Case Study Description of Multiple Regression and Marketing-Mix Models
This technical note provides a basic introduction to multiple linear regression. The concept of regression using a single independent variable is first introduced and then some of the practical challenges associated with it--including multiple independent variables in a regression--are discussed. Particular attention is paid to bias in the regression coefficients in the presence of omitted variables. The concept of the economic significance of a model is introduced and is contrasted with statistical significance. At Darden, it is used in a course elective titled "Big Data in Marketing."
Swot Analysis of "Multiple Regression and Marketing-Mix Models" written by Rajkumar Venkatesan, Shea Gibbs includes – strengths weakness that are internal strategic factors of the organization, and opportunities and threats that Regression Significance facing as an external strategic factors. Some of the topics covered in Multiple Regression and Marketing-Mix Models case study are - Strategic Management Strategies, Marketing and Sales & Marketing.
Some of the macro environment factors that can be used to understand the Multiple Regression and Marketing-Mix Models casestudy better are - – competitive advantages are harder to sustain because of technology dispersion, challanges to central banks by blockchain based private currencies, central banks are concerned over increasing inflation, there is backlash against globalization, customer relationship management is fast transforming because of increasing concerns over data privacy, talent flight as more people leaving formal jobs, increasing inequality as vast percentage of new income is going to the top 1%,
supply chains are disrupted by pandemic , banking and financial system is disrupted by Bitcoin and other crypto currencies, etc
Introduction to SWOT Analysis of Multiple Regression and Marketing-Mix Models
SWOT stands for an organization’s Strengths, Weaknesses, Opportunities and Threats . At Oak Spring University , we believe that protagonist in Multiple Regression and Marketing-Mix Models case study can use SWOT analysis as a strategic management tool to assess the current internal strengths and weaknesses of the Regression Significance, and to figure out the opportunities and threats in the macro environment – technological, environmental, political, economic, social, demographic, etc in which Regression Significance 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 Multiple Regression and Marketing-Mix Models can be done for the following purposes –
1. Strategic planning using facts provided in Multiple Regression and Marketing-Mix Models case study
2. Improving business portfolio management of Regression Significance
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 Significance
Strengths Multiple Regression and Marketing-Mix Models | Internal Strategic Factors
What are Strengths in SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
The strengths of Regression Significance in Multiple Regression and Marketing-Mix Models Harvard Business Review case study are -
Successful track record of launching new products
– Regression Significance has launched numerous new products in last few years, keeping in mind evolving customer preferences and competitive pressures. Regression Significance 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
– Regression Significance has one of the best training and development program in the industry. The effectiveness of the training programs can be measured in Multiple Regression and Marketing-Mix Models 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.
Sustainable margins compare to other players in Sales & Marketing industry
– Multiple Regression and Marketing-Mix Models firm has clearly differentiated products in the market place. This has enabled Regression Significance to fetch slight price premium compare to the competitors in the Sales & Marketing industry. The sustainable margins have also helped Regression Significance to invest into research and development (R&D) and innovation.
Effective Research and Development (R&D)
– Regression Significance 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 Multiple Regression and Marketing-Mix Models - staying ahead in the industry in terms of – new product launches, superior customer experience, highly competitive pricing strategies, and great returns to the shareholders.
Analytics focus
– Regression Significance 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 Rajkumar Venkatesan, Shea Gibbs 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.
Cross disciplinary teams
– Horizontal connected teams at the Regression Significance 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.
Low bargaining power of suppliers
– Suppliers of Regression Significance in the sector have low bargaining power. Multiple Regression and Marketing-Mix Models has further diversified its suppliers portfolio by building a robust supply chain across various countries. This helps Regression Significance to manage not only supply disruptions but also source products at highly competitive prices.
Diverse revenue streams
– Regression Significance is present in almost all the verticals within the industry. This has provided firm in Multiple Regression and Marketing-Mix Models 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.
Superior customer experience
– The customer experience strategy of Regression Significance in the segment is based on four key concepts – personalization, simplification of complex needs, prompt response, and continuous engagement.
Ability to lead change in Sales & Marketing field
– Regression Significance 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 Significance in – penetrating new markets, reaching out to new customers, and providing different value propositions to different customers in the international markets.
Organizational Resilience of Regression Significance
– The covid-19 pandemic has put organizational resilience at the centre of everthing that Regression Significance does. Organizational resilience comprises - Financial Resilience, Operational Resilience, Technological Resilience, Organizational Resilience, Business Model Resilience, and Reputation Resilience.
Highly skilled collaborators
– Regression Significance 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 Multiple Regression and Marketing-Mix Models HBR case study have helped the firm to develop new products and bring them quickly to the marketplace.
Weaknesses Multiple Regression and Marketing-Mix Models | Internal Strategic Factors
What are Weaknesses in SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
The weaknesses of Multiple Regression and Marketing-Mix Models are -
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 Significance supply chain. Even after few cautionary changes mentioned in the HBR case study - Multiple Regression and Marketing-Mix Models, it is still heavily dependent upon the existing supply chain. The existing supply chain though brings in cost efficiencies but it has left Regression Significance vulnerable to further global disruptions in South East Asia.
Workers concerns about automation
– As automation is fast increasing in the segment, Regression Significance 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.
Increasing silos among functional specialists
– The organizational structure of Regression Significance is dominated by functional specialists. It is not different from other players in the Sales & Marketing segment. Regression Significance needs to de-silo the office environment to harness the true potential of its workforce. Secondly the de-silo will also help Regression Significance to focus more on services rather than just following the product oriented approach.
High cash cycle compare to competitors
Regression Significance 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.
Skills based hiring
– The stress on hiring functional specialists at Regression Significance 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.
No frontier risks strategy
– After analyzing the HBR case study Multiple Regression and Marketing-Mix Models, 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.
Interest costs
– Compare to the competition, Regression Significance 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.
Capital Spending Reduction
– Even during the low interest decade, Regression Significance 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 decision making process
– As mentioned earlier in the report, Regression Significance 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 Significance 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 operating costs
– Compare to the competitors, firm in the HBR case study Multiple Regression and Marketing-Mix Models 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 Significance 's lucrative customers.
High dependence on star products
– The top 2 products and services of the firm as mentioned in the Multiple Regression and Marketing-Mix Models 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 Regression Significance has relatively successful track record of launching new products.
Opportunities Multiple Regression and Marketing-Mix Models | 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 Multiple Regression and Marketing-Mix Models are -
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 Significance can tie-up with other value chain partners to explore new opportunities regarding meeting customer demands and building a rewarding and engaging relationship.
Lowering marketing communication costs
– 5G expansion will open new opportunities for Regression Significance in the field of marketing communication. It will bring down the cost of doing business, provide technology platform to build new products in the Sales & Marketing segment, and it will provide faster access to the consumers.
Using analytics as competitive advantage
– Regression Significance 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 Multiple Regression and Marketing-Mix Models - to build a competitive advantage using analytics. The analytics driven competitive advantage can help Regression Significance to build faster Go To Market strategies, better consumer insights, developing relevant product features, and building a highly efficient supply chain.
Low interest rates
– Even though inflation is raising its head in most developed economies, Regression Significance 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.
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. Regression Significance 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. Regression Significance 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.
Reforming the budgeting process
- By establishing new metrics that will be used to evaluate both existing and potential projects Regression Significance 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
– Regression Significance 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.
Buying journey improvements
– Regression Significance can improve the customer journey of consumers in the industry by using analytics and artificial intelligence. Multiple Regression and Marketing-Mix Models 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.
Better consumer reach
– The expansion of the 5G network will help Regression Significance to increase its market reach. Regression Significance 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.
Remote work and new talent hiring opportunities
– The widespread usage of remote working technologies during Covid-19 has opened opportunities for Regression Significance 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 Significance to hire the very best people irrespective of their geographical location.
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 Regression Significance in the consumer business. Now Regression Significance can target international markets with far fewer capital restrictions requirements than the existing system.
Loyalty marketing
– Regression Significance 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.
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 Significance 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.
Threats Multiple Regression and Marketing-Mix Models External Strategic Factors
What are Threats in the SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
The threats mentioned in the HBR case study Multiple Regression and Marketing-Mix Models are -
Learning curve for new practices
– As the technology based on artificial intelligence and machine learning platform is getting complex, as highlighted in case study Multiple Regression and Marketing-Mix Models, Regression Significance 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 .
Increasing wage structure of Regression Significance
– 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 Regression Significance.
Easy access to finance
– Easy access to finance in Sales & Marketing field will also reduce the barriers to entry in the industry, thus putting downward pressure on the prices because of increasing competition. Regression Significance can utilize it by borrowing at lower rates and invest it into research and development, capital expenditure to fortify its core competitive advantage.
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 Significance needs to understand the core reasons impacting the Sales & Marketing industry. This will help it in building a better workplace.
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.
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.
Increasing international competition and downward pressure on margins
– Apart from technology driven competitive advantage dilution, Regression Significance 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 Multiple Regression and Marketing-Mix Models .
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 Significance in the Sales & Marketing sector and impact the bottomline of the organization.
Environmental challenges
– Regression Significance 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 Significance can take advantage of this fund but it will also bring new competitors in the Sales & Marketing industry.
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 Significance.
High dependence on third party suppliers
– Regression Significance 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.
Consumer confidence and its impact on Regression Significance 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.
Regulatory challenges
– Regression Significance 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.
Weighted SWOT Analysis of Multiple Regression and Marketing-Mix Models 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 Multiple Regression and Marketing-Mix Models 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 Multiple Regression and Marketing-Mix Models 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 Multiple Regression and Marketing-Mix Models 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 Multiple Regression and Marketing-Mix Models 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 Significance needs to make to build a sustainable competitive advantage.