Prediction Markets at Google, Spanish Version SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
Technology & Operations
Strategy / MBA Resources
Case Study SWOT Analysis Solution
Case Study Description of Prediction Markets at Google, Spanish Version
In its eight quarters of operation, Google's internally developed prediction market has delivered accurate and decisive predictions about future events of interest to the company. Google must now determine how to increase participation in the market, and how to best use its predictions.
Authors :: Peter A. Coles, Karim R. Lakhani, Andrew McAfee
Swot Analysis of "Prediction Markets at Google, Spanish Version" written by Peter A. Coles, Karim R. Lakhani, Andrew McAfee includes – strengths weakness that are internal strategic factors of the organization, and opportunities and threats that Prediction Predictions facing as an external strategic factors. Some of the topics covered in Prediction Markets at Google, Spanish Version case study are - Strategic Management Strategies, Forecasting, IT, Market research, Strategic planning and Technology & Operations.
Some of the macro environment factors that can be used to understand the Prediction Markets at Google, Spanish Version casestudy better are - – cloud computing is disrupting traditional business models, increasing commodity prices, banking and financial system is disrupted by Bitcoin and other crypto currencies, talent flight as more people leaving formal jobs, technology disruption, geopolitical disruptions, increasing government debt because of Covid-19 spendings,
there is backlash against globalization, supply chains are disrupted by pandemic , etc
Introduction to SWOT Analysis of Prediction Markets at Google, Spanish Version
SWOT stands for an organization’s Strengths, Weaknesses, Opportunities and Threats . At Oak Spring University , we believe that protagonist in Prediction Markets at Google, Spanish Version case study can use SWOT analysis as a strategic management tool to assess the current internal strengths and weaknesses of the Prediction Predictions, and to figure out the opportunities and threats in the macro environment – technological, environmental, political, economic, social, demographic, etc in which Prediction Predictions operates in.
According to Harvard Business Review, 75% of the managers use SWOT analysis for various purposes such as – evaluating current scenario, strategic planning, new venture feasibility, personal growth goals, new market entry, Go To market strategies, portfolio management and strategic trade-off assessment, organizational restructuring, etc.
SWOT Objectives / Importance of SWOT Analysis and SWOT Matrix
SWOT analysis of Prediction Markets at Google, Spanish Version can be done for the following purposes –
1. Strategic planning using facts provided in Prediction Markets at Google, Spanish Version case study
2. Improving business portfolio management of Prediction Predictions
3. Assessing feasibility of the new initiative in Technology & Operations field.
4. Making a Technology & Operations topic specific business decision
5. Set goals for the organization
6. Organizational restructuring of Prediction Predictions
Strengths Prediction Markets at Google, Spanish Version | Internal Strategic Factors
What are Strengths in SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
The strengths of Prediction Predictions in Prediction Markets at Google, Spanish Version Harvard Business Review case study are -
Organizational Resilience of Prediction Predictions
– The covid-19 pandemic has put organizational resilience at the centre of everthing that Prediction Predictions does. Organizational resilience comprises - Financial Resilience, Operational Resilience, Technological Resilience, Organizational Resilience, Business Model Resilience, and Reputation Resilience.
Operational resilience
– The operational resilience strategy in the Prediction Markets at Google, Spanish Version Harvard Business Review case study comprises – understanding the underlying the factors in the industry, building diversified operations across different geographies so that disruption in one part of the world doesn’t impact the overall performance of the firm, and integrating the various business operations and processes through its digital transformation drive.
Analytics focus
– Prediction Predictions is putting a lot of focus on utilizing the power of analytics in business decision making. This has put it among the leading players in the industry. The technology infrastructure suggested by Peter A. Coles, Karim R. Lakhani, Andrew McAfee can also help it to harness the power of analytics for – marketing optimization, demand forecasting, customer relationship management, inventory management, information sharing across the value chain etc.
Innovation driven organization
– Prediction Predictions is one of the most innovative firm in sector. Manager in Prediction Markets at Google, Spanish Version Harvard Business Review case study can use Clayton Christensen Disruptive Innovation strategies to further increase the scale of innovtions in the organization.
Sustainable margins compare to other players in Technology & Operations industry
– Prediction Markets at Google, Spanish Version firm has clearly differentiated products in the market place. This has enabled Prediction Predictions to fetch slight price premium compare to the competitors in the Technology & Operations industry. The sustainable margins have also helped Prediction Predictions to invest into research and development (R&D) and innovation.
Training and development
– Prediction Predictions has one of the best training and development program in the industry. The effectiveness of the training programs can be measured in Prediction Markets at Google, Spanish Version Harvard Business Review case study by analyzing – employees retention, in-house promotion, loyalty, new venture initiation, lack of conflict, and high level of both employees and customer engagement.
Digital Transformation in Technology & Operations segment
- digital transformation varies from industry to industry. For Prediction Predictions digital transformation journey comprises differing goals based on market maturity, customer technology acceptance, and organizational culture. Prediction Predictions has successfully integrated the four key components of digital transformation – digital integration in processes, digital integration in marketing and customer relationship management, digital integration into the value chain, and using technology to explore new products and market opportunities.
Ability to lead change in Technology & Operations field
– Prediction Predictions is one of the leading players in its industry. Over the years it has not only transformed the business landscape in its segment but also across the whole industry. The ability to lead change has enabled Prediction Predictions in – penetrating new markets, reaching out to new customers, and providing different value propositions to different customers in the international markets.
High brand equity
– Prediction Predictions has strong brand awareness and brand recognition among both - the exiting customers and potential new customers. Strong brand equity has enabled Prediction Predictions to keep acquiring new customers and building profitable relationship with both the new and loyal customers.
Strong track record of project management
– Prediction Predictions is known for sticking to its project targets. This enables the firm to manage – time, project costs, and have sustainable margins on the projects.
Effective Research and Development (R&D)
– Prediction Predictions has innovation driven culture where significant part of the revenues are spent on the research and development activities. This has resulted in, as mentioned in case study Prediction Markets at Google, Spanish Version - staying ahead in the industry in terms of – new product launches, superior customer experience, highly competitive pricing strategies, and great returns to the shareholders.
Successful track record of launching new products
– Prediction Predictions has launched numerous new products in last few years, keeping in mind evolving customer preferences and competitive pressures. Prediction Predictions has effective processes in place that helps in exploring new product needs, doing quick pilot testing, and then launching the products quickly using its extensive distribution network.
Weaknesses Prediction Markets at Google, Spanish Version | Internal Strategic Factors
What are Weaknesses in SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
The weaknesses of Prediction Markets at Google, Spanish Version are -
Low market penetration in new markets
– Outside its home market of Prediction Predictions, firm in the HBR case study Prediction Markets at Google, Spanish Version needs to spend more promotional, marketing, and advertising efforts to penetrate international markets.
Skills based hiring
– The stress on hiring functional specialists at Prediction Predictions has created an environment where the organization is dominated by functional specialists rather than management generalist. This has resulted into product oriented approach rather than marketing oriented approach or consumers oriented approach.
High bargaining power of channel partners
– Because of the regulatory requirements, Peter A. Coles, Karim R. Lakhani, Andrew McAfee suggests that, Prediction Predictions is facing high bargaining power of the channel partners. So far it has not able to streamline the operations to reduce the bargaining power of the value chain partners in the industry.
Aligning sales with marketing
– It come across in the case study Prediction Markets at Google, Spanish Version that the firm needs to have more collaboration between its sales team and marketing team. Sales professionals in the industry have deep experience in developing customer relationships. Marketing department in the case Prediction Markets at Google, Spanish Version can leverage the sales team experience to cultivate customer relationships as Prediction Predictions is planning to shift buying processes online.
Lack of clear differentiation of Prediction Predictions products
– To increase the profitability and margins on the products, Prediction Predictions needs to provide more differentiated products than what it is currently offering in the marketplace.
Need for greater diversity
– Prediction Predictions has taken concrete steps on diversity, equity, and inclusion. But the efforts so far has resulted in limited success. It needs to expand the recruitment and selection process to hire more people from the minorities and underprivileged background.
Slow to harness new channels of communication
– Even though competitors are using new communication channels such as Instagram, Tiktok, and Snap, Prediction Predictions is slow explore the new channels of communication. These new channels of communication mentioned in marketing section of case study Prediction Markets at Google, Spanish Version can help to provide better information regarding products and services. It can also build an online community to further reach out to potential customers.
High dependence on existing supply chain
– The disruption in the global supply chains because of the Covid-19 pandemic and blockage of the Suez Canal illustrated the fragile nature of Prediction Predictions supply chain. Even after few cautionary changes mentioned in the HBR case study - Prediction Markets at Google, Spanish Version, it is still heavily dependent upon the existing supply chain. The existing supply chain though brings in cost efficiencies but it has left Prediction Predictions vulnerable to further global disruptions in South East Asia.
Increasing silos among functional specialists
– The organizational structure of Prediction Predictions is dominated by functional specialists. It is not different from other players in the Technology & Operations segment. Prediction Predictions needs to de-silo the office environment to harness the true potential of its workforce. Secondly the de-silo will also help Prediction Predictions to focus more on services rather than just following the product oriented approach.
High dependence on star products
– The top 2 products and services of the firm as mentioned in the Prediction Markets at Google, Spanish Version HBR case study still accounts for major business revenue. This dependence on star products in has resulted into insufficient focus on developing new products, even though Prediction Predictions has relatively successful track record of launching new products.
High cash cycle compare to competitors
Prediction Predictions has a high cash cycle compare to other players in the industry. It needs to shorten the cash cycle by 12% to be more competitive in the marketplace, reduce inventory costs, and be more profitable.
Opportunities Prediction Markets at Google, Spanish Version | External Strategic Factors
What are Opportunities in the SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
The opportunities highlighted in the Harvard Business Review case study Prediction Markets at Google, Spanish Version are -
Finding new ways to collaborate
– Covid-19 has not only transformed business models of companies in Technology & Operations industry, but it has also influenced the consumer preferences. Prediction Predictions can tie-up with other value chain partners to explore new opportunities regarding meeting customer demands and building a rewarding and engaging relationship.
Changes in consumer behavior post Covid-19
– Consumer behavior has changed in the Technology & Operations industry because of Covid-19 restrictions. Some of this behavior will stay once things get back to normal. Prediction Predictions can take advantage of these changes in consumer behavior to build a far more efficient business model. For example consumer regular ordering of products can reduce both last mile delivery costs and market penetration costs. Prediction Predictions can further use this consumer data to build better customer loyalty, provide better products and service collection, and improve the value proposition in inflationary times.
Using analytics as competitive advantage
– Prediction Predictions has spent a significant amount of money and effort to integrate analytics and machine learning into its operations in the sector. This continuous investment in analytics has enabled, as illustrated in the Harvard case study Prediction Markets at Google, Spanish Version - to build a competitive advantage using analytics. The analytics driven competitive advantage can help Prediction Predictions to build faster Go To Market strategies, better consumer insights, developing relevant product features, and building a highly efficient supply chain.
Remote work and new talent hiring opportunities
– The widespread usage of remote working technologies during Covid-19 has opened opportunities for Prediction Predictions to expand its talent hiring zone. According to McKinsey Global Institute, 20% of the high end workforce in fields such as finance, information technology, can continously work from remote local post Covid-19. This presents a really great opportunity for Prediction Predictions to hire the very best people irrespective of their geographical location.
Reconfiguring business model
– The expansion of digital payment system, the bringing down of international transactions costs using Bitcoin and other blockchain based currencies, etc can help Prediction Predictions to reconfigure its entire business model. For example it can used blockchain based technologies to reduce piracy of its products in the big markets such as China. Secondly it can use the popularity of e-commerce in various developing markets to build a Direct to Customer business model rather than the current Channel Heavy distribution network.
Redefining models of collaboration and team work
– As explained in the weaknesses section, Prediction Predictions is facing challenges because of the dominance of functional experts in the organization. Prediction Markets at Google, Spanish Version case study suggests that firm can utilize new technology to build more coordinated teams and streamline operations and communications using tools such as CAD, Zoom, etc.
Buying journey improvements
– Prediction Predictions can improve the customer journey of consumers in the industry by using analytics and artificial intelligence. Prediction Markets at Google, Spanish Version suggest that firm can provide automated chats to help consumers solve their own problems, provide online suggestions to get maximum out of the products and services, and help consumers to build a community where they can interact with each other to develop new features and uses.
Increase in government spending
– As the United States and other governments are increasing social spending and infrastructure spending to build economies post Covid-19, Prediction Predictions can use these opportunities to build new business models that can help the communities that Prediction Predictions operates in. Secondly it can use opportunities from government spending in Technology & Operations sector.
Harnessing reconfiguration of the global supply chains
– As the trade war between US and China heats up in the coming years, Prediction Predictions can build a diversified supply chain model across various countries in - South East Asia, India, and other parts of the world. This reconfiguration of global supply chain can help, as suggested in case study, Prediction Markets at Google, Spanish Version, to buy more products closer to the markets, and it can leverage its size and influence to get better deal from the local markets.
Low interest rates
– Even though inflation is raising its head in most developed economies, Prediction Predictions can still utilize the low interest rates to borrow money for capital investment. Secondly it can also use the increase of government spending in infrastructure projects to get new business.
Better consumer reach
– The expansion of the 5G network will help Prediction Predictions to increase its market reach. Prediction Predictions will be able to reach out to new customers. Secondly 5G will also provide technology framework to build new tools and products that can help more immersive consumer experience and faster consumer journey.
Leveraging digital technologies
– Prediction Predictions can leverage digital technologies such as artificial intelligence and machine learning to automate the production process, customer analytics to get better insights into consumer behavior, realtime digital dashboards to get better sales tracking, logistics and transportation, product tracking, etc.
Manufacturing automation
– Prediction Predictions can use the latest technology developments to improve its manufacturing and designing process in Technology & Operations segment. It can use CAD and 3D printing to build a quick prototype and pilot testing products. It can leverage automation using machine learning and artificial intelligence to do faster production at lowers costs, and it can leverage the growth in satellite and tracking technologies to improve inventory management, transportation, and shipping.
Threats Prediction Markets at Google, Spanish Version External Strategic Factors
What are Threats in the SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
The threats mentioned in the HBR case study Prediction Markets at Google, Spanish Version are -
Regulatory challenges
– Prediction Predictions needs to prepare for regulatory challenges as consumer protection groups and other pressure groups are vigorously advocating for more regulations on big business - to reduce inequality, to create a level playing field, to product data privacy and consumer privacy, to reduce the influence of big money on democratic institutions, etc. This can lead to significant changes in the Technology & Operations industry regulations.
Increasing international competition and downward pressure on margins
– Apart from technology driven competitive advantage dilution, Prediction Predictions can face downward pressure on margins from increasing competition from international players. The international players have stable revenue in their home market and can use those resources to penetrate prominent markets illustrated in HBR case study Prediction Markets at Google, Spanish Version .
Barriers of entry lowering
– As technology is more democratized, the barriers to entry in the industry are lowering. It can presents Prediction Predictions with greater competitive threats in the near to medium future. Secondly it will also put downward pressure on pricing throughout the sector.
Increasing wage structure of Prediction Predictions
– Post Covid-19 there is a sharp increase in the wages especially in the jobs that require interaction with people. The increasing wages can put downward pressure on the margins of Prediction Predictions.
Environmental challenges
– Prediction Predictions needs to have a robust strategy against the disruptions arising from climate change and energy requirements. EU has identified it as key priority area and spending 30% of its 880 billion Euros European post Covid-19 recovery funds on green technology. Prediction Predictions can take advantage of this fund but it will also bring new competitors in the Technology & Operations industry.
Technology acceleration in Forth Industrial Revolution
– Prediction Predictions has witnessed rapid integration of technology during Covid-19 in the Technology & Operations industry. As one of the leading players in the industry, Prediction Predictions needs to keep up with the evolution of technology in the Technology & Operations sector. According to Mckinsey study top managers believe that the adoption of technology in operations, communications is 20-25 times faster than what they planned in the beginning of 2019.
Consumer confidence and its impact on Prediction Predictions demand
– There is a high probability of declining consumer confidence, given – high inflammation rate, rise of gig economy, lower job stability, increasing cost of living, higher interest rates, and aging demography. All the factors contribute to people saving higher rate of their income, resulting in lower consumer demand in the industry and other sectors.
Aging population
– As the populations of most advanced economies are aging, it will lead to high social security costs, higher savings among population, and lower demand for goods and services in the economy. The household savings in US, France, UK, Germany, and Japan are growing faster than predicted because of uncertainty caused by pandemic.
Easy access to finance
– Easy access to finance in Technology & Operations field will also reduce the barriers to entry in the industry, thus putting downward pressure on the prices because of increasing competition. Prediction Predictions can utilize it by borrowing at lower rates and invest it into research and development, capital expenditure to fortify its core competitive advantage.
Capital market disruption
– During the Covid-19, Dow Jones has touched record high. The valuations of a number of companies are way beyond their existing business model potential. This can lead to capital market correction which can put a number of suppliers, collaborators, value chain partners in great financial difficulty. It will directly impact the business of Prediction Predictions.
Learning curve for new practices
– As the technology based on artificial intelligence and machine learning platform is getting complex, as highlighted in case study Prediction Markets at Google, Spanish Version, Prediction Predictions may face longer learning curve for training and development of existing employees. This can open space for more nimble competitors in the field of Technology & Operations .
Trade war between China and United States
– The trade war between two of the biggest economies can hugely impact the opportunities for Prediction Predictions in the Technology & Operations industry. The Technology & Operations industry is already at various protected from local competition in China, with the rise of trade war the protection levels may go up. This presents a clear threat of current business model in Chinese market.
High level of anxiety and lack of motivation
– the Great Resignation in United States is the sign of broader dissatisfaction among the workforce in United States. Prediction Predictions needs to understand the core reasons impacting the Technology & Operations industry. This will help it in building a better workplace.
Weighted SWOT Analysis of Prediction Markets at Google, Spanish Version Template, Example
Not all factors mentioned under the Strengths, Weakness, Opportunities, and Threats quadrants in the SWOT Analysis are equal. Managers in the HBR case study Prediction Markets at Google, Spanish Version needs to zero down on the relative importance of each factor mentioned in the Strengths, Weakness, Opportunities, and Threats quadrants.
We can provide the relative importance to each factor by assigning relative weights. Weighted SWOT analysis process is a three stage process –
First stage for doing weighted SWOT analysis of the case study Prediction Markets at Google, Spanish Version is to rank the strengths and weaknesses of the organization. This will help you to assess the most important strengths and weaknesses of the firm and which one of the strengths and weaknesses mentioned in the initial lists are marginal and can be left out.
Second stage for conducting weighted SWOT analysis of the Harvard case study Prediction Markets at Google, Spanish Version is to give probabilities to the external strategic factors thus better understanding the opportunities and threats arising out of macro environment changes and developments.
Third stage of constructing weighted SWOT analysis of Prediction Markets at Google, Spanish Version is to provide strategic recommendations includes – joining likelihood of external strategic factors such as opportunities and threats to the internal strategic factors – strengths and weaknesses. You should start with external factors as they will provide the direction of the overall industry. Secondly by joining probabilities with internal strategic factors can help the company not only strategic fit but also the most probably strategic trade-off that Prediction Predictions needs to make to build a sustainable competitive advantage.
