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 - – competitive advantages are harder to sustain because of technology dispersion, digital marketing is dominated by two big players Facebook and Google, there is increasing trade war between United States & China, increasing commodity prices, central banks are concerned over increasing inflation, increasing household debt because of falling income levels, increasing transportation and logistics costs,
wage bills are increasing, talent flight as more people leaving formal jobs, 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 -
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.
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.
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.
Superior customer experience
– The customer experience strategy of Prediction Filtering in the segment is based on four key concepts – personalization, simplification of complex needs, prompt response, and continuous engagement.
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.
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.
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.
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.
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.
Ability to lead change in Sales & Marketing field
– Prediction Filtering 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 Filtering in – penetrating new markets, reaching out to new customers, and providing different value propositions to different customers in the international markets.
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 -
Slow decision making process
– As mentioned earlier in the report, Prediction Filtering has a very deliberative decision making approach. This approach has resulted in prudent decisions, but it has also resulted in missing opportunities in the industry over the last five years. Prediction Filtering even though has strong showing on digital transformation primary two stages, it has struggled to capitalize the power of digital transformation in marketing efforts and new venture efforts.
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.
Skills based hiring
– The stress on hiring functional specialists at Prediction Filtering 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.
Need for greater diversity
– Prediction Filtering 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.
High cash cycle compare to competitors
Prediction Filtering 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.
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.
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 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.
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.
Aligning sales with marketing
– It come across in the case study The Prediction Lover's Handbook 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 The Prediction Lover's Handbook can leverage the sales team experience to cultivate customer relationships as Prediction Filtering is planning to shift buying processes online.
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.
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 -
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.
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 Filtering in the consumer business. Now Prediction Filtering can target international markets with far fewer capital restrictions requirements than the existing system.
Manufacturing automation
– Prediction Filtering can use the latest technology developments to improve its manufacturing and designing process in Sales & Marketing 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.
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.
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.
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 Filtering 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.
Remote work and new talent hiring opportunities
– The widespread usage of remote working technologies during Covid-19 has opened opportunities for Prediction Filtering 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 Filtering to hire the very best people irrespective of their geographical location.
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.
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.
Creating value in data economy
– The success of analytics program of Prediction Filtering has opened avenues for new revenue streams for the organization in the industry. This can help Prediction Filtering to build a more holistic ecosystem as suggested in the The Prediction Lover's Handbook case study. Prediction Filtering can build new products and services such as - data insight services, data privacy related products, data based consulting services, etc.
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.
Better consumer reach
– The expansion of the 5G network will help Prediction Filtering to increase its market reach. Prediction Filtering 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.
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.
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 -
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.
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.
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 .
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.
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.
Stagnating economy with rate increase
– Prediction Filtering 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.
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.
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 Filtering.
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.
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.
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 Filtering needs to understand the core reasons impacting the Sales & Marketing industry. This will help it in building a better workplace.
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.
Barriers of entry lowering
– As technology is more democratized, the barriers to entry in the industry are lowering. It can presents Prediction Filtering with greater competitive threats in the near to medium future. Secondly it will also put downward pressure on pricing throughout the sector.
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.