This white paper discusses Discovery Patterns' analytic platform for unstructured big data that can discover expanded market sentiments. It defines key terms like prevailing and expanded market sentiment. It also describes Discovery Patterns' use of an ultra-granular industry context database called Industry Building Blocks, competitive analytic engines, and trend visualization engines to discover expanded market sentiments. Two case studies are presented that show how expanded market sentiments can provide insights into future market movements and identify investment opportunities when they diverge from prevailing sentiments.
Movement of Share Prices and Sectoral Analysis: A Reflection Through Interact...Waqas Tariq
Interaction in graphs gives the user with an advantage to analyze the data in greater depth. With the help of interactive graphics users can get better insight of the data in comparison to the static graphical tools. This paper introduces an interactive graphical tool consisting of two graphs, a line diagram complemented by a boxplot. The line diagram helps to understand how successive values of a variable are related to time and box plot can help the visual comparison of several such variables. Here the line diagram is used to visualize share prices of a company corresponding to a number of days and the boxplot displays the position of the Share price of all companies in a particular sector. An investor in share market needs to consider a number of factors before making any decision about investment. Some of the factors influencing the decision are the performance of the particular security in recent past, its position in terms of share price in its own sector. The graphical technique used in this software tool shall be helpful while making investment decision.
Competitor analysis involves three main steps:
1) Identifying competitors and grouping them into strategic groups based on similarities.
2) Gathering intelligence on competitors' performance, strategies, capabilities by examining public sources.
3) Analyzing competitors' objectives, assumptions, strategies, and resources to understand their strengths/weaknesses and how they may respond to strategic actions. Conducting a thorough competitor analysis helps firms make strategic decisions and predict competitive dynamics in their industry.
This white paper introduces the Industry Building Blocks (IBB) classification system, which divides the global economy into over 17,000 ultra-granular industries. IBB classifications are based on Michael Porter's five competitive forces and define industries based on unique combinations of competitors, buyers, suppliers, substitutes, and potential entrants. This creates a more granular system than traditional industry classifications. The paper argues that understanding industries at this level of granularity provides insights into companies, themes, and mergers that are not possible with less granular classifications. It illustrates how IBB can be used to analyze companies like Apple, which it divides into 50 distinct industries based on competitive dynamics.
Are good companies good stocks evidence from nairobi stock exchangeAlexander Decker
This document summarizes a research study that examined the relationship between company performance and stock performance on the Nairobi Stock Exchange. The study hypothesized that there would be a strong positive correlation between "good companies", defined as those with strong earnings and sales growth, and their stock performance. The researchers analyzed 32 listed companies using correlation analysis and descriptive statistics. The results indicated there is a strong positive correlation between good company performance and good stock performance on the NSE, supporting the hypothesis that good companies tend to be good stocks.
The document discusses strategic marketing forecasting and its relationship to market segment selection and firm performance. It presents four hypotheses:
1) Firms select market segments that have high market attractiveness and where the firm has strong business strengths.
2) A firm's forecasts are more accurate for targeted market segments than non-targeted segments.
3) A firm's forecast accuracy increases as the level of competition in a targeted market segment increases.
4) Superior long-term strategic forecasting accuracy is positively associated with better firm performance.
The document proposes testing these hypotheses using strategic forecasts from a marketing simulation to investigate the relationships between strategic forecasting, market segment selection, and firm performance.
This document discusses Porter's Five Forces model and its application to analyzing the competitive environment of Nokia's business. It provides an overview of each of the five competitive forces - threat of new entrants, threat of substitutes, bargaining power of suppliers, bargaining power of buyers, and competitive rivalry. It then gives a brief history of Nokia, describing its growth into a leading telecommunications equipment manufacturer with a strong brand presence globally and in local Indian markets.
Ajekwe et al. 2017 testing the random walk theory in the nigerian stock marketNicholas Adzor
This document analyzes whether stock returns in the Nigerian stock market follow a random walk distribution by testing the weak-form efficiency of the market. The study uses daily return data from 2010 to 2014 of the top 20 most active stocks on the Nigerian Stock Exchange. Autocorrelation and runs tests were performed and found that daily stock returns were randomly distributed, indicating the market is informationally efficient at the weak form level. This means past stock price information cannot be used to consistently earn abnormal returns. The study recommends further efforts to improve the market to attract more domestic and foreign investment.
This document provides an overview of marketing information systems and demand measurement. It discusses the key components of a marketing information system, including the internal records system, marketing intelligence system, marketing research system, and marketing decision support system. Various methods for estimating current and future market demand are also outlined, such as total market potential, area market potential, and analyzing past sales trends. The document also discusses how marketing information systems are currently practiced in Nepal, noting that while large multinational companies utilize such systems, they are not widely used overall in the country.
Movement of Share Prices and Sectoral Analysis: A Reflection Through Interact...Waqas Tariq
Interaction in graphs gives the user with an advantage to analyze the data in greater depth. With the help of interactive graphics users can get better insight of the data in comparison to the static graphical tools. This paper introduces an interactive graphical tool consisting of two graphs, a line diagram complemented by a boxplot. The line diagram helps to understand how successive values of a variable are related to time and box plot can help the visual comparison of several such variables. Here the line diagram is used to visualize share prices of a company corresponding to a number of days and the boxplot displays the position of the Share price of all companies in a particular sector. An investor in share market needs to consider a number of factors before making any decision about investment. Some of the factors influencing the decision are the performance of the particular security in recent past, its position in terms of share price in its own sector. The graphical technique used in this software tool shall be helpful while making investment decision.
Competitor analysis involves three main steps:
1) Identifying competitors and grouping them into strategic groups based on similarities.
2) Gathering intelligence on competitors' performance, strategies, capabilities by examining public sources.
3) Analyzing competitors' objectives, assumptions, strategies, and resources to understand their strengths/weaknesses and how they may respond to strategic actions. Conducting a thorough competitor analysis helps firms make strategic decisions and predict competitive dynamics in their industry.
This white paper introduces the Industry Building Blocks (IBB) classification system, which divides the global economy into over 17,000 ultra-granular industries. IBB classifications are based on Michael Porter's five competitive forces and define industries based on unique combinations of competitors, buyers, suppliers, substitutes, and potential entrants. This creates a more granular system than traditional industry classifications. The paper argues that understanding industries at this level of granularity provides insights into companies, themes, and mergers that are not possible with less granular classifications. It illustrates how IBB can be used to analyze companies like Apple, which it divides into 50 distinct industries based on competitive dynamics.
Are good companies good stocks evidence from nairobi stock exchangeAlexander Decker
This document summarizes a research study that examined the relationship between company performance and stock performance on the Nairobi Stock Exchange. The study hypothesized that there would be a strong positive correlation between "good companies", defined as those with strong earnings and sales growth, and their stock performance. The researchers analyzed 32 listed companies using correlation analysis and descriptive statistics. The results indicated there is a strong positive correlation between good company performance and good stock performance on the NSE, supporting the hypothesis that good companies tend to be good stocks.
The document discusses strategic marketing forecasting and its relationship to market segment selection and firm performance. It presents four hypotheses:
1) Firms select market segments that have high market attractiveness and where the firm has strong business strengths.
2) A firm's forecasts are more accurate for targeted market segments than non-targeted segments.
3) A firm's forecast accuracy increases as the level of competition in a targeted market segment increases.
4) Superior long-term strategic forecasting accuracy is positively associated with better firm performance.
The document proposes testing these hypotheses using strategic forecasts from a marketing simulation to investigate the relationships between strategic forecasting, market segment selection, and firm performance.
This document discusses Porter's Five Forces model and its application to analyzing the competitive environment of Nokia's business. It provides an overview of each of the five competitive forces - threat of new entrants, threat of substitutes, bargaining power of suppliers, bargaining power of buyers, and competitive rivalry. It then gives a brief history of Nokia, describing its growth into a leading telecommunications equipment manufacturer with a strong brand presence globally and in local Indian markets.
Ajekwe et al. 2017 testing the random walk theory in the nigerian stock marketNicholas Adzor
This document analyzes whether stock returns in the Nigerian stock market follow a random walk distribution by testing the weak-form efficiency of the market. The study uses daily return data from 2010 to 2014 of the top 20 most active stocks on the Nigerian Stock Exchange. Autocorrelation and runs tests were performed and found that daily stock returns were randomly distributed, indicating the market is informationally efficient at the weak form level. This means past stock price information cannot be used to consistently earn abnormal returns. The study recommends further efforts to improve the market to attract more domestic and foreign investment.
This document provides an overview of marketing information systems and demand measurement. It discusses the key components of a marketing information system, including the internal records system, marketing intelligence system, marketing research system, and marketing decision support system. Various methods for estimating current and future market demand are also outlined, such as total market potential, area market potential, and analyzing past sales trends. The document also discusses how marketing information systems are currently practiced in Nepal, noting that while large multinational companies utilize such systems, they are not widely used overall in the country.
Defining the organization's strategic direction RohanaDaulay
This document discusses tools for assessing a firm's strategic position, including Porter's five forces model. The five forces are: 1) the degree of rivalry among existing competitors, 2) the threat of new entrants, 3) the bargaining power of suppliers, 4) the bargaining power of buyers, and 5) the threat of substitute products. Each force is influenced by various factors such as the number and size of competitors, entry barriers, reliance on suppliers or customers, and availability of substitutes. Analyzing these five competitive forces helps identify opportunities and threats in a firm's external environment.
Trend-Setting Market Segmentation - A New Wave In The German Born Energy MarketSteven843Summers
1) Some energy companies in Germany are using innovative psychographic segmentation techniques to differentiate themselves in the increasingly competitive energy market. These techniques group customers based on lifestyle and values rather than just demographics.
2) One example is Sinus-Milieus which clusters people into groups based on shared life aspirations, values and lifestyles. Another is semometrie which evaluates how people rate words to quantify their values.
3) These segmentation models help energy companies understand customer needs better to develop tailored products, services and communications for different target segments. Companies using these innovative approaches are showing improved customer retention and market share gains.
This document summarizes a research paper that examines the relationship between product market strategies (innovator vs imitator), financing strategies (venture capital vs other), and product market outcomes. The authors find that innovator firms are more likely to obtain venture capital financing than imitator firms. They also find that venture capital is associated with a significantly faster time to market, especially for innovator firms. This suggests venture capital plays an important role in supporting innovative companies.
This document provides an overview of marketing opportunities analysis and planning. It discusses identifying market opportunities through factors like target market and market size. Sources of opportunities include short supply, innovation, weak competitors, and changes in customer wants. The document also covers marketing environment scanning, strategic planning at the corporate and business unit levels, and tools for assigning resources like the BCG matrix and GE matrix. It provides details on the nature and contents of marketing plans.
Porter's five forces analysis is a framework for industry analysis and business strategy development. It draws upon industrial organization economics to analyze five competitive forces that determine the attractiveness and therefore profitability of an industry. The five forces are: the threat of new entrants, the threat of substitutes, the bargaining power of suppliers, the bargaining power of buyers, and the intensity of rivalry among existing competitors. Analyzing these forces can help companies identify whether an industry is attractive to compete in from a profitability perspective.
This document provides an overview of a research project on IPO pricing and growth rates implied in offer prices. It contains an introduction that discusses challenges in valuing IPO firms and approaches used. It also includes a literature review, research methodology with objectives and data sources, and a summary of key findings. The research derives implied cash flow growth rates from 184 European IPOs priced using discounted cash flow models. It finds the average IPO firm is expected to grow cash flows by 33% annually, though actual post-IPO growth rates are slightly positive. Forecast errors are associated with factors like market-to-book ratio and leverage, and negatively impact long-term stock returns.
This document discusses factors that influence how multinational enterprises devise strategies, including industry structure and changes in the industry environment. It provides examples of how disruptive innovations in products, processes, and politics can dramatically change industry structure. Strategies must assess the impact of these changes and adjustments may be needed to elements like research and development, product positioning, and pricing. Understanding industry dynamics is key to developing effective strategies for long-term success.
This document summarizes a study that tests market efficiency using share repurchase announcements as events. The study uses an event study methodology to analyze stock price movements around repurchase announcement dates for 28 companies. It finds some evidence supporting market efficiency but also finds evidence of underreaction and selective response depending on companies' recent earnings growth. The results provide mixed evidence regarding the efficient market hypothesis.
This document summarizes a research paper that analyzes supermarket pricing strategies. The paper uses a unique store-level dataset to estimate a discrete choice model of pricing strategy selection as a static game. The model addresses three questions: 1) How do local demographics influence strategy choice? 2) Do some chains have advantages for certain strategies? 3) How do firms react to rival strategies? The key finding is that firms tend to choose strategies that match their rivals rather than differentiate, contradicting theories viewing pricing strategy as differentiation.
This document summarizes a journal article that examines how firms' marketing spending before initial public offerings (IPOs) can impact investor responses to the IPO. The summary is:
[1] The article analyzes how pre-IPO marketing spending may help reduce IPO underpricing and increase trading volume by providing information to investors and reducing uncertainty about firm value.
[2] Results from analyzing a large dataset of IPOs from 1996-2005 found that higher pre-IPO marketing spending is associated with lower underpricing and higher trading, though the effects vary depending on firm efficiency and market conditions.
[3] The findings suggest prudent investors could better identify promising IPOs by considering both pre
How Advanced Analytics Will Inform and Transform U.S. RetailCognizant
Macroeconomic trends, changing consumer behavior and increased data volumes – these trends are forcing retailers to devise mechanisms or seek out partners who can quickly transform raw data into bankable insights.
A study on the chain restaurants dynamic negotiation games of the optimizatio...ijcsit
In the era of meager profit, production costs often become an important factor affecting SMEs’ operating
conditions, and how to effectively reduce production costs has become an issue of in-depth consideration
for the business owners. Especially, the food and beverage (F&B) industry cannot accurately predict the
demand. It many cause demand forecast fall and excess or insufficient inventory pressure. Companies of
the F&B industry may be even unable to meet immediate customer needs. They are faced great challenges
in quick response and inventory pressure. This study carried out the product inventory model analysis of
the most recent year’s sales data of the fresh food materials for chain restaurants in a supply chain region
with raw material suppliers and demanders. Moreover, this study adopted the multi-agent dynamic strategy
game to establish the joint procurement decision model negotiation algorithm for analysis and verification
by simulation cases to achieve the design of dynamic negotiation optimization mechanism for the joint
procurement of food materials. Coupled with supply chain management 3C theory for food material
inventory management, we developed the optimization method for determining the order quantities of the
chain restaurants. For product demand forecast, we applied the commonality model, production and
delivery capacity model, and the model of consumption and replenishment based on market demand
changes in categorization and development. Moreover, with the existence of dependencies between product
demands as the demand forecast basis, we determined the appropriate inventory model accordingly.
This document summarizes key points from Chapter 10 on relationship marketing, information technology, and sales forecasting. It discusses how relationship marketing focuses on developing long-term links with customers for mutual benefit. Information technology enables collecting customer data and using it to target customers more efficiently. Sales forecasting methods include executive judgement, surveys, time series analysis, and market tests to estimate a company's expected sales over different periods.
This document provides an overview of identifying market segment, target, and position strategies. It begins by outlining the learning objectives, which are to understand market segmentation bases, the segmentation process, target market evaluation and selection, positioning strategies, and practices in Nepal. Several key aspects of segmentation are then defined and explained, including the concept, requirements, bases for consumer and industrial segmentation, the segmentation process, target market evaluation, selection, and developing positioning strategies. Specific variables, types, and the process are outlined for each topic.
The Market’s Reaction to Corporate Diversification: What Deserves More Punish...RyanMHolcomb
The document summarizes a paper that examines whether the market rewards or punishes corporate diversification. It begins by reviewing relevant investment theory and prior studies. Lang and Stulz (1993) found a negative relationship between diversification and Tobin's Q, but the authors aim to examine if this relationship holds in 2009 using different diversification measures. They hypothesize firms will be "punished more" for unrelated diversification. The document defines key terms like the Herfindahl-Hirschman Index and Tobin's Q that will be used in the analysis. Prior literature presented mixed views on the costs and benefits of diversification.
For answers go to
https://www.homeworksimple.com/downloads/busi-330-midterm/
BUSI 330 Midterm
1. Which of the following conditions are necessary for marketing to occur?
2. In a marketing context, a market refers to
3. The trade of things of value between buyer and seller so that each is better off after the trade is referred to as __________.
4. Which of the following products mentioned in Chapter 1 of the textbook failed in the marketplace?
5. The marketing manager's controllable factors—product, price, promotion, and place—that can be used to solve a marketing problem are referred to as the __________.
6. An organization that focuses its efforts on: (1) continuously collecting information about customers' needs; (2) sharing this information across departments; and (3) using it to create customer value is said to have a
7. The value to consumers that comes from making an item easy to purchase through the provision of credit cards and financial arrangements constitutes _________ utility.
8. Which of the following statements about marketing departments is most accurate?
This document summarizes sales promotion models. It begins by providing background on the growth of sales promotion modeling in both academia and industry. Descriptive models measure the effects of promotions, while prescriptive models make recommendations. The document reviews key phenomena and models for coupons, trade promotions, and retailer promotions. For coupons, redemption rates and incremental sales are critical to model. Regression is commonly used to predict redemption rates based on factors like distribution method, brand loyalty, and previous promotions. Descriptive models are needed to understand promotional effects before building prescriptive models that optimize profitability.
Marketline provides in-depth reports and analyses on specific companies and entire industries. Users can search for company or industry profiles to access summaries and full PDF reports that include overviews, market analyses, company strengths and weaknesses, forecasts, and more. Searches can be narrowed using filters, and reports vary in length from short summaries to longer, more detailed analyses covering topics such as market trends and leading competitors.
Michael Levie, Rattan Chadha, and Robin Chadha founded a new hotel chain called citizenM in 2008 with the goal of innovating the hotel industry and better serving frequent travelers. To develop their strategy, they analyzed the external environment using tools like Porter's five forces model and stakeholder analysis. Porter's five forces model examines the degree of industry rivalry, potential for new entrants, bargaining power of suppliers and buyers, and threat of substitutes. Stakeholder analysis identifies entities with interests in the firm, what they want from the company, and any claims they can make. Together, these tools help assess opportunities and threats in the marketplace to inform the firm's strategic direction.
Global Analytics: Text, Speech, Sentiment, and SenseSeth Grimes
The document discusses global analytics including text, speech, sentiment, and sense. It provides a history of analytics from the 1950s to present day, covering developments in extracting information from text, modeling patterns and insights, and connecting information. It also explores challenges and opportunities in natural language processing, including context, interaction, sentiment analysis, question answering, and cross-lingual implementation.
Apache Storm es un sistema de procesamiento de datos en tiempo real y tolerante a fallos que permite procesar grandes volúmenes de datos de forma distribuida. Storm consta de spouts que actúan como fuentes de datos, bolts que realizan el procesamiento, y topologías que definen el flujo de datos entre spouts y bolts. Las topologías de Storm se ejecutan de forma continua e indefinidamente en un cluster, procesando datos en tiempo real.
Defining the organization's strategic direction RohanaDaulay
This document discusses tools for assessing a firm's strategic position, including Porter's five forces model. The five forces are: 1) the degree of rivalry among existing competitors, 2) the threat of new entrants, 3) the bargaining power of suppliers, 4) the bargaining power of buyers, and 5) the threat of substitute products. Each force is influenced by various factors such as the number and size of competitors, entry barriers, reliance on suppliers or customers, and availability of substitutes. Analyzing these five competitive forces helps identify opportunities and threats in a firm's external environment.
Trend-Setting Market Segmentation - A New Wave In The German Born Energy MarketSteven843Summers
1) Some energy companies in Germany are using innovative psychographic segmentation techniques to differentiate themselves in the increasingly competitive energy market. These techniques group customers based on lifestyle and values rather than just demographics.
2) One example is Sinus-Milieus which clusters people into groups based on shared life aspirations, values and lifestyles. Another is semometrie which evaluates how people rate words to quantify their values.
3) These segmentation models help energy companies understand customer needs better to develop tailored products, services and communications for different target segments. Companies using these innovative approaches are showing improved customer retention and market share gains.
This document summarizes a research paper that examines the relationship between product market strategies (innovator vs imitator), financing strategies (venture capital vs other), and product market outcomes. The authors find that innovator firms are more likely to obtain venture capital financing than imitator firms. They also find that venture capital is associated with a significantly faster time to market, especially for innovator firms. This suggests venture capital plays an important role in supporting innovative companies.
This document provides an overview of marketing opportunities analysis and planning. It discusses identifying market opportunities through factors like target market and market size. Sources of opportunities include short supply, innovation, weak competitors, and changes in customer wants. The document also covers marketing environment scanning, strategic planning at the corporate and business unit levels, and tools for assigning resources like the BCG matrix and GE matrix. It provides details on the nature and contents of marketing plans.
Porter's five forces analysis is a framework for industry analysis and business strategy development. It draws upon industrial organization economics to analyze five competitive forces that determine the attractiveness and therefore profitability of an industry. The five forces are: the threat of new entrants, the threat of substitutes, the bargaining power of suppliers, the bargaining power of buyers, and the intensity of rivalry among existing competitors. Analyzing these forces can help companies identify whether an industry is attractive to compete in from a profitability perspective.
This document provides an overview of a research project on IPO pricing and growth rates implied in offer prices. It contains an introduction that discusses challenges in valuing IPO firms and approaches used. It also includes a literature review, research methodology with objectives and data sources, and a summary of key findings. The research derives implied cash flow growth rates from 184 European IPOs priced using discounted cash flow models. It finds the average IPO firm is expected to grow cash flows by 33% annually, though actual post-IPO growth rates are slightly positive. Forecast errors are associated with factors like market-to-book ratio and leverage, and negatively impact long-term stock returns.
This document discusses factors that influence how multinational enterprises devise strategies, including industry structure and changes in the industry environment. It provides examples of how disruptive innovations in products, processes, and politics can dramatically change industry structure. Strategies must assess the impact of these changes and adjustments may be needed to elements like research and development, product positioning, and pricing. Understanding industry dynamics is key to developing effective strategies for long-term success.
This document summarizes a study that tests market efficiency using share repurchase announcements as events. The study uses an event study methodology to analyze stock price movements around repurchase announcement dates for 28 companies. It finds some evidence supporting market efficiency but also finds evidence of underreaction and selective response depending on companies' recent earnings growth. The results provide mixed evidence regarding the efficient market hypothesis.
This document summarizes a research paper that analyzes supermarket pricing strategies. The paper uses a unique store-level dataset to estimate a discrete choice model of pricing strategy selection as a static game. The model addresses three questions: 1) How do local demographics influence strategy choice? 2) Do some chains have advantages for certain strategies? 3) How do firms react to rival strategies? The key finding is that firms tend to choose strategies that match their rivals rather than differentiate, contradicting theories viewing pricing strategy as differentiation.
This document summarizes a journal article that examines how firms' marketing spending before initial public offerings (IPOs) can impact investor responses to the IPO. The summary is:
[1] The article analyzes how pre-IPO marketing spending may help reduce IPO underpricing and increase trading volume by providing information to investors and reducing uncertainty about firm value.
[2] Results from analyzing a large dataset of IPOs from 1996-2005 found that higher pre-IPO marketing spending is associated with lower underpricing and higher trading, though the effects vary depending on firm efficiency and market conditions.
[3] The findings suggest prudent investors could better identify promising IPOs by considering both pre
How Advanced Analytics Will Inform and Transform U.S. RetailCognizant
Macroeconomic trends, changing consumer behavior and increased data volumes – these trends are forcing retailers to devise mechanisms or seek out partners who can quickly transform raw data into bankable insights.
A study on the chain restaurants dynamic negotiation games of the optimizatio...ijcsit
In the era of meager profit, production costs often become an important factor affecting SMEs’ operating
conditions, and how to effectively reduce production costs has become an issue of in-depth consideration
for the business owners. Especially, the food and beverage (F&B) industry cannot accurately predict the
demand. It many cause demand forecast fall and excess or insufficient inventory pressure. Companies of
the F&B industry may be even unable to meet immediate customer needs. They are faced great challenges
in quick response and inventory pressure. This study carried out the product inventory model analysis of
the most recent year’s sales data of the fresh food materials for chain restaurants in a supply chain region
with raw material suppliers and demanders. Moreover, this study adopted the multi-agent dynamic strategy
game to establish the joint procurement decision model negotiation algorithm for analysis and verification
by simulation cases to achieve the design of dynamic negotiation optimization mechanism for the joint
procurement of food materials. Coupled with supply chain management 3C theory for food material
inventory management, we developed the optimization method for determining the order quantities of the
chain restaurants. For product demand forecast, we applied the commonality model, production and
delivery capacity model, and the model of consumption and replenishment based on market demand
changes in categorization and development. Moreover, with the existence of dependencies between product
demands as the demand forecast basis, we determined the appropriate inventory model accordingly.
This document summarizes key points from Chapter 10 on relationship marketing, information technology, and sales forecasting. It discusses how relationship marketing focuses on developing long-term links with customers for mutual benefit. Information technology enables collecting customer data and using it to target customers more efficiently. Sales forecasting methods include executive judgement, surveys, time series analysis, and market tests to estimate a company's expected sales over different periods.
This document provides an overview of identifying market segment, target, and position strategies. It begins by outlining the learning objectives, which are to understand market segmentation bases, the segmentation process, target market evaluation and selection, positioning strategies, and practices in Nepal. Several key aspects of segmentation are then defined and explained, including the concept, requirements, bases for consumer and industrial segmentation, the segmentation process, target market evaluation, selection, and developing positioning strategies. Specific variables, types, and the process are outlined for each topic.
The Market’s Reaction to Corporate Diversification: What Deserves More Punish...RyanMHolcomb
The document summarizes a paper that examines whether the market rewards or punishes corporate diversification. It begins by reviewing relevant investment theory and prior studies. Lang and Stulz (1993) found a negative relationship between diversification and Tobin's Q, but the authors aim to examine if this relationship holds in 2009 using different diversification measures. They hypothesize firms will be "punished more" for unrelated diversification. The document defines key terms like the Herfindahl-Hirschman Index and Tobin's Q that will be used in the analysis. Prior literature presented mixed views on the costs and benefits of diversification.
For answers go to
https://www.homeworksimple.com/downloads/busi-330-midterm/
BUSI 330 Midterm
1. Which of the following conditions are necessary for marketing to occur?
2. In a marketing context, a market refers to
3. The trade of things of value between buyer and seller so that each is better off after the trade is referred to as __________.
4. Which of the following products mentioned in Chapter 1 of the textbook failed in the marketplace?
5. The marketing manager's controllable factors—product, price, promotion, and place—that can be used to solve a marketing problem are referred to as the __________.
6. An organization that focuses its efforts on: (1) continuously collecting information about customers' needs; (2) sharing this information across departments; and (3) using it to create customer value is said to have a
7. The value to consumers that comes from making an item easy to purchase through the provision of credit cards and financial arrangements constitutes _________ utility.
8. Which of the following statements about marketing departments is most accurate?
This document summarizes sales promotion models. It begins by providing background on the growth of sales promotion modeling in both academia and industry. Descriptive models measure the effects of promotions, while prescriptive models make recommendations. The document reviews key phenomena and models for coupons, trade promotions, and retailer promotions. For coupons, redemption rates and incremental sales are critical to model. Regression is commonly used to predict redemption rates based on factors like distribution method, brand loyalty, and previous promotions. Descriptive models are needed to understand promotional effects before building prescriptive models that optimize profitability.
Marketline provides in-depth reports and analyses on specific companies and entire industries. Users can search for company or industry profiles to access summaries and full PDF reports that include overviews, market analyses, company strengths and weaknesses, forecasts, and more. Searches can be narrowed using filters, and reports vary in length from short summaries to longer, more detailed analyses covering topics such as market trends and leading competitors.
Michael Levie, Rattan Chadha, and Robin Chadha founded a new hotel chain called citizenM in 2008 with the goal of innovating the hotel industry and better serving frequent travelers. To develop their strategy, they analyzed the external environment using tools like Porter's five forces model and stakeholder analysis. Porter's five forces model examines the degree of industry rivalry, potential for new entrants, bargaining power of suppliers and buyers, and threat of substitutes. Stakeholder analysis identifies entities with interests in the firm, what they want from the company, and any claims they can make. Together, these tools help assess opportunities and threats in the marketplace to inform the firm's strategic direction.
Global Analytics: Text, Speech, Sentiment, and SenseSeth Grimes
The document discusses global analytics including text, speech, sentiment, and sense. It provides a history of analytics from the 1950s to present day, covering developments in extracting information from text, modeling patterns and insights, and connecting information. It also explores challenges and opportunities in natural language processing, including context, interaction, sentiment analysis, question answering, and cross-lingual implementation.
Apache Storm es un sistema de procesamiento de datos en tiempo real y tolerante a fallos que permite procesar grandes volúmenes de datos de forma distribuida. Storm consta de spouts que actúan como fuentes de datos, bolts que realizan el procesamiento, y topologías que definen el flujo de datos entre spouts y bolts. Las topologías de Storm se ejecutan de forma continua e indefinidamente en un cluster, procesando datos en tiempo real.
Ai based character recognition and speech synthesisAnkita Jadhao
The document discusses an AI seminar on character recognition and speech synthesis. It describes how optical character recognition can convert scanned images or text into machine code, and speech synthesis can artificially produce human speech. It provides details on preprocessing techniques for character recognition, such as de-noising and binarization of images. It also explains the processes of text analysis, phoneme generation and prosody generation used in speech synthesis engines.
Introduction to Predictive Analytics with IBM SPSS. Predictive analytics helps organizations use their data to make better decisions by allowing them to draw reliable, data-driven conclusions about current conditions and future events.
Predictive analytics encompasses a variety of techniques such as Statistics, Game theory and Data mining to do this analysis,
and make these predictions.
So by deploying predictive analytics, organizations are addressing their business issues proactively to get the best outcomes.
Discussion Forum data, sourced from sites like Reddit and other social media platforms, as well other sources of textual information, provides tremendous opportunity for insight and innovation. This presentation focuses on how an analysis of unstructured data can be used to innovate in Life/Health Science organizations
1) The Industry Graph provides contextual information about competitive industries to enhance searching, discovery, and decision-making. It maps over 16,000 granular industries and the relationships between competitors, suppliers, buyers, and other forces within each industry.
2) The Industry Graph allows users to search for a company like Toyota within a specific industry context, such as the transportation energy industry. This provides relevant competitive intelligence instead of broad results across all industries.
3) Discovery algorithms analyze news, social media, and other unstructured data to surface interesting trends and changes within industries. This helps users gain real-time situational awareness without manually searching large amounts of data.
Dissertation Part 2 - Academic DiscussionWill Scott
This document provides a summary and analysis of IBM PowerNet's business strategy of partnering with small independent software vendors (ISVs) to develop and sell software and hardware solutions. The strategy targets "white space" customers who have not purchased from IBM in the past 3 years. While revenue increased steadily until 2009, it declined that year due in part to the economic downturn and lack of marketing materials. IBM PowerNet produced new marketing materials, aiming to continue the strategy of engaging new customers and ISVs. The document evaluates whether this strategy is sustainable by analyzing academic literature on strategic capabilities, value-added business partnerships, external market analysis, threats to sustainability, and marketing strategies. It assesses factors like pricing, relationships, value creation
This document discusses market segmentation and reviews relevant literature. It begins by defining market segmentation as dividing a market into smaller groups with distinct characteristics and needs. The key bases for segmentation are identified as geographic, demographic, psychographic, and behavioral.
The document then reviews several studies and their findings. One study found that customers are willing to pay more for products tailored to their specific needs. Another argued that any proposed segmentation should meet tests for measurability, accessibility, stability, and substantiality. A third proposed segmenting based on factors causally related to future purchasing behavior.
The benefits of market segmentation are then discussed. Segmentation allows tailoring marketing mixes to specific target segments, which can increase profits and reduce competition.
The Boston Consulting Group growth-share matrix is a management tool used to evaluate a company's portfolio of business units. It classifies products based on their relative market share and the market growth they operate in. This assigns them to categories of stars, cash cows, question marks, and dogs. Stars have high share in growing markets, while cash cows have high share in stagnant markets. Question marks have low share in growing markets, and dogs have low share in stagnant markets. The matrix is used to determine resource allocation and strategy for different products.
This document discusses industry and competitor analysis. It defines industry analysis as focusing on the potential of an industry by examining its actual and potential size, growth, structure, setting, attractiveness, performance, key success factors, and cost structure. It also discusses Porter's five forces model for analyzing competitive forces in an industry. Competitor analysis examines competitors' strategies, strengths/weaknesses, objectives, and response profiles. Porter's generic competitive strategies of cost leadership, differentiation, and focus are also summarized. The importance of competitive intelligence in understanding competitors' short and long-term strategies, organization, culture, and cost structure is highlighted.
This document discusses different market structures including perfect competition, monopoly, monopolistic competition, and oligopoly. It begins by introducing the Structure-Conduct-Performance model which links market structure to firm behavior and performance. The key aspects of market structure are the number of firms, product differentiation, barriers to entry/exit, resource mobility, and information availability. Perfect competition is characterized by many small firms, homogeneous products, free entry and exit, and perfect information. A monopoly involves a single seller of unique products with high barriers to entry. The document explores firm behavior and equilibrium under different market structures.
A Business Market Segmentation Procedure For Product PlanningBrittany Brown
This document outlines a market segmentation procedure for business product planning and marketing. It begins by discussing the need for segmentation in business markets to identify groups of customers with distinct needs. It then reviews past research on business market segmentation, noting shortcomings like a lack of agreement on criteria and difficulties in data collection. The document proposes criteria for an effective segmentation model and outlines a multi-stage procedure using past purchase behavior to identify early adopters. It applies this procedure to the US information processing market as an example.
Competitive Intelligence Analysis Tools For Economic DevelopmemtIntelegia Group
This document provides an overview of 9 competitive intelligence analysis tools: SWOT analysis, TOWS analysis, Boston Consulting Group matrix, competitor profile, GE McKinsey screen matrix, STEEP analysis, Porter's five forces model, product life cycle analysis, and SPACE matrix. For each tool, a brief description is given of its objective and the types of information needed to conduct the analysis. Tips are provided at the end on applying the tools effectively and developing competitive intelligence skills.
The document discusses the GE Nine Cell Matrix, which is a portfolio analysis tool developed by McKinsey & Company for General Electric in the 1970s. It evaluates business units based on their market attractiveness and business strength. Market attractiveness depends on factors like market size, growth rate, and profit margins. Business strength is assessed by metrics such as market share, brand strength, and competitiveness. The matrix plots business units into nine cells that indicate whether a unit should be invested in, maintained, or harvested. It provides a more nuanced analysis than the Boston Consulting Group matrix.
Business portfolio analysis is a technique that analyzes a company's different business units or products in the same way an investment portfolio is analyzed. It uses tools like the BCG matrix and GE nine-cell matrix to evaluate business units based on factors such as market share and market growth. This helps companies allocate resources more effectively by identifying strong business units in attractive markets that should receive more investment, and weak units in unattractive markets that may need to be improved or divested. While portfolio analysis provides a systematic approach and encourages strategic evaluation, the analyses can oversimplify strategies and produce static snapshots that may not account for changing market conditions.
Presents frameworks and methodology for building those segments of a company which are vital to long term sustainability. The systematic process of identifying business strategy, marketing, and a mission statements which articulates the developed value proposition. This framework enables companies to build a brand that helps target the identified market.
Porter’s Five Force Model:
THREAT TO NEW ENTRANTS
THREAT TO SUBSTITUTE
RIVALRY AMONG EXISTING FIRMS
BARGAINING POWER OF BUYERS & SUPPLIERS
COMPETITIVE CHANGES DURING INDUSTRY LIFE CYCLE:
Industry lifecycle comprises four stages including fragmentation, growth, maturity and decline. An understanding of the industry lifecycle can help competing companies survive during periods of transition.
The Fragmentation or birth stage of the organization is dominated by the entrepreneur as it is based on concentration and niche marketing.
The growth is carried out with the help of integration of both horizontal & vertical. Here focus is given more on the functional management.
In maturity stage the industry focuses on concentration and diversification. The centers like: profit, investment etc. are established in this stage.
Decline stage is followed by retrenchment strategy. The stage after decline I death which is known as liquidation or bankruptcy.
Strategic Group:
A strategic group consists of those rival firms with similar competitive approaches and positions in the market. The identification of strategic groups within an industry enables the competitive structure of the industry to be redefined to compare strategies of various competitors for similarities and differences.22-Jan-2015
Strategic groups are sets of firms that follow similar strategies to one another (Hunt, 1972; Short et al., 2007). More specifically, a strategic group consists of a set of industry competitors that have similar characteristics to one another but differ in important ways from the members of other groups.
A simple example of a strategic group would be the fast-food restaurant chains in the foodservice industry. Other strategic groups in this industry include fine-dining restaurants, cafes, and family restaurants among many others.30-Sept-2022
Strategic Group Analysis:
Strategic group analysis is used to examine the competitive environment and the rivalry among competitors within an industry.
It helps,
Identify the strategic direction of the direct rivals in the industry. This will in turn help shape the strategic moves of your own organization.
Identify the strategies used by companies in other strategic groups. In certain difficult situations, your organization can use these alternative paths to success as solutions.
Discover untapped opportunities in the industry by revealing the gaps (i.e. disclose areas where there is limited or no competition)
Mapping Strategic Groups:
The strategy group map is used as the primary tool in the analysis of strategic groups. It helps visualize and analyse the competitive positions of industry rivals based on variables (common characteristics) relevant to their strategic significance.
This market feasibility report summarizes key elements to include when conducting a feasibility study for a new innovation project. It describes analyzing the current market, industry trends, competition, sales projections, and potential customers. The report emphasizes using factual information from research and cites sources to justify assumptions. Conducting in-depth analysis of these components can help determine if a project is both needed in the market and competitive against alternatives.
A latest report on home security solutions market by Allied Market Research provides a stellar copy of the overall performance of different home security products worldwide.
Read more at : https://www.alliedmarketresearch.com/home-security-solutions-market
This document provides guidance on analyzing competitors. It discusses the objectives of competitor analysis as understanding competitors' strengths/weaknesses and likely strategies/responses. Key aspects covered include:
1. Identifying direct competitors in the same product/customer markets using substitution-in-use analysis and purchase data.
2. Also identifying indirect competitors serving similar customer needs through different resources, and potential competitors with the capabilities to enter the market.
3. Gathering information on competitors' strategies, costs, resources to assess vulnerabilities and predict actions/responses to strategic moves.
The document provides a framework for systematically analyzing competitors at different levels to inform strategic decision making.
Supply Market Analysis for a Competitive Advantage Davi.docxcalvins9
The document provides a detailed guide for conducting a supply market analysis to gain competitive advantage. It outlines key elements that should be examined, including developing a commodity profile, determining cost structures, researching suppliers, and identifying key market indicators. Conducting primary research is emphasized as essential for developing unique insights. Tracking market indicators over time allows anticipating trends to improve sourcing strategies and reduce supply risk. A thorough supply market analysis can help procurement decisions by providing intelligence on market dynamics, the supply base, and optimal times to purchase.
Military Lighting Market Competitive Research And Precise Outlook 2023 To 2030subishsam
The research firm Contrive Datum Insights has just recently added to its database a report with the heading global Military Lighting Market. Both primary and secondary research methodologies have been utilised in order to conduct an analysis of the worldwide Military Lighting Market. In order to provide a comprehensive comprehension of the topic at hand, it has been summed up using appropriate and accurate market insights. According to Contrive Datum Insights, this worldwide comprehensive report is broken up into several categories in order to present the data in a way that is understandable, succinct, and presented in a professional manner.On the base and by the people who work there, military lighting structures and equipment are used.
GBS Sample 1Name_ID_GBS Task 1.pdf1 P a g e .docxshericehewat
GBS Sample 1/Name_ID_GBS Task 1.pdf
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Global Business Strategy
Level 7 - Unit 7.2
International Business
Environment Analysis.
Report – Activity 1
Revised 18 Sept 2015
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Contents
Section Details Page
Activity 1
Introduction Company profile 4
1a International business environment Analysis Techniques 4 – 7
1b Analysis of the micro and macro of Marks & Spencer‟s PLC 7 – 8
1c The impact of international business environment on Marks & Spencer‟s 8 – 9
1d What does globalization mean for Marks & Spencer‟s? 9 – 10
1a (2) What is the extent of globalization on organizations? 10
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1b (2) Operating structures different organizations in international markets. 10 – 11
References 12
Introduction
Marks & Spencer PLC was founded in 1884.It has grown from a single market stall to an
international multi-channel retailer. They sell stylish, high quality value clothing and home
products as well as food, responsibly sourced from around 3,000 suppliers globally. Their
portfolio covers general merchandise, food, international and multi-channel across 54
international territories with nearly 86,000 employees.(Marks and Spencer, 2014).
International business environment Analysis Techniques
Business environment is the combination of internal and external factors that influence a
company‟s operating situation and the overall business. It is both Micro and Macro in nature.
Micro or internal factors are controllable and could include management style, organizational
culture, mission and value statement. Whereas Macro or external factors are uncontrollable these
http://www.businessdictionary.com/definition/combination.html
4 | P a g e
factors are often both dynamic & complex. Business environment factors can include new
policies, procedures, government changes, improvements in technology, social and economic
trends(Nonaka, I., and Takeuchi, H, 1995).The reason for analyzing the business environment is
to highlight opportunities and threats. Knowing the opportunities and threats to the business
allows the company to set a strong business strategy and understand better where to invest,
expand, diversify and downscale. There are a number of different tools we can use to analyse
both the Micro & Marco factors within a business.
Micro can be analysed with Porters 5 forces model.Porter identified that there are 5 key
forces that influence business that needed to be analysed in order to develop a competitive
advantage (Porter, 1985). These forces are supplier power, buyer power, competitive rivalry,
threat of substitution &threat of new entryand are used for strategic industry analysis. The
positives of using this technique to analyze is that it looks at a wider range of competitors and it
forces the business to look externally. However this is a relatively old model that may not be
suitable ...
This document provides an overview of capital market research, which examines the impact of financial accounting and disclosure decisions on share prices and returns. Capital market research analyzes statistical relationships between financial information and share price movements to assess how investors react in aggregate to new information. It relies on the assumption that markets are semi-strong form efficient and quickly reflect all public information in security prices. The research is useful for understanding how alternative accounting methods and disclosures influence investment decisions.
Similar to Investing_With_Expanded_Market_Sentiments_02 (20)
Reactions of capital markets to financial reporting inggris
Investing_With_Expanded_Market_Sentiments_02
1. Client application white paper
March 2016
Unstructured Big Data: Investing advantages with
expanded market sentiments
James J Andrus
2. Discovery Patterns
Expanded Market Sentiments
2
Contents
1. Executive Summary
2. Some Baseline Terms
3. The Value & Process of Discovering Expanded Market Sentiments
4. Case Study 2009: Sentiment Mismatch About Nokia Smartphones (300 day)
5. Case Study 2015: Sentiment Mismatch of iPhone Sales Deceleration (30 day)
6. Implications for Analysts, Portfolio Managers and Strategic Planners
Executive Summary
Discovery Patterns [DP] has created an analytic platform for unstructured big data that amplifies
the worldwide market insights of human analysts, investors, competitive intelligence professionals
and strategic planners. One of the outputs of DP analytics are Expanded Market Sentiments.
These expanded sentiment discoveries can be used to anticipate actual market movements
ahead of prevailing market sentiments. Two case studies will illustrate the process of creating
expanded market sentiments, then discovering mismatches and investing opportunities based
prevailing market sentiments. This paper demonstrates the power of DP analytics for superior
investor/analyst decisions in the era of big data.
Important Terms Used in This Paper
Prevailing Market Sentiment
“Market sentiment is the [current] feeling or tone of a market, or its crowd psychology, as revealed
through the activity and price movement of the securities traded in that market. For example,
rising prices would indicate a bullish market sentiment, while falling prices would indicate a
bearish market sentiment.1”
Expanded Market Sentiment
Is the revealing of an expanded crowd psychology of the
feeling or tone of a marketplace, if investors had a
greater scope and depth of competitive insights.
Discovery Patterns derives these deeper insights from
innovative context, analytic and visualization engines. In
general, expanded market sentiments lead prevailing
market sentiments. Over time, market sentiments
approach expanded market sentiments.
1
What is Market Sentiment? Investopedia
3. Discovery Patterns
Expanded Market Sentiments
3
Market Theme
A general definition of a theme is a “unifying or dominant idea, motif, etc., as in a work of art.”2
McKinsey further defines a theme as opportunities created by long term structural trends.3 In
regard to markets, a theme is a unifying idea about a market premise that needs to be maintained
or proved to be relevant over time. Themes are rich grounds for early trend discoveries and
market sentiment mismatch opportunities.
Structured versus Unstructured Data
Structured data are those information streams that are characterized by numbers associated with
quantified market events – as in revenues, earnings, stock prices, future estimates, market
shares, etc. The more difficult data sibling of structured data are unstructured data, where it is
estimated that 80-90% of all potentially usable business information may originate4. Unstructured
data is characterized by news articles, blogs, texts messages, social media, reports or any other
communication that is typical of words and the imprecise ways that humans use them. As such,
unstructured big data has the potential to capture the “crowd psychology” of a marketplace.
The Value & Process of Discovering Expanded Market Sentiments
Expanded market sentiments can offer insights into future market directions. And mismatches
between prevailing market sentiment and expanded market sentiment can be investment
opportunities. Expanded sentiments can be compared with an elevated or even satellite vantage
point. More visibility in a useful context is achieved versus a ground level vantage. With
expanded market sentiment discoveries, one simply sees more in a useful competitive context
over short and long periods of time.
Discovery Patterns employees three analytic engines that enable expanded market sentiments
to be discovered and tracked:
1. Industry Context Engines: The ultra-granular Industry Building Blocks [IBB] database
2. Competitive Analytic Engines: Big data analytics based on market force relationships
3. Trend Visualization Engines: Animated visualizations of emerging and dissipating market
forces among each other over time.
2
Theme definition, (Dictionary.Reference.com, March 2016)
3
V. Berube, S. Ghai and J. Tetrault, From Indexing to insights: The rise of thematic investing, (McKinsey, Dec. 2014)
4 Big Content: The Unstructured Side of Big Data, (Gartner, May 1, 2013)
4. Discovery Patterns
Expanded Market Sentiments
4
Ultra-Granular Industry Context Engine
Industry Building Blocks [IBB] is the world’s most
granular industry classification system, created by
Alan S. Michaels who was a strategic planner for
technology, banking and insurance companies. It
was Alan’s goal to focus on granular market
competiveness to create a superior classification,
different from correlated stock classifications. At
present, there are over 17,000 IBB industries in the
IBB database, which is more than ten times the
number of industries in SIC and NAICS industrial
classification codes.
IBB classifications are based on Michael Porter’s five archetypical competitive forces5:
competitors, buyers, suppliers, substitutes, potential market entrants and all the relationships
among these forces. The controlling Porter and IBB rationale is that each industry has a unique
competitive dynamic among these archetypical industry forces. If one or more of these five forces
is significantly different from a related industry, then IBB will define them as different industries in
its classification system6. Winning and losing companies
and products are better understood in this basic
competitive context. Market trends and evolving industries
can be better identified and projected into the future once
these complex competitive interactions are identified and
tracked. Unrecognized industries can also be identified
early within IBB. Stock and asset values are coupled with
these changing archetypical forces and industry
classifications.
IBB arose out of the needs of corporate planning for multinationals. Granular IBB industries or
business units are resourced and planned based on their competitive standings and likely
financial performances. Within a company all these financial measures are visible and therefore
a truer measure of market performance. Even though a company may not use IBB classifications
for its corporate planning, these companies are nonetheless using classifications that approach
IBB in granularity versus standard industrial classifications. Public financial markets crave this
level of detail for market evaluation.
For example, infant diapers is a different industry from adult diapers; cloud computing represents
dozens of unique IBB industries; Alphabet now competes in 82 industries; and GE competes in
203 industries. IBB also delivers insight complexity among all the interactions of market forces in
5 Michael E. Porter, Competitive Strategy, (Free Press, New York, 1980)
6
Alan S Michaels, IBB Overview Video, (February 18, 2015)
Figure 1: The Five Porter Forces Defining IBB Industries
Figure 2: Force Complexity within Industries
5. Discovery Patterns
Expanded Market Sentiments
5
each industry and among multiple industries. For example, the recent merger of Dell with EMC
created a portfolio of 278 combined IBB industries in which only 31 of those industries were initial
overlaps, a smaller number than many might have predicted.
The sum of all a company’s IBB industries, with their embedded competitive force relationships,
defines the overall expected performance of a company. These forces govern shareholder
returns because they influence prices, quantities sold, costs, investment, and the riskiness of
firms in an industry. Theses force variables, in turn, are the building blocks for the value driver
determinants of shareholder value. IBB categories enable granular industry comparisons
between companies, where overlapping industries serve as points of comparison, and non-
overlapping IBB industries serve as points of contrast.
Table 1 gives an example of Apple IBB ultra-granularity. Today, Apple is now composed of 50
IBB industries, 45 of which are detailed in Table 1. Each of these industries composing Apple
has a unique combination of Porter market forces characteristics to make it a distinct IBB industry.
Specifically, each of these industries has a unique combination of competitors, buyers, suppliers,
substitutes and entrants as defined by Porter. This specification yields the most granular and
unique industries in accordance with Porter’s five archetypical competitive forces. As such, each
Industry is unique, without overlaps with other industries. Additionally, each IBB industry can be
modular inputs for combinations like market themes and electronic traded funds (ETF’s).
The overall market performance of Apple is the sum of all Apple’s market performances in each
IBB industry7. iPhone® [IBB Industry = Smart Phones] may be important to Apple, yet there are
49 other Apple lines of business (industries) that also define the cumulative market performance
of Apple. IBB industry context engine enables a realistic capture and reduction of true market
complexity that is often unrealized in traditional market classifications.
7
Alfred Rappaport, Creating Shareholder Value, (The Free Press, 1986)
6. Discovery Patterns
Expanded Market Sentiments
6
Table 1: Sample IBB Industries Defining Apple – (45 of 50, March 2016)
# IBB Industry Name
1 Payments / Mobile Payment Services & Digital Wallets
2 Cameras / 3D Embedded Camera Manufacturing
3 Digital Music Players / Portable Media Player Manufacturing
4 MP3 Player / Portable CD + MP3 Player Manufacturing
5 DMPs / Digital Media Players & Digital Media Receiver Network Device Manufacturing
6 Smartwatches / Smart Watch & Computerized Wristwatch Manufacturing
7 Social Media / Social Gaming Services
8 Digital Media Services
9 Publishing / Digital Publishing Software
10 Collaboration Software & Workgroup Team Collaboration Software & Services
11 Email / Internet Services for Email, Contacts & Calendars
12 Health Data Platforms
13 Hardware Servers / Rackmount Server & Rack Server Manufacturing
14 Routers / WLAN Routers & Wireless Local Area Network Router Manufacturing
15 Displays / Flat Display & Flat Panel Screen Monitors
16 Displays / HDTV - High-Definition TV Monitors & Flat Panels
17 Displays / LCD - Liquid Crystal Display Computer LCD Monitors
18 Cables / Lightning to USB Cable Manufacturing
19 Notebooks / Business Laptop & Notebook Computer Manufacturing
20 Notebooks / Consumer Laptop & Notebook Computer Manufacturing
21 PCs / All In One PCs & All-In-One PC Manufacturing
22 PCs / PC Desktop Stand-Alone System Units
23 Tablets / Apple iOS iPad Tablet Manufacturing
24 Ink / Ink-jet Printer & All-In-One Inkjet Replacement Cartridges
25 SANs / Storage Area Networks Infrastructure Products
26 Web Browser Software
27 Video Conferencing & Web Conferencing Software & Services
28 Business Intelligence: Analytics - Big Data Analytics & Social Media Analytics Software
29 PC Databases
30 Content Authoring Tools
31 SDK / iOS Software Developers Kits
32 Collaboration / Cloud File Sync & File Sharing, Storage & Collaboration Solutions
33 Digital Media Player Application Software
34 Music / Consumer Music Creation Software
35 DVD Application Software
36 Photo Editing Software / Digital Photo Application Software
37 Video Software / Digital Video Editing Software
38 Auto Operating System, Car OS & Commercial Vehicle Operating System Software
39 PC OS / Personal Computer Operating Systems Software
40 Portable Intelligent Device OS Software for Handheld Computer, Cell Phone, Tablet & Smart Phone
41 Tablet PC Operation System Software
42 TV & Home Entertainment Media Center Operating Systems Software
43 Phones / Smart Phones - Smartphone Manufacturing
44 Internet - Music Downloads Subscription Services
45 Internet Music / Digital Music Service & Personalized Music Listening 95
Report Date = March 21, 2016
7. Discovery Patterns
Expanded Market Sentiments
7
DP Analytic Engine
DP Analytic engines sift through unstructured big data streams, seeking ever changing relevant
market forces defined by the Porter five forces. This DP analytic process starts with three key
advantages:
• The analytics are programmed that they are not just seeking any pattern, but the patterns
that matter most for market sentiments - relevant market forces and their inter-
relationships based on the Porter competitive archetypes,
• The IBB database seeds DP analytics with ultra-granular industry market forces and their
interrelationships, and
• The discoveries of the analytic processes become inputs to the IBB industry definitions as
new competitors, buyers, suppliers, entrants and substitutes emerge.
Some of the key outputs from DP analytics include:
• Most relevant market forces over time
• Emerging market forces
• Declining market forces
• Most relevant relationships among market forces over time
DP analytics first refer to “relevant” market forces and relationships. This idea
of DP relevancy derives from the idea that important market forces have the
most influence on market outcomes, whether anyone notices them or
broadcasts their existence. With this idea of relevancy, market truths exist, if
only they might be discovered. There are absolute market insights to be
discovered, hit upon or missed.
It is additionally important that key outputs are discovered, tracked and prioritized over time. The
inclusion of daily, weekly, monthly and longer time period changes enables DP analytics to
discover market trends and themes before they might be recognized in prevailing market
sentiment. DP analytics enable the creation of expanded market sentiments.
There are many existing methods of analyzing prevailing market sentiment as
developed by leading universities8 9 10
news organizations11 12
, intelligence
services companies13 14
and social media analytic15 16
companies. Most of these
methods focus on the positive-negative sentiment of single articles, or extend
interpretations of single events. They complement DP Analytics and yield the
8 Zhai, Cohen and Atreya, Sentiment analysis of news articles for financial signal prediction, (Stanford University, 2008)
9 Tetlock, Saar, Tschansky, and Macskassy, Columbia, More Than Words: Quantifying Language to Measure Firms’
Fundamentals, (The Journal of Finance, June 2008)
10 Pablo Azar, Sentiment Analysis in Financial News, (Harvard, April 1, 2009)
11 Thomson Reuters Adds Unique Twitter and News Sentiment Analysis to Thomson Reuters Eikon, (Thomson Reuters, Feb.
2014)
12 Dow Jones News Analytics: Transforming News Into Data, (Dow Jones and RavenPack, March 2016)
13 Peter Hafez, Creating Thematic Alphas with News Sentiment, (RavenPack, July 15, 2015)
14 Sentiment Analysis API, (Alchemy-IBM, 2016)
15 Radian6 scan bares netizens' sentiment on 'fight of the century', (Rappler, May 3, 2015)
16 How Esurance Engineered Its Way To Winning The Hashtag Bowl [about Crimson Hexagon] (Forbes, Feb. 8, 2016)
8. Discovery Patterns
Expanded Market Sentiments
8
advantages of fast positive or negative sentiment summations used for automated stock trading.
The primary difference between these methods and DP analytics is that they start with the
presumption that the user or analyst knows the right company criteria for analytic inputs. A
targeted company name is the most often default input. Single company sentiment analytics
neglect the greater competitive context as defined by Porter forces and IBB classification context.
If one is focusing too narrowly, and one does not start with all the archetypical market forces and
relationships - as in a realistic competitive context - it is improbable that expanded market
sentiments could be discovered.
Trend Visualization Engine
Trend Radars are animated visualizations of
DP analytic outputs. Even though
visualizations are not required to discover
expanded market sentiments, they offer a
very fast17 neuroscience method for
analysts and investors to tame the
complexity of markets and to be alerted to
changing market forces. Trend Radars
condense thousands of daily unstructured
data articles into granular IBB competitive
context market forces, where analytics
highlight and animate the most relevant
market forces and trends over time.
DP Trend Radars use elements of graph
theory18 to display the key outputs of DP
analytics. With Trend Radars…
• most relevant market forces win the
center of the radar space over time;
• emerging market forces move from the radar periphery to the center, displacing existing
market forces over time;
• declining market forces are pushed to the radar periphery by more relevant market forces
over time; and
• most relevant relationships among market forces are highlighted by bold connecting lines
17
Humans read at bandwidth of 200 bits per second, whereas they perceive images at 10,000,000 bits per second. Therefore,
useful visualizations have a huge information delivery advantage over text alone which is the common method of unstructured
data reporting.
18 Graph Theory Definition, Stanford University
Figure 3: Example Trend Radar of Apple, including many IBB industries
9. Discovery Patterns
Expanded Market Sentiments
9
Figure 3 is an example Trend Radar for Apple19
competitive ecosystem on February 26, 2016. It
includes many key IBB industries as part of greater competitive ecosystem inputs. Unstructured
data inputs are news and blog articles at the rate of 22,000 articles per week. One can see at the
radar center, the most relevant market forces on February 26 were Apple versus Android. The
orange connecting lines among Xiaomi, Asian Competitors and Qualcomm highlighted potentially
significant market events as part of an expanded market sentiment about Apple:
• The Wall Street Journal: Chinese giant Xiaomi takes aim at Apple iPhone with launch of Mi5
handset, February 24, 2016
• Computer World: Xiaomi Mi 5 smartphone: Snapdragon 820 [QUALCOMM] for $300? Sign me
up! February 24, 2016
The combination of three engines - IBB ultra-granular context, DP analytics and trend
visualizations creates a rich intelligence environment to discover expanded market sentiments.
Investment and risk avoidance opportunities emerge when there are mismatches between these
expanded sentiments and prevailing market sentiments.
Case Study 2009: Sentiment Mismatch about Nokia Smartphones
Discovery Patterns [DP] analytics were first developed to provide a superior situational awareness
of competitive marketplaces for corporate enterprises. Some of the early work of DP market
analytics focused on smartphone industries. In late 2009 and into early 2010, DP analytics
discovered the expanded market sentiment about Nokia - that Nokia was becoming increasingly
irrelevant as a smartphone competitor. This early and expanded market sentiment was unnoticed
by the stock market with its prevailing market sentiment about Nokia.
Throughout 2009, the stock price of Nokia was reacting to increased competition from the Apple
iPhone. Nevertheless, throughout the second half of 2009, NOK was relatively stable as seen in
its stock history of Figure 4. Prevailing market sentiment was remembering that Nokia was a 40%
smartphone market share company. No data had been released to the public through 2009 that
contradicted this positive sentiment about Nokia.
19
Apple Trend Radar, public Discovery Patterns site (this radar is constantly evolving with new unstructured data.) iPhone is not
plotted as a separate market force due to the very high market context correlation of Apple with iPhone during this time period.
Figure 4: Nokia Stock Price Second Half of 2009
10. Discovery Patterns
Expanded Market Sentiments
10
During most this same period of 2009, DP expanded market sentiment correlated with prevailing
market sentiments about Nokia, continuing to display Nokia as a central market force in its DP
Analytics and Trend Radar for the smartphone industry. This expanded market sentiment was
calculated from hundreds of thousands of unstructured news, blog and message board articles
without social media like Twitter or Facebook.
Yet in September of 2009, the expanded market sentiment of Nokia started a significant
disconnect with the prevailing market sentiment about Nokia. By November 1, 2009, Nokia’s
greater market relevancy had fallen by 80%. During this same sixty day period, the stock price
of Nokia dropped only 8%. Figure 5 shows the significant change in smartphone market relevancy
about Nokia as displayed in Trend Radar graphics. Nokia, once of central relevance, was reduced
to low peripheral competitive relevancy.
Figure 5: Changing Nokia Market Relevance Displayed on Trend Radar Displays – Sept. 1 versus Nov. 1, 2009
Figure 6 compares the indexed stock price of Nokia with the indexed DP analytic relevance of
Nokia, starting on September 1, 2009. These two indexes serve as respective proxies for
prevailing market sentiment and expanded market sentiment about Nokia. Over this ten month
time frame of Figure 6, one can observe that the prevailing Nokia market sentiment was stable or
slightly rising until Nokia’s public announcements on April 22, 2010, when Nokia revealed that its
smartphone sales were projected to be significantly below analyst estimates.
11. Discovery Patterns
Expanded Market Sentiments
11
April 22, 2010, Reuters - Smartphone Competition Hits Nokia Oyj, Shares Dive; Cuts FY 2010 Profit Outlook
Reuters reported that Nokia Oyj cut its profit outlook and delayed the launch of phones it needs to compete with the
iPhone and Blackberry. The Company cut the outlook for its fiscal 2010 operating profit margin at its key phone unit
to 11%-13%. 20
In contrast to the prevailing market sentiment about Nokia, Figure 6 shows that the expanded
market sentiment of Nokia crashed prior to November 1, 2009. During this time, not only was the
expanded market sentiment aware of iPhone competition, it was also noticing the emergence of
the Android operating system as another competitor. [Noted as “Google Wireless” in Figure 5.]
In fact, Android, not Blackberry, completely displaced Nokia by year end of 2009 as the primary
competitor to the iPhone.
DP Analytics discovered this free fall in Nokia competitive relevancy in November of 2009, and
thereby offered an expanded market sentiment that diverged from the prevailing market sentiment
until April 22, 2010. The mismatch of market sentiments offered significant investment or risk
reduction opportunities. After April 22nd
, Nokia prevailing market sentiment began to catch-up
with the DP analytic expanded market sentiment.
In January of 2010, prevailing market sentiment did not reflect that Nokia was on the precipice of
a short term 41% decline in stock price, and an expanded stock decline of 87%. DP Analytics
was able to factor in the greater context of Nokia’s changing competitive position by considering
Android, a Porter market entrant force.
20
Interesting that Nokia was not considering the market force entrant threat of Android OS smartphones at this announcement,
instead referring to Blackberry.
Figure 6: Normalized Nokia Stock Price versus DP Analytic Nokia Relevancy
12. Discovery Patterns
Expanded Market Sentiments
12
Table 2: Nokia Theme That Unexpected Smartphone Competition Was Emerging
300 day theme performance
Market Theme
September 4, 2009
Prevailing Market
Sentiment
Expanded Market
Sentiment
Theme 088 Tracking
July 2, 2010
NOK = $14.38
NOK is at risk from
emerging competitors and
existing competitors in
greater smartphone
ecosystem.
Nokia is the preeminent
smartphone manufacturer,
with a recent worldwide
smartphone market share
approaching 40%.
Android is emerging to
replace Nokia as one of
the smartphone leaders
with iPhone – a sea
change in ecosystem
competition.
NOK = $8.43
Over the 300 day period
from theme start, NOK
stock declined by 41%.
Case Study 2015: Sentiment Mismatch of Apple iPhone Sales Deceleration
The following four steps outline the discovery of potential sentiment mismatches and the market
theme that captures and tracks the sentiment mismatch opportunity.
Step 1: creating a market theme as part of DP Analytics platform
In January of 2016, DP analytics started to indicate that there might be a market sentiment
mismatch with AAPL stock. Discovery Patterns created a market theme that might capture these
potential mismatches. Theme 088 was created as seen in Figure 7. This theme creation and
tracking process was an advancement in DP analytics since the 2010 Nokia case study. These
themes originate from the many outputs of the DP analytic platform. Themes can be created to
capture almost any “dominant or unifying idea” about a marketplace. In this case, a rather specific
theme was created about Apple.
13. Discovery Patterns
Expanded Market Sentiments
13
Figure 7: Apple Theme 088 derived by and tracked by DP Context, Analytics and Visualization Engines
Theme 088 was originated on January 23, 2016, days ahead of Apple’s first quarter earnings
release on January 26, 2016. This theme was created because it appeared that prevailing market
sentiments about AAPL were overly optimistic in comparison to expanded market sentiments
about AAPL. A DP Theme first defines the unifying market idea. Then the process outlines the
rationale of the theme in a hypothesis. And finally the theme tracks the ongoing market relevance
or validity of the theme and any sentiment mismatches with DP analytics. Key market events
either validate or refute the theme over time.
Over the twelve months of 2015, Apple attempted to create the “next big thing” after the iPhone
with products like the Apple Watch®, Apple Pay®, Apple TV®, Apple Music®, HomeKit® and
HealthKit®. Nevertheless, 2015 DP Analytics indicated, based on relevancy measures, that it
was improbable that these Apple products would soon be able to replace the long standing
prevailing market sentiment of iPhone driven Apple. Throughout 2015, DP Analytics showed that
Apple smartphone competitors were not losing market relevancy. Likewise throughout 2015, no
game changing new Apple products appeared to be on the near term horizon. At the start of
2015, there was market sentiment that the Apple Watch could be the redefining product for Apple.
Nevertheless this scenario did not materialize. Additionally, through 2015, smartphone
competitors to iPhone were not falling into market irrelevancy. In fact Chinese and Indian
smartphone competitors were gaining market strength. Was the prevailing market sentiment
overly optimistic about AAPL on January 23, 2016?
Step 2: Assessing Prevailing Market Sentiment about Apple
Over the five years of 2011 through 2015, Apple revenues grew by 116% [Figure 8], fueled by
iPhone sales that accelerated from 44% of total Apple revenues in 2011 to 66% of total revenues
in 2015 [Figure 9]. Therefore, the ongoing market sentiment about Apple was tightly coupled with
the market performance of iPhone.
14. Discovery Patterns
Expanded Market Sentiments
14
Figure 8: Apple Sales History, 2004-2015
Figure 9: iPhone Sales as Percentage of Total Apple Sales, 2009-2015
15. Discovery Patterns
Expanded Market Sentiments
15
Over the 2011-2015 five year period, the stock price of AAPL reflected the market’s awareness
of iPhone as part of Apple’s overall market success. Over this time, AAPL peaked at a 300%
increase versus the peak increase of the S&P 500 Index of 80%, or a peak positive advantage of
220% for AAPL. [Figure 10]
Figure 10: Six Year Apple Stock History Index versus S&P 500
Nevertheless, all good things must come to an end. During the second half 2015 and up to the
date of Apple earnings release in January of 2016, the market sentiment for AAPL started to shift
as news of iPhone supply chain slowdowns started to be made public21 22 23
Over this six month
period [Figure 11], AAPL dropped 24% versus S&P 500 dropping 12%. The five year advantage
of AAPL at 2015 year end was a 108% gain versus the S&P 500 gain of 51%, or a five year AAPL
advantage of 57%.
Figure 11: Apple Monthly Stock History versus S&P 500 Index
21
Taiwan Gives The iPhone a Vote of Confidence as Apple Sales in Asia Go Down. (Buzz Orange, August 24, 2015)
22 Here's why the iPhone isn't going to catch up to Android any time soon, (Business Insider, August 26, 2015)
23 Apple gives itself an extra 6 days to sell 10M iPhones, hinting at slower pre-orders, (Computer World, September 14, 2015)
220%
AAPL Index
S&P 500 Index
AAPL Index
S&P 500 Index
16. Discovery Patterns
Expanded Market Sentiments
16
Step 3 – Seeking Potential Mismatches between Apple Prevailing Market Sentiment
and Expanded Market Sentiment
Mismatches between prevailing market sentiment versus expanded market sentiment can yield
investment or risk reduction opportunities. The prevailing market sentiment on January 23, 2016
was that Apple’s stock price was already considering an iPhone deceleration through the end of
2015, showing a negative 12% drop [24% drop for Apple, versus 12% for S&P 500 index]. The
prevailing market sentiment on January 23, 2016 was optimistic that Apple would not give back
more of its five year 57% advantage over the S&P 500 index.
In the context of an expanded market sentiment, Apple would soon need to rejuvenate iPhone
sales or boost up market enthusiasm for other Apple products if AAPL were to hold onto this 57%
advantage into 2016. Was this probable?
Figure 12 displays the DP competitive relevancy index of Apple and its smartphone competitors
during the second half of 2015 and up to January 23, 2016. Over 1,000,000 news and blog
articles were used as unstructured inputs over 2015 and into 2016 to create the expanded
sentiment outputs. Note that Apple and Android were in sync with each other as most relevant
smartphone competitors. Also note the competitive relevancy of major Apple competitors
remained steady based on DP competitive analytics. Apple’s chief operating system rival Android
remained as the primary iPhone competitor. None of the major iPhone rivals decayed into market
irrelevancy as Nokia did in 2009. The DP Analytic indices used in Figure 12 are similar to the
Nokia indices used in Figure 6. The expanded market sentiment tells the story that if iPhone were
to rally in 2016, it would not be at the expense of floundering smartphone competitors.
Figure 12: Index of DP Analytic Relevancy of Apple Competitors (July 1, 2015 – Jan. 23, 2016)
17. Discovery Patterns
Expanded Market Sentiments
17
DP analytics were also adding new product influences on AAPL’s expanded market sentiment.
Figure 13 shows competitive relevancy trends of major Apple new product groups from the
second half of 2015 to January 23, 2016. These indexed measures of comparative market
relevancy are similar to Figures 6 and 12. Figure 13 indicates that none of the Watch, Pay, Music,
Health, Home, TV or Tablet products appeared to be ramping to greater market excitement. Apple
Watch did generate great market excitement at the start of 2015. Yet this excitement dissipated
by the second half of 2015.
In summary, the expanded market sentiment for AAPL on January 23, 2016 was more pessimistic
of Apple than prevailing market sentiment.
Step 4: Incorporating theme tracking as part of investment decision making
Ultimate financial decisions are the sum of many market insights. DP Analytics with expanded
market sentiments, are an innovative and complementary insight. As market themes are given
probable validity through discovered DP Analytic sentiment mismatches, investment decisions
can be taken and tracked. Tracked themes are validated or refuted over time.
Figure 13: Index of DP Analytic Relevancy of non-iPhone Apple products (July 1, 2015 – Jan. 23, 2016)
18. Discovery Patterns
Expanded Market Sentiments
18
Table 3 and Figure 14 show how the prevailing market sentiment approached the expanded
market sentiment between January 23, 2016 and February 22, 2016. The market reality that
AAPL dropped 5.2% over this 30 day period is objective support for Theme 088.
Theme 088 was created and then tracked based on all DP insight engines.
• The ever changing granular competitive context of Apple was captured in IBB database
[example Table 1]. Theme 088 analytics and tracking makes direct access to this IBB
ultra-granular classification data. [Figure 7]
• Throughout 2015 and into 2016, over 1,000,000 unstructured data articles were used as
DP analytic inputs. [Social media data like Twitter and Facebook were again excluded.]
• Throughout 2015 and into 2016, billions of weekly DP analytic calculations sought the
most relevant market forces, emerging market forces, declining market forces and most
relevant relationships among market forces. [Figure 3 is a visualization of Apple
Ecosystem Trend Radar where these market force trends were animated.]
• Theme 088 also includes “Event Tracking” where DP or client human analysts can
complement DP analytics and highlight key market events that either validate or support
the respective market theme. [see Figure 15 for Theme 088 tracking example.]
Table 3: Theme 088 Sentiment Composition and Tracking
30 day theme performance
Market Theme 088
January 23, 2016
Prevailing Market
Sentiment
Expanded Market
Sentiment
Theme 088 Tracking
February 22, 2016
AAPL = $101.42
AAPL still has down side
risk as iPhone sales
decelerate, while Apple
Pay, Watch, Home, Music,
Health and TV mature.
The prevailing AAPL
market sentiment
indicates that AAPL can
retain its 57%, five year
advantage over the S&P
500 even as iPhone sales
slow.
The expanded market
sentiment of AAPL shows
a competitive context of
ongoing smartphone
competition and unlikely
near-term replacement
sales from Apple’s other
product developments.
AAPL = $96.16
Over the 30 day period
from theme start, AAPL
stock declined by 5.2%.
Theme Start
AAPL = 101.42
Theme End
AAPL = 96.16
Figure 14: AAPL thirty day stock price
20. Discovery Patterns
Expanded Market Sentiments
20
Implications for Market Analysts, Portfolio Managers and Strategic Planners
All sophisticated investors and analysts have a wide variety of analysis tools for their market and
investment decisions. Recent developments in big data analytics have offered a wide variety of
analysis tools that address the potential insights in unstructured data. Discovery Patterns has
advanced the science of big data analytics by leveraging and seamlessly combining granular
industry context, competitive relationship analytics and visualizations of key emerging trends and
relevant market forces.
One of the key insight discoveries of DP Analytics are potential mismatches between prevailing
market sentiment and the expanded market sentiments. Because expanded market sentiments
include a wider scope of the true competitive situation of a company, an investor can see more of
the market.
In the 2009 Nokia case study, expanded market sentiment foretold of a collapse of Nokia
relevancy as a smartphone competitor. In the 2015 Apple case study, expanded market
sentiment highlighted the downside risk of AAPL stock as iPhone sales cooled. Every day there
are numerous scenarios where the prevailing market sentiment would adjust if only the market
could see more of the complexities of a company and the many Porter competitive force
environments where these companies actually compete. Seeing these complexities early creates
an investing competitive advantage.
Times of complexity and change are rich environments for expanded market sentiments. The two
case studies of this paper referred to dynamic mobility markets. Before mobility, there was the
dynamism of Internet markets. Now we are in the era of Internet of Things markets where
ubiquitous data collection and analytics are revolutionizing almost every industry. Discovery
Patterns offers operational analytic ecosystems and packaged market themes [including ongoing
tracking for validity] that can be used as simple modular inputs for sentiment mismatch discoveries
and general situational awareness. DP clients can subscribe to existing themes, create their own
themes or build entirely new competitive ecosystems.