This session was the first ever math-based session for advanced revenue management practitioners to discuss computational challenges in revenue management and customer data analytics. Pricing isn’t just about looking at data on spreadsheets. You actually have to do math. Complex math. Well, you have to do complex math if your revenue management software system doesn’t bring the science and analytics together.
Private Label for Brokerage and Property Management FirmsMatt Angerer
VerticalRent offers a 100% private and whitelabeled version of it's platform to Brokerage and Property Management firms. Lock-in the industry lowest pricing on credit, criminal, and eviction reports with our FCRA-compliant platform.
The Send Social Media white label product allows agencies, franchises, and large corporations to offer social media management while maintaining their own branding identity. It includes a customized hosted site, control panel, and branded social media applications. The monthly license fee is $799 for 40 users, with additional users available for $22.50 per month. The initial term is six months, and additional services like emails, SMS, mobile apps, and dedicated hosting are available for extra fees.
This document discusses different strategies for pricing private label products compared to national brands. It identifies five categories of private label products - standard, gourmet, super premium, organic, and commodity. For standard products, the author recommends pricing them 10-15% lower than the leading national brand. Gourmet products should be priced based on consumer research to determine the highest value. Super premium products can be priced equal to or up to 10% higher than national brands. Commodity products should be 15-25% lower in price. The overall strategy is to determine the value proposition for private label products based on quality and price compared to national brands.
Competing on pricing analytics by Privaledge - pricing strategies solutionsprivaledge
Goals for this presentation about pricing analytics
1. To leave you with an understanding or a deeper understanding of the Importance of Pricing & its potential bottom line impact
2. To Show how Pricing & Value analytics could help you measure, manage & improve your pricing effectiveness
3. To show you also some of the limits of analytics & give you a few simple recipes to get more out of them
For more information : http://privaledge.net
Indian consumers are generally price sensitive due to limited incomes. The degree of price sensitivity varies between products and consumers. Producers need to consider different strategies like downsizing products, increasing prices, or offering promotions, depending on how sensitive a product is to price changes. For highly price sensitive essential goods, downsizing may minimize volume losses compared to a price increase. Premium products are less sensitive and can drive more revenue. Producers also need to consider small, large, and distributed pack sizes differently based on sensitivity. The goal is to protect revenues during inflation while focusing on penetration and consumption.
As the world struggles to free itself from global recession, the one silver lining in an otherwise grim scenario is the expected growth of private label brands. Indeed, the continued growth of private labels remains one of the major macro trends affecting FMCG (and other) sectors.
In this short round up, Cocoon Group summarizes a few of the trends driving and affecting private label brands - with a particular focus on opportunities for (Central and Eastern) Europe.
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.
Private Label for Brokerage and Property Management FirmsMatt Angerer
VerticalRent offers a 100% private and whitelabeled version of it's platform to Brokerage and Property Management firms. Lock-in the industry lowest pricing on credit, criminal, and eviction reports with our FCRA-compliant platform.
The Send Social Media white label product allows agencies, franchises, and large corporations to offer social media management while maintaining their own branding identity. It includes a customized hosted site, control panel, and branded social media applications. The monthly license fee is $799 for 40 users, with additional users available for $22.50 per month. The initial term is six months, and additional services like emails, SMS, mobile apps, and dedicated hosting are available for extra fees.
This document discusses different strategies for pricing private label products compared to national brands. It identifies five categories of private label products - standard, gourmet, super premium, organic, and commodity. For standard products, the author recommends pricing them 10-15% lower than the leading national brand. Gourmet products should be priced based on consumer research to determine the highest value. Super premium products can be priced equal to or up to 10% higher than national brands. Commodity products should be 15-25% lower in price. The overall strategy is to determine the value proposition for private label products based on quality and price compared to national brands.
Competing on pricing analytics by Privaledge - pricing strategies solutionsprivaledge
Goals for this presentation about pricing analytics
1. To leave you with an understanding or a deeper understanding of the Importance of Pricing & its potential bottom line impact
2. To Show how Pricing & Value analytics could help you measure, manage & improve your pricing effectiveness
3. To show you also some of the limits of analytics & give you a few simple recipes to get more out of them
For more information : http://privaledge.net
Indian consumers are generally price sensitive due to limited incomes. The degree of price sensitivity varies between products and consumers. Producers need to consider different strategies like downsizing products, increasing prices, or offering promotions, depending on how sensitive a product is to price changes. For highly price sensitive essential goods, downsizing may minimize volume losses compared to a price increase. Premium products are less sensitive and can drive more revenue. Producers also need to consider small, large, and distributed pack sizes differently based on sensitivity. The goal is to protect revenues during inflation while focusing on penetration and consumption.
As the world struggles to free itself from global recession, the one silver lining in an otherwise grim scenario is the expected growth of private label brands. Indeed, the continued growth of private labels remains one of the major macro trends affecting FMCG (and other) sectors.
In this short round up, Cocoon Group summarizes a few of the trends driving and affecting private label brands - with a particular focus on opportunities for (Central and Eastern) Europe.
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.
This document summarizes the findings of a survey conducted to understand price sensitivity in the Indian telecom sector. The survey involved 180 respondents from Mumbai, India. Key findings include:
1) For most mobile subscribers, call cost and network quality are the top criteria for choosing a service provider.
2) Tata Telecom had the highest customer satisfaction while MTNL had the lowest.
3) The most used service is making local calls.
4) 40% of Indian customers change mobile providers every year.
Understanding the Card Fraud Lifecycle : A Guide For Private Label IssuersChristopher Uriarte
With credit card fraud dramatically on the rise, particularly in the form of card-not-present (CNP) fraud across Internet and Mail Order/Telephone Order (MOTO) channels, it is important for private label issuers to understand the depth of this problem and how it affects their merchant portfolio and their ability to accept private label cards. Private label cards were often considered to be “low risk”, relative to traditional bank cards, but our current analysis has shown the contrary: fraudsters are increasingly using private label cards as the payment instrument in CNP channels and merchants are at great risk if specific strategies are not put in place to stop it.
How to Set Pricing Using the Van Westendorp Price Sensitivity MeterQuestionPro
In this webinar, Dana Stanley of market research software firm Survey Analytics explains the common pricing research technique called the Van Westendorp Price Sensitivity Meter.
The 2016 Amazon Virtual Summit is designed to arm high-volume, professional Amazon sellers with the proven strategies and tactics to effectively grow a profitable brand on the Marketplace.
This year, the focal points will be on how to increase product discoverability on the Amazon SERP, scaling Sponsored Products campaigns, and pricing strategies for private label brands.
How retail business - small, large and online - can benefit from private label brands and products. From developing a niche to the legal requirement of partnerships, discover how to make private label work for your retail business. A great piece of content I made for ASD Market Week as part of our lead generation efforts.
comparison between private labels and brands of Shoppers stop limited.Shukla Dev
This presentation will help you to understand the difference between private labels and brands in retail industry.The pricing and promotional strategy used by retailers in Indian retail industry.
This document compares six methods for conducting pricing research: concept tests, conjoint analysis, price sensitivity meter (PSM), brand price trade off (BPTO), monadic scenarios, and discrete choice modelling. Each method is described in terms of how it is executed, the type of information it can provide about customer preferences and willingness to pay, and its limitations. The document provides examples and details on each pricing research method.
The document discusses private label trends in the U.S. It finds that private label dollar share is increasing more due to rising food prices than consumers switching from brands. Key insights include: private label sales growth is outpacing unit growth; the top private label categories are dairy, eggs, and packaged meat; organic private label sales are growing despite economic challenges; and retailers like Walmart are expanding their exclusive private label brands across many product categories to increase market share.
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.
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.
The document discusses retail pricing strategies. It begins by defining the goals of setting the right price that is acceptable to both consumers and retailers. It then outlines various external factors that influence pricing decisions. The document goes on to explain different pricing elements, objectives, and dependent variables that must be considered. It provides details on several specific pricing strategies retailers can employ, such as customary pricing, variable pricing, price lining, and leader pricing. It also discusses cost-oriented pricing approaches like markup pricing and markdown pricing. The overall document serves as a guide for retailers to understand how to establish prices for their merchandise.
The document discusses private label products, which are store brand or generic brand alternatives to national brands sold in retail stores. Private labels allow retailers to earn higher margins than branded products and give them an advantage over branded manufacturers. As the middle class grows in India and lifestyles and consumer preferences change, demand for private label products is increasing faster than national brands. Retailers can expand their private label offerings and markets by ensuring quality, developing value-added regional products, and lobbying the government to include private label pulses in welfare programs.
05 price elasticity of demand and supplyNepDevWiki
Price elasticity of demand measures how responsive quantity demanded is to price changes. It is calculated as the percentage change in quantity divided by the percentage change in price. Elastic demand occurs when this is above 1, inelastic below 1, and unitary elastic at 1. Perfectly elastic demand is horizontal, while perfectly inelastic is vertical. Factors like substitutes, budget share, and adjustment time influence elasticity. Income elasticity measures responsiveness to income changes, while cross elasticity measures responsiveness between related goods. Price elasticity of supply measures responsiveness of quantity supplied to price. Tax incidence depends on demand elasticity, with inelastic demand leading to consumers paying more of the tax.
This is the last and final ppt of the retail sector project done by us. Hope that it helps a lot of students & profeessionals. Vishal Retail is a very interesting story as its business model is very similar to wal-mart\'s.
Private labels, also known as store brands, are products that are exclusively designed and sold by retailers under the retailer's brand name rather than a national brand name. Private labels originated in the 1960s-1970s as cheaper generic alternatives but have since improved in quality and expanded across price points. Some retailers offer premium private label products. Private labels are produced by both large brand manufacturers and retailers and provide benefits like higher margins for retailers and good value and quality for consumers. However, national brands remain more desirable to some consumers.
This document discusses pricing strategies and considerations. It covers:
1) Assessing customer value perceptions and price sensitivities using methods like economic value analysis and conjoint analysis.
2) Identifying optimal pricing structures like quantity discounts, bundle pricing, and mixed bundling.
3) Considering competitive reactions and using techniques like price signaling, asymmetric pricing, and game theory.
4) Monitoring transaction prices and assessing customer emotional responses to pricing like reference prices and perceptions of fairness.
Here are the key PESTEL factors affecting More Private Labels:
Political: FDI policy changes, land acquisition policies, local politics
Economic: Inflation, economic growth, financial institution support, free market competition, infrastructure quality
Social: Changing demographics, lifestyle changes, social media influence
Technological: E-commerce growth, supply chain technologies, automation
Environmental: Resource scarcity, waste management regulations
Legal: Taxation policies, labor laws, product safety laws
This highlights both opportunities and threats across political, economic, social, technological, environmental and legal external factors. Careful monitoring of these macro trends is important for More's private label strategy.
The document discusses forecasting techniques. It outlines the learning objectives which include listing elements of a good forecast, describing qualitative and quantitative forecasting approaches, and explaining measures of forecast accuracy. The document also describes various forecasting techniques such as qualitative judgmental forecasts, quantitative time-series forecasts including naive forecasts, moving averages, weighted moving averages, exponential smoothing, and linear trend analysis. It provides examples and discusses advantages and disadvantages of each technique.
The document discusses various forecasting techniques used to predict future values based on historical patterns. It describes qualitative methods like executive judgment and quantitative time series methods including naive forecasting, simple moving averages, and weighted moving averages. Forecasting is important for business planning in areas like production, inventory, sales and more. Accurate forecasts are challenging to achieve but provide better guidance than no forecasts at all.
General overview of analytical models. Presentation covers, the following topics:
• What is analytical model?
• What are business requirements for the model?
• How to fulfill those requirements ?
Lec-3 Forecasting.pdf Data science collegemsherazmalik1
The document discusses various forecasting techniques used to predict future events or trends based on historical data patterns. It describes qualitative forecasting methods that rely on expert judgment and quantitative time series methods. Some key time series forecasting techniques mentioned include naive methods, simple moving averages, and weighted moving averages. The document emphasizes that while forecasts are often imperfect, having some prediction or "educated guess" about the future is generally better than no forecast at all for business planning purposes.
This document summarizes the findings of a survey conducted to understand price sensitivity in the Indian telecom sector. The survey involved 180 respondents from Mumbai, India. Key findings include:
1) For most mobile subscribers, call cost and network quality are the top criteria for choosing a service provider.
2) Tata Telecom had the highest customer satisfaction while MTNL had the lowest.
3) The most used service is making local calls.
4) 40% of Indian customers change mobile providers every year.
Understanding the Card Fraud Lifecycle : A Guide For Private Label IssuersChristopher Uriarte
With credit card fraud dramatically on the rise, particularly in the form of card-not-present (CNP) fraud across Internet and Mail Order/Telephone Order (MOTO) channels, it is important for private label issuers to understand the depth of this problem and how it affects their merchant portfolio and their ability to accept private label cards. Private label cards were often considered to be “low risk”, relative to traditional bank cards, but our current analysis has shown the contrary: fraudsters are increasingly using private label cards as the payment instrument in CNP channels and merchants are at great risk if specific strategies are not put in place to stop it.
How to Set Pricing Using the Van Westendorp Price Sensitivity MeterQuestionPro
In this webinar, Dana Stanley of market research software firm Survey Analytics explains the common pricing research technique called the Van Westendorp Price Sensitivity Meter.
The 2016 Amazon Virtual Summit is designed to arm high-volume, professional Amazon sellers with the proven strategies and tactics to effectively grow a profitable brand on the Marketplace.
This year, the focal points will be on how to increase product discoverability on the Amazon SERP, scaling Sponsored Products campaigns, and pricing strategies for private label brands.
How retail business - small, large and online - can benefit from private label brands and products. From developing a niche to the legal requirement of partnerships, discover how to make private label work for your retail business. A great piece of content I made for ASD Market Week as part of our lead generation efforts.
comparison between private labels and brands of Shoppers stop limited.Shukla Dev
This presentation will help you to understand the difference between private labels and brands in retail industry.The pricing and promotional strategy used by retailers in Indian retail industry.
This document compares six methods for conducting pricing research: concept tests, conjoint analysis, price sensitivity meter (PSM), brand price trade off (BPTO), monadic scenarios, and discrete choice modelling. Each method is described in terms of how it is executed, the type of information it can provide about customer preferences and willingness to pay, and its limitations. The document provides examples and details on each pricing research method.
The document discusses private label trends in the U.S. It finds that private label dollar share is increasing more due to rising food prices than consumers switching from brands. Key insights include: private label sales growth is outpacing unit growth; the top private label categories are dairy, eggs, and packaged meat; organic private label sales are growing despite economic challenges; and retailers like Walmart are expanding their exclusive private label brands across many product categories to increase market share.
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.
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.
The document discusses retail pricing strategies. It begins by defining the goals of setting the right price that is acceptable to both consumers and retailers. It then outlines various external factors that influence pricing decisions. The document goes on to explain different pricing elements, objectives, and dependent variables that must be considered. It provides details on several specific pricing strategies retailers can employ, such as customary pricing, variable pricing, price lining, and leader pricing. It also discusses cost-oriented pricing approaches like markup pricing and markdown pricing. The overall document serves as a guide for retailers to understand how to establish prices for their merchandise.
The document discusses private label products, which are store brand or generic brand alternatives to national brands sold in retail stores. Private labels allow retailers to earn higher margins than branded products and give them an advantage over branded manufacturers. As the middle class grows in India and lifestyles and consumer preferences change, demand for private label products is increasing faster than national brands. Retailers can expand their private label offerings and markets by ensuring quality, developing value-added regional products, and lobbying the government to include private label pulses in welfare programs.
05 price elasticity of demand and supplyNepDevWiki
Price elasticity of demand measures how responsive quantity demanded is to price changes. It is calculated as the percentage change in quantity divided by the percentage change in price. Elastic demand occurs when this is above 1, inelastic below 1, and unitary elastic at 1. Perfectly elastic demand is horizontal, while perfectly inelastic is vertical. Factors like substitutes, budget share, and adjustment time influence elasticity. Income elasticity measures responsiveness to income changes, while cross elasticity measures responsiveness between related goods. Price elasticity of supply measures responsiveness of quantity supplied to price. Tax incidence depends on demand elasticity, with inelastic demand leading to consumers paying more of the tax.
This is the last and final ppt of the retail sector project done by us. Hope that it helps a lot of students & profeessionals. Vishal Retail is a very interesting story as its business model is very similar to wal-mart\'s.
Private labels, also known as store brands, are products that are exclusively designed and sold by retailers under the retailer's brand name rather than a national brand name. Private labels originated in the 1960s-1970s as cheaper generic alternatives but have since improved in quality and expanded across price points. Some retailers offer premium private label products. Private labels are produced by both large brand manufacturers and retailers and provide benefits like higher margins for retailers and good value and quality for consumers. However, national brands remain more desirable to some consumers.
This document discusses pricing strategies and considerations. It covers:
1) Assessing customer value perceptions and price sensitivities using methods like economic value analysis and conjoint analysis.
2) Identifying optimal pricing structures like quantity discounts, bundle pricing, and mixed bundling.
3) Considering competitive reactions and using techniques like price signaling, asymmetric pricing, and game theory.
4) Monitoring transaction prices and assessing customer emotional responses to pricing like reference prices and perceptions of fairness.
Here are the key PESTEL factors affecting More Private Labels:
Political: FDI policy changes, land acquisition policies, local politics
Economic: Inflation, economic growth, financial institution support, free market competition, infrastructure quality
Social: Changing demographics, lifestyle changes, social media influence
Technological: E-commerce growth, supply chain technologies, automation
Environmental: Resource scarcity, waste management regulations
Legal: Taxation policies, labor laws, product safety laws
This highlights both opportunities and threats across political, economic, social, technological, environmental and legal external factors. Careful monitoring of these macro trends is important for More's private label strategy.
The document discusses forecasting techniques. It outlines the learning objectives which include listing elements of a good forecast, describing qualitative and quantitative forecasting approaches, and explaining measures of forecast accuracy. The document also describes various forecasting techniques such as qualitative judgmental forecasts, quantitative time-series forecasts including naive forecasts, moving averages, weighted moving averages, exponential smoothing, and linear trend analysis. It provides examples and discusses advantages and disadvantages of each technique.
The document discusses various forecasting techniques used to predict future values based on historical patterns. It describes qualitative methods like executive judgment and quantitative time series methods including naive forecasting, simple moving averages, and weighted moving averages. Forecasting is important for business planning in areas like production, inventory, sales and more. Accurate forecasts are challenging to achieve but provide better guidance than no forecasts at all.
General overview of analytical models. Presentation covers, the following topics:
• What is analytical model?
• What are business requirements for the model?
• How to fulfill those requirements ?
Lec-3 Forecasting.pdf Data science collegemsherazmalik1
The document discusses various forecasting techniques used to predict future events or trends based on historical data patterns. It describes qualitative forecasting methods that rely on expert judgment and quantitative time series methods. Some key time series forecasting techniques mentioned include naive methods, simple moving averages, and weighted moving averages. The document emphasizes that while forecasts are often imperfect, having some prediction or "educated guess" about the future is generally better than no forecast at all for business planning purposes.
1. The document outlines a six-step process for developing scoring models: research design, data checking and variable creation, creating analysis files, calibrating the scoring model, model evaluation, and model implementation.
2. Several modeling techniques are discussed including linear regression, logistic regression, and neural networks. Key factors in choosing a technique include the target variable type and the software environment.
3. Model evaluation is done using lift tables and gains tables to assess how well the model ranks and selects customers. Graphs of these tables help understand model performance in selecting respondents and generating revenue or profit.
Doing Analytics Right - Designing and Automating AnalyticsTasktop
There is no “one-sized fits all” of development analytics. It is not as simple as “here are the measures you need, go implement them.” The world of software delivery is too complex, and software organizations differ too significantly, to make it that simple. As discussed in the first webinar, the analytics you need depend on your unique business goals and environment.
That said, the design of your analytics solution will still require:
* The dashboards,
* the required data, and
* an appropriate choice of analytical techniques and statistics to apply to the data.
This webinar will describe a straightforward method for finding your analytic solution. In particular, we will explain how to adapt the Goal, Question, Metric (GQM) method to development processes. In addition, we will explain how to avoid “the light is brighter here” analytics anti-pattern: the idea that organizations tend to design metrics programs around the data they can easily get, rather than figuring out how to get the data they really need.
This document discusses forecasting methods used in production and operations management. It defines forecasting as predicting future values based on historical data. The key types of forecasts discussed are judgmental forecasts using subjective inputs, time series forecasts using historical data patterns, and associative models using explanatory variables. Time series methods covered include simple moving averages, weighted moving averages, and exponential smoothing. Exponential smoothing gives more weight to recent periods to generate forecasts. Quantitative forecasting methods are chosen based on the forecast horizon and data available.
This document provides an overview of a training module on problem solving techniques. It includes definitions of AQC, SQC, and SPC and their differences. It discusses the importance of data and different types of data. Basic statistical concepts like average and standard deviation are introduced. Various tools for problem solving are described such as flow diagrams, brainstorming, graphs, and stratification. Flow diagrams can be used to depict processes and different types include macro, micro, and matrix diagrams. Brainstorming is a technique to generate ideas in a team setting. Different types of graphs like line, bar, pie, belt, compound, and strata graphs are used to represent data visually. Stratification involves separating data into categories to identify problem
Machine Learning with Big Data using Apache SparkInSemble
"Machine Learning with Big Data
using Apache Spark" was presented to Lansing Big Data and Hadoop User Group by Muk Agaram and Amit Singh on 3/31/2015. It goes over the basics of machine learning and demos a use case of predicting recession using Apache Spark through Logistic Regression, SVM and Random Forest Algorithm
Here are the exponential smoothing forecasts for periods 2-10 using a smoothing constant (α) of 0.1:
2: 815.50
3: 801.95
4: 787.26
5: 783.53
6: 785.38
7: 786.64
8: 784.17
9: 782.29
10: 780.41
Operational research models can help organizations in various sectors. Some key examples include:
1) British Telecom used an OR model to schedule over 40,000 field engineers, saving $150 million annually from 1997-2000.
2) Continental Airlines developed a crew scheduling model to help resume normal operations just days after 9/11.
3) Ford Motor Company reduced annual prototype costs by $250 million using an optimization model to share prototype vehicles between testing needs.
This document discusses demand forecasting techniques used to predict future demand for products and services. It covers qualitative methods like executive opinion and surveys, as well as quantitative time series and causal models. Time series methods explained include moving averages, trend projection using least squares, and exponential smoothing. Causal models relate demand to factors like income, price, and leading economic indicators. The document notes uncertainties in demand forecasting arise from limitations of past data, unrealistic assumptions in models, and changes that are difficult to predict.
This document outlines learning objectives and concepts related to forecasting techniques. It discusses key aspects of forecasts such as expected demand levels and accuracy. Common features of all forecasts are listed, such as the assumption that past causal systems will persist. Qualitative and quantitative forecasting approaches are described. Time series forecasts like naive, moving average, weighted moving average, exponential smoothing, linear trend, and trend-adjusted exponential smoothing are explained. The document also covers techniques for incorporating seasonality such as seasonal relatives and additive/multiplicative models.
Marketo Revenue Cycle Model and Lead Lifecycle How ToJosh Hill
Join Marketo experts Josh Hill and Jeff Coveney to learn how to design and implement a Marketo Revenue Cycle Model and gain visibility into your sales funnel metrics.
See the video here: http://summit.marketo.com/2015/sessions/the-tale-of-two-lifecycles-simplify-your-funnel-analysis/
This document provides an overview of operations management forecasting models and their applications. It defines forecasting and lists its common uses. The key components of a forecast and the forecasting process are described. Both qualitative and quantitative forecasting approaches are discussed, along with their advantages and disadvantages. Specific forecasting techniques covered include time series methods, regression methods, moving averages, exponential smoothing, and naive forecasts. Examples are provided to illustrate weighted moving averages and exponential smoothing.
This document discusses predicting stock prices over the short term using vector autoregressive (VAR) and vector autoregressive moving average (VARMA) models. It first describes acquiring and preprocessing stock price data from Kaggle to make it suitable for these time series models. It then explains formulating the prediction problem in MATLAB and using tools to estimate VAR and VARMA model parameters. Results show that while neither model reaches benchmarks, VARMA(10,4) slightly outperforms VAR(4) with a mean absolute error of 0.27 for one-day-ahead forecasts. The document concludes more work is needed to improve short-term prediction, such as incorporating additional features.
This document discusses various quantitative forecasting techniques including time series models. It provides an overview of moving averages, exponential smoothing, trend projections, and decomposition models. Examples are given to illustrate computing forecasts using a three-month simple moving average and a three-month weighted moving average. Exponential smoothing is also introduced as a type of moving average that requires less data to compute forecasts.
During my summer internship at Anheuser-Busch, I worked with the Category Management and Solutions department to develop a predictive model for SKU unit movement. I utilized machine learning techniques to process 100+ variables including total facings, price, capacity, as well as demographics variables per store ZIP code.
Similar to NAA Maximize 2015 - Presentation on In-depth Analytics of Pricing Discovery (20)
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdfGetInData
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation
3. #MAMConf15
Forecast Model Options and Design
Theoretical Probability:
Coin:
P(heads) = 1 head on a 2 sided coin
= 1 out of 2
=
1
2
Dice:
P(6) = 1 side out of 6 sides of a die
(1,2,3,4,5,6)
= 1 out of 6
=
1
6
Both Heads and a 6 together:
= P(heads) * P(6)
=
1
2
*
1
6
=
1
12
or 8.3%
Experimental Probability:
Identify a trial:
• One trial consists of flipping a coin once and
rolling a die once
• Conduct 25 trials and record your data in the
table below:
Question: You are handed one die and one quarter. What’s the probability of rolling a 6
and getting a heads at the same time?
Legend:
Coin: H = Heads, T = Tails
Die: 1,2,3,4,5,6 = number rolled on the die
Head & 6: Y : Heads & 6 occurred, N: All other results
Results:
1 trial out of 25 resulted in a heads and a 6
= 1/25
Therefore,
P(heads,6) = 4%
1. Forecasting Model Options
2. Principles of Forecasting
3. Forecasting Methods
4. Time Series Models
5. Forecast Accuracy
Trial 1 2 3 4 5 6 7 8 9 10 11 12 13
Coin T T T H T T T T H T T T T
Die 4 1 1 6 2 5 5 6 5 1 1 5 6
Head & 6 N N N Y N N N N N N N N N
Trial 14 15 16 17 18 19 20 21 22 23 24 25
Coin H H H H T H H T T H H H
Die 2 1 2 1 5 1 2 3 2 1 4 2
Head & 6 N N N N N N N N N N N N
ResultsResults
4. #MAMConf15
Principles of Forecasting
1. Forecasting Model Options
2. Principles of Forecasting
3. Forecasting Methods
4. Time Series Models
5. Forecast Accuracy
Grouping
of Data
Forecast
Accuracy
Quantity
of Data
Forecast
Accuracy
Recent
Data
Forecast
Accuracy
• Forecasts contain risk and uncertainty - they are rarely perfect
• Some characteristics of the data used to forecast can improve accuracy
• Forecasts should be systematically evaluated over time for accuracy
5. #MAMConf15
Principle of Aggregating Data
• Since many times we must forecast off of sparse data, what are
some of the ways we aggregate data in our revenue management
forecasts?
- Lease type – Conventional New & Renewal, Affordable, Student, etc.
- Lead Source – ILS Vendor, Craig’s List, Property Website, Outdoor, etc.
- Unit types
- Lease terms
- Week types
- Move-in weeks
- Clustered communities
- Market
• Need “enough” observations/transactions to have predictive
capabilities
1. Forecasting Model Options
2. Principles of Forecasting
3. Forecasting Methods
4. Time Series Models
5. Forecast Accuracy
6. #MAMConf15
Forecasting Methods
• Qualitative Methods
- Educated guesses based
on human judgement and
opinion
- Subjective and non-
mathematical
Executive Opinion
Market Research
Delphi Method
• Quantitative Methods
- Based on mathematics
- Consistent and objective
- Only as good as the data
on which they are based
Time Series Models
Causal Models
Associative Models
1. Forecasting Model Options
2. Principles of Forecasting
3. Forecasting Methods
4. Time Series Models
5. Forecast Accuracy
7. #MAMConf15
Time Series Model
• Many of the forecasts used in revenue management
leverage time series models
• Time series models use historical data as the basis for
estimating future outcomes
- Moving average
- Weighted moving average
- Kalman filtering
- Exponential smoothing
- Autoregressive moving average (ARMA)
- Autoregressive integrated moving average (ARIMA)
- Extrapolation
- Linear prediction
- Trend estimation
- Growth curve
1. Forecasting Model Options
2. Principles of Forecasting
3. Forecasting Methods
4. Time Series Models
5. Forecast Accuracy
8. #MAMConf15
Time Series Examples
Uniform distribution
between 1 and 2
Increasing trend
Quadratic growth
trend
Seasonal Model
1. Forecasting Model Options
2. Principles of Forecasting
3. Forecasting Methods
4. Time Series Models
5. Forecast Accuracy
9. #MAMConf15
Time Series Problem - Seasonality
• A community manager must develop forecasts for the next
year’s quarterly or seasonal leads.
• The community has collected quarterly lead data for the
past two years.
• She has forecast total leads for next year to be 9000.
• What is the forecast for each quarter or season of next
year?
1. Forecasting Model Options
2. Principles of Forecasting
3. Forecasting Methods
4. Time Series Models
5. Forecast Accuracy
10. #MAMConf15
Time Series Problem
2-period Moving Average
Quarter 2014 ‘14 Index 2015 ’15 Index Avg.
Index
2016
Fall 1900 ? 1900 ? ? ?
Winter 1400 ? 1700 ? ? ?
Spring 2300 ? 2200 ? ? ?
Summer 2400 ? 2600 ? ? ?
Total 8000 8400 9000
Average ? ? ?
=8000/42000
=1900/20000.95 =1900/21000.90
=8400/4 =9000/422502100
=(0.95+0.90)/20.925 =2250*.9252081
1. Forecasting Model Options
2. Principles of Forecasting
3. Forecasting Methods
4. Time Series Models
5. Forecast Accuracy
1. Calculate the average leads per season for each of the past two years
2. Calculate a seasonal index for each season of the year
3. Average the indices by season
4. Calculate the average leads per season for next year by using total
forecast leads for the next year divided by the number of seasons
5. Multiply next year’s average seasonal leads by each average seasonal
index to get forecasted leads per season
11. #MAMConf15
Time Series Problem
Solution
Quarter 2014 ‘14 Index 2015 ’15 Index Avg.
Index
2016
Fall 1900 0.95 1900 0.90 0.925 2081
Winter 1400 0.70 1700 0.81 0.755 1699
Spring 2300 1.15 2200 1.05 1.100 2475
Summer 2400 1.20 2600 1.24 1.220 2745
Total 8000 8400 9000
Average 2000 2100 2250
1. Forecasting Model Options
2. Principles of Forecasting
3. Forecasting Methods
4. Time Series Models
5. Forecast Accuracy
13. #MAMConf15
Measuring Forecasting Accuracy
• Forecasts are never perfect
• The forecast error is the difference between the actual value and the forecast value for
the corresponding period
Et = At - Ft
where E is the forecast error at period t, A is the actual value at period t, and F is the
forecast for period t.
• Measures of aggregate error:
- Mean Absolute Error (MAE) or Mean Absolute Deviation (MAD)
- Mean Absolute Percentage Error (MAPE) or Mean Absolute Percentage Deviation
(MAPD)
- Mean Squared Error (MSE) or Mean Squared Prediction Error (MSPE)
- Cumulative Forecast Error (CFE)
1. Forecasting Model Options
2. Principles of Forecasting
3. Forecasting Methods
4. Time Series Models
5. Forecast Accuracy
14. #MAMConf15
Forecast Accuracy Problem
• An asset manager is measuring the accuracy of
her forecasts using data from the past 5
Thursdays.
• Average difference = (4+6-3-6-2)/5 = -0.2
• Is this an accurate forecast?
Forecast Actual Difference
43 39 4
40 34 6
34 37 -3
36 42 -6
38 40 -2
1. Forecasting Model Options
2. Principles of Forecasting
3. Forecasting Methods
4. Time Series Models
5. Forecast Accuracy
15. #MAMConf15
Forecast Actual Difference
Absolute
Difference
43 39 4 4
40 34 6 6
34 37 -3 3
36 42 -6 6
38 40 -2 2
MAE 4.2
MAE: Mean Absolute Error
1. Forecasting Model Options
2. Principles of Forecasting
3. Forecasting Methods
4. Time Series Models
5. Forecast Accuracy
16. #MAMConf15
Forecast Actual Difference
Absolute
Difference
% of Actual
43 39 4 4 10.3%
40 34 6 6 17.6%
34 37 -3 3 8.1%
36 42 -6 6 14.3%
38 40 -2 2 5.0%
MAPE 11.1%
MAPE: Mean Absolute Percent Error
1. Forecasting Model Options
2. Principles of Forecasting
3. Forecasting Methods
4. Time Series Models
5. Forecast Accuracy
17. #MAMConf15
Week Type Unit Category
Lease Term
Category
Move-in Week Etc.
Level of Granularity
Number of Days Out
Measure accuracy where the forecast has the best potential for performing well
Measure accuracy with appropriate lead time so that your yielding decisions will have value
Too far out:
- Decisions mean little
- Typically less
accurate
Too close in:
- Decisions made
too late
Key Questions when
Measuring Accuracy
1. Forecasting Model Options
2. Principles of Forecasting
3. Forecasting Methods
4. Time Series Models
5. Forecast Accuracy
18. #MAMConf15
Using T-tests to Assess Unit Amenity Values
• The Problem: how do we know whether our unit
amenities are priced too high or too low (or just right)?
• The Solution: Use Days on Market (DOM) as a proxy for
market response and assess how statistically significantly
different the average DOM is for leases with versus
without the amenity
• Application: Any individual or bundle of unit-level
amenities including renovations
19. #MAMConf15
Example 1
T-test examines whether 2
samples are different;
commonly used with
small sample sizes
First two parameters are the
ranges of the two samples
Third parameter is set to 1
for one-tailed distribution
and 2 for two-tailed
Fourth parameter is set to 1
for paired data, 2 for equal
variance and 3 for unequal
variance
Conclusion: PRICED RIGHT
20. #MAMConf15
Example 2
Only 3 bundles can be analyzed
BA partial and Kitchen partial (26)
BA full and Kitchen full (65)
No renovations (12)
BA Minor BA Partial Kitchen Appliance Kitchen Partial BA Full Kitchen Full LseCount AvgDOM
50 75 150 175 No Amenity No Amenity 1 1.0
No Amenity 75 150 175 No Amenity No Amenity 1 33.0
No Amenity No Amenity No Amenity 1 30.0
No Amenity 175 No Amenity No Amenity 26 43.5
No Amenity 150 No Amenity No Amenity No Amenity 2 9.5
No Amenity 175 250 No Amenity 1 16.0
No Amenity No Amenity 2 44.5
No Amenity 250 450 65 78.4
No Amenity No Amenity 12 46.8
Grand Total 111 62.9
29. #MAMConf15
"the map is not the territory"
“...no matter how many instances of white
swans we may have observed, this does not
justify the conclusion that all swans are white.”
33. #MAMConf15
Analyzing Performance:
Measurement Methodology
1. Methodology
2. Performance Results
3. Intangible Benefits
1. Measure “Rental Revenue”
• Account for both rent and occupancy
- Method 1 – Month End Financials
- Method 2 – RPU (Revenue per Unit)
2. Incorporate a Benchmark
• Before and After - Pre vs. Post Revenue Management
• 3rd party “market” data
• Test vs. Control Data Set
3. Measure over Time
• Revenue management is a marathon, not a sprint
4. Account for the Intangibles
34. #MAMConf15
Method 1 - Month End Financials
1. Methodology
2. Performance Results
3. Intangible Benefits
• Measure the month end revenue line items that Rev Mgmt can directly
impact:
› Market Rent
› Vacancy Loss
› Loss & Gain to Lease
› Concessions – New & Renewal
› Month to Month and Short Term Lease Fees
• Don’t incorporate line items that Rev Mgmt cannot control like Bad Debt,
Write Offs, etc…
July Aug Sept Oct Nov Dec Jan Feb Mar Apr May June Baseline July Aug Sept
Market Rent $883,825 $884,575 $884,575 $884,575 $884,575 $884,635 $884,635 $885,850 $885,050 $885,050 $885,075 $878,940 $878,955 $878,980 $878,965
Vacancy Loss ($100,575) ($105,145) ($113,045) ($124,755) ($129,710) ($138,758) ($145,801) ($148,955) ($152,526) ($132,854) ($116,498) ($112,907) ($101,941) ($97,407) ($94,924)
Loss to Lease ($16,966) ($15,784) ($14,793) ($13,518) ($12,378) ($11,836) ($11,221) ($11,301) ($10,686) ($10,975) ($10,126) ($10,084) ($9,965) ($10,897) ($14,484)
Gain to Lease $110 $125 $105 $230 $100 $100 $110 $135 $135 $110 $110 $5,890 $5,885 $6,413 $6,250
Concessions - Renewals ($31,629) ($34,866) ($36,552) ($14,469) ($10,343) ($13,925) ($12,010) ($3,110) ($7,820) ($17,015) ($22,490) ($19,290) ($31,230) ($24,030) ($34,430)
Concessions ($11,412) ($12,225) ($18,875) ($11,826) ($19,769) ($22,280) ($19,241) ($4,880) ($6,440) ($21,082) ($15,620) ($19,947) ($22,206) ($19,699) ($15,447)
Month to Month Fee $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0
Short Term Monthly Fee $775 $1,115 $64 $701 $843 $835 $706 $590 $500 $400 $675 $770 $970 $990 $1,463
Total Rev $724,128 $717,795 $701,479 $720,938 $713,318 $698,771 $697,178 $718,329 $708,213 $703,634 $721,126 $723,372 $712,357 $720,468 $734,350 $727,393
YOY -0.5% 2.3% 3.7%
35. #MAMConf15
Method 2 – Revenue per Unit (RPU)
1. Methodology
2. Performance Results
3. Intangible Benefits
36. #MAMConf15
Analyzing Performance:
Incorporate a Benchmark
1. Methodology
2. Performance Results
3. Intangible Benefits
86%
88%
90%
92%
94%
96%
98%
100%
102%
Baseline July Aug Sept Oct
%ofIndex
Test (Rev Mgmt) vs. Control (No Rev Mgmt)
Avg Net Rental Income - Test (Rev Mgmt)
Avg Net Rental Income - Control (No Rev Mgmt)
37. #MAMConf15
Analyzing Performance:
Account for the Intangibles
1. Methodology
2. Performance Results
3. Intangible Benefits
• Steady pricing with measured market
response
• Strategic approach to pricing with more
attention and visibility to amenity-based
pricing
• Better, more consistent insight into
competitive market space
• Movement away from market rent and
toward net effective pricing