An introduction to the two most common types of demand curves (linear and power), which can be used to estimate the price for a product or service that maximizes profit margins. Includes hands-on real-world examples using Excel.
The “best” price for a product or service is one that maximizes profits, not necessarily the price that sells the most units. This presentation uses real-world examples to explore how Excel’s Solver functionality can be used to calculate the optimal price for any product or service.
Pricing Analytics: Estimating Demand Curves Without Price ElasticityMichael Lamont
Most techniques used to created demand curves depend on the product’s price elasticity. But what if you don’t have or can’t obtain the price elasticity figures for a particular product? If you can make reasonable estimates of demand for a product at a high, median, and low price point, then you can still construct a reasonable estimate of the demand curve over the range of those prices. This presentation shows how to use Excel’s line fitting and Solver functionality to construct a demand curve without knowing the product’s price elasticity, and determine the optimal price for the product that maximizes profit margin.
pricing methods, perceived value pricing, competition based pricing, total cost based pricing or floor pricing, mark up pricing, target return pricing.
The “best” price for a product or service is one that maximizes profits, not necessarily the price that sells the most units. This presentation uses real-world examples to explore how Excel’s Solver functionality can be used to calculate the optimal price for any product or service.
Pricing Analytics: Estimating Demand Curves Without Price ElasticityMichael Lamont
Most techniques used to created demand curves depend on the product’s price elasticity. But what if you don’t have or can’t obtain the price elasticity figures for a particular product? If you can make reasonable estimates of demand for a product at a high, median, and low price point, then you can still construct a reasonable estimate of the demand curve over the range of those prices. This presentation shows how to use Excel’s line fitting and Solver functionality to construct a demand curve without knowing the product’s price elasticity, and determine the optimal price for the product that maximizes profit margin.
pricing methods, perceived value pricing, competition based pricing, total cost based pricing or floor pricing, mark up pricing, target return pricing.
Meaning and nature of buyer behavior, differences between consumer buying and organizational buying in terms of characteristics and process, Strategic use of consumer behavior knowledge in marketing and public policy decisions. Modern Consumerism and the global consumer movement
Sales and promotional discounts let retailers reach pools of customers that value the same product differently. Modeling the pool of potential buyers, and how it changes over time, lets you optimize how and when sales and discounts are applies. This presentation provides a hands-on demonstration of modeling the pool of potential buyers, and using Excel’s Solver tool to optimize revenue from that shopper pool by manipulating price.
It defines the relations, promises and marketing efforts between the three key stakeholders in services marketing - companies, providers (employees), and customers. Internal marketing is done between company and providers, external marketing is performed between companies and customers, and interactive marketing takes place between customers and providers.Marketing service triangle plays a very important role in service industries.
This presentation has been prepared for the students of MBA or any other departments. It includes all the details regarding consumer psychology and pricing. Certain points have been taken from the book of marketing management- Philip Kotler
Marketing is one of the most important part for any company. And for that companies have to follow certain guidelines, which are different for different countries. Also, ethics plays major part in marketing, therefore all marketing campaigns should be ethical according to particular country (community). This presentation shows our view point of marketing campaigns by different companies and it is made on Indian Laws and Ethics
Meaning and nature of buyer behavior, differences between consumer buying and organizational buying in terms of characteristics and process, Strategic use of consumer behavior knowledge in marketing and public policy decisions. Modern Consumerism and the global consumer movement
Sales and promotional discounts let retailers reach pools of customers that value the same product differently. Modeling the pool of potential buyers, and how it changes over time, lets you optimize how and when sales and discounts are applies. This presentation provides a hands-on demonstration of modeling the pool of potential buyers, and using Excel’s Solver tool to optimize revenue from that shopper pool by manipulating price.
It defines the relations, promises and marketing efforts between the three key stakeholders in services marketing - companies, providers (employees), and customers. Internal marketing is done between company and providers, external marketing is performed between companies and customers, and interactive marketing takes place between customers and providers.Marketing service triangle plays a very important role in service industries.
This presentation has been prepared for the students of MBA or any other departments. It includes all the details regarding consumer psychology and pricing. Certain points have been taken from the book of marketing management- Philip Kotler
Marketing is one of the most important part for any company. And for that companies have to follow certain guidelines, which are different for different countries. Also, ethics plays major part in marketing, therefore all marketing campaigns should be ethical according to particular country (community). This presentation shows our view point of marketing campaigns by different companies and it is made on Indian Laws and Ethics
Demand and Supply
The Demand Curve
The Supply Curve
Market Equilibrium
Surplus
Effects of Government Intervention—Price Controls
Elasticity and revenue
Presenting this set of slides with name - Supply Chain Management Kpi Dashboard Showing Cost Reduction And Procurement Roi. This is a five stage process. The stages in this process are Demand Forecasting, Predicting Future Demand, Supply Chain Management. https://bit.ly/3y0JFjb
The cost of production/Chapter 7(pindyck)RAHUL SINHA
content
•MEASURING COST: WHICH COSTS MATTER?
•Fixed and variable cost
•Fixed versus sunk cost
•Amortizing Sunk Costs
•Marginal cost
•Average cost
•Determinants of short run cost
•Diminishing marginal returns
•The shapes of cost curves
•The Average–Marginal Relationship
•Costs in a long run
•Cost minimizing input choices
•Isocost lines
•Marginal rate of technical substitution
•Expansion path
•The Inflexibility of Short-Run Production
•Long run average cost
•Economies and Diseconomies of Scale
•The Relationship Between Short-Run and Long-Run Cost
•Break even analysis
Introduction to Demand
We buy products for their utility- the pleasure, usefulness, or satisfaction they give us.
What is your utility for the following products? (Measure your utility by the maximum amount you would be willing to pay for this product)Do we have the same utility for these goods?Introduction to Demand
One reason the demand curve slopes downward is due to diminish marginal utility
The principle of diminishing marginal utility says that our additional satisfaction tends to go down as we consume more and more units.
To make a buying decision, we consider whether the satisfaction we expect to gain is worth the money we must give up.
Changes in Demand
Demand Curves can also shift in response to the following factors:
Buyers (# of): changes in the number of consumers
Income: changes in consumers' income
Tastes: changes in preference or popularity of product/ service
Expectations: changes in what consumers expect to happen in the future
Related goods: compliments and substitutes.
BITER: factors that shift the demand curve
Changes in Demand
Prices of related goods affect on demand
Substitute goods a substitute is a product that can be used in the place of another.
The price of the substitute good and demand for the other good are directly related
For example, Coke Price
Pepsi Demand
Complementary goods a compliment is a good that goes well with another good.
When goods are complements, there is an inverse relationship between the price of one and the demand for the other
For example, Peanut Butter Price
Jam Demand
Introduction to Supply
Supply refers to the various quantities of a good or service that producers are willing to sell at all possible market prices.
Supply can refer to the output of one producer or to the total output of all producers in the market (market supply).Introduction to Supply
• A supply schedule can be shown as points on a graph.
• The graph lists prices on the vertical axis and quantities supplied on the horizontal axis.
• Each point on the graph shows how many units of the product or service a producer (or group of producers) would willing sell at a particular price.
• The supply curve is the line that connects these points.
Introduction to Supply
As the price for a good rises, the quantity supplied rises and the quantity demanded falls. As the price falls, the quantity supplied falls and the quantity demanded rises.
The law of supply holds that producers will normally offer more for sale at higher prices and less at lower prices.Introduction to Supply
• The reason the supply curve slopes upward is due to costs and profit.
• Producers purchase resources and use them to produce output.
• Producers will incur costs as they bid resources away from their alternative uses.Introduction to Supply
• Businesses provide goods and services hoping to make a profit.
Profit is the money a business has left over after it covers its costs.
Businesses try to sell at prices high enough to cover it
The TCP/IP protocol system is used by virtually every modern data network to quickly and reliably move data from node to node. This presentation covers what TCP/IP is, what it does, it’s most important features, and how it was developed.
This presentation explores the reasons why software projects are significantly more difficult to manage than other types of projects. Software-specific issues related to scope, resources, and time are explored, as well as how software projects differ from other projects in the physical world. An argument for why software constitutes a “Wicked Problem” is expanded, and numerous software development myths are attacked with real-world anecdotes and solutions.
Pricing Analytics: Segmenting Customers To Maximize RevenueMichael Lamont
Potential customers for a product or service can be segmented into valuation groups. High valuation groups are willing to pay more for the product or service, while low valuation groups are only willing to pay a lesser amount for the same product or service. This presentation provides a basic background on yield management through customer segmentation, and a hands-on example of modeling airline customer segmentation using Excel.
The prices of several product classes – notably fashion and technology – tend to drop over time. One possible reason for the drop over time is different customers assigning a different value to the same product or service. Price skimming models can be used to maximize a product or service’s revenue by planning price reductions over time in a manner that slowly cuts tranches of higher-value customers out of the market. This presentation provides a hands-on demonstration of constructing a price skimming model in Excel, and optimizing planned price reductions.
Business Intelligence: Multidimensional AnalysisMichael Lamont
An introduction to multidimensional business intelligence and OnLine Analytical Processing (OLAP) suitable for both a technical and non-technical audience. Covers dimensions, attributes, measures, Key Performance Indicators (KPIs), aggregates, hierarchies, and data cubes.
HP Tech Forum 2009 presentation covering some of the ways spammers harvest email addresses on the Internet (and how you can prevent it), including an in-depth look at three commonly used software packages.
Slides from my wildly popular presentation at HP World 2005. Who knew? Grossly over-simplified signal processing methodology and sample photos of models in bikinis was a winning combo, even in San Francisco.
Evaluating and Implementing Anti-Spam SolutionsMichael Lamont
Presentation from HP World 2004 that explores common anti-spam technologies including how they work, how effective they are, their relative strengths/weaknesses, and how spammers try to circumvent them. Also has a section on evaluating anti-spam software packages.
Methodology for a technical evaluation of software-based spam filters - a hot topic back in 2005. It was originally going to be given at the HP Tech Forum in New Orleans in Sept 2005 - Katrina forced the conference to cancel while I was literally on the way to the Boston airport. Ended up giving this presentation at the rescheduled conference in Orlando in Oct 2005.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
2. Demand Curve
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Demand
Curve describing how many units of product the market demands for every possible price point
3. Demand Curves
•Used to estimate price that should be charged for maximum profits
•The best price for a product maximizes margins – not unit sales 12 units * $5 = $60 50 units * $1 = $50
4. Estimating Best Price
•Need two things to estimate best price:
•Variable cost to produce one unit of product
•Product’s demand curve
5. Estimating Best Price
•COG: variable cost to produce one unit of product
•p: price we charge customers for 1 unit of product
•D(p): customer demand, in units of product, at price p
•Profit margin formula:
Margin = (p – COG) * D(p)
Profit margin per unit
Demand for product
6. Demand Curves
•Demand curves are subject to frequent change
•Affected by:
•Competitive pressures
•Customer sentiment
•Macroeconomic factors
7. Price Elasticity
•The amount demand decreases if prices increased by 1%
•Product is price elastic if its elasticity > 1
•Decreasing price of product will increase revenue
•Product is price inelastic if its elasticity < 1
•Decreasing price of product will decrease revenue
8. Price Elasticity
•Examples of price elasticity values in Boston MSA:
•Good pricing decisions require understanding of products’ price elasticity
Product/Service
Elasticity
Salt
0.09
Coffee
0.20
Beer
0.95
LCD monitors
1.73
Restaurant meals
2.90
Travel to Ireland
5.27
9. Demand Curves
•Two most popular types of demand curves:
•Linear demand curves
•Power demand curves
10. Linear Demand Curves
•Straight-line relationship between price and demand
D = a – bp
•D: units of product demanded by customers
•p: per-unit price
•a and b: adjust curve to fit product’s price elasticity
•Excel can auto-calculate a and b for us
11. Power Demand Curves
•Arc that shows relationship between price and demand, when product’s price elasticity isn’t affected by product’s price
D = apb
•D: units of product demanded by customers
•p: per-unit price
•a and b: adjust curve to fit product’s price elasticity
•b is additive inverse of price elasticity (ex: b = -2 if elasticity = 2)
•Excel can auto-calculate a for us
12. Which Curve to Use?
•Price elasticity properties tell us which curve is appropriate
•Linear demand curve: if product’s price elasticity changes as price changes
•Power demand curve: if product’s price elasticity remains constant as price changes
13. Constructing Linear Demand Curves
•Scenario:
•We’re selling polo shirts for Ralph Lauren
•Current price per unit p = $90
•Current demand D = 1,000 shirts
•Price elasticity of product: 2.0
•We need two points to construct our line:
•We already know ($90, 1000) is on the curve
•Increase price by 1% ($0.90), demand will decrease by 2% (20 shirts)
•Calculated point on curve: ($90.90, 980)
24. Constructing Linear Demand Curves
•Linear demand curve equation for this example:
D = 3000 – 22.2p
•Implication: Every $0.90 increase in shirt price is going to cost demand for ~22 shirts
•Error rate for linear demand curves increases with distance from current price point
•Pretty good approximation +/- 5% of current price
25. Constructing Power Demand Curves
•Use power demand curves when product’s price elasticity doesn’t change when price changes
•Same scenario:
•We’re selling polo shirts for Ralph Lauren
•Current price per unit p = $90
•Current demand D = 1,000 shirts
•Price elasticity of product: 2.0
•Price elasticity doesn’t change when price changes
•Excel’s Goal Seek function calculates value of a for us
47. Constructing Power Demand Curves
•Value of a determined to be 8,100,000
D = 8,100,000p-2
•Price elasticity remains constant for every price on the demand curve