This document outlines a 6-step process for making more profitable pricing decisions using analytics. The steps include: 1) Assessing the business situation and setting yield objectives. 2) Conducting analytics to model own-brand and competitive price elasticities. 3) Assessing the risk of competitive response. 4) Generating pricing scenarios and planning timing. 5) Summarizing key pricing principles. 6) Optimizing profit yield. The goal is to provide managers with tools to estimate the sales, revenue and profit impacts of pricing actions, better understand competitor behavior, and make pricing decisions that maximize profits.
Marketing attribution is complex due to multiple variables that influence sales over various time periods through direct and indirect relationships. New analytical techniques can help determine the contributions, effects, and returns of different marketing investments and combinations over the short, medium and long term. These include vector auto regression, dynamic linear models, and nonlinear programming. Accurately measuring attribution allows for optimizing media spending and understanding price elasticity and copy testing results.
The document discusses budget optimization for a CPG client's marketing mix. Using techniques like non-linear programming and time series regression, the budgets were optimized to reallocate funds from less productive elements like TV, print and radio towards more productive digital, BTL, trade promotions and consumer promotions. This resulted in a 10% lift in sales for the same overall budget. After accounting for other variables, the optimization translated to a 3% lift in overall sales.
This document discusses marketing mix optimization and the key factors that drive sales. It outlines the 5 P's of marketing - product, place, price, promotion, and people - as well as external factors that affect sales. It then describes a marketing mix optimization process that involves understanding the market and data, collecting data, visualizing insights, and re-allocating budgets to find an optimal allocation that can increase sales. The process involves consolidating data, describing sales patterns, preparing models, and fully optimizing the marketing mix where every step is important.
This document discusses measuring the long-term effects of advertising for the brand Alpha. It finds that long-term ad effects account for 23% of Alpha's annual sales and are 15 times greater than short-term ad effects. Improved ad creative has helped boost long-term effects and Alpha's sales growth. A model found advertising returns of $2.7 million compared to a loss of $100k if only considering short-term effects, suggesting under-investment in advertising.
Marketing Mix Models In a Changing EnvironmentAquent
Marketing Mix Models have been used successfully for years at consumer package goods (CPG) companies to increase their marketing effectiveness and efficiency. The four Ps (Product, Placement, Price, and Promotion) were as far as the models needed to go. Broad–based media was and is very expensive, which kept competition to a minimum. However, the marketing environment has changed in many ways and must be considered when looking to these models to improve marketing performance.
Market mix modelling is used to estimate the effectiveness of media investments on sales. A statistical model is estimated using historical sales data and explanatory variables like marketing activities, price, seasonality, and other macro factors. The simplest model is linear regression. The output is then used to analyze media effectiveness and return on investment. The case study involved structuring regional sales and marketing data, exploring relationships between variables, fitting mixed models to account for regional differences, and finding seasonality had the highest contribution to sales followed by online and radio marketing.
This document discusses market mix models and some of the challenges involved in their use. It begins by explaining how marketers turned to more scientific approaches like market mix models to address demands for accountability and ROI assessment. However, several challenges were overlooked, including misunderstanding what models can and cannot do, data integration issues, and the limitations of only using observational data. The document then provides an overview of how market mix models work today and some of the technical issues that must be addressed, such as collinearity of factors, data quality problems, and endogeneity. It concludes by noting the need to accommodate different effects like carryover, diminishing returns, and cross-sectional differences across segments.
Marketing mix modeling provides a holistic view of how marketing activities impact business performance. While some see it as a "black box", it actually considers unique factors for each activity and assigns quantifiable measures. When done right, it can attribute sales to customer segments, inform new activities based on past data, and include complex digital channels like search. Critics argue it lacks real-time value or overvalues promotions, but quality models provide real-time insights and identify high-return promotional events without eroding long-term margins. Overall, marketing mix modeling is a valuable tool for optimizing marketing spend when properly customized to each business.
Marketing attribution is complex due to multiple variables that influence sales over various time periods through direct and indirect relationships. New analytical techniques can help determine the contributions, effects, and returns of different marketing investments and combinations over the short, medium and long term. These include vector auto regression, dynamic linear models, and nonlinear programming. Accurately measuring attribution allows for optimizing media spending and understanding price elasticity and copy testing results.
The document discusses budget optimization for a CPG client's marketing mix. Using techniques like non-linear programming and time series regression, the budgets were optimized to reallocate funds from less productive elements like TV, print and radio towards more productive digital, BTL, trade promotions and consumer promotions. This resulted in a 10% lift in sales for the same overall budget. After accounting for other variables, the optimization translated to a 3% lift in overall sales.
This document discusses marketing mix optimization and the key factors that drive sales. It outlines the 5 P's of marketing - product, place, price, promotion, and people - as well as external factors that affect sales. It then describes a marketing mix optimization process that involves understanding the market and data, collecting data, visualizing insights, and re-allocating budgets to find an optimal allocation that can increase sales. The process involves consolidating data, describing sales patterns, preparing models, and fully optimizing the marketing mix where every step is important.
This document discusses measuring the long-term effects of advertising for the brand Alpha. It finds that long-term ad effects account for 23% of Alpha's annual sales and are 15 times greater than short-term ad effects. Improved ad creative has helped boost long-term effects and Alpha's sales growth. A model found advertising returns of $2.7 million compared to a loss of $100k if only considering short-term effects, suggesting under-investment in advertising.
Marketing Mix Models In a Changing EnvironmentAquent
Marketing Mix Models have been used successfully for years at consumer package goods (CPG) companies to increase their marketing effectiveness and efficiency. The four Ps (Product, Placement, Price, and Promotion) were as far as the models needed to go. Broad–based media was and is very expensive, which kept competition to a minimum. However, the marketing environment has changed in many ways and must be considered when looking to these models to improve marketing performance.
Market mix modelling is used to estimate the effectiveness of media investments on sales. A statistical model is estimated using historical sales data and explanatory variables like marketing activities, price, seasonality, and other macro factors. The simplest model is linear regression. The output is then used to analyze media effectiveness and return on investment. The case study involved structuring regional sales and marketing data, exploring relationships between variables, fitting mixed models to account for regional differences, and finding seasonality had the highest contribution to sales followed by online and radio marketing.
This document discusses market mix models and some of the challenges involved in their use. It begins by explaining how marketers turned to more scientific approaches like market mix models to address demands for accountability and ROI assessment. However, several challenges were overlooked, including misunderstanding what models can and cannot do, data integration issues, and the limitations of only using observational data. The document then provides an overview of how market mix models work today and some of the technical issues that must be addressed, such as collinearity of factors, data quality problems, and endogeneity. It concludes by noting the need to accommodate different effects like carryover, diminishing returns, and cross-sectional differences across segments.
Marketing mix modeling provides a holistic view of how marketing activities impact business performance. While some see it as a "black box", it actually considers unique factors for each activity and assigns quantifiable measures. When done right, it can attribute sales to customer segments, inform new activities based on past data, and include complex digital channels like search. Critics argue it lacks real-time value or overvalues promotions, but quality models provide real-time insights and identify high-return promotional events without eroding long-term margins. Overall, marketing mix modeling is a valuable tool for optimizing marketing spend when properly customized to each business.
Marketing ROI isn't all cut and dry these days. This deck gives you a brief run down of 9 things you really need to thinking about in planning the measurement of your marketing activities across all sales channels.
ARF Rethink 2016 - IRI-Atkins Case Segment Marketing MixJoy Joseph
As Atkins’ product portfolio expands, maintaining innovation incrementality requires a better understanding of consumer segment engagement to ensure need-states do not overlap and cannibalize growth.
Message Mix Modeling - Leveraging Creative for Sales GrowthMasood Akhtar
The document discusses Message Mix Modelling, which is a reformulation and extension of traditional Media Mix Modelling. Message Mix Modelling focuses on measuring the sales uplift and ROI of different creative messages, rather than solely on media channels. It involves coding media activity into campaign and message type in order to evaluate the ROI of specific messages behind campaigns. This allows marketers to optimize media spend across channels, campaigns, and messages. Key benefits are quantifying the incremental sales impact of different messages to inform brand communications strategies and optimizing future investments by focusing on delivering the right message.
Many CMOs at leading firms are overhauling marketing operations to improve agility, decrease cycle times, and increase throughput and efficiency. They are significantly increasing marketing productivity. One of the tools that helps them drive these improvements is Lean thinking.
How continuously improve on marketing ROI with lessons learned from the Total Quality Movement. Create a Plan-Do-Learn-Change process with a continuous cycle on measuring what you need to learn how to improve, then keep it going with increases in ROI thresholds as you improve.
Top Strategies for Marketing Signal MeasurementOrigami Logic
This document summarizes the top 5 strategies for marketing signal measurement:
1. Measure against your objectives by setting clear goals and metrics for each campaign.
2. Choose signals that answer "what" happened and "why" by selecting primary metrics for tracking performance and secondary signals for analysis.
3. View performance holistically by measuring cross-channel interactions and cumulative impact.
4. Establish a standard measurement practice with consistent metrics across teams for accountability.
5. Measure marketing signals continuously through near real-time monitoring to discover opportunities and optimize strategies.
Measuring the “carryover” effects of pricingJoy Joseph
Standard pricing models evaluate point in time consumer price sensitivity, looking at the relationship between consumption changes vis-à-vis pricing or promotional changes one week at a time. Consumer price sensitivity is a more gradual phenomenon that builds over time, with shocks on consumption reverberating several weeks following a price change. This is true for own as well as competitive pricing effects- it is easy to underestimate how much impact a competitor’s price has on a brand if one is just looking at one week at a time- the shock carries over or “persists” in later purchase cycles, regardless of price stabilizing to a new level or reverting back. We use a Dynamic Time Series model (Vector Autoregression) to capture the contemporaneous as well as lagged effect of pricing and promotions (own as well as competitive) to capture this “carryover” effect. This can help prevent marketers from underestimating the extent and duration of own as well as competitive pricing action.
Measure Marketing Like It's 2016: A Guided Tour of the Newest Tools & TechniquesOrigami Logic
There are currently more than 4,000 marketing technology companies worldwide, according to industry thought leader Scott Brinker. With marketers placing increasing emphasis on ROI and accountability, the solution category that focuses on cross-channel measurement and analytics is seeing explosive growth. Leading brands are investing in technology to automate campaign measurement and enhance their real-time understanding of strategies that drive marketing performance.
Join Origami Logic for an overview of the latest marketing measurement tools and techniques, and learn how these innovations will interact with your current technology stack and processes.
In this webinar, you’ll get:
- Snapshot of the fastest-growing category of marketing technology: marketing measurement and analytics
- Review of each of the major solutions in the space, and what value they provide to your business
- Guidelines for how to assess and prioritize marketing measurement investments in 2016
Marketing ROI case for banking & financeMichael Wolfe
Following is a case study showing marketing effectiveness analytics for a banking and financial services firm in the South Central US. A part of the challenge here involved estimating the impact that Hurricane Katrina had on this banks and the measurement of marketing ROI and impact in some new markets. in the end,. as is true for the banking sector, actual ROI of marketing is quite high and there are substantial opportunities for accelerate revenue growth with more effective marketing spend.
Modeling The Market Mix Modeling Problem (Media Mix Optimization)Amit Satsangi
Channel Attribution Modeling is not the best way to decide on Media Mix Optimization (Channel ROI). Here I present results by formulating the problem as a Marketing Mix using two models:
(a) Linear Regression Analysis
(b) Log-linear Multiplicative Model
Driving marketing performance in financial services is subject to unique considerations. Diverse set of distribution channels, complex customer segments, a need to balance branding and promotion, and multiple outcome measures impacting customer value are factors to consider.
This document discusses ways to improve marketing mix models (MMMs) used to measure the effectiveness of print advertising. It notes that MMMs currently do not provide a sharp contrast between promoted and unpromoted sales. The document recommends using single-source data that links advertising exposures to purchases, which would create a clearer treatment vs. control comparison. It also suggests enhancing measurement of print advertising occurrences and audiences. With these improvements, the document argues MMMs could show advertising's greater contribution to sales and shift more marketing spending back to advertising, including print advertising specifically.
Marketing and media managers are looking for ways to:
1) Reach more viewers and users in the least amount of time and cost in order to increase returns on ad spending.
2) Accurately identify target segments for specific media and creative campaigns to build the brand and improve performance.
3) Connect performance measurement tools to existing CRM systems in order to better understand results and the impact of different media.
Thomas Betts has over 25 years of experience in strategic marketing and multi-channel brand development. He has held several director roles developing marketing strategies that drove over $1 billion in sales for various brands. Most recently, he has been an independent marketing consultant helping companies improve declining sales, outdated creative strategies, weak brands, and ineffective segmentation.
Marketing Optimization for Natural Gas UtilitiesMichael Wolfe
This is a story of how a retail natural gas firm used marketing/media response model to re-engineer its marketing spend to drive higher growth and climb out of a stagnant business situation
Metrics Credentials For Slideshare December 29, 2008Blair Currie
Metrics is Japan's first marketing consultancy focused on auditing the performance of marketing communications. It aims to bring more transparency and fairness to Japan's marketing industry. Metrics offers various auditing services including media audits, creative audits, PR audits, and agency audits. It also provides marketing management consulting. Metrics conducts national and global audits to benchmark client performance against market standards and identify areas for improvement. The goal is to help clients improve marketing ROI and get better value from their marketing investments and suppliers.
This document discusses the rise of big data and data-driven marketing. It notes that data now plays a central role for organizations in maintaining customer relationships, personalizing offerings, and automating marketing processes. Big data has led to new forms of digital marketing like recommendations, geofencing, and retargeting. There is now an urgent demand for analytics capabilities to help firms make sense of new data sources and problems. The document outlines several issues that will impact data-rich marketing environments in the future, including increased privacy/security concerns limiting data collection/retention, the need for an organizational culture that values data/analytics, and the interdisciplinary nature of marketing analytics work.
4 Simple Steps to Profits through a Painless Pricing FrameworkMichael Nelson
Pricing can often feel like an arcane art rife with phrases such as; value pricing, pricing equilibrium, cost plus pricing, etc. If we get it wrong, our business suffers. The WRAP Pricing Framework gives small business owners the ability to simplify pricing and maximize both their profit and the value provided to their customers. Get a free copy of the framework here: http://thecogentcoach.com/wrap-pricing-framework
Marketing ROI isn't all cut and dry these days. This deck gives you a brief run down of 9 things you really need to thinking about in planning the measurement of your marketing activities across all sales channels.
ARF Rethink 2016 - IRI-Atkins Case Segment Marketing MixJoy Joseph
As Atkins’ product portfolio expands, maintaining innovation incrementality requires a better understanding of consumer segment engagement to ensure need-states do not overlap and cannibalize growth.
Message Mix Modeling - Leveraging Creative for Sales GrowthMasood Akhtar
The document discusses Message Mix Modelling, which is a reformulation and extension of traditional Media Mix Modelling. Message Mix Modelling focuses on measuring the sales uplift and ROI of different creative messages, rather than solely on media channels. It involves coding media activity into campaign and message type in order to evaluate the ROI of specific messages behind campaigns. This allows marketers to optimize media spend across channels, campaigns, and messages. Key benefits are quantifying the incremental sales impact of different messages to inform brand communications strategies and optimizing future investments by focusing on delivering the right message.
Many CMOs at leading firms are overhauling marketing operations to improve agility, decrease cycle times, and increase throughput and efficiency. They are significantly increasing marketing productivity. One of the tools that helps them drive these improvements is Lean thinking.
How continuously improve on marketing ROI with lessons learned from the Total Quality Movement. Create a Plan-Do-Learn-Change process with a continuous cycle on measuring what you need to learn how to improve, then keep it going with increases in ROI thresholds as you improve.
Top Strategies for Marketing Signal MeasurementOrigami Logic
This document summarizes the top 5 strategies for marketing signal measurement:
1. Measure against your objectives by setting clear goals and metrics for each campaign.
2. Choose signals that answer "what" happened and "why" by selecting primary metrics for tracking performance and secondary signals for analysis.
3. View performance holistically by measuring cross-channel interactions and cumulative impact.
4. Establish a standard measurement practice with consistent metrics across teams for accountability.
5. Measure marketing signals continuously through near real-time monitoring to discover opportunities and optimize strategies.
Measuring the “carryover” effects of pricingJoy Joseph
Standard pricing models evaluate point in time consumer price sensitivity, looking at the relationship between consumption changes vis-à-vis pricing or promotional changes one week at a time. Consumer price sensitivity is a more gradual phenomenon that builds over time, with shocks on consumption reverberating several weeks following a price change. This is true for own as well as competitive pricing effects- it is easy to underestimate how much impact a competitor’s price has on a brand if one is just looking at one week at a time- the shock carries over or “persists” in later purchase cycles, regardless of price stabilizing to a new level or reverting back. We use a Dynamic Time Series model (Vector Autoregression) to capture the contemporaneous as well as lagged effect of pricing and promotions (own as well as competitive) to capture this “carryover” effect. This can help prevent marketers from underestimating the extent and duration of own as well as competitive pricing action.
Measure Marketing Like It's 2016: A Guided Tour of the Newest Tools & TechniquesOrigami Logic
There are currently more than 4,000 marketing technology companies worldwide, according to industry thought leader Scott Brinker. With marketers placing increasing emphasis on ROI and accountability, the solution category that focuses on cross-channel measurement and analytics is seeing explosive growth. Leading brands are investing in technology to automate campaign measurement and enhance their real-time understanding of strategies that drive marketing performance.
Join Origami Logic for an overview of the latest marketing measurement tools and techniques, and learn how these innovations will interact with your current technology stack and processes.
In this webinar, you’ll get:
- Snapshot of the fastest-growing category of marketing technology: marketing measurement and analytics
- Review of each of the major solutions in the space, and what value they provide to your business
- Guidelines for how to assess and prioritize marketing measurement investments in 2016
Marketing ROI case for banking & financeMichael Wolfe
Following is a case study showing marketing effectiveness analytics for a banking and financial services firm in the South Central US. A part of the challenge here involved estimating the impact that Hurricane Katrina had on this banks and the measurement of marketing ROI and impact in some new markets. in the end,. as is true for the banking sector, actual ROI of marketing is quite high and there are substantial opportunities for accelerate revenue growth with more effective marketing spend.
Modeling The Market Mix Modeling Problem (Media Mix Optimization)Amit Satsangi
Channel Attribution Modeling is not the best way to decide on Media Mix Optimization (Channel ROI). Here I present results by formulating the problem as a Marketing Mix using two models:
(a) Linear Regression Analysis
(b) Log-linear Multiplicative Model
Driving marketing performance in financial services is subject to unique considerations. Diverse set of distribution channels, complex customer segments, a need to balance branding and promotion, and multiple outcome measures impacting customer value are factors to consider.
This document discusses ways to improve marketing mix models (MMMs) used to measure the effectiveness of print advertising. It notes that MMMs currently do not provide a sharp contrast between promoted and unpromoted sales. The document recommends using single-source data that links advertising exposures to purchases, which would create a clearer treatment vs. control comparison. It also suggests enhancing measurement of print advertising occurrences and audiences. With these improvements, the document argues MMMs could show advertising's greater contribution to sales and shift more marketing spending back to advertising, including print advertising specifically.
Marketing and media managers are looking for ways to:
1) Reach more viewers and users in the least amount of time and cost in order to increase returns on ad spending.
2) Accurately identify target segments for specific media and creative campaigns to build the brand and improve performance.
3) Connect performance measurement tools to existing CRM systems in order to better understand results and the impact of different media.
Thomas Betts has over 25 years of experience in strategic marketing and multi-channel brand development. He has held several director roles developing marketing strategies that drove over $1 billion in sales for various brands. Most recently, he has been an independent marketing consultant helping companies improve declining sales, outdated creative strategies, weak brands, and ineffective segmentation.
Marketing Optimization for Natural Gas UtilitiesMichael Wolfe
This is a story of how a retail natural gas firm used marketing/media response model to re-engineer its marketing spend to drive higher growth and climb out of a stagnant business situation
Metrics Credentials For Slideshare December 29, 2008Blair Currie
Metrics is Japan's first marketing consultancy focused on auditing the performance of marketing communications. It aims to bring more transparency and fairness to Japan's marketing industry. Metrics offers various auditing services including media audits, creative audits, PR audits, and agency audits. It also provides marketing management consulting. Metrics conducts national and global audits to benchmark client performance against market standards and identify areas for improvement. The goal is to help clients improve marketing ROI and get better value from their marketing investments and suppliers.
This document discusses the rise of big data and data-driven marketing. It notes that data now plays a central role for organizations in maintaining customer relationships, personalizing offerings, and automating marketing processes. Big data has led to new forms of digital marketing like recommendations, geofencing, and retargeting. There is now an urgent demand for analytics capabilities to help firms make sense of new data sources and problems. The document outlines several issues that will impact data-rich marketing environments in the future, including increased privacy/security concerns limiting data collection/retention, the need for an organizational culture that values data/analytics, and the interdisciplinary nature of marketing analytics work.
4 Simple Steps to Profits through a Painless Pricing FrameworkMichael Nelson
Pricing can often feel like an arcane art rife with phrases such as; value pricing, pricing equilibrium, cost plus pricing, etc. If we get it wrong, our business suffers. The WRAP Pricing Framework gives small business owners the ability to simplify pricing and maximize both their profit and the value provided to their customers. Get a free copy of the framework here: http://thecogentcoach.com/wrap-pricing-framework
Conheça a história do primeiro gato guia do mundo, bem como exemplos mais recentes onde os gatos assumiram esta função geralmente executada por cães. https://www.mundodosanimais.pt/
O documento discute o abandono cruel de animais de estimação e a importância do vínculo entre donos e seus cães e gatos. Relata histórias de donos que abandonam seus animais sem culpa e de um homem idoso que criou um laço forte com seu cão Yorkshire ao longo de caminhadas diárias.
O documento discute os sistemas circulatórios de diferentes animais. Os cnidários e platelmintas não possuem um sistema circulatório verdadeiro, enquanto minhocas e insetos têm sistemas abertos e vertebrados têm sistemas fechados com coração e vasos sanguíneos.
O documento fornece uma lista de alimentos perigosos para cães e gatos, incluindo chocolate, café, álcool, uvas, ovos crus, ossos, alho, cebola, alimentos gordos, leite e derivados. Recomenda dar aos animais apenas ração balanceada desenvolvida especificamente para eles.
Kiran Johny is a mechanical engineer from India seeking a role in the oil and gas industry. He currently works as a roustabout in Abu Dhabi for National Drilling Company, where he has received awards for safety and performance. Kiran has a bachelor's degree in mechanical engineering and certifications in safety training. He aims to develop his career in a well-established company using his communication, teamwork, and technical skills.
Este documento presenta el cronograma de la primera ronda del XI Concurso Interuniversitario de Litigación Oral en Materia de Familia, que se llevará a cabo del 22 al 28 de agosto de 2013 entre varias universidades salvadoreñas en la ciudad de San Salvador. Se enumeran las fechas, horarios y universidades participantes en cada debate.
Integrates brand track scores with sales in a marketing mix modelling based technique. Optimizes media usage to longer term brand equity growth as opposed to only sales.
The document discusses the history and evolution of the English language from its origins as Anglo-Frisian dialects brought to Britain by Anglo-Saxon settlers in the 5th century AD. It details how Old English emerged as the dominant language by the 7th century and later transformed into Middle English after the Norman conquest in 1066, when it absorbed elements from Norman French. The document also notes how English eventually became a global language due to British colonial expansion starting in the 16th century.
We declare that the Case Studies entitled
“1. A case study on Rain Water Harvestment.
2. Studies on the ecological impacts of Kolleru lake (Eutrophication).
3 . A case study on Vanasamrakshana programme by Government of Andhra Pradesh
4. A case study on present condition of agricultural lands in Andhra Pradesh capital region.
5. A case study on tribal evacuation and impact on indigenous knowledge”
Il report presenta i risultati di una serie di test svoltasi nel 2014 presso una cantina sociale emiliana. I test erano volti a valutare l’utilizzo dell’ozono come agente sanificante su vasche in acciaio inox da 600 hl, utilizzate per la fermentazione e lo stoccaggio dei vini.
Why coca cola is dominating in the beverage industryIMT ProHunt
This document discusses the history and dominance of Coca-Cola in the beverage industry. It notes that Coca-Cola was founded in 1886 in Atlanta, Georgia and became the largest beverage company by the 20th century due to its marketing tactics. The document outlines Coca-Cola's various products including its signature Coca-Cola drink as well as Diet Coke and Sprite. It attributes Coca-Cola's dominance to its secret formula, widespread marketing, and ability to consistently produce its signature taste. The document also discusses both benefits and risks of drinking Coca-Cola products.
This document discusses different frameworks for online marketing research and segmentation. It provides details on 5 key techniques for segmenting customers: (1) demographics, (2) current and predicted customer value, (3) customer lifecycle groups, (4) RFM analysis, and (5) multi-channel customer behavior. It then discusses performance metrics and segmentation strategies for different online business models, including eCommerce/retail sites, lead generation sites, advertising/media sites, and customer support sites. The goal is to understand customers at a deeper level in order to create targeted marketing strategies and content for each segment.
most pricing decisions are made at the SKU level, yet analysed at the brand level. BLA propitiatory modeling approach to optimising SKU level pricing allows for strategic decision making.
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
Pricing should be a critical issue for the CEO as it is one of the most powerful levers in the business. Successful pricing also depends on clear goal and strategy alignment, which is the role of the CEO. This presentation was made in Seattle to a group of business leaders interested in improving pricing leadership.
This document discusses the role of strategic pricing for product managers. It begins by covering the need for strategic pricing to anticipate market changes rather than just reacting. It then discusses basic economic concepts for pricing like estimating demand curves and revenue maximization. It also covers common pricing techniques like cost-based, customer-based, and market-based pricing. The document emphasizes that value-based, proactive, and profit-driven strategies are most effective. It provides steps for identifying customer value drivers, estimating economic value, and using techniques like conjoint analysis to understand willingness to pay. Finally, it discusses the key role of product managers in collaborating across functions to define value propositions and drive strategic pricing based on market and customer insights.
The document provides guidance on effective pricing strategies during difficult economic times. It recommends reaffirming your pricing strategy while gaining a thorough understanding of customers, costs, and competitors. Key steps include segmenting customers, establishing pricing metrics and incentives, developing options based on analysis rather than emotions, and training salespeople on pricing fundamentals. Tools like pricing simulations and reporting systems can help implement and monitor the pricing strategy. The overall aim is to optimize pricing decisions and maximize profitability.
Mather Disciplined Pricing Approach For Banking Summarydfischer
This document discusses developing a disciplined pricing approach for banks. It outlines an 8-phase process: 1) Review bank strategy and pricing implications, 2) Review regulatory constraints, 3) Develop pricing strategies, 4) Build a database and pricing model, 5) Develop preliminary pricing tactics, 6) Test and refine pricing tactics, 7) Implement pricing guidelines, and 8) Measure and track results. The goal is to help banks maximize revenue and profitability through strategic pricing when new regulations eliminate some bank fees. Advanced analytics are used to understand customer behavior and optimize prices.
The document discusses developing pricing strategies and programs. It addresses how consumers evaluate prices, how companies should initially set prices and adapt prices over time. A company should set prices by selecting objectives, estimating demand and costs, analyzing competitors, and choosing a pricing method. They must also determine when to change prices in response to costs or competitors. There are various strategies for adapting prices for different contexts and responding to competitive challenges through price or quality adjustments.
This document discusses factors that influence pricing decisions in marketing management. It covers objectives of pricing like return on investment, market penetration, and market skimming. Factors that affect pricing include production costs, perceived value, demand, and competitor prices. The document also outlines different methods of pricing like cost-based pricing, value pricing, and competition-based pricing. Finally, it concludes that pricing strategy is an important marketing tool that determines the success of a product and helps maximize revenue while taking into account various factors.
The document discusses strategy workshops and concepts related to developing business strategy. It covers:
- Business drivers which can help clarify strategy, communicate objectives, plan targets, and provide strategic feedback.
- Market position in terms of market share, and different challenges and strategies for market leaders, challengers, followers, and market penetrators.
- Estimating market share using four factors: market awareness, product acceptance, company ability, and competitive edge. Multiplying these factors provides an understanding of strengths and weaknesses.
A pricing strategy is an approach taken by businesses to decide how much to charge for their goods and services. The interaction between margin, price, and selling level is given specific consideration while pricing products. Therefore, it’s important and complicated to design a proper pricing plan that ensures business success.
This document outlines 10 common pitfalls that retailers face when implementing price optimization and how to avoid them. It recommends starting with executive sponsorship to drive necessary changes. It also stresses preparing for change management since price optimization can require fundamental shifts in business processes. Additionally, the document emphasizes educating users and gaining team buy-in on why a data-driven approach is better than individual intuition for setting prices. Addressing issues like non-compliance and examining interactions between business units can help optimize prices for increased revenue and profit.
Marketelligent Capabilities & Offerings for Sales AnalyticsMarketelligent
The document summarizes Marketelligent's capabilities in sales analytics for consumer packaged goods (CPG) companies. It provides examples of how Marketelligent helps clients track sales performance, identify drivers of share loss, and conduct pricing simulations. Marketelligent also offers forecasting, trade promotion optimization, market mix modeling, and SKU rationalization to improve business decisions. The management team descriptions suggest Marketelligent provides data-driven consulting services to consumer industries globally.
What we can do in Retail analytics by bhawani nandanprasadBhawani N Prasad
1. The document discusses various retail analytics strategies and solutions including: retail product life cycle price optimization, markdown optimization for short and long life cycle products, data mining for price optimization, key value item analysis, assortment and space optimization, market basket analytics, optimization intelligence reporting, promotion strategy and measurement, social commerce, and cluster analysis.
2. The solutions aim to optimize pricing, promotions, inventory, and assortments to increase sales and profits through techniques like price elasticity analysis, simulation, localization, and predictive analytics.
3. Benefits cited include increased sales 1-12%, gross margin gains 2-20%, ROI of $10-20 for every $1 invested, and better decision making through
The document summarizes a panel discussion on pricing systems and best practices for the wine industry. The panelists discussed how pricing is important from different perspectives within a company. They also addressed common challenges with pricing and outlined best practices, such as centralizing pricing data and modeling pricing throughout the supply chain. The panel recommended companies develop a pricing roadmap and solutions to improve their pricing models over time.
This document discusses pricing methods and strategies. It defines price and discusses factors that affect price decisions like marketing objectives, costs, the nature of the market, and competitors. It also covers major considerations in setting price like pricing objectives, strategies, and procedures. Common pricing mistakes and major pricing strategies like cost-based, value-based, and product mix pricing are summarized as well.
This document discusses marketing policies related to sales, including product, distribution, and pricing policies. It provides details on key considerations for each type of policy, such as determining product lines, distribution intensity strategies, approaches to pricing relative to competition and costs. The document also discusses sales strategies, including account targeting, relationship strategies, selling strategies, and sales channel strategies. Overall, the document provides an overview of various sales and marketing policies and strategies that can guide a company's sales efforts.
Retail Webinar - How to Stay 10 Steps Ahead of Retail Competitors?JK Tech
Today, profiling competitors is an imperative strategy. The proactive approach to competitor pricing strategy analysis will assist your retail business to anticipate other competitors’ business strategies. Having such proactive knowledge fosters strategic business agility.
Learn how retail competitive analysis helps you to achieve that competitive edge and optimize your business’s ROI.
Key Takeaways:
1) Monitoring Product Pricing and competitor-tracking.
2) Analytics and insights on competitor pricing & promotions.
3) How to use competitor data to reprice your products at scale?
4) How to detect competitor strategy and avoid the “race to the bottom”?
5) The need to compare the assortment of a brand to competitors in the market.
6) Tracking Manufacturer Suggested Retail Price compliance by resellers.
7) Product mapping using the latest techniques.
8) Also, competition analysis offers many other benefits that you should not miss!
Watch the recorded session here: https://jktech.com/webcast/how-to-stay-10-steps-ahead-of-retail-competitors/
This document discusses pricing strategies and factors that affect pricing decisions. It defines price and explores the importance of pricing objectives. Key factors that influence pricing include costs, competition, customers and economic conditions. The document also analyzes pricing strategies such as competitive pricing, penetration pricing and premium pricing. It provides examples of how Samsung and Apple employ different pricing approaches. Finally, it concludes that selecting pricing objectives and strategies requires consideration of business goals and allowing flexibility to change over time.
This document analyzes drivers of customer churn in the wireless telecom industry. It finds that network message advertising is more effective at reducing churn than non-network messages. Higher competitive advertising from companies like Beta Corp increases Alpha Mobility's churn. The analysis models churn based on media variables and finds an optimized media mix and execution level that could reduce Alpha's churn by 0.4 basis points. Key insights include network messages being more effective at reducing churn, some markets being more impacted by media than others, and non-network executions having a stronger unfavorable impact on churn than network executions.
This document discusses measuring and improving ad effectiveness by incorporating copy test scores into marketing mix models. It proposes that Bottom Line Analytics and Global Analytics Partners have partnered with Advertising Benchmark to bring disruptive change by combining copy test scoring with predictive analytics and marketing mix modeling. This would allow advertisers to monetize and measure the impact of ad creative, get a 360 degree view of effectiveness, and improve ads over time based on validated brand revenue.
Radial Landscape Mapping is a new statistical technique for perceptual mapping that displays brand attributes and consumer perceptions of brands in a radial map format. This overcomes limitations of traditional 2D perceptual maps which can be cluttered and difficult to interpret. The radial format arranges brand attributes around the perimeter and positions brands inside based on their associations. Several examples are provided of how Radial Landscape Maps can be used to analyze brand positioning for industries such as restaurants, detergents, beverages, and more. Moving forward, the technique can help companies develop branding strategies through comparative analysis of consumer perceptions.
1. Measuring customer experience is important for business performance but companies struggle with accurately measuring social media sentiment and linking it to real business outcomes like sales and ROI.
2. The Social Engagement Index (SEI) uses stance-shift analysis to more accurately analyze social media conversations and has shown strong correlations between social media sentiment measured by SEI and sales for various brands.
3. Case studies demonstrate how SEI analysis can provide insights into key drivers of customer experience for different types of companies and inform marketing strategies to improve sales and ROI.
The document discusses sales optimization strategies used by an ACPG major company that sells over 200 SKUs to small retail stores. It implemented analytics to (1) reduce order time by prioritizing high probability SKUs, (2) increase average SKUs sold per visit, and (3) increase invoice values. The strategies included store segmentation, logistic regression models to predict focus SKU purchases, and a "heat map" showing past SKU purchases. An algorithm combining these strategies customized SKU lists for each store. This led to a 20% reduction in order time, 8% higher sales values, and 20% more SKUs sold on average per visit, along with increased margins from focus product sales.
Alpha Construction Group commissioned Global Analytics Partners to develop a housing components demand forecast model and interactive tool. The model covers monthly demand from 2009 to present using five drivers: recurring seasonality, housing starts, home remodeling spending, order backlog, and price to customer. The tool accurately forecasts sales with 93% accuracy. For 2015, the model predicts 7.2% demand growth based on modest growth in key drivers and flat pricing, compared to 3.1% growth in 2014. Remodeling spending, seasonality, and housing starts are the most important drivers of monthly demand. The interactive forecasting tool allows clients to simulate forecasts based on changes to model drivers.
The document provides analysis from a customer segmentation case study and conjoint study for a new home beverage appliance product. Key findings include:
1) Two customer segments - "Life-of-the-Party Sam" and "Techie Party Hostess Soccer-Mom" - showed the highest purchase intent for the product and represent 77% of the estimated market potential.
2) Owners of other beverage appliances like SodaStream had significantly higher purchase intent for the new product compared to others.
3) Projections estimate 695,000 appliances and 456 million drink pods would be sold annually at a $179 price point.
Relationship between Paid, Owned and Earned in the marketing ecosystem. Describes the new consumer path to purchase while keeping all influences in view
The document discusses innovations in marketing effectiveness measurement by Global Analytics Partners. It provides examples of projects where they developed models to measure ROI for various companies' marketing initiatives. These include quantifying the impact of iPhone launch for AT&T, optimizing ad spend for Splenda, and measuring drivers of hotel satisfaction for TripAdvisor. It also discusses measuring long-term advertising effects, marketing synergies between channels, quantifying social media engagement through a Social Engagement Index, and using this data to simulate marketing mix scenarios.
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
Natural Language Processing (NLP), RAG and its applications .pptxfkyes25
1. In the realm of Natural Language Processing (NLP), knowledge-intensive tasks such as question answering, fact verification, and open-domain dialogue generation require the integration of vast and up-to-date information. Traditional neural models, though powerful, struggle with encoding all necessary knowledge within their parameters, leading to limitations in generalization and scalability. The paper "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks" introduces RAG (Retrieval-Augmented Generation), a novel framework that synergizes retrieval mechanisms with generative models, enhancing performance by dynamically incorporating external knowledge during inference.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
State of Artificial intelligence Report 2023kuntobimo2016
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
1. 1
The Price is Right?
Six Steps to More Profitable Pricing
Leveraging Analytics to Generate More Profitable Pricing Strategies and Decisions
New Developments in Measurement and Analytics
2. 2
Copyright 2013 Bottom Line Analytics All rights reserved.
Content
Forward, Objectives & Destination
The 6 Step Process
1. Situation Assessment & Set Yield Objectives
2. Do the Analytics
Own Brand Price Elasticity by SKU
Competitive Price Elasticities
Pass-Through Assessment
3. Assess Competitive Response Risk
4. Generate Scenarios & Plan Timing
5. Summarize 6 Principles
6. Optimize Profit Yield
3. 3
Copyright 2013 Bottom Line Analytics All rights reserved.
Forward: The Imperative and Challenge
of Pricing
• Pricing is considered one of the Four-P’s of marketing and a critical
part of the marketing mix
• In fact, no single part of the marketing mix has as much impact on
sales performance and profitability than pricing
• Yet, managers tend to spend less time and research on improving
pricing decisions and, likewise, are less knowledgeable of the
specific impact that individual pricing decisions have on their
brand’s performance in advance of a pricing action
• Consequently, sub-optimal pricing decisions are common and
managers literally leave millions of dollars of profit and revenue on
the table due to poor pricing decisions
• Pricing analytics has a huge upside and is a relatively easy sell, if
done correctly.
4. 4
Copyright 2013 Bottom Line Analytics All rights reserved.
Pricing research and analytic approaches
• The following three approaches are the most used methods for addressing
issues of pricing, price sensitivity and pricing strategy for marketers.
Method Description Advantages Limitations
Von Westendorp Method A survey tecnique which seeks to find the For providing price guidance for new products, Based on perceptions and not on actual mar-
intersection between "perceived too expensive" `` ket behavior.
and "too cheap to be of adequate quality". perience or data.
Conjoint/Discrete Choice Based on trade-off surveys; where price is paired Great for new products and linking price to Provides share or preference simulations, but
Models with various product features and attributes. value-added product features and attributes. also is not linked to actual purchase behavior
Econometric Models Based on statistical linkage or retail prices to actual Requires sufficient historical price and end- Best for existing products and does provide the
product sales. user sales data. Greater uncertainty when critical linkage with actual customer sales or
pricing decisions are outside the range of behavior.
historical experience.
5. 5
Copyright 2013 Bottom Line Analytics All rights reserved.
Objective
• In order to adequately plan, evaluate and implement
profitable pricing decision, managers must have a
precise understanding of the sales and profit impact of
given price changes. This will require development of
advanced models or analytic tools which measure:
• Brand or Own Product Price Elasticity, which is the
percent change in brand sales due to a given
percent change in brand price over time, and
• Cross or Competitive Price Elasticity, which is the
percent change in brand sales due to a given
percent change in competitors’ pricing over time.
6. 6
Copyright 2013 Bottom Line Analytics All rights reserved.
Destination
• This outlines a 6-step process that has shown to provide
companies the information and tools needed to make more
profitable pricing decisions
• While not eliminating all uncertainty due to pricing decisions, this 6-
step process will greatly enhance the likelihood that pricing
decisions will be optimized due to:
• Having models and simulation tools that precisely estimate the
impact of any given pricing action on sales, revenue and net
profitability
• Provide intelligence on the likelihood and level of competitive
pricing following your brand’s pricing move
• Provide facts and insight into the best timing for pricing
• Provide the specifics of which SKUs are most critical in the
pricing decision
• Identify which markets or channels where your brand’s price is
not in competitive alignment and what can be done about it
• Uncover the key principles for effective pricing strategy
7. 7
Copyright 2013 Bottom Line Analytics All rights reserved.
Content
Forward, Objectives & Destination
The 6 Step Process
1. Situation Assessment & Set Yield Objectives
2. Do the Analytics
Own Brand Price Elasticity by SKU
Competitive Price Elasticities
Pass-Through Assessment
3. Assess Competitive Response Risk
4. Generate Scenarios & Plan Timing
5. Summarize 6 Principles
6. Optimize Profit Yield
8. 8
Copyright 2013 Bottom Line Analytics All rights reserved.
1- 3 Plan & Assess
4- 5 Prepare
6 Optimize
Assess &
Set Yield
Targets
Situation &
Objectives
Model
Price
Elasticities
Analytics Competitor
Response
Evaluate
Risk
When &
What
to
Implement
Generate
Scenarios &
Plan Timing
Consolidate Insights
Summarize
6 Principles
Profit
Yield
Optimize
A Proven 6 Step Process
9. 9
Copyright 2013 Bottom Line Analytics All rights reserved.
Content
Forward, Objectives & Destination
The 6 Step Process
1. Situation Assessment & Set Yield Objectives
2. Do the Analytics
Own Brand Price Elasticity by SKU
Competitive Price Elasticities
Pass-Through Assessment
3. Assess Competitive Response Risk
4. Generate Scenarios & Plan Timing
5. Summarize 6 Principles
6. Optimize Profit Yield
10. 10
Copyright 2013 Bottom Line Analytics All rights reserved.
Step 1 Situation Assessment & Objectives: Review
Sales Trends
80
85
90
95
100
105
110
115
120
125
130
Market 1 Market 2 Market 3
Standardized Index of Sales Trends
Reviewing your current business situation and performance is the
first step for developing pricing goals and objectives. You clearly
need to find the weak links in your business to uncover possible
pricing issues.
11. 11
Copyright 2013 Bottom Line Analytics All rights reserved.
Step 1 Situation Assessment & Objectives:
Summarize Key Driver Trends
• Doing a thorough review of yours and competitor sales and pricing trends will
enable you to focus where pricing needs to be leveraged to rectify potential
competitive imbalances. This is the “smoking gun” which shows a pricing
issue in Market 3.
Competitve
Price Trend
Own
Price
Tremd
Sales
Trend
Your
Price
Competitor
Price
Market 1 -5.1% 2.1% 1.3% 45.50$ 43.50$
Market 2 2.1% 4.6% 4.5% 49.50$ 49.00$
Market 3 -14.3% 5.8% -11.3% 52.20$ 48.60$
12. 12
Copyright 2013 Bottom Line Analytics All rights reserved.
Step 1 Situation Assessment & Objectives: Identify
Competitive Price Misalignments
Market 3 Sales & Competitive Pricing Trends
Your Brand Retail Sales Trends Market 3 Compet.Price Trends Market 3
Uncovering the problem areas in your business will often reveal issues
with competitive price alignment for your brand. This is an area
where pricing remedies will be front and center
13. 13
Copyright 2013 Bottom Line Analytics All rights reserved.
Step 1 Six Situation Assessment & Objectives:
Determining Historical Price Pass-Through
• In markets or channels where your brand’s price is not in alignment with competitive
brands, it is not uncommon to find less than 100%of prior pricing actions being totally
passed through to the end customer. This is certainly the case with Market 3
13
Your
Price
Pass Through
Now
Pass Through in
6 Months
Market 1 4.4% 4.1% 4.5%
Market 2 7.0% 6.1% 8.2%
Market 3 8.1% 3.4% 7.3%
14. 14
Copyright 2013 Bottom Line Analytics All rights reserved.
Step 1 Situation Assessment & Objectives: Set Yield
Objectives
• At the core of every pricing action is a trade-off between
sales growth and improving profitability.
• Every pricing action starts with specific volume sales
objectives and profit yield estimates consistent with
profit goals.
• These goals are actualized through careful planning,
estimating and execution. The analytics we will be
doing is specifically aimed towards an accurate
estimate of the sales growth and profit impact or yield
due to the desired pricing action.
• Every pricing decision is the culmination of an effective
balance between competing sales growth and profit
goals.
15. 15
Copyright 2013 Bottom Line Analytics All rights reserved.
Content
Forward, Objectives & Destination
The 6 Step Process
Situation Assessment & Set Yield Objectives
Do the Analytics
Own Brand Price Elasticity by SKU
Competitive Price Elasticities
Pass-Through Assessment
Assess Competitive Response Risk
Generate Scenarios & Plan Timing
6 Principles & Optimize Profit Yield
16. 16
Copyright 2013 Bottom Line Analytics All rights reserved.
Step 2Analytics: Sales Model Architecture
Brand
SKU
Price
Comptv.
Price
Macro-
Economic
Season-
ality
Weekly Retail
Sales by
Market
Determining Price Elasticities starts with a Predictive Econometric
Model of Brand Sales
Weekly Retail Sales
are driven by
Brand SKU Price
Plus
Competitor’s Price
Plus
Macro-Economic Factor
Plus
Seasonality
17. 17
Copyright 2013 Bottom Line Analytics All rights reserved.
Step 2Analytics: Sales Model Validation
-
5,000
10,000
15,000
20,000
25,000
30,000
35,000
Actual
Model
Models show an excellent predictive fit to actual sales
R2 =97.3, Holdout R2 =89.9, MAP = +/- 1.9%
18. 18
Copyright 2013 Bottom Line Analytics All rights reserved.
Step 2Analytics: Derive Own Brand Price & Profit
Elasticities with and without Competitor Reciprocation
1.08
1.10
1.12
1.14
1.16
1.18
1.20
10.0
12.0
14.0
16.0
18.0
20.0
22.0
-15% -10% -5% 0% 5% 10% 15%
UnitSalesMil
NetProfitMil.
Price Change
Profit Mil wo Reciprocation
Profit Mil w Reciprocation
Unit Sales Mil wo Reciprocation
Unit Sales Mil w Reciprication
When competitors match Brand price changes, it reduces the impact
from that pricing change
Brand Price & Profit Elasticity
19. 19
Copyright 2013 Bottom Line Analytics All rights reserved.
Step 2Analytics: Retail Sales Variance Drivers: Annual Unit
Sales Trend Due To:
4.5%
1.3%
-11.3%
-1.0%
-15.0% -10.0% -5.0% 0.0% 5.0% 10.0%
Market 1
Market 2
Market 3
Total
2012 Sales % Impact
Competitor Pricing
Your Brand's Pricing
Macro-Economic Impact
Base Momentum
From the econometric model, we can determine the impact of different
drivers and pricing on overall performance. The problem Market 3
is the one area dragging down overall brand growth
20. 20
Copyright 2013 Bottom Line Analytics All rights reserved.
Step 2Analytics: Pricing Impact on retail unit sales
by SKU
0.1%
0.1%
0.2%
0.2%
0.2%
0.2%
0.2%
0.2%
0.5%
1.6%
2.5%
3.2%
5.4%
7.2%
7.3%
9.7%
25.7%
35.2%
0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0% 40.0%
SKU 1
SKU 2
SKU 3
SKU 4
SKU 5
SKU 6
SKU 7
SKU 8
SKU 9
SKU 10
SKU 11
SKU 12
SKU 13
SKU 14
SKU 15
SKU 16
SKU 17
SKU 18
Retail Unit Sales Impact % Due to Pricing
Retail Sales Impact %
BLA models pricing elasticity at the SKU level, where pricing decisions
are made. Evidence usually shows that a relatively small proportion
of a brand’s SKUs drive most of the pricing impact.
21. 21
Copyright 2013 Bottom Line Analytics All rights reserved.
Step 2Analytics: Pricing Impact of SKU’s on brand
net profit
-1.5%
0.0%
0.9%
1.3%
1.3%
1.5%
2.3%
2.3%
3.3%
4.4%
4.5%
4.7%
5.6%
7.3%
9.7%
13.8%
15.9%
22.8%
-5.0% 0.0% 5.0% 10.0% 15.0% 20.0% 25.0%
SKU 1
SKU 2
SKU 3
SKU 4
SKU 5
SKU 6
SKU 7
SKU 8
SKU 9
SKU 10
SKU 11
SKU 12
SKU 13
SKU 14
SKU 15
SKU 16
SKU 17
SKU 18
Net Margin Yield Impact % Due to Pricing
Retail Sales Impact %
And similarly, a few SKUs tend to drive most of the profit impact due to
pricing
22. 22
Copyright 2013 Bottom Line Analytics All rights reserved.
Step 2Analytics: Price Elasticity with and without Full
Competitor Price Reciprocation by Market
-0.3%
-0.6%
-1.4%
-0.5%
-0.2%
-0.3%
-1.0%
-0.3%
-1.6%
-1.4%
-1.2%
-1.0%
-0.8%
-0.6%
-0.4%
-0.2%
0.0%
Market 1 Market 2 Market 3 Total
Price Elasticity: Change in Retail Unit Sales
Due to a 1% Increase in Retail Price
WO Reciprocation Full Reciprocation
Our price elasticity model looks at market/SKU level and with and
without competitor reciprocation of your price change
23. 23
Copyright 2013 Bottom Line Analytics All rights reserved.
Step 2Analytics: Impact of Housing Starts on Brand
Retail Sales
7,85,000
7,90,000
7,95,000
8,00,000
8,05,000
8,10,000
8,15,000
8,20,000
8,25,000
8,30,000
8,35,000
8,40,000
-15% -10% -5% 0% 5% 10% 15%
AnnualRetailSales
Change in Housing Starts
Housing Starts and Unit Sales
Unit Sales
Our Model is also able to isolate the impact of key macro-economic
drivers
24. 24
Copyright 2013 Bottom Line Analytics All rights reserved.
Step 2Analytics: Pricing Impact Matrix
• Quadrant Plots visually show which SKUs are the most critical in terms of
pricing
Bubble size represents net revenue
25. 25
Copyright 2013 Bottom Line Analytics All rights reserved.
Content
Forward, Objectives & Destination
The 6 Step Process
1. Situation Assessment & Set Yield Objectives
2. Do the Analytics
Own Brand Price Elasticity by SKU
Competitive Price Elasticities
Pass-Through Assessment
3. Assess Competitive Response Risk
4. Generate Scenarios & Plan Timing
5. Summarize 6 Principles
6. Optimize Profit Yield
26. 26
Copyright 2013 Bottom Line Analytics All rights reserved.
Step 3Assess Risk: Competitive Price Response
$-
$5.00
$10.00
$15.00
$20.00
$25.00
$30.00
$35.00
$40.00
09-01-2010 09-01-2011 09-01-2012
Your Brand Price
Competitive Price
Tracing competitor pricing behavior is critical to understanding the
likelihood of full/partial pricing reciprocation and the timing thereof
Full price reciprocation
occurs, but with a 5-8 week
lag.
27. 27
Copyright 2013 Bottom Line Analytics All rights reserved.
Content
Forward, Objectives & Destination
The 6 Step Process
1. Situation Assessment & Set Yield Objectives
2. Do the Analytics
Own Brand Price Elasticity by SKU
Competitive Price Elasticities
Pass-Through Assessment
3. Assess Competitive Response Risk
4. Generate Scenarios & Plan Timing
5. Summarize 6 Principles
6. Optimize Profit Yield
28. 28
Copyright 2013 Bottom Line Analytics All rights reserved.
Step 4 Generate Scenarios & Plan Timing: Generate
Pricing Scenarios
$0.86
$1.04
$1.28
$1.14
$1.42
$1.77
$-
$0.20
$0.40
$0.60
$0.80
$1.00
$1.20
$1.40
$1.60
$1.80
$2.00
Yield $Mil
Incremental Revenue & Profit Yields from Optimized
Price Scenarios
Revenue 3% Increase
Revenue 4% Increase
Revenue 5% Increase
Net Profit 3% Increase
Net Profit 4% Increase
Net Profit 5% Increase
The final pricing decision is from running various optimized pricing
scenarios and selecting that which achieves profit goals
29. 29
Copyright 2013 Bottom Line Analytics All rights reserved.
Step 4 Generate Scenarios & Plan Timing: Price
Elasticity Trends by Period
0.31
0.32
0.33
0.34
0.35
0.36
0.37
0.38
0.39
0.40
0.41
0.42
Total Elasticity (Absolute Value)
BLA’s modeling approach can identify points-in-time when price
sensitivities are highest and when is the strategically best time to
implement pricing increases or decreases
30. 30
Copyright 2013 Bottom Line Analytics All rights reserved.
Content
Forward, Objectives & Destination
The 6 Step Process
1. Situation Assessment & Set Yield Objectives
2. Do the Analytics
Own Brand Price Elasticity by SKU
Competitive Price Elasticities
Pass-Through Assessment
3. Assess Competitive Response Risk
4. Generate Scenarios & Plan Timing
5. Summarize 6 Principles
6. Optimize Profit Yield
31. 31
Copyright 2013 Bottom Line Analytics All rights reserved.
Step 5 Six Principles: Six Strategic
Principles for effective pricing
1. Setting a Sales & Profit Yield Target: What profit improvement is required? Note that,
with pricing, there is always a trade off with business growth.
2. Evaluating whether your overall price level is inalignment with competitors. Determine
which markets and channels where price misalignment might exist
3. Assessing the likelihood of retail price pass-through. Are retailers passing-through your
price changes to customers?
4. Assessing the likelihood of competitive reciprocation and their timing. Are competitors
matching price changes and is there a lag?
5. Estimating scenarios and optimizing the decision, by SKU. Estimating sales and profit
of different pricing scenarios and putting aplan into place., as well as the best time to
implement pricing decisions.
6. Selecting a pricing level and rate from alternatives and optimizing profit yield
Your
Price
Pass Through
Now
Pass Through in
6 Months
Market 1 4.4% 4.1% 4.5%
Market 2 7.0% 6.1% 8.2%
Market 3 8.1% 3.4% 7.3%
32. 32
Copyright 2013 Bottom Line Analytics All rights reserved.
Content
32
Forward, Objectives & Destination
The 6 Step Process
1. Situation Assessment & Set Yield Objectives
2. Do the Analytics
Own Brand Price Elasticity by SKU
Competitive Price Elasticities
Pass-Through Assessment
3. Assess Competitive Response Risk
4. Generate Scenarios & Plan Timing
5. Summarize 6 Principles
6. Optimize Profit Yield
33. 33
Copyright 2013 Bottom Line Analytics All rights reserved.
Step 6: Optimize: Optimized Pricing
A simulation tool which permits us to estimate different scenarios and
optimize profitability within specified targets and constraints, is essential
34. 34
Copyright 2013 Bottom Line Analytics All rights reserved.
Key Summary and Recommendations
Recommendations
• Plan pricing actions in advance.
• Set specific sales growth and profit objectives from pricing
• Evaluate current situation and pricing/sales trends. Identify where there might be
competitive pricing imbalances
• Due diligence by developing analytics for price elasticity
• Analyze prior pricing actions for competitive response, timing and the level of
price pass through fromprior pricing actions
• Simulation and optimization tools for evaluating different pricing scenarios
• Analytics to determine the best timing and level of pricing adjustments
• Select & optimize the pricing rate which will generate the desired sales & profit
yield
35. 35
Bangalore, IN Office:
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