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 - – increasing transportation and logistics costs, increasing inequality as vast percentage of new income is going to the top 1%, increasing commodity prices, digital marketing is dominated by two big players Facebook and Google, banking and financial system is disrupted by Bitcoin and other crypto currencies, central banks are concerned over increasing inflation, geopolitical disruptions,
increasing energy prices, customer relationship management is fast transforming because of increasing concerns over data privacy, 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 -
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.
Operational resilience
– The operational resilience strategy in the Multiple Regression and Marketing-Mix Models 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.
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.
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.
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.
Strong track record of project management
– Regression Significance is known for sticking to its project targets. This enables the firm to manage – time, project costs, and have sustainable margins on the projects.
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.
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.
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.
High switching costs
– The high switching costs that Regression Significance 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.
High brand equity
– Regression Significance has strong brand awareness and brand recognition among both - the exiting customers and potential new customers. Strong brand equity has enabled Regression Significance to keep acquiring new customers and building profitable relationship with both the new and loyal customers.
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 -
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.
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.
Ability to respond to the competition
– As the decision making is very deliberative, highlighted in the case study Multiple Regression and Marketing-Mix Models, in the dynamic environment Regression Significance has struggled to respond to the nimble upstart competition. Regression Significance has reasonably good record with similar level competitors but it has struggled with new entrants taking away niches of its business.
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.
Lack of clear differentiation of Regression Significance products
– To increase the profitability and margins on the products, Regression Significance needs to provide more differentiated products than what it is currently offering in the marketplace.
Employees’ incomplete understanding of strategy
– From the instances in the HBR case study Multiple Regression and Marketing-Mix Models, it seems that the employees of Regression Significance don’t have comprehensive understanding of the firm’s strategy. This is reflected in number of promotional campaigns over the last few years that had mixed messaging and competing priorities. Some of the strategic activities and services promoted in the promotional campaigns were not consistent with the organization’s strategy.
Aligning sales with marketing
– It come across in the case study Multiple Regression and Marketing-Mix Models 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 Multiple Regression and Marketing-Mix Models can leverage the sales team experience to cultivate customer relationships as Regression Significance is planning to shift buying processes online.
Compensation and incentives
– The revenue per employee as mentioned in the HBR case study Multiple Regression and Marketing-Mix Models, is just above the industry average. Regression Significance needs to redesign the compensation structure and incentives to increase the revenue per employees. Some of the steps that it can take are – hiring more specialists on project basis, etc.
Products dominated business model
– Even though Regression Significance 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 - Multiple Regression and Marketing-Mix Models should strive to include more intangible value offerings along with its core products and services.
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.
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.
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 -
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.
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.
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.
Learning at scale
– Online learning technologies has now opened space for Regression Significance 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.
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.
Redefining models of collaboration and team work
– As explained in the weaknesses section, Regression Significance is facing challenges because of the dominance of functional experts in the organization. Multiple Regression and Marketing-Mix Models 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.
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.
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.
Manufacturing automation
– Regression Significance 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.
Building a culture of innovation
– managers at Regression Significance 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.
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.
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.
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 -
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.
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.
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.
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 .
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 Significance 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.
Backlash against dominant players
– US Congress and other legislative arms of the government are getting tough on big business especially technology companies. The digital arm of Regression Significance business can come under increasing regulations regarding data privacy, data security, etc.
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.
Stagnating economy with rate increase
– Regression Significance 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.
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.
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.
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.
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 .
Barriers of entry lowering
– As technology is more democratized, the barriers to entry in the industry are lowering. It can presents Regression Significance 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 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.
