Case Study Description of The Prediction Lover's Handbook
This is an MIT Sloan Management Review article. When picking assessment tools to inform better decisions about future paths, executives are faced with a wide variety of options--some of which are well established, while others are in early stages of development. The authors provide an insider's guide to prediction and recommendation techniques and technologies. They cover prediction tools including attributized Bayesian analysis, biological responses analysis, cluster analysis, collaborative filtering, content-based filtering/decision trees, neural network analysis, prediction (or opinion) markets, regression analysis, social network-based recommendations and textual analytics. With each potential tool, they briefly describe the technique, who uses it and for what purpose, its strengths and weaknesses, and its future prospects as a prediction tool. Finally, the authors offer up an indication of the best time in the decision process to begin using the tool.
Swot Analysis of "The Prediction Lover's Handbook" written by Thomas H. Davenport, Jeanne G. Harris includes – strengths weakness that are internal strategic factors of the organization, and opportunities and threats that Prediction Filtering facing as an external strategic factors. Some of the topics covered in The Prediction Lover's Handbook case study are - Strategic Management Strategies, Innovation, Marketing, Technology and Sales & Marketing.
Some of the macro environment factors that can be used to understand the The Prediction Lover's Handbook casestudy better are - – challanges to central banks by blockchain based private currencies, cloud computing is disrupting traditional business models, customer relationship management is fast transforming because of increasing concerns over data privacy, central banks are concerned over increasing inflation, talent flight as more people leaving formal jobs, there is backlash against globalization, banking and financial system is disrupted by Bitcoin and other crypto currencies,
competitive advantages are harder to sustain because of technology dispersion, increasing transportation and logistics costs, etc
Introduction to SWOT Analysis of The Prediction Lover's Handbook
SWOT stands for an organization’s Strengths, Weaknesses, Opportunities and Threats . At Oak Spring University , we believe that protagonist in The Prediction Lover's Handbook case study can use SWOT analysis as a strategic management tool to assess the current internal strengths and weaknesses of the Prediction Filtering, and to figure out the opportunities and threats in the macro environment – technological, environmental, political, economic, social, demographic, etc in which Prediction Filtering 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 The Prediction Lover's Handbook can be done for the following purposes –
1. Strategic planning using facts provided in The Prediction Lover's Handbook case study
2. Improving business portfolio management of Prediction Filtering
3. Assessing feasibility of the new initiative in Sales & Marketing field.
4. Making a Sales & Marketing topic specific business decision
5. Set goals for the organization
6. Organizational restructuring of Prediction Filtering
Strengths The Prediction Lover's Handbook | Internal Strategic Factors
What are Strengths in SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
The strengths of Prediction Filtering in The Prediction Lover's Handbook Harvard Business Review case study are -
Strong track record of project management
– Prediction Filtering is known for sticking to its project targets. This enables the firm to manage – time, project costs, and have sustainable margins on the projects.
Highly skilled collaborators
– Prediction Filtering 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 The Prediction Lover's Handbook HBR case study have helped the firm to develop new products and bring them quickly to the marketplace.
Sustainable margins compare to other players in Sales & Marketing industry
– The Prediction Lover's Handbook firm has clearly differentiated products in the market place. This has enabled Prediction Filtering to fetch slight price premium compare to the competitors in the Sales & Marketing industry. The sustainable margins have also helped Prediction Filtering to invest into research and development (R&D) and innovation.
High brand equity
– Prediction Filtering has strong brand awareness and brand recognition among both - the exiting customers and potential new customers. Strong brand equity has enabled Prediction Filtering to keep acquiring new customers and building profitable relationship with both the new and loyal customers.
Diverse revenue streams
– Prediction Filtering is present in almost all the verticals within the industry. This has provided firm in The Prediction Lover's Handbook case study a diverse revenue stream that has helped it to survive disruptions such as global pandemic in Covid-19, financial disruption of 2008, and supply chain disruption of 2021.
Successful track record of launching new products
– Prediction Filtering has launched numerous new products in last few years, keeping in mind evolving customer preferences and competitive pressures. Prediction Filtering 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.
Digital Transformation in Sales & Marketing segment
- digital transformation varies from industry to industry. For Prediction Filtering digital transformation journey comprises differing goals based on market maturity, customer technology acceptance, and organizational culture. Prediction Filtering 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.
Innovation driven organization
– Prediction Filtering is one of the most innovative firm in sector. Manager in The Prediction Lover's Handbook Harvard Business Review case study can use Clayton Christensen Disruptive Innovation strategies to further increase the scale of innovtions in the organization.
Organizational Resilience of Prediction Filtering
– The covid-19 pandemic has put organizational resilience at the centre of everthing that Prediction Filtering does. Organizational resilience comprises - Financial Resilience, Operational Resilience, Technological Resilience, Organizational Resilience, Business Model Resilience, and Reputation Resilience.
Ability to recruit top talent
– Prediction Filtering is one of the leading recruiters in the industry. Managers in the The Prediction Lover's Handbook are in a position to attract the best talent available. The firm has a robust talent identification program that helps in identifying the brightest.
Learning organization
- Prediction Filtering 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 Filtering is open place that encourages instructiveness, ideation, open minded discussions, and creativity. Employees and leaders in The Prediction Lover's Handbook Harvard Business Review case study emphasize – knowledge, initiative, and innovation.
Operational resilience
– The operational resilience strategy in the The Prediction Lover's Handbook 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.
Weaknesses The Prediction Lover's Handbook | Internal Strategic Factors
What are Weaknesses in SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
The weaknesses of The Prediction Lover's Handbook are -
High bargaining power of channel partners
– Because of the regulatory requirements, Thomas H. Davenport, Jeanne G. Harris suggests that, Prediction Filtering 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.
No frontier risks strategy
– After analyzing the HBR case study The Prediction Lover's Handbook, it seems that company is thinking about the frontier risks that can impact Sales & Marketing strategy. But it has very little resources allocation to manage the risks emerging from events such as natural disasters, climate change, melting of permafrost, tacking the rise of artificial intelligence, opportunities and threats emerging from commercialization of space etc.
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 Filtering supply chain. Even after few cautionary changes mentioned in the HBR case study - The Prediction Lover's Handbook, it is still heavily dependent upon the existing supply chain. The existing supply chain though brings in cost efficiencies but it has left Prediction Filtering vulnerable to further global disruptions in South East Asia.
High dependence on star products
– The top 2 products and services of the firm as mentioned in the The Prediction Lover's Handbook 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 Filtering has relatively successful track record of launching new products.
Capital Spending Reduction
– Even during the low interest decade, Prediction Filtering 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.
Products dominated business model
– Even though Prediction Filtering has some of the most successful products in the industry, this business model has made each new product launch extremely critical for continuous financial growth of the organization. firm in the HBR case study - The Prediction Lover's Handbook should strive to include more intangible value offerings along with its core products and services.
High operating costs
– Compare to the competitors, firm in the HBR case study The Prediction Lover's Handbook has high operating costs in the. This can be harder to sustain given the new emerging competition from nimble players who are using technology to attract Prediction Filtering 's lucrative customers.
Increasing silos among functional specialists
– The organizational structure of Prediction Filtering is dominated by functional specialists. It is not different from other players in the Sales & Marketing segment. Prediction Filtering needs to de-silo the office environment to harness the true potential of its workforce. Secondly the de-silo will also help Prediction Filtering to focus more on services rather than just following the product oriented approach.
Workers concerns about automation
– As automation is fast increasing in the segment, Prediction Filtering 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.
Ability to respond to the competition
– As the decision making is very deliberative, highlighted in the case study The Prediction Lover's Handbook, in the dynamic environment Prediction Filtering has struggled to respond to the nimble upstart competition. Prediction Filtering has reasonably good record with similar level competitors but it has struggled with new entrants taking away niches of its business.
Interest costs
– Compare to the competition, Prediction Filtering 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 The Prediction Lover's Handbook | 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 The Prediction Lover's Handbook are -
Low interest rates
– Even though inflation is raising its head in most developed economies, Prediction Filtering 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.
Reforming the budgeting process
- By establishing new metrics that will be used to evaluate both existing and potential projects Prediction Filtering can not only reduce the costs of the project but also help it in integrating the projects with other processes within the organization.
Finding new ways to collaborate
– Covid-19 has not only transformed business models of companies in Sales & Marketing industry, but it has also influenced the consumer preferences. Prediction Filtering can tie-up with other value chain partners to explore new opportunities regarding meeting customer demands and building a rewarding and engaging relationship.
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 Filtering can explore opportunities that can attract volunteers and are consistent with its mission and vision.
Changes in consumer behavior post Covid-19
– Consumer behavior has changed in the Sales & Marketing industry because of Covid-19 restrictions. Some of this behavior will stay once things get back to normal. Prediction Filtering 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 Filtering 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.
Leveraging digital technologies
– Prediction Filtering 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.
Learning at scale
– Online learning technologies has now opened space for Prediction Filtering to conduct training and development for its employees across the world. This will result in not only reducing the cost of training but also help employees in different part of the world to integrate with the headquarter work culture, ethos, and standards.
Using analytics as competitive advantage
– Prediction Filtering 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 The Prediction Lover's Handbook - to build a competitive advantage using analytics. The analytics driven competitive advantage can help Prediction Filtering to build faster Go To Market strategies, better consumer insights, developing relevant product features, and building a highly efficient supply chain.
Loyalty marketing
– Prediction Filtering has focused on building a highly responsive customer relationship management platform. This platform is built on in-house data and driven by analytics and artificial intelligence. The customer analytics can help the organization to fine tune its loyalty marketing efforts, increase the wallet share of the organization, reduce wastage on mainstream advertising spending, build better pricing strategies using personalization, etc.
Buying journey improvements
– Prediction Filtering can improve the customer journey of consumers in the industry by using analytics and artificial intelligence. The Prediction Lover's Handbook 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.
Redefining models of collaboration and team work
– As explained in the weaknesses section, Prediction Filtering is facing challenges because of the dominance of functional experts in the organization. The Prediction Lover's Handbook 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.
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 Filtering can use these opportunities to build new business models that can help the communities that Prediction Filtering operates in. Secondly it can use opportunities from government spending in Sales & Marketing sector.
Developing new processes and practices
– Prediction Filtering can develop new processes and procedures in Sales & Marketing 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.
Threats The Prediction Lover's Handbook External Strategic Factors
What are Threats in the SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
The threats mentioned in the HBR case study The Prediction Lover's Handbook are -
Trade war between China and United States
– The trade war between two of the biggest economies can hugely impact the opportunities for Prediction Filtering in the Sales & Marketing industry. The Sales & Marketing 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 Filtering has witnessed rapid integration of technology during Covid-19 in the Sales & Marketing industry. As one of the leading players in the industry, Prediction Filtering needs to keep up with the evolution of technology in the Sales & Marketing 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.
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 Filtering in the Sales & Marketing sector and impact the bottomline of the organization.
Regulatory challenges
– Prediction Filtering 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 Sales & Marketing industry regulations.
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 Filtering
– 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 Filtering.
Shortening product life cycle
– it is one of the major threat that Prediction Filtering is facing in Sales & Marketing sector. It can lead to higher research and development costs, higher marketing expenses, lower customer loyalty, etc.
Increasing international competition and downward pressure on margins
– Apart from technology driven competitive advantage dilution, Prediction Filtering 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 The Prediction Lover's Handbook .
Consumer confidence and its impact on Prediction Filtering 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.
High dependence on third party suppliers
– Prediction Filtering 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.
Instability in the European markets
– European Union markets are facing three big challenges post Covid – expanded balance sheets, Brexit related business disruption, and aggressive Russia looking to distract the existing security mechanism. Prediction Filtering will face different problems in different parts of Europe. For example it will face inflationary pressures in UK, France, and Germany, balance sheet expansion and demand challenges in Southern European countries, and geopolitical instability in the Eastern Europe.
Environmental challenges
– Prediction Filtering 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 Filtering can take advantage of this fund but it will also bring new competitors in the Sales & Marketing industry.
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.
Weighted SWOT Analysis of The Prediction Lover's Handbook 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 The Prediction Lover's Handbook 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 The Prediction Lover's Handbook 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 The Prediction Lover's Handbook 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 The Prediction Lover's Handbook 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 Filtering needs to make to build a sustainable competitive advantage.
