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.

 

 


crm2

 


 

 

 


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.

 

 

MDM 4

 

 

 

 

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.

 

 


 

MDM 2

 

 


 

 

 

Value Proposition of MDM:

 

 

Revenue

  • 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.

 

Cost

  • 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.

 

 

Quality

  • 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

 


MDM 3

 

 

 

 

  • 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.

 

 

Retail:

  • 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?

 

Hospitality:

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

 

Healthcare:

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

 

Insurance:

  • 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:

 

Charts

 

 

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

 

 

Global Sourcing

In this day and age there is an assumed maturity in the way initiatives within a business are sourced and out-sourced. When it comes to IT applications and their development and maintenance, there are 4 possible scenarios that companies deal with:

  • Insource  - Maintain control internally (usually for reasons of intellectual property, privacy, or strategic responsiveness)
  • Staff Augmentation - Save money while maintaining responsibility for application support and maintenance activities
  • Co-source- Leverage external cost structure benefits and expertise while maintaining an appropriate level of control
  • Outsource – Delegate IT (or selected functions therein) to an external organization for which it is a core competency

With this industry evolved over the years, the rationale for IT outsourcing decisions has shifted from cost being the sole consideration to include a number of strategic factors. No doubt cost is still top of the mind, especially with this economy. But a lot of other considerations are in play:

  • Strategic Importance
    • Relative impact of a service area on the company’s revenues and overall profitability
    • How strategic is the function to my organization today? How does it fit
      into our future plans?
  • Current Capability
    • Relative strength of a service area’s technical & business know-how, processes, and tools
    • What are the capabilities of the function?  How do those capabilities compare to our requirements, and to our peers?
  • Perceived Value / Cost
    • Perceived value of a service area relative to the costs incurred
    • What is the function’s capacity to adapt and change?
  • Ownership Preference
    • Relative preference of management to own, share, or transfer out IT assets based on company beliefs, values, and sourcing experience
    • How easily can the function be transitioned to another sourcing strategy?

 

 

Business Quarterly indicates 75% of US executives considered financial motivations as secondary to other strategic objectives when outsourcing. Business Week reports, “The really smart business owners have figured out how to use outsourcing as a strategic tool instead of simply looking for savings.” CIO magazine reveals strategic value rivals cost reductions for outsourcing motivations.

 

Based on some reports by The Outsourcing Institute the top reasons for outsourcing look as below:

Sourcing

 

No matter what the goals, the key success factors of outsourcing are always:

  • Be clear about objectives– cost, process improvement, and the ability to focus on the core business are the most common
  • Incorporate business outcomes as a performance measure from the outset of the arrangement
  • Look beyond price and promises of cost reductions for an outsourcing provider that brings a wide set of skills and strengths, and a long-term track record of delivering results
  • Give as much attention to performance measurement and the quality of your relationship with your provider as you do to the contract
  • Use active governance to manage the outsourcing relationship for maximum performance
  • Task talented executives with optimizing outsourcing arrangements

 

Business Cases – Show me the Money !

Ever since Jerry Maguire blurted this out, people have been using this as a corporate euphemism for ROI/ Business case.

 


sales

 


One of the critical roles for any organization is to manage the value achievement of the initiatives they pursue. They need to ensure sponsor and executive ownership of the business case. The business case allows the stakeholders in IT projects to jointly address their key concerns with project investments:

 


stakeholders

 


 

Business cases highlight the initiatives that create the greatest value, support decision- making, and help track program performance. It is good to define the business case early and plan on many iterations since it:

  • Demonstrates how a major investment creates value
  • Includes both quantitative and qualitative rationale
  • Supports business decisions by weighing choices or options
  • Creates a way to track performance and measure success after a decision has been made
  • Gains alignment and management consensus for a project

 

In some organizations, the term ‘Business case’ may also be referred to as

  • Cost/benefit analysis
  • ROI analysis
  • Feasibility study
  • Capital funding request
  • Case for action

bizcase2

 

  • Once the team has understood the importance of having a business case to guide the investment decisions of the initiatives, there is debate on what level of detail should it have. There are many approaches to building out a business case and the main elements are
    • Benefit models
    • Cost models
    • Cash flow models
    • Assumptions (timing, dependencies)
    • Sensitivity Analysis
    • Qualitative Factors Analysis (non-financial benefits, risks)

 

The financial models can be Top-Down (more high level and helps form an initial hypothesis wider ranges to reflect uncertainty) or Bottoms-Up (more quantitative and time spent on thorough data collection and analyses). But the key point is that you need to build the business case with ranges and confidence levels. Once the numbers were compelling, the ranges could change but they would not change the decision.

 

 

IT Service Management

At a BPM event recently in Orlando, I was chatting with a colleague about IT and the BPM responsibility. This guy is the SVP of IT operations and handles Infrastructure for his company. When someone asked him who from the business was responsible for the BPM aspects in his firm from the business side, his response was “We in IT are actually responsible for the BPM aspects and optimization therein.” Another guys goes, “The only real applications the business is concerned about is e-mail”

 

That set me thinking about IT Service Management, etc. Having spent some time doing ITIL work, I am familiar with the concept of IT service management, which involves moving:

 


 
From…

  • Multiple points of contact with the business
  • Service defined and measured in technical terms (if at all)
  • Work driven by technology
  • Organized to support systems

 

To…

  • Managed relationships established with customers
  • Service defined, measured and reported on in business terms
  • Work driven by service requirement
  • Organized to deliver service

So ITSM is all about better service at lower cost. But the challenges with a full blown ITIL deployment is that ITIL is far too generic for an organization to implement at a fast pace, in totality. Process reengineering and change management are always required and are rarely considered. Some practitioners have said that it complements other IT management methodologies like CMMI, etc. But the way I look at this is that CMM focuses on improving and appraises the maturity of application development.  ITIL is focused on best practices around IT Operations and Services. This kind of demarcation:

 

ITSM 1

 

The ITIL v2 broke these Operations into Service Support (ensuring that the customer has access to appropriate services to support business functions) and Service Delivery (IT services are provided as agreed between the Service Provider and the Customer).

 

But the key to achieving good IT service management even at a small scale is by using the following guiding principles:

  • Business Relationship Management: Ongoing liaison and relationship building with Client community.  Maintain an understanding of the business and IT requirements.
  • Service Delivery Management: Understand the IT Services provided and the businesses reliance on these Services.  Carry out the appropriate business liaison and escalation for Service issues.
  • Service Performance Review: Formally review service performance against agreed upon SLAs. And good luck with that J
  • Service Level Agreement Management: Maintain service definitions and assess implications of any changes
  • Service Enhancement Request: Receive and shape requests for new/enhanced services