Le jeu de données met à disposition, pour les années 2011 à 2015, les principales caractéristiques des pôles de compétitivité : localisation, entreprises et établissements membres, projets. Fournies par la Direction Générale des Entreprises, ces données sont issues de l’enquête annuelle auprès des gouvernances des pôles de compétitivité puis de leur appariement avec les bases de données de l’Insee. Ces tableaux décrivent les principales caractéristiques des entreprises membres pour chacun des pôles de compétitivité ainsi que pour l’ensemble des pôles (nombre d’entreprises membres, localisation des établissements des entreprises membres, répartition PME / ETI / grandes entreprises, taux d’exportation,…).

Le jeu de données, mis à disposition par la Direction Générale des Entreprises, propose pour les années 2006 à 2016 le nombre de projets de RDI de chacun des pôles de compétitivité et les aides financières associées, par pôle.

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Liste et coordonnées des Pôles de Compétitivité en 2013.

Competitive Collaboration is a generic framework in which networks learn to collaborate and compete, thereby achieving specific goals. Competitive Collaboration is a three player game consisting of two players competing for a resource that is regulated by a third player, moderator. This framework is similar in spirit to expectation-maximization (EM) but is formulated for neural network training.

Competitive intelligence monitoring goes beyond your sales team. Our CI solutions also bring powerful insights to your production, logistics, operation & marketing departments.

Why should you use our Competitive intelligence data? 1. Increase visibility: Our geolocation approach allows us to “get inside” any facility in the US, providing visibility in places where other solutions do not reach. 2. In-depth 360º analysis: Perform a unique and in-depth analysis of competitors, suppliers and customers. 3. Powerful Insights: We use alternative data and big data methodologies to peel back the layers of any private or public company. 4. Uncover your blind spots against leading competitors: Understand the complete business environment of your competitors, from third-tier suppliers to main investors. 5. Identify business opportunities: Analyze your competitor's strategic shifts and identify unnoticed business opportunities and possible threats or disruptions. 6. Keep track of your competitor´s influence around any specific area: Maintain constant monitoring of your competitors' actions and their impact on specific market areas.

How other companies are using our CI Solution? 1. Enriched Data Intelligence: Our Market Intelligence data bring you key insights from different angles. 2. Due Diligence: Our data provide the required panorama to evaluate a company’s cross-company relations to decide whether or not to proceed with an acquisition. 3. Risk Assessment: Our CI approach allows you to anticipate potential disruptions by understanding behavior in all the supply chain tiers. 4. Supply Chain Analysis: Our advanced Geolocation approach allows you to visualize and map an entire supply chain network. 5. Insights Discovery: Our relationship identifiers algorithms generate data matrix networks that uncover new and unnoticed insights within a specific market, consumer segment, competitors' influence, logistics shifts, and more.

From "digital" to the real field: Most competitive intelligence companies focus their solutions analysis on social shares, review sites, and sales calls. Our competitive intelligence strategy consists on tracking the real behavior of your market on the field, so that you can answer questions like: -What uncovered need does my market have? -How much of a threat is my competition? -How is the market responding to my competitor´s offer? -How my competitors are changing? -Am I losing or winning market?

Gain in-depth insights on competitors’ prices on an ongoing basis which enables you to price your own products better and drive more revenue and margin gains. Match your products with competition for a benchmarked view on pricing, on shelf availability, and promotion insights at a granular level of SKU, store, and location-based. Easily consume these insights on an in-house-built dashboard with timely updates as frequently as required.

Dans ce document, nous commençons par étudier la version on-line du problème du sous-graphe héréditaire de poids maximum, WHG, ci-dessous défini : étant donné un graphe G et une propriété héréditaire, trouver un sous-graphe de G de poids maximum satisfaisant. Ensuite, nous étudierons le cas particulier du problème du stable pondéré. Dans notre modèle on-line, nous supposons que l'instance finale de taille n'est révélée en t étapes (ou paquets), 2 ≤ t ≤ n. Nous analysons le comportement des algorithmes on-line résolvant le problème WHG et déterminons des rapports compétitifs (résultats positifs) et des résultats négatifs. Ces derniers résultats rendent compte aussi bien de la difficulté du problème que de la qualité des algorithmes élaborés pour les résoudre. In this paper, we study the on-line version of maximum-weighted hereditary subgraph problems. In our on-line model, the final instance-graph (which has n vertices) is revealed in t clusters, 2 ≤ t ≤ n. We first focus on the on-line version of the following problem: finding a maximum-weighted subgraph satisfying some hereditary property. Then, we deal with the particular case of the independent set. For all these problems, we are interested in two types of results: the competitivity ratio guaranteed by the on-line algorithm and hardness results that account for the difficulty of the problems and for the quality of algorithms developed to solve them.

We use the introduction of a means-tested voucher program in Florida to examine whether increased competitive pressure on public schools affects students' test scores. We find greater score improvements in the wake of the program introduction for students attending schools that faced more competitive private school markets prior to the policy announcement, especially those that faced the greatest financial incentives to retain students. These effects suggest modest benefits for public school students from increased competition. The effects are consistent across several geocoded measures of competition and isolate competitive effects from changes in student composition or resource levels in public schools.

Description: This data set contains non-confidential information on approved Non-Competitively Bid (NCB) contracts, Special Category NCB Requests (SCR), and Limited-to-Brand Requests (LTB) approved for $1 million or more. This dataset is limited to requests made to (and approved by) the California Department of General Services (DGS). It does not contain requests made to (or approved by) the California Department of Technology (CDT). For definitions of key terms, please see the attached Data Dictionary. If you have any questions regarding a specific NCB, SCR, or LTB, please contact the department or agency identified in the “Requesting Organization” column. For any other questions, please contact PDNCB@dgs.ca.gov.

Competition is an important biotic interaction that influences survival and reproduction. While competition on ecological timescales has received great attention, little is known about competition on evolutionary timescales. Do competitive abilities change over hundreds of thousands to millions of years? Can we predict competitive outcomes using phenotypic traits? How much do traits that confer competitive advantage and competitive outcomes change? Here we show, using communities of encrusting marine bryozoans spanning more than 2 million years, that size is a significant determinant of overgrowth outcomes: colonies with larger zooids tend to overgrow colonies with smaller zooids. We also detected temporally coordinated changes in average zooid sizes, suggesting that different species responded to a common external driver. Although species-specific average zooid sizes change over evolutionary timescales, species-specific competitive abilities seem relatively stable, suggesting that traits other than zooid size also control overgrowth outcomes and/or that evolutionary constraints are involved.

Organizations that learn from others’ successful policies not only become more competitive because their policies improve but also avoid the costs of policy innovation. While economists have widely recognized latecomer advantage, the policy diffusion literature in political science has failed to emphasize the connection between learning and competition. This article distinguishes competitive learning from learning that is not driven by competitive pressure (that is, ‘pure learning’). It models policy diffusion as a game played on social networks that govern competitive pressure and the possibilities of information transfer. The article develops an empirical test for competitive learning using spatial lags, which are applied to data on the performance of larger English local authorities from 2002 to 2006. Evidence is found for both competitive learning and pure learning. The sharper distinction between causal mechanisms proposed in this article should be widely applicable to diffusion across international boundaries and sub-national units.

We help our clients to draw connections between companies and facilities, map markets and supply chains and uncover and visualize hidden relations between companies.

Given a specific U.S. or Europe-based company, we draw a connections map, including all detected relationships between that company and other companies and facilities nationwide, statewide, and between countries.

Markets we can address

We can identify and detect connections between facilities and companies across the United States, and across countries and within them in Europe.

What makes us different?

We have developed a brand-new technology that combines the use of alternative data and machine learning methodologies, what allows us to perform competitive analysis in a non-traditional way, by studying a specific facility and detecting and drawing all the connections between the facility and other locations. We use publicly available information such as location data and building footprints to identify and geolocate company facilities. With a machine learning algorithm, we identify movement patterns between these locations.

Already Validated Use Cases

• Market Intelligence & Due Diligence

Company: A large multinational company that provides specialized materials to different industries.

Challenge: How to find a cost-effective way to analyze the competition and identify potential customers?

Solution: Relationship data with a geolocation approach to unveil the DNA of a facility.

Results: Identify competitors' clients and suppliers.

• Risk Assessment

Company: A leading data analytics provider serving customers in insurance, energy and specialized markets, and financial services.

Challenge: How to anticipate and prevent possible disruptions in all the tiers of the supply chain?

Solution: Inter-company data with a geolocation approach to detect and measure relations between companies and facilities in the US. Using the movements of physical goods between different companies and locations, the supply chains can be inferred and analyzed.

Results: Anticipate potential disruptions by understanding behavior in all the tiers of the supply chain.

• Asset Protection & Loss Prevention

Company: A large multinational corporation in the industrial and logistics sector.

Challenge: How to optimize the asset recovery strategy in a network of more than 1 million locations nationwide?

Solution: A machine learning model that combines client´s own data and a geolocation approach to track assets´ movements between facilities.

Results: Improvement in the identification of facilities where the presence of assets to be recovered is higher.

Other Use Cases

• Monitor company supply chains (paired with import-export data if additive).
• Provide a proxy for manufacturing/production (e.g., monitoring activity from factories to warehouses).

Competition for limiting resources is among the most fundamental ecological interactions and has long been considered a key driver of species coexistence and biodiversity. Species' minimum resource requirements, their R*s, are key traits that link individual physiological demands to the outcome of competition. However, a major question remains unanswered - to what extent are species' competitive traits able to evolve in response to resource limitation? To address this knowledge gap, we performed an evolution experiment in which we exposed Chlamydomonas reinhardtii for approximately 285 generations to seven environments in chemostats which differed in resource supply ratios (including nitrogen, phosphorus and light limitation) and salt stress. We then grew the ancestors and descendants in common garden and quantified their competitive abilities for essential resources. We investigated constraints on trait evolution by testing whether changes in resource requirements for different resources were correlated. Competitive abilities for phosphorus improved in all populations, while competitive abilities for nitrogen and light increased in some populations and decreased in others. In contrast to the common assumption that there are trade-offs between competitive abilities for different resources, we found that improvements in competitive ability for a resource came at no detectable cost. Instead, improvements in competitive ability for multiple resources were either positively correlated or not significantly correlated. Using resource competition theory, we then demonstrated that rapid adaptation in competitive traits altered the predicted outcomes of competition. These results highlight the need to incorporate contemporary evolutionary change into predictions of competitive community dynamics over environmental gradients.

This survey is a supplement to the 2018 CCES and was designed to focus on vote choices in competitive US House races. This dataset includes interviews with 17,000 American adults, 13,000 of whom were living in House districts that were classified as competitive or potentially competitive during the 2018 campaign, and the remaining living in non-competitive districts. We identified 52 House districts as ``competitive'' for the purpose of this study, and we interviewed an average of 250 respondents in each district. These districts are identified and described below. The remaining 4,000 cases were evenly distributed among the remaining 383 uncompetitive congressional districts.

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The competitive intelligence tools market is projected to grow by USD 28.90 billion with a CAGR of 10.38% during the forecast period 2021 to 2026.

The market research report provides valuable insights into the post-COVID-19 impact on the market, which will help companies evaluate their business approaches. Furthermore, this report extensively covers the market segmentation by deployment (cloud-based and on-premise) and geography (North America, APAC, Europe, the Middle East and Africa, and South America). The market report also offers information on several market vendors, including Brandwatch, BuzzSumo Ltd., CI Radar LLC, Clootrack Software Labs Pvt. Ltd., Comintelli AB, Crayon Inc., Crunchbase Inc., Digimind, Evalueserve Inc., G2.com Inc., Klue Labs Inc., Meltwater BV, NetBase Solutions Inc., Pathmatics Inc., Semrush Holdings Inc., Similarweb Ltd., Slintel LLC, SpyFu, Talkwalker Inc., and Adthena Ltd. among others.

What will the Competitive Intelligence Tools Market Size be During the Forecast Period?

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Competitive Intelligence Tools Market: Key Drivers, Trends, and Challenges

The research studied the historical data considered for years, with 2021 as the base year and 2022 as the estimated year, and produced drivers, trends, and challenges for the market.

Key Competitive Intelligence Tools Market Driver

The exponential increase in data is one of the key drivers fueling the market's growth. Large volumes of data are generated from various sources such as mobile devices, the Internet, and social media. Organizations find it challenging to manage and analyze large databases efficiently due to the increasing variety, volume, and velocity of data. In addition, veracity, the fourth component in big data management systems, eliminates irrelevant data that involve high costs. Moreover, competitive intelligence tools help organizations transform unstructured and semi-structured data into structured and meaningful information. Therefore, an exponential increase in data will positively impact the growth of the market during the forecast period.

Key Competitive Intelligence Tools Market Trend

The rising adoption of smart connected devices is one of the key market trends propelling the market growth. Data from various technologies, including radio-frequency identification (RFID), sensors, barcodes, and the global positioning system (GPS), are used in business analytics. These technologies contribute to the efficient monitoring and management of physical assets in many industries. In addition, companies are now benefiting from significant improvements in their business processes through the adoption of competitive intelligence tools. Closer monitoring of business processes leads to effective decisions in real time. Therefore, the increasing adoption of smart devices is expected to accelerate the growth of the market during the forecast period.

Key Competitive Intelligence Tools Market Challenge

Data privacy and security concerns are one of the factors limiting the market's growth. Concerns about the security and privacy of data on the cloud have increased worldwide. Enterprises that adopt the public cloud-based storage model have limited control over the security of their data. Therefore, they transfer the less-important data to the public cloud. In a multi-tenant model, there is a risk of leakage of business-critical information that could be misused or manipulated. The security of data is a vital aspect of cloud infrastructure and applications. Therefore, data privacy and security concerns will negatively impact the growth of the market during the forecast period.

This market analysis report also provides detailed information on other upcoming trends and challenges that will have a far-reaching effect on the market growth. The actionable insights on the trends and challenges will help companies evaluate and develop growth strategies for 2022-2026.

Parent Market Analysis

Technavio categorizes the global competitive intelligence tools market as a part of the global application software market. Our research report has extensively covered external factors influencing the parent market growth potential in the coming years, which will determine the levels of growth of the market during the forecast period.

Who are the Major Competitive Intelligence Tools Market Vendors?

The report analyzes the market’s competitive landscape and offers information on several market vendors, including:

Brandwatch
BuzzSumo Ltd.
CI Radar LLC
Clootrack Software Labs Pvt. Ltd.
Comintelli AB
Crayon Inc.
Crunchbase Inc.
Digimind
Evalueserve Inc.
G2.com Inc.
Klue Labs Inc.
Meltwater BV
NetBase Solutions Inc.
Pathmatics Inc.
Semrush Holdings Inc.
Similarweb Ltd.
Slintel LLC
SpyFu
Talkwalker Inc.
Adthena Ltd.

This statistical study of the mar

Artificial intelligence (AI) adopters worldwide report modernizing their data infrastructure for AI as a top initiative for increasing their competitive advantage, according to a 2020 global survey on AI. Another 19 percent view gaining access to the newest and best AI technologies as a top initiative.

Scholars have proposed two distinct explanations for why policies diffuse across American states: (1) policymakers learn by observing the experiences of nearby states, and (2) states seek a competitive economic advantage over other states. The most common empirical approach for studying interstate influence is modeling an indicator of a state's policy choice as a function of its neighbors' policies, with each neighbor weighted equally. This can appropriately specify one form of learning model, but it does not adequately test for interstate competition: when a policy diffuses due to competition, states' responses to other states vary depending on the size and location of specific populations. The authors of this article illustrate with two substantive applications how geographic information systems (GIS) can be used to test for interstate competition. They find that lottery adoptions diffuse due to competition, rather than learning, but find no evidence of competition in state choices about welfare benefits. The authors' empirical approach can also be applied to competition among nations and local jurisdictions.

We investigate and generalize an existing model of competitive helping within a biological market, first introduced for a population of competing individuals in which one individual provides help to all others; the rest compete for the help available from this individual by providing help themselves. Our generalized model comprises two strategies in which each individual of a specific type provides the same amount of help as all other individuals of that type. Each individual's fitness function is dependent on this level of help, the cost of providing the help, and the fact that help is proportionally reciprocated by other individuals. Competitive helping occurs when individuals actively try to help more than other individuals. To assess the emergence of equilibrium help strategies as adopted by proportions of the population, we examine the competition over available help within two settings: replicator dynamics and agent-based numerical simulations. To move one step further in our generalization, we use the agent-based model to study the N-person competitive helping game, where all individuals in the population are heterogeneous with respect to help provided. Our results show that helping does not increase indefinitely with the population size, as concluded previously, and while there are some instances of an increase in help provided as a result of competition, this competition can be detrimental to all individuals and in most cases, one type simply gives up (thus evolving to a "no help" strategy). The degree to which an individual's help is reciprocated by the others in the population has strong implications in the long-term behaviour of equilibrium help levels of types of individuals (and of individuals themselves); these equilibrium help levels diverge from existing conjectures in current literature. Lastly, small amounts of passively provided (costless) help results in runaway competition among all individuals.