Archive for Business Intelligence
May
25
Building Customer Retention Models
Posted by: | Comments
building customer retention models
Customer retention is one of the major challenges faced in any industry.
In case of any business it is said that retaining a customer is always cheaper than getting a new customer.
Here with help of business intelligence it is possible to build certain customer retention models basically looking customer data and their call behaviors. Read More→
May
25
Analysis Sales Strategy Business Plan
Posted by: | Comments
sales strategy business plan
Sales Analysis can be in terms of:
• Customers
• Performance
• Revenue
• Volume
• Margin
This can be viewed in terms of reports or charts thus competitive plan can be developed .
By this analysis any strategic decision taken by management will be based on proof and not by just guessing and thus can also predict the performance of the company for the next quarter or so.
Improving customer loyalty services. Read More→
May
25
Strong Marketing Management Support
Posted by: | Comments
sales marketing management support
To give a good marketing support it is necessary to understand the market well. Having a good strategy is the foundation for a strong marketing.
Two basic Strategies which can be pursued, are:
A. General strategy
B. Customer strategy
A. General strategy
Companies here comes up with a product, which is common, and try marketing to the masses. Advertising plays a major role here. Even though this has been a major practice, profit margin out of this has been very low, because individual needs of the customers are not taken into account. Read More→
May
22
Business Intelligence: the Right Diet for Telecom Expense Management
Posted by: | Comments
Nina Wales asked:
Business Intelligence (BI) today promises faster, more automated control over telecom expenses. The change is happening and it’s happening now. The advancement in business monitoring capabilities within next generation telecom expense management solutions lends for some exciting developments and possibilities.
The “eating healthy and regular exercise” regiment remains in the forefront of headline news. In the wake of a harried obesity crisis, fast-food empires are taking efforts to revamp their menus by eliminating trans-fat and offering healthier choices beyond the double-cheeseburger and chocolate shake. Schools are replacing sugary treats and salty snacks in the vending machine with granola bars and all-natural juice drinks. People are becoming increasingly aware that what they do today, will affect their lives tomorrow. A similar realization has gained momentum within the telecom landscape. But just like maintaining a balanced diet and exercising regularly, it takes work to get an organization’s telecom expenses under control: it requires getting it right and keeping it right with enhanced business intelligence
Plan: No Diets Necessary
Many individuals are recognizing that fad diets and weight-loss pills are glamorized shortcuts that promise unrealistic results. This is similar to past practices in the telecom expense management industry, where companies would conduct historical audits to identify overcharges. While successful in the short-term, the issue of improving the overall management of telecom expenses is not solved with one time audits, leading to similar mistakes (and overcharges) year after year.
Instead of waiting until the problem is unmanageable, begin by establishing clear objectives about getting your telecom expenses in control: start with a plan. Maybe you want to eliminate the use of cumbersome spreadsheets and organize all expenses into one robust reporting tool. Or maybe the capability to track your organization’s total monthly telecom spend across all vendors is a better place to start. Whatever the desired results may be, establishing the objective in the beginning will avoid the pitfalls of a “crash” initiative.
Budget: Cake for Breakfast, Lunch and Dinner is not Okay
Planning for slip-ups and unexpected gorge-fests means you are being proactive in trying to maintain a healthy lifestyle. Similarly, this measure is applied to the budgeting aspect of business intelligence. Business intelligence reporting capabilities track previous spend and usage. The data captured from these reports provides quick, accurate analysis of the state of your organization’s telecom environment. Anticipating when your organization will be adding several services at once, or recognizing that certain months produce higher telecom costs, is valuable information. Funneling that information into a dashboard with drill-down capabilities provides accurate executive information and sophisticated analysis capabilities. Gauges and graphs support quick, visual analysis.
Forecast: Map it Out
Noticing trends and anticipating higher consumption, makes it easier to “map-out” the next steps. Predicting that you will eventually succumb to that occasional chocolate éclair will allow you to compensate for that expenditure and readjust your plan and budget. Companies looking to apply similar concepts to their telecom expenses are realizing the advantage of this type of analysis.
Consolidate: Less is Better
The premise behind staying healthy relies on getting rid of the bad, to make way for the good. Beneficial reductions are needed and the realization that desserts at the end of dinner is no longer a part of your four-course meal can be difficult to accept – but – referencing the initial plan can aid in achieving your goal. The consolidation within business intelligence takes diligent action. Locating lines that need disconnecting, identifying where electronic data can be used instead of paper, and over and above everything else, becoming more proactive towards the telecom environment is a key differentiator within BI.
In order for an organization to achieve their desired results, certain long lasting changes need to occur. Those changes and plans differ from company-to-company but the goal remains the same: gain control and visibility to achieve reductions in telecom expenses by taking action based on the increased knowledge that is gained. Business Intelligence is about being smarter and more aggressive towards proactively managing telecom expenses. It requires sticking with the plan and actively spot-checking to make sure your business intelligence initiatives are still relevant. And just like maintaining a healthy lifestyle, performing regular exercise will lead to a leaner and healthier you – your organizations results, relating to business intelligence, will also be rewarding; enabling the management of your telecom expenses to become leaner, more efficient and competitive.
Business Intelligence (BI) today promises faster, more automated control over telecom expenses. The change is happening and it’s happening now. The advancement in business monitoring capabilities within next generation telecom expense management solutions lends for some exciting developments and possibilities.
The “eating healthy and regular exercise” regiment remains in the forefront of headline news. In the wake of a harried obesity crisis, fast-food empires are taking efforts to revamp their menus by eliminating trans-fat and offering healthier choices beyond the double-cheeseburger and chocolate shake. Schools are replacing sugary treats and salty snacks in the vending machine with granola bars and all-natural juice drinks. People are becoming increasingly aware that what they do today, will affect their lives tomorrow. A similar realization has gained momentum within the telecom landscape. But just like maintaining a balanced diet and exercising regularly, it takes work to get an organization’s telecom expenses under control: it requires getting it right and keeping it right with enhanced business intelligence
Plan: No Diets Necessary
Many individuals are recognizing that fad diets and weight-loss pills are glamorized shortcuts that promise unrealistic results. This is similar to past practices in the telecom expense management industry, where companies would conduct historical audits to identify overcharges. While successful in the short-term, the issue of improving the overall management of telecom expenses is not solved with one time audits, leading to similar mistakes (and overcharges) year after year.
Instead of waiting until the problem is unmanageable, begin by establishing clear objectives about getting your telecom expenses in control: start with a plan. Maybe you want to eliminate the use of cumbersome spreadsheets and organize all expenses into one robust reporting tool. Or maybe the capability to track your organization’s total monthly telecom spend across all vendors is a better place to start. Whatever the desired results may be, establishing the objective in the beginning will avoid the pitfalls of a “crash” initiative.
Budget: Cake for Breakfast, Lunch and Dinner is not Okay
Planning for slip-ups and unexpected gorge-fests means you are being proactive in trying to maintain a healthy lifestyle. Similarly, this measure is applied to the budgeting aspect of business intelligence. Business intelligence reporting capabilities track previous spend and usage. The data captured from these reports provides quick, accurate analysis of the state of your organization’s telecom environment. Anticipating when your organization will be adding several services at once, or recognizing that certain months produce higher telecom costs, is valuable information. Funneling that information into a dashboard with drill-down capabilities provides accurate executive information and sophisticated analysis capabilities. Gauges and graphs support quick, visual analysis.
Forecast: Map it Out
Noticing trends and anticipating higher consumption, makes it easier to “map-out” the next steps. Predicting that you will eventually succumb to that occasional chocolate éclair will allow you to compensate for that expenditure and readjust your plan and budget. Companies looking to apply similar concepts to their telecom expenses are realizing the advantage of this type of analysis.
Consolidate: Less is Better
The premise behind staying healthy relies on getting rid of the bad, to make way for the good. Beneficial reductions are needed and the realization that desserts at the end of dinner is no longer a part of your four-course meal can be difficult to accept – but – referencing the initial plan can aid in achieving your goal. The consolidation within business intelligence takes diligent action. Locating lines that need disconnecting, identifying where electronic data can be used instead of paper, and over and above everything else, becoming more proactive towards the telecom environment is a key differentiator within BI.
In order for an organization to achieve their desired results, certain long lasting changes need to occur. Those changes and plans differ from company-to-company but the goal remains the same: gain control and visibility to achieve reductions in telecom expenses by taking action based on the increased knowledge that is gained. Business Intelligence is about being smarter and more aggressive towards proactively managing telecom expenses. It requires sticking with the plan and actively spot-checking to make sure your business intelligence initiatives are still relevant. And just like maintaining a healthy lifestyle, performing regular exercise will lead to a leaner and healthier you – your organizations results, relating to business intelligence, will also be rewarding; enabling the management of your telecom expenses to become leaner, more efficient and competitive.
May
22
The Illusion of Business Intelligence
Posted by: | Comments
Max J. Pucher asked:
Occam’s Razor: ‘Of all possible answers to a question the most simple one is usually the right one.’
The proponents of BI however hope to fulfill the vision of Laplace; who suggested: ‘… with an intelligence sufficiently vast to submit all comprehensable data to analysis … nothing would be uncertain and the future and past present to his eyes.’
Business Intelligence is not a productive system, such as accounting, stockkeeping, or supply chain management. Customer care or relationship management should first be consodered as a tool to manage and track your customer service, but is often seen as an analysis tool for customer behavior to improve for example cross-selling rather than service quality.
Business Intelligence is complex and expensive software for gathering and analyzing masses of data that supposedly will be of help to make better business decisions. It relies on a faith in mathematics that were obtained by Bernoulli, Coombs, Edwards, Neumann and Morgenstern. They saw heuristic approaches to decision making as defective because it takes resource saving short-cuts.
The proponents of BI now claim that it provides the computing power to optimize decision making by calculating probability for maximum utitlity, as described by Simon (1955). Optimisation however, relies on a number of restrictive assumptions, such as that the process of decision analysis has to be followed precisely and that the data available are correct and relevant. Let’s for a moment assume that the garbage-in problem has been solved and the data given to the business executives are good.
Klein (1999) provides a list of these restrictions and requirements that were identified in many studies :
1. The goals must be well defined in quantitative terms.
2. The decision makers values must be stable.
3. The situation must be stable.
4. The task is restricted to the selection of options.
5. Exhaustive generation of alternatives.
6. Optimal choice must be possible in reasonable time.
7. Thorough comparison of options.
8. Use compensatory strategy
9. Probaility estimates must be coherent and accurate.
10. Failure prediction must be exhaustive.
11. Evaluation must be exhaustive
There are many studies about human decision making and most of them come to the conclusion that ‘less is more’. Less information about a subject makes for better decisions. Bi can be used to gather and consolidate information that then seems to be simpler and easier to use for decisions. The problem is one of comprehension and trust. Can the user comprehend what the data values truly mean? Do the metadata make sense to the deciding person? Can past averages, means, standard deviations and periodic data be used to predict the future? I propose that just a few people within any organization might even sensibly comprehend what those data could mean. The old adage of garbage-in-garbage-out still holds. Who knows if the mathematical methods used to process the data are well chosen.
Klein (1999) proposes that forcing people to give up their heuristic approach to decision making puts them into ‘information overload’ and questions optimization as the gold standard for decision making.
Now that users of Business Intelligence data do not find them too helpful and seem overwhelmed, the new idea is to aid or replace human decision making with predictive analytics, using probability calculated from past data. Probability computing about future events is the next illusion that BI proponents sell.
Here is a list of what the executives and managers really need for decision making and don’t get from Business Inteligence:
- what customers want.
- what to do to be competitive.
- where business has to innovate.
- how the market will react to current and upcoming changes.
- what competitors are currently doing.
- how employees really see the company.
- how to impove the profitability of the business.
- how to improve internal communications.
- the quality of business processes (not the quality of execution).
You might recognize a common element in the above list: Knowledge is not about knowing a lot of data, or taking decisions based on data. One has to come up with an ACTION or a list of alternative actions and then take a decision which one to perform. Finding out from BI that revenue is dropping only says that the management is out of touch with the business.
Simon (1972) was concerned that optimization was not practical in a field setting because of its restrictions. I propose that there is no proof available that BI solves the problem of the restrictions listed above. Business Intelligence can enforce the optimization process for the decision maker and seemingly create the conditions necessary. It can however only propose options based on the data available to the system. It can not propose to the user to go outside the system and analyze other information as it brakes the optimization process. As a consequence it BLINDS the decision maker to the real world. Klein (1999) proposes that it the enforced optimization process stops the decision maker from gaining experience for future benefit. Outside opportunities and constraints would be totally ignored.
Business Intellilgence enforces a better decision making process but not a better quality of decision making.
Timely and good quality information is not like having a crystal ball. It is abstract information and will tell the user nothing that he doesn’t know. We can only make decisions based on analogies to previously perceived patterns that have to be fairly simple. Statistical software does not take better decisions just because it can process more data. Any data given to the user that is not truly relevant turns into noise that reduces the quality of the communication channel and obfuscates the important information. Less data means less noise.
What information should it be that truly represents a company’s competitive position in the market? Past sales data and comparisons of market share? What decision will that offer? The best way to find out about the competion is to ask a customer who decided for another product why. A competitor is a friend who helps the business to improve what it does. Without competitors companies would become complacent. Inteviewing five of those customers who switched – ideally face to face – will provide much more decision making input than statistics. That certain groups of randomly classified people spent a certain amount on randomly classified goods is not knowledge. A qualified manager who attends sales calls or walks into the store to speak with customers will see those changes in customer behaviour in real-time. He can ask about customer preferences at the time they happen and not months later in a filtered and watered down manner that is completely abstract.
March (1978) and Simon (1983) propose that people do not decide by calculating expected utility and question its mathematical foundation (that is used in BI) for real-world situations. Klein (1999) proposes that training people by exposing them to experience of decision making is more important than abstract optimization processes. This falls in line with Thomas Sowell in ‘Knowledge and Decisions’ who calculates the cost of knowledge by its practical usefullness and not by the amount of abstract eductation.
Antonio Damasio (1995) has virtually proven the influence of our emotional center on human decision making and Steven Johnson paints a wonderful picture of the power of our human mind in its connection of instincts, intuitions and emotions created by the link between the neocortex and hippocampus, and amygdala. Good decisions come from feeling good about a decision and mathematical optimization strategies fail to do that.
Massive business intelligence data do not eliminate the guesswork but create a substantial amount of new guesses that have no connection with the real world situation. Yes, gather business relevant data, filter it and link just the neceassary detail right into your business service, that will improve decision making. Our uncertainty is not reduced by knowing more, as we all have experienced. That is the approach we propose at ISIS Papyrus Software.
Bbliography:
A. Damasio (1995) Descartes’ Error: Emotion, Reason, and the Human Brain
D. MacKay (2003) Information Theory, Inference, Learning Agorithms
G.Gigerenzer, R. Selten (1999), Bounded Rationality
G.Gigerenzer, P. Todd (1999) Simple Heuristics that make us smart
Thomas Sowell (1996), Knowledge and Decisions
S. Johnson (2004), Mind Wide Open
Occam’s Razor: ‘Of all possible answers to a question the most simple one is usually the right one.’
The proponents of BI however hope to fulfill the vision of Laplace; who suggested: ‘… with an intelligence sufficiently vast to submit all comprehensable data to analysis … nothing would be uncertain and the future and past present to his eyes.’
Business Intelligence is not a productive system, such as accounting, stockkeeping, or supply chain management. Customer care or relationship management should first be consodered as a tool to manage and track your customer service, but is often seen as an analysis tool for customer behavior to improve for example cross-selling rather than service quality.
Business Intelligence is complex and expensive software for gathering and analyzing masses of data that supposedly will be of help to make better business decisions. It relies on a faith in mathematics that were obtained by Bernoulli, Coombs, Edwards, Neumann and Morgenstern. They saw heuristic approaches to decision making as defective because it takes resource saving short-cuts.
The proponents of BI now claim that it provides the computing power to optimize decision making by calculating probability for maximum utitlity, as described by Simon (1955). Optimisation however, relies on a number of restrictive assumptions, such as that the process of decision analysis has to be followed precisely and that the data available are correct and relevant. Let’s for a moment assume that the garbage-in problem has been solved and the data given to the business executives are good.
Klein (1999) provides a list of these restrictions and requirements that were identified in many studies :
1. The goals must be well defined in quantitative terms.
2. The decision makers values must be stable.
3. The situation must be stable.
4. The task is restricted to the selection of options.
5. Exhaustive generation of alternatives.
6. Optimal choice must be possible in reasonable time.
7. Thorough comparison of options.
8. Use compensatory strategy
9. Probaility estimates must be coherent and accurate.
10. Failure prediction must be exhaustive.
11. Evaluation must be exhaustive
There are many studies about human decision making and most of them come to the conclusion that ‘less is more’. Less information about a subject makes for better decisions. Bi can be used to gather and consolidate information that then seems to be simpler and easier to use for decisions. The problem is one of comprehension and trust. Can the user comprehend what the data values truly mean? Do the metadata make sense to the deciding person? Can past averages, means, standard deviations and periodic data be used to predict the future? I propose that just a few people within any organization might even sensibly comprehend what those data could mean. The old adage of garbage-in-garbage-out still holds. Who knows if the mathematical methods used to process the data are well chosen.
Klein (1999) proposes that forcing people to give up their heuristic approach to decision making puts them into ‘information overload’ and questions optimization as the gold standard for decision making.
Now that users of Business Intelligence data do not find them too helpful and seem overwhelmed, the new idea is to aid or replace human decision making with predictive analytics, using probability calculated from past data. Probability computing about future events is the next illusion that BI proponents sell.
Here is a list of what the executives and managers really need for decision making and don’t get from Business Inteligence:
- what customers want.
- what to do to be competitive.
- where business has to innovate.
- how the market will react to current and upcoming changes.
- what competitors are currently doing.
- how employees really see the company.
- how to impove the profitability of the business.
- how to improve internal communications.
- the quality of business processes (not the quality of execution).
You might recognize a common element in the above list: Knowledge is not about knowing a lot of data, or taking decisions based on data. One has to come up with an ACTION or a list of alternative actions and then take a decision which one to perform. Finding out from BI that revenue is dropping only says that the management is out of touch with the business.
Simon (1972) was concerned that optimization was not practical in a field setting because of its restrictions. I propose that there is no proof available that BI solves the problem of the restrictions listed above. Business Intelligence can enforce the optimization process for the decision maker and seemingly create the conditions necessary. It can however only propose options based on the data available to the system. It can not propose to the user to go outside the system and analyze other information as it brakes the optimization process. As a consequence it BLINDS the decision maker to the real world. Klein (1999) proposes that it the enforced optimization process stops the decision maker from gaining experience for future benefit. Outside opportunities and constraints would be totally ignored.
Business Intellilgence enforces a better decision making process but not a better quality of decision making.
Timely and good quality information is not like having a crystal ball. It is abstract information and will tell the user nothing that he doesn’t know. We can only make decisions based on analogies to previously perceived patterns that have to be fairly simple. Statistical software does not take better decisions just because it can process more data. Any data given to the user that is not truly relevant turns into noise that reduces the quality of the communication channel and obfuscates the important information. Less data means less noise.
What information should it be that truly represents a company’s competitive position in the market? Past sales data and comparisons of market share? What decision will that offer? The best way to find out about the competion is to ask a customer who decided for another product why. A competitor is a friend who helps the business to improve what it does. Without competitors companies would become complacent. Inteviewing five of those customers who switched – ideally face to face – will provide much more decision making input than statistics. That certain groups of randomly classified people spent a certain amount on randomly classified goods is not knowledge. A qualified manager who attends sales calls or walks into the store to speak with customers will see those changes in customer behaviour in real-time. He can ask about customer preferences at the time they happen and not months later in a filtered and watered down manner that is completely abstract.
March (1978) and Simon (1983) propose that people do not decide by calculating expected utility and question its mathematical foundation (that is used in BI) for real-world situations. Klein (1999) proposes that training people by exposing them to experience of decision making is more important than abstract optimization processes. This falls in line with Thomas Sowell in ‘Knowledge and Decisions’ who calculates the cost of knowledge by its practical usefullness and not by the amount of abstract eductation.
Antonio Damasio (1995) has virtually proven the influence of our emotional center on human decision making and Steven Johnson paints a wonderful picture of the power of our human mind in its connection of instincts, intuitions and emotions created by the link between the neocortex and hippocampus, and amygdala. Good decisions come from feeling good about a decision and mathematical optimization strategies fail to do that.
Massive business intelligence data do not eliminate the guesswork but create a substantial amount of new guesses that have no connection with the real world situation. Yes, gather business relevant data, filter it and link just the neceassary detail right into your business service, that will improve decision making. Our uncertainty is not reduced by knowing more, as we all have experienced. That is the approach we propose at ISIS Papyrus Software.
Bbliography:
A. Damasio (1995) Descartes’ Error: Emotion, Reason, and the Human Brain
D. MacKay (2003) Information Theory, Inference, Learning Agorithms
G.Gigerenzer, R. Selten (1999), Bounded Rationality
G.Gigerenzer, P. Todd (1999) Simple Heuristics that make us smart
Thomas Sowell (1996), Knowledge and Decisions
S. Johnson (2004), Mind Wide Open