point Transformation



These days customers interact with companies at many touch points—call centers, online, mobile apps, point of sales in the case of the retail industry, etc. In order to offer a complete and holistic experience for the customer, the company should look at contact touch point transformation at every level and have a decent integrated contact-management system


Contact center




With this basic framework in mind, one has to recognize that some of the recent world phenomenon makes this even a bigger mandate:


  • Exponential expansion of media options makes targeting consumers far more complex
  • “Cash-rich, time-poor” consumers are demanding more relevant offerings, experiences and communication
  • Consumers are far more technology-savvy and more active in controlling the consumption cycle
  • Demographic and social changes are creating a more diverse, fragmented consumer base and buyer values
  • Products/services, stores and messages are proliferating and becoming increasingly commoditized



Master Data Management

Master Data are the fundamental business data in the company, typically long-lived and used across multiple applications, inherently including the subset of Master Reference Data.


Master Data, including Reference Data, is not to be considered “Metadata”. Metadata is data about data. It describes data content but it is NOT the content.  There is no formal and universal definition of how deep to take the definition from a content perspective. Only those metadata whose management will
bring more value to the enterprise than the cost of the labor needed to create and maintain it should be managed and integrated formally. For example, “Master Data” definitions (customers, suppliers, products, organization, etc.) which will be the most ever-present and shared data across an enterprise will be most critical.








Master Reference Data are used to understand, navigate and query information based on or related to the Core Master Data from various business level and/or user level perspectives.










Value Proposition of MDM:




  • Enables 360º view of the enterprise and corporate performance management improvement.
  • Provides competitive advantage by enabling customer behavior insight & predictive modeling.
  • Improves the forecast management through more effective logistics and inventory control.



  • Reduces cost of manual master data reconciliation & alignment efforts, error fixing, etc.
  • Reduces the data redundancy cost by consolidating & eliminating duplicate masters (DB/apps).
  • Reduces application development & maintenance costs, by having clear master data interfaces.




  • Improves the decision quality by securing reliable, high quality master data “just in time”.
  • Improves the availability of key data – speed of access, data timeliness, common (user) support.
  • Eliminates/prevents redundant & non-coordinated master data activities within the company.




Executing components of the MDM Strategy







  • Data Governance
    • Key roles
    • Desire to roll-out data governance to other areas of the business
    • Data Quality
      • Incorporating DQ approach into EDW instance consolidation project
      • Process Improvement

Competitive Intelligence using Analytics

Having spent so much time in the Entertainment and Hospitality industry, I see everywhere the key systems being CRM and Revenue Management systems. Yield Management is a market segmentation strategy to capture the consumer surplus, widely used in the Entertainment and Hospitality industry. Airlines, hotels, theme parks, car rentals, cruise ships, broadcasting (TV, radio), and Utilities (telecom, electricity), etc. always used segmentation and peak-load pricing. But with the advent in computing power of systems this technique is now able to do excessive and data intensive calculations with linear programming to optimize revenue. Just like airlines realized long ago that a flight flying with an empty seat is forgone revenue, many companies use historical data to optimize differential revenue gains. American Airlines had added $1.4B additional revenue over three-year period in the early 1990s. Hertz added 1-5% revenue annually and so did companies like Marriott Hotels, Royal Caribbean Cruise Line, etc.  Computer algorithms can use variables like time of purchase/usage (advance/spot purchase, day-of-week/season), purchase restrictions (cancellation options, minimum term, Saturday night stay), purchase volume (individual vs. group), and duration of usage (single night/weekly rate) to get the right price to the right customer. Next time you are stuck an airport and an airlines mentioned over-booked flight, know that some computer algorithm was doing some marginal analysis based on capacity of flight, typical cancellation rate, revenue from booking seat and cost of denied boarding (these days with the pressure airline are also adding costs like loss of goodwill along with the free flights they give you).

Other analytic systems within an organization depending upon what the goals of a business or its units are.

  • Monte Carlo Simulation – This technique uses data to establish a pattern between a domain of possible inputs. The calculations generate inputs randomly from a probability distribution over the domain, perform computations on these and aggregate the results. This tries to minimize reinventing the wheel by reuse of research results. In the Pharmaceutical industry, for example, during the Discovery phase of their value chain, data mining is used to search contextual information based on secondary relationships,
  • Regression Analysis – This is a set of techniques used for modeling and analyzing several variables and establish a relationship between a dependent variable and one or more independent variables. It is widely used in Marketing and Sales part of a company’s value chain since it can provide deep insight on customer behavior and provide an enhanced decision-making for future customer interactions. Companies are using industrialized analytics which uses closed-loop promotion and data mining to optimize marketing budget allocation (i.e. optimizes marketing channel and product mix). The closed-loop-promotion ensures feedback of mission-critical data to marketing and provides on-demand access to marketing decision-support. This helps creates an optimal marketing mix (as Jerome McCarthy called this the 4P’s – product, price, place, and promotion). It started with improvements in systems for statistical analysis that helped understand why certain things were happening in the business environment. These were heavily used to do web traffic analyses during the emergence of eCommerce. Then these systems were extended to forecasting and extrapolation to see what would happen if the trends continued. Then the era of predictive modeling built systems what could try to predict what will happen next, based on empirical data and heuristics. Of course all these systems are used to help with optimization of spend and maximize the revenue. As an example, within the financial services industry, specifically Banks, such models are used to predict Retention & Loyalty, perform Portfolio Analytics, help with Fraud (Transaction/ Payment) and Anti Money Laundering efforts. They help answer questions like Bank Servicing – Which customers/ segments are at risk? Which ones are profitable? How do I retain them? How do I win back customers? They can also help with Loyalty Programs – What drives loyalty for my customer base? How do I design an effective loyalty program? They are used for Cross Selling and Upselling – Which deposit account holders would be interested in an auto loan? Can we sell insurance along with auto loan? Which products can be sold on credit card welcome call? Which ‘Gold’ customers would increase their spend if we upgrade them to ‘Platinum’? Can I identify customer requiring short term loan? It helps with Campaign Management with segmentation modeling, profitability analysis, retention campaigns, win-back campaigns, etc.
  • Neural Network Analysis  – Apart from the obvious impactions in the study of real biological neurons, this technique is used in areas like distribution & logistics, sociology, economics, etc. Any company that produces a product is chartered with getting that product to its customers (whether that’s the final customer, retailer, wholesaler, etc.) in the least expensive manner. They are constantly trying to reduce total inventory while maintaining service levels of supply at each warehouse or distribution center location. Neural network models help with optimizing with the variables involved: number of warehouses to have, location of each warehouse, size of each warehouse, allocation of products to the different warehouses, allocation of customers to each warehouse, etc.  The objective is to balance service level of supply against production/ purchasing costs, inventory carrying costs, facility costs (storage, handling and fixed costs), and transportation costs.

Comp ANalytics

Data Visualization

Sure a picture is worth a thousand words. But didn’t the dude in Matrix see numbers and know the stories behind this? Well, only in movies. According to research, Data visualization is so powerful because the human visual cortex converts objects into information quickly. As we continue the journey of Data – Information – Knowledge – Wisdom, the feedback loop of models and visualization to see patterns is key.


Data Visualiaztion 1



As Big Data grows, it’s clear that the technology to gather and store data far EXCEEDS the ability to Analyze it. However, not all visualizations are actually that helpful. You may be all too familiar with lifeless bar graphs, or line graphs made with software defaults and couched in a slideshow presentation or lengthy document. The best data visualizations are ones that expose something new about the underlying patterns and relationships contained within the data. Understanding those relationships — and being able to observe them — is key to good decision making.




  • Pizza and Cola sell together more often than any other combo – is there a cross-marketing opportunity?
  • Does Plant and Clay Pot sales IMPLY sales of Soil?
  • Milk sells well with everything – people probably come here specifically to buy it. Should we raise prices since less price elasticity?
  • What is the one item you want to have in your store in case of a hurricane?



  • Does buying any kind of pepper also denote sales of  banana?.



  • Does buying any kind of pepper also denote sales of  banana?.



  • Which customers are most likely not to have an accident?


An important distinction lies between visualization for exploring and visualization for explaining. Exploring data is all about statistical acumen and understanding the nature of what the data represents in your enterprise. Visualization tools are an aid but they have been around for eons. Once you have explored, you will almost always find less than a handful of factors stand out and need explanation. Your presentation should not be about fancy graphs but the right power point / keynote /video storyline for your audience. It seldom needs voluptuous graphs … if you are trying to describe more than this handful of points, then you are already lost in your quest.



The key is use the right Visualization for the right Data at the right Time. I found this chart very helpful to decide the decision tree for which types of visualizations to use for different scenarios:





There are so many tools to do this kind of analyzes:

  • Qlik, SAP, SAS, and Tableau Software deliver the latest table stakes in visual discovery: storyboard capabilities.
  • Google Fusion Tables: Bust your data out of its silo and combine it with other data on the web. Collaborate, visualize and share
  • Datawrapper: An open source tool helping anyone to create simple, correct and embeddable charts in minutes
  • Infogram: Infogr.am is user-friendly interface to help develop creative, interactive infographics
  • Piktochart: Piktochart is a simple WYSIWYG editor to help develop and design charts and infographics



Visualization for explaining is best when it is cleanest. Here, the ability to pare down the information to its simplest form — to strip away the noise entirely — will increase the efficiency with which a decision maker can understand it. As big data becomes bigger, and more companies deal with complex datasets with dozens of variables, data visualization will become even more important.



Data Visualiaztion 2





Techniques in Predictive Analytics

So last week when I wrote about Predictive Analytics, I got responses from folks saying, “The value from such areas is clearly there. But the challenge is which technique to use and the ever-sliding sword of showing ROI so there is buy in for these analyses”.



In framing the Analytics problem – we need to balance data, SME knowledge, and performance. One of the things I have noticed in my work is when the analysts build models the real skill in creating effective analytic model is knowing which models and algorithms to use. They can use different techniques: neural networks, decision trees, linear regression, naïve Bayes, etc. But these days many analytic workbenches now automatically apply multiple models to a problem to find the combination that works best. One needs to explore different paths – they look at the problem from different perspectives. When these algorithms are combined there is resulting synergy. Once the modeling data sets were finalized, the largest incremental gain was not achieved by fine tuning the training parameters of an individual algorithm, but by combining predictions from multiple algorithms.



So with the myriad tools and techniques that exist, the way to approach this is to ask the questions that are really important for what the company is trying to solve:


  • Strategic Customer Questions
    • Who are the most/least profitable customers?
    • Who are the most/least satisfied customers?
    • What is fastest/slowest customer segment?
    • What are the reasons for customer attrition?
    • What are the costs of customer transactions?
  • Strategic Product Questions
    • What are our most/least profitable products?
    • What are our production costs & how can we lower them?
    • What is our cycle time & how can we lower it?
  • Strategic Employee Questions
    • Who are the most productive salespeople, employee?
    • Which managers have the highest retention rates? What do they do?
    • What is the cost of turnover?
  • Strategic Financial Questions
    • How accurate are the financial forecasts?
    • How much financial data is used to answer business decisions?
    • What impacts the demand of our product?
    • What items are affecting our margins the most?


Based on this one has to look at some of the following techniques:


  • Classification – predicting an item class, “Decision Tree”
  • Association – what occurs together, “Market Basket”
  • Estimation and Time Series – predicting a continuous value
  • Web and Text Mining – extracting information from unstructured data
  • Clustering – finding natural clusters or groups in data
  • Deviation Detection – finding changes or outliers
  • Link Analysis – finding relationships



Predictive Analytics

These days it’s tough to walk out of any meeting with Business or IT organizations without touching the topic of Big Data or Analytics. Lot of people struggle with what this is – everyone BELIEVES that it can help if done rightly. But what is it?


The way as I look it: As organizations mature on their Business Intelligence capability, the questions they ask mature too. It’s not about only looking at what the data tells about problems you need to solve. But can data tell you to THINK OF NEW PROBLEMS that you can solve. Things you didn’t know. THINK of something different. Organizations are faced with ever increasing business challenges: Driving new sources of growth, Cost management and cash conservation, Increased business complexity and the need for operational excellence, or Business restructuring in an increasingly global business environment. Ubiquitous computing and technology capabilities have increased dramatically the volume of data at companies’ disposal, yet there remains little in the way of actionable insights (Big Data). Companies need timely, in-depth actionable insights if they are to remain competitive globally to effect a “whole business” approach to big data analytics to deliver business results. Analytics-driven optimization of key business processes

  • Staking out distinctive market strategy (CRM Strategy and Loyalty programs)
  • Finding the best customers, and charging them the right price (Revenue Management )
  • Minimizing inventory and maximizing availability in supply chains (Inventory Optimization)
  • Understanding and managing financial performance (Forecasting)



Predictive Analytics



Business Intelligence technologies are deductive in nature validating the hypotheses of the business problems you want to solve. Examples:

  • Product shortage by market
  • Vendor spend by category
  • Brand health by market
  • Periodic trend analysis
  • Periodic P&L and Financial      Reports


Predictive Analytics is Inductive in nature – pull out meaningful relationships and patterns and tells you of different things that might be addressing the same or new problems. Example:

  • Business Mix Optimization      (Product, Geography, etc.)
  • Price sensitivity by consumer      segment
  • Customer Behavior Modeling
  • Performance/profitability      analysis


As an example, NETFLIX, a US movie delivery company, asked engineers and scientists around the world to solve what might have seemed like a simple problem: improve Netflix’s ability to predict what movies users would like by a modest 10%. From $5 million revenue in 1999 reached $4.3 billion revenue in 2013 as a result of becoming an analytics competitor. By analyzing customer behavior and buying patterns created a recommendation engine which optimizes both customer tastes and inventory condition.


As another example, Analyzing Love: Data Mining on Match.com in AllAnalytics, Match.com, online dating service, tries to predict the likelihood of attractions between people.  95% of relationship can be predicted by analyzing as few as 10 characteristics in each profile. The find things like:

  • Members with accounts on Twitter, which only allows for messages of no more than 140 characters, have shorter relationships.
  • People identifying themselves as Republicans are more willing to connect with Democrats than the reverse


Predictive Analytics 2

Predictive Analytics 3