This technical note presents the reason for using a binomial logic regression in marketing applications. It is used in Darden's "Big Data in Marketing" course elective. The issues surrounding the use of a linear regression model when the dependent variable is a dummy variable are identified. A consumer-utility-based behavioral rationale is presented for the applicability of the binomial logistic regression for modeling dummy variables. Simulated and real data examples are used to present the mechanics of the logistic regression and the interpretation of the outputs. The relationship between odds ratio and the logistic regression probabilities are presented. Application areas such as brand choice and customer retention are discussed.
Swot Analysis of "Logistic Regression" written by Rajkumar Venkatesan, Shea Gibbs includes – strengths weakness that are internal strategic factors of the organization, and opportunities and threats that Regression Logistic facing as an external strategic factors. Some of the topics covered in Logistic Regression case study are - Strategic Management Strategies, and Sales & Marketing.
Some of the macro environment factors that can be used to understand the Logistic Regression casestudy better are - – there is backlash against globalization, increasing household debt because of falling income levels, supply chains are disrupted by pandemic , increasing energy prices, increasing transportation and logistics costs, customer relationship management is fast transforming because of increasing concerns over data privacy, increasing commodity prices,
cloud computing is disrupting traditional business models, banking and financial system is disrupted by Bitcoin and other crypto currencies, etc
Introduction to SWOT Analysis of Logistic Regression
SWOT stands for an organization’s Strengths, Weaknesses, Opportunities and Threats . At Oak Spring University , we believe that protagonist in Logistic Regression case study can use SWOT analysis as a strategic management tool to assess the current internal strengths and weaknesses of the Regression Logistic, and to figure out the opportunities and threats in the macro environment – technological, environmental, political, economic, social, demographic, etc in which Regression Logistic 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 Logistic Regression can be done for the following purposes –
1. Strategic planning using facts provided in Logistic Regression case study
2. Improving business portfolio management of Regression Logistic
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 Regression Logistic
Strengths Logistic Regression | Internal Strategic Factors
What are Strengths in SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
The strengths of Regression Logistic in Logistic Regression Harvard Business Review case study are -
Diverse revenue streams
– Regression Logistic is present in almost all the verticals within the industry. This has provided firm in Logistic Regression 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.
Ability to lead change in Sales & Marketing field
– Regression Logistic 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 Regression Logistic in – penetrating new markets, reaching out to new customers, and providing different value propositions to different customers in the international markets.
Low bargaining power of suppliers
– Suppliers of Regression Logistic in the sector have low bargaining power. Logistic Regression has further diversified its suppliers portfolio by building a robust supply chain across various countries. This helps Regression Logistic to manage not only supply disruptions but also source products at highly competitive prices.
Learning organization
- Regression Logistic 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 Regression Logistic is open place that encourages instructiveness, ideation, open minded discussions, and creativity. Employees and leaders in Logistic Regression Harvard Business Review case study emphasize – knowledge, initiative, and innovation.
Sustainable margins compare to other players in Sales & Marketing industry
– Logistic Regression firm has clearly differentiated products in the market place. This has enabled Regression Logistic to fetch slight price premium compare to the competitors in the Sales & Marketing industry. The sustainable margins have also helped Regression Logistic to invest into research and development (R&D) and innovation.
Effective Research and Development (R&D)
– Regression Logistic 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 Logistic Regression - staying ahead in the industry in terms of – new product launches, superior customer experience, highly competitive pricing strategies, and great returns to the shareholders.
Cross disciplinary teams
– Horizontal connected teams at the Regression Logistic 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.
Superior customer experience
– The customer experience strategy of Regression Logistic in the segment is based on four key concepts – personalization, simplification of complex needs, prompt response, and continuous engagement.
High switching costs
– The high switching costs that Regression Logistic 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.
Innovation driven organization
– Regression Logistic is one of the most innovative firm in sector. Manager in Logistic Regression 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 Regression Logistic
– The covid-19 pandemic has put organizational resilience at the centre of everthing that Regression Logistic does. Organizational resilience comprises - Financial Resilience, Operational Resilience, Technological Resilience, Organizational Resilience, Business Model Resilience, and Reputation Resilience.
High brand equity
– Regression Logistic has strong brand awareness and brand recognition among both - the exiting customers and potential new customers. Strong brand equity has enabled Regression Logistic to keep acquiring new customers and building profitable relationship with both the new and loyal customers.
Weaknesses Logistic Regression | Internal Strategic Factors
What are Weaknesses in SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
The weaknesses of Logistic Regression are -
Capital Spending Reduction
– Even during the low interest decade, Regression Logistic 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.
Aligning sales with marketing
– It come across in the case study Logistic Regression 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 Logistic Regression can leverage the sales team experience to cultivate customer relationships as Regression Logistic is planning to shift buying processes online.
Low market penetration in new markets
– Outside its home market of Regression Logistic, firm in the HBR case study Logistic Regression needs to spend more promotional, marketing, and advertising efforts to penetrate international markets.
High operating costs
– Compare to the competitors, firm in the HBR case study Logistic Regression 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 Regression Logistic 's lucrative customers.
Lack of clear differentiation of Regression Logistic products
– To increase the profitability and margins on the products, Regression Logistic needs to provide more differentiated products than what it is currently offering in the marketplace.
Products dominated business model
– Even though Regression Logistic 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 - Logistic Regression should strive to include more intangible value offerings along with its core products and services.
Slow decision making process
– As mentioned earlier in the report, Regression Logistic 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. Regression Logistic 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.
High bargaining power of channel partners
– Because of the regulatory requirements, Rajkumar Venkatesan, Shea Gibbs suggests that, Regression Logistic 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.
Ability to respond to the competition
– As the decision making is very deliberative, highlighted in the case study Logistic Regression, in the dynamic environment Regression Logistic has struggled to respond to the nimble upstart competition. Regression Logistic has reasonably good record with similar level competitors but it has struggled with new entrants taking away niches of its business.
High cash cycle compare to competitors
Regression Logistic 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.
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 Regression Logistic supply chain. Even after few cautionary changes mentioned in the HBR case study - Logistic Regression, it is still heavily dependent upon the existing supply chain. The existing supply chain though brings in cost efficiencies but it has left Regression Logistic vulnerable to further global disruptions in South East Asia.
Opportunities Logistic Regression | 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 Logistic Regression are -
Using analytics as competitive advantage
– Regression Logistic 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 Logistic Regression - to build a competitive advantage using analytics. The analytics driven competitive advantage can help Regression Logistic to build faster Go To Market strategies, better consumer insights, developing relevant product features, and building a highly efficient supply chain.
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 Regression Logistic 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.
Buying journey improvements
– Regression Logistic can improve the customer journey of consumers in the industry by using analytics and artificial intelligence. Logistic Regression 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.
Harnessing reconfiguration of the global supply chains
– As the trade war between US and China heats up in the coming years, Regression Logistic 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, Logistic Regression, to buy more products closer to the markets, and it can leverage its size and influence to get better deal from the local markets.
Better consumer reach
– The expansion of the 5G network will help Regression Logistic to increase its market reach. Regression Logistic 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.
Loyalty marketing
– Regression Logistic 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 Regression Logistic has opened avenues for new revenue streams for the organization in the industry. This can help Regression Logistic to build a more holistic ecosystem as suggested in the Logistic Regression case study. Regression Logistic 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, Regression Logistic is facing challenges because of the dominance of functional experts in the organization. Logistic Regression 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.
Building a culture of innovation
– managers at Regression Logistic can make experimentation a productive activity and build a culture of innovation using approaches such as – mining transaction data, A/B testing of websites and selling platforms, engaging potential customers over various needs, and building on small ideas in the Sales & Marketing segment.
Manufacturing automation
– Regression Logistic 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 Regression Logistic can not only reduce the costs of the project but also help it in integrating the projects with other processes within the organization.
Remote work and new talent hiring opportunities
– The widespread usage of remote working technologies during Covid-19 has opened opportunities for Regression Logistic 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 Regression Logistic to hire the very best people irrespective of their geographical location.
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. Regression Logistic can tie-up with other value chain partners to explore new opportunities regarding meeting customer demands and building a rewarding and engaging relationship.
Threats Logistic Regression External Strategic Factors
What are Threats in the SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
The threats mentioned in the HBR case study Logistic Regression are -
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 Regression Logistic in the Sales & Marketing sector and impact the bottomline of the organization.
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. Regression Logistic 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 Regression Logistic.
Regulatory challenges
– Regression Logistic 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.
Consumer confidence and its impact on Regression Logistic 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 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. Regression Logistic needs to understand the core reasons impacting the Sales & Marketing industry. This will help it in building a better workplace.
Technology acceleration in Forth Industrial Revolution
– Regression Logistic has witnessed rapid integration of technology during Covid-19 in the Sales & Marketing industry. As one of the leading players in the industry, Regression Logistic 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.
Trade war between China and United States
– The trade war between two of the biggest economies can hugely impact the opportunities for Regression Logistic 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.
Barriers of entry lowering
– As technology is more democratized, the barriers to entry in the industry are lowering. It can presents Regression Logistic with greater competitive threats in the near to medium future. Secondly it will also put downward pressure on pricing throughout the sector.
Environmental challenges
– Regression Logistic 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. Regression Logistic can take advantage of this fund but it will also bring new competitors in the Sales & Marketing industry.
Increasing international competition and downward pressure on margins
– Apart from technology driven competitive advantage dilution, Regression Logistic 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 Logistic Regression .
Stagnating economy with rate increase
– Regression Logistic 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.
Learning curve for new practices
– As the technology based on artificial intelligence and machine learning platform is getting complex, as highlighted in case study Logistic Regression, Regression Logistic may face longer learning curve for training and development of existing employees. This can open space for more nimble competitors in the field of Sales & Marketing .
Weighted SWOT Analysis of Logistic Regression 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 Logistic Regression 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 Logistic Regression 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 Logistic Regression 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 Logistic Regression 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 Regression Logistic needs to make to build a sustainable competitive advantage.
