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 - – increasing transportation and logistics costs, increasing household debt because of falling income levels, there is backlash against globalization, talent flight as more people leaving formal jobs, competitive advantages are harder to sustain because of technology dispersion, banking and financial system is disrupted by Bitcoin and other crypto currencies, customer relationship management is fast transforming because of increasing concerns over data privacy,
cloud computing is disrupting traditional business models, there is increasing trade war between United States & China, 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 -
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.
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.
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.
Ability to recruit top talent
– Prediction Predictions is one of the leading recruiters in the industry. Managers in the Prediction Markets at Google, Spanish Version are in a position to attract the best talent available. The firm has a robust talent identification program that helps in identifying the brightest.
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.
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.
Superior customer experience
– The customer experience strategy of Prediction Predictions in the segment is based on four key concepts – personalization, simplification of complex needs, prompt response, and continuous engagement.
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.
Learning organization
- Prediction Predictions is a learning organization. It has inculcated three key characters of learning organization in its processes and operations – exploration, creativity, and expansiveness. The work place at Prediction Predictions is open place that encourages instructiveness, ideation, open minded discussions, and creativity. Employees and leaders in Prediction Markets at Google, Spanish Version Harvard Business Review case study emphasize – knowledge, initiative, and innovation.
High switching costs
– The high switching costs that Prediction Predictions has built up over years in its products and services combo offer has resulted in high retention of customers, lower marketing costs, and greater ability of the firm to focus on its customers.
Highly skilled collaborators
– Prediction Predictions has highly efficient outsourcing and offshoring strategy. It has resulted in greater operational flexibility and bringing down the costs in highly price sensitive segment. Secondly the value chain collaborators of the firm in Prediction Markets at Google, Spanish Version HBR case study have helped the firm to develop new products and bring them quickly to the marketplace.
Cross disciplinary teams
– Horizontal connected teams at the Prediction Predictions are driving operational speed, building greater agility, and keeping the organization nimble to compete with new competitors. It helps are organization to ideate new ideas, and execute them swiftly in the marketplace.
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 -
Ability to respond to the competition
– As the decision making is very deliberative, highlighted in the case study Prediction Markets at Google, Spanish Version, in the dynamic environment Prediction Predictions has struggled to respond to the nimble upstart competition. Prediction Predictions has reasonably good record with similar level competitors but it has struggled with new entrants taking away niches of its business.
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 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.
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.
Workers concerns about automation
– As automation is fast increasing in the segment, Prediction Predictions needs to come up with a strategy to reduce the workers concern regarding automation. Without a clear strategy, it could lead to disruption and uncertainty within the organization.
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.
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 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.
Capital Spending Reduction
– Even during the low interest decade, Prediction Predictions has not been able to do capital spending to the tune of the competition. This has resulted into fewer innovations and company facing stiff competition from both existing competitors and new entrants who are disrupting the industry using digital technology.
Slow to strategic competitive environment developments
– As Prediction Markets at Google, Spanish Version HBR case study mentions - Prediction Predictions takes time to assess the upcoming competitions. This has led to missing out on atleast 2-3 big opportunities in the industry in last five years.
Interest costs
– Compare to the competition, Prediction Predictions has borrowed money from the capital market at higher rates. It needs to restructure the interest payment and costs so that it can compete better and improve profitability.
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 -
Identify volunteer opportunities
– Covid-19 has impacted working population in two ways – it has led to people soul searching about their professional choices, resulting in mass resignation. Secondly it has encouraged people to do things that they are passionate about. This has opened opportunities for businesses to build volunteer oriented socially driven projects. Prediction Predictions can explore opportunities that can attract volunteers and are consistent with its mission and vision.
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.
Use of Bitcoin and other crypto currencies for transactions
– The popularity of Bitcoin and other crypto currencies as asset class and medium of transaction has opened new opportunities for Prediction Predictions in the consumer business. Now Prediction Predictions can target international markets with far fewer capital restrictions requirements than the existing system.
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.
Lowering marketing communication costs
– 5G expansion will open new opportunities for Prediction Predictions in the field of marketing communication. It will bring down the cost of doing business, provide technology platform to build new products in the Technology & Operations segment, and it will provide faster access to the consumers.
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.
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.
Developing new processes and practices
– Prediction Predictions can develop new processes and procedures in Technology & Operations industry using technology such as automation using artificial intelligence, real time transportation and products tracking, 3D modeling for concept development and new products pilot testing etc.
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.
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.
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.
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.
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.
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 -
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.
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.
High dependence on third party suppliers
– Prediction Predictions high dependence on third party suppliers can disrupt its processes and delivery mechanism. For example -the current troubles of car makers because of chip shortage is because the chip companies started producing chips for electronic companies rather than car manufacturers.
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.
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.
New competition
– After the dotcom bust of 2001, financial crisis of 2008-09, the business formation in US economy had declined. But in 2020 alone, there are more than 1.5 million new business applications in United States. This can lead to greater competition for Prediction Predictions in the Technology & Operations sector and impact the bottomline of the organization.
Stagnating economy with rate increase
– Prediction Predictions can face lack of demand in the market place because of Fed actions to reduce inflation. This can lead to sluggish growth in the economy, lower demands, lower investments, higher borrowing costs, and consolidation in the field.
Technology disruption because of hacks, piracy etc
– The colonial pipeline illustrated, how vulnerable modern organization are to international hackers, miscreants, and disruptors. The cyber security interruption, data leaks, etc can seriously jeopardize the future growth of the organization.
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.
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.
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 .
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.
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.
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.