Marketing Mix (4Ps) Analysis of Assumptions Behind the Linear Regression Model

About 4Ps Model

The 4p model of marketing comprises elements of the product, price, promotion, and place (Chernev, 2018; Kucuk, 2017). The model is commonly referred to as the marketing mix. The marketing mix of the Assumptions Behind the Linear Regression Model allows and facilitates it in achieving its marketing objectives as well as in positively influencing the target audience (Baines, Fill, & Rosengren, 2017). The elements identified in the marketing mix are typically used by the Assumptions Behind the Linear Regression Model for marketing its product and service, and for brand development and building activities. These elements are critically fundamental for the development and creation of marketing plans and marketing strategies by the Assumptions Behind the Linear Regression Model – especially for developing and sustaining competitive advantage (Chernev, 2018; Stead & Hastings, 2018; Grewal & Levy, 2021). Assumptions Behind the Linear Regression Model ensures that the elements identified for the marketing mix model work together cohesively, and complement each other in all its marketing strategies and plans (Abratt & Bendixen, 2018; Deepak & Jeyakumar, 2019).

Product

The product refers to the actual good or service that is being marketed to the consumers by Assumptions Behind the Linear Regression Model, and which will be consumed by the target audience of the Assumptions Behind the Linear Regression Model (Groucutt & Hopkins, 2015). The product or the service being offered by Assumptions Behind the Linear Regression Model largely aims to fulfill a market need and demand, as well as works to create demand by providing a unique and fulfilling customer experience (Stead & Hastings, 2018; Sahaf, 2019).

Quality

Product quality for Assumptions Behind the Linear Regression Model largely refers to how well the company is able to satisfy the customers’ needs and demands through its product and service offerings (Baines, Fill, & Rosengren, 2017; Deepak & Jeyakumar, 2019). In addition to this, the product quality for Assumptions Behind the Linear Regression Model further includes the adherence of the company and its product and service offerings to industry standards and benchmarks as well as the ability of the same to serve its meaning and purpose comprehensively (Iacobucci, 2021; Groucutt & Hopkins, 2015; Chernev, 2018).

Customer demand fulfillment

The ability of the product and service to fulfill customer demands as well as its purpose, and to work efficiently and effectively are important facets of product quality for Assumptions Behind the Linear Regression Model (Iacobucci, 2021; Deepak & Jeyakumar, 2019). Assumptions Behind the Linear Regression Model ensures that its products are available for customers at affordable prices by controlling internal costs (Wu & Li, 2018).

Warranty

The warranty extended by Assumptions Behind the Linear Regression Model includes the guarantee that the company to its customers regarding the functioning and the quality of the purchased food and service (Abratt & Bendixen, 2018). In addition, Assumptions Behind the Linear Regression Model’s warranty also includes any compensation that the company has promised to give the customers in case the product and service fall short of the marketed benefits and functionalities (Išoraitė, 2016; Grewal & Levy, 2021; Kucuk, 2017).

Packaging

Assumptions Behind the Linear Regression Model focuses thoroughly on the packaging and makes sure it includes the process of designing, evaluating, and developing a container for the products and services being manufactured and marketed (Deepak & Jeyakumar, 2019; Baines, Fill, & Rosengren, 2017). The packaging of the product and the service allows Assumptions Behind the Linear Regression Model to highlight the product's purpose, as well as provides ease in transportation, gives room for more prolonged shelf life, and creates a unique and delightful customer experience (Kareh, 2018; Park, 2020).

Brand

The Assumptions Behind the Linear Regression Model invests in developing brands out of its products and service offerings. This means that the Assumptions Behind the Linear Regression Model engages in brand-building activities for its offerings i.e. associating specific designs and communications with its products to ensure differentiation, and easier communication with the target audience (Gillespie & Swan, 2021).

Building the brand

The branding-building activities undertaken by the Assumptions Behind the Linear Regression Model ensure that its target audience is better able to relate to the offerings (Abratt & Bendixen, 2018). Through this, the Assumptions Behind the Linear Regression Model ensures higher loyalty and repeat purchases, as well as positive perception creation for its offerings (Khan, 2014; Kareh, 2018).

Features

Product features or characteristics refer to the product traits and attributes present in the offerings of Assumptions Behind the Linear Regression Model that allow the company to successfully deliver unique value to customers through the products and services manufactured and offered (Varadarajan, 2015; Kotler & Keller, 2021). The product traits and features also allow Assumptions Behind the Linear Regression Model to create points of differentiation from the competition for its offering (Kotler & Keller, 2021; Park, 2020).

Product style

Assumptions Behind the Linear Regression Model makes sure to focus on the design and the look of the product, and the ability of the same to meet the expectations and lifestyle of the target audience (Groucutt & Hopkins, 2015). The Assumptions Behind the Linear Regression Model ensures that the product style and design complement its features and purpose.

Functionality

Assumptions Behind the Linear Regression Model makes sure that the product manufactured fulfills its purpose, and meets customer expectations (Abratt & Bendixen, 2018). Assumptions Behind the Linear Regression Model focuses on the product design, and how well it is able to fulfill the demands of the customers, as well as fill in the market gap (Baines, Fill, & Rosengren, 2017)

Experience

Assumptions Behind the Linear Regression Model products provide the customers with an exceptional and unique experience upon consumption (Kotler & Keller, 2021). This experience includes interaction with the products that leads to different unique and positive customer feelings and helps the Assumptions Behind the Linear Regression Model maintain differentiation from the competition (Varadarajan, 2015; Kotabe & Helsen, 2020).

Availability

Assumptions Behind the Linear Regression Model ensures that its product and service offerings are available for its target consumers at various retail setups. The easy availability ensures that consumers are able to purchase the offerings of Assumptions Behind the Linear Regression Model from various locations, allowing the Assumptions Behind the Linear Regression Model to create an advantage over competing players (Kotler & Keller, 2021; Chernev, 2018).

Convenience

One point of focus for Assumptions Behind the Linear Regression Model in its product offering is convenience. The Assumptions Behind the Linear Regression Model ensures that its products and service are easy and convenient to use. The factor of convenience allows Assumptions Behind the Linear Regression Model to enjoy a higher consumption rate, as well as increased sales and trials (Kotabe & Helsen, 2020; Kucuk, 2017).

After-sales service

Assumptions Behind the Linear Regression Model caters to after-sales queries and demands of customers, which also includes processes of returns as well as exchanges. The after-sales service of company Assumptions Behind the Linear Regression Model is detrimental and critical in determining customer satisfaction with its offerings (Iacobucci, 2021; Chernev, 2018).

Sizes

Assumptions Behind the Linear Regression Model has different SKUs in the product available. Assumptions Behind the Linear Regression Model has its products available in various SKU sizes which helps the company boost its sales, as different customer groups have different demands for the product quantity – depending on their usage, income as well as lifestyle (Grewal & Levy, 2021; Deepak & Jeyakumar, 2019).

Price

The element of price in the marketing mix refers to the value that customers pay for the service or the product offered by Assumptions Behind the Linear Regression Model. The pricing strategy and the price of the offerings are critical because it determines three success for Assumptions Behind the Linear Regression Model by directly influencing the profit levels and revenue for the company (Kotabe & Helsen, 2020; Kotler & Keller, 2021; Deepak & Jeyakumar, 2019).

Discounts

One of the ways through which the Assumptions Behind the Linear Regression Model influences its pricing strategies is through offering discounts on its product and service offerings. Discounted pricing for the Assumptions Behind the Linear Regression Model means that Assumptions Behind the Linear Regression Model decreases the price of the product and service in order to generate interest, or even unload excessive inventory and stock; as well as for boosting sales (Baines, Fill, & Rosengren, 2017).

Margins

Assumptions Behind the Linear Regression Model makes room for margins through the additional value charged in price over the cost – which allows the Assumptions Behind the Linear Regression Model to build profit for its offerings (Kucuk, 2017). The margins available to the Assumptions Behind the Linear Regression Model largely depend on the offering and its quality itself, in addition to the brand equity and brand value of the company.

Payment method

A significant factor of the pricing element of the marketing mix for the Assumptions Behind the Linear Regression Model includes the payment methods that the company offers (Kotler & Keller, 2021; Abratt & Bendixen, 2018). Since the Assumptions Behind the Linear Regression Model largely operates distribution to retail via agents and retailers, it ensures the inclusion of different payment methods. This includes digital payment, cash payment, as well as credit allowances (Grewal & Levy, 2021; Groucutt & Hopkins, 2015).

Pricing strategy

Penetrative pricing strategy

For Assumptions Behind the Linear Regression Model, the penetrative pricing strategy is adopted as it allows the company higher trial generation of its products and services in the desired target market, as well as allows the building of a broader reach for its product offerings by ensuring easier affordability (Baines, Fill, & Rosengren, 2017).

Introductory pricing strategy

For new products that the company launches, Assumptions Behind the Linear Regression Model ensures to adopt an introductory pricing strategy. This means that the company prices its products and service offerings at relatively lower prices than the competition. This introductory pricing strategy allows the company to increase trial generation, achieve higher penetration, as well as lead to the generation of increased brand awareness and recall (Kucuk, 2017).

Aggressive/competitive pricing strategy

For existing products, Assumptions Behind the Linear Regression Model uses a competitive and aggressive pricing strategy. This ensures that the products are available readily at competitive prices. Aggressive and competitive pricing strategies allow the Assumptions Behind the Linear Regression Model to experience high rates of growth and experience by allowing the buildup of consumer loyalty and following based largely on product attributes and quality instead of price– leading to the generation of higher brand equity and value for Assumptions Behind the Linear Regression Model (Deepak & Jeyakumar, 2019).

Place

The element of place within the 4Ps model of the marketing mix largely refers to the locations where company Assumptions Behind the Linear Regression Model stocks its product and service offerings for consumers' accessibility and purchase. Assumptions Behind the Linear Regression Model ensures to include all possible placements which are easily accessible to and available for the company's target audience (Iacobucci, 2021; Išoraitė, 2016). With the advancement of technology, Assumptions Behind the Linear Regression Model has expanded the placement of its products beyond the traditional brick-and-mortar retail spaces, to include modern Omni channel retail platforms as well (Iacobucci, 2021).

Physical stores/retail

The physical retail and stores i.e. the traditional brick and mortar spaces continue to be the prioritized locations for product placement by Assumptions Behind the Linear Regression Model (Iacobucci, 2021; Groucutt & Hopkins, 2015; Abratt & Bendixen, 2018).

Retail types

These include hypermarkets, upper markets, and smaller grocery stores - all of which allow increased accessibility and availability of Assumptions Behind the Linear Regression Model’s products and services to its target audience. Physical retail has a higher footfall and allows direct interaction of the Assumptions Behind the Linear Regression Model brand and its product offerings with the consumers (Groucutt & Hopkins, 2015; Groucutt & Hopkins, 2015; Chernev, 2018).

E-commerce

E-tailers

The Assumptions Behind the Linear Regression Model also stocks its products on e-commerce retail shops – such as amazon. This allows the Assumptions Behind the Linear Regression Model higher access and penetration in other markets, as well as in secondary consumer groups. Moreover, e-commerce retailing is more cost-effective for the Assumptions Behind the Linear Regression Model (Wu & Li, 2018; Chernev, 2018; Baines, Fill, & Rosengren, 2017).

Company-owned website

In addition to stocking products with other e-trailers, the Assumptions Behind the Linear Regression Model also manages orders through its own website, where consumers can place orders for Assumptions Behind the Linear Regression Model’s products directly. This allows the Assumptions Behind the Linear Regression Model greater control over stock and inventory management, as well as distribution networks – allowing the buildup of stronger relations with consumers.

Lastly, the Assumptions Behind the Linear Regression Model also takes limited orders through social media pages and platforms (Wu & Li, 2018; Baines, Fill, & Rosengren, 2017).

Aggregators

Another way through which Assumptions Behind the Linear Regression Model uses e-commerce is by stocking its offerings with aggregators (Kucuk, 2017). This allows the Assumptions Behind the Linear Regression Model to maximize its reach and increase penetration. At the same time, it also allows increased trial generation and repeats purchases for the Assumptions Behind the Linear Regression Model product offerings (Išoraitė, 2016; Groucutt & Hopkins, 2015).

Specialty stores

Interestingly, the Assumptions Behind the Linear Regression Model also stocks its products with specialty stores (Grewal & Levy, 2021). This gives the company direct exposure to its target market and audience and allows the consumers to directly interact with the brand and its offerings- without too much clutter (Kotler & Keller, 2021; Gillespie & Swan, 2021). The specialty stores are located in prime locations, and allow Assumptions Behind the Linear Regression Model higher penetration and reach, leading to increased brand awareness for its product offerings (Groucutt & Hopkins, 2015; Išoraitė, 2016).

Direct sales

The Assumptions Behind the Linear Regression Model also has a trained sales team for making direct sales (Kotler & Keller, 2021). Assumptions Behind the Linear Regression Model targets not only B2C consumers but also B2-B consumers (Chernev, 2018; Grewal & Levy, 2021). Both these categories, also make use of direct marketing whereby the sales agents and teams visit the target audience and business directly and detail the product features and benefits (Kotler & Keller, 2021; Groucutt & Hopkins, 2015).

B2B and direct sales

Assumptions Behind the Linear Regression Model’s team makes sales instantly during field visits for the company (Sahaf, 2019; Stead & Hastings, 2018). The target audience is carefully profiled and selected by the Assumptions Behind the Linear Regression Model so that the sales representatives are able to filter out the clutter (Gillespie & Swan, 2021; Išoraitė, 2016). Assumptions Behind the Linear Regression Model is able to easily contact and communicate with the desired business groups only (Groucutt & Hopkins, 2015; Abratt & Bendixen, 2018).

Inventory management

Assumptions Behind the Linear Regression Model effectively manages its inventory and fulfills the retailer's demand in time to ensure that it manages customer relations efficiently – and does not lose any customers. Assumptions Behind the Linear Regression Model has also introduced automation in inventory management which allows it to improve efficiency and speed, and reduce error rates (Park, 2020; Gillespie & Swan, 2021; Kucuk, 2017).

Transportation

For Assumptions Behind the Linear Regression Model, this includes choosing cos effective transportation means for inventory handling, as well as order deliveries to customers, as well as retailers. The company uses third-party transportation, as well as manages its own in-house transportation networks for ensuring on-time order deliveries (Abratt & Bendixen, 2018; Chernev, 2018; Grewal & Levy, 2021).

Promotion

The element of promotion in the marketing mix for Assumptions Behind the Linear Regression Model largely refers to the tactics and activities of communication that the company has adopted for promoting its products and services – including the brand, and its offerings, as well as other product features, characteristics, and activities (Varadarajan, 2015; Gillespie & Swan, 2021). The communication is largely targeted toward the Assumptions Behind the Linear Regression Model's target audience and is aimed to increase brand awareness, brand loyalty as well as sales of the company (Wu & Li, 2018; Grewal & Levy, 2021).

Direct marketing

For its more specific products and offerings, Assumptions Behind the Linear Regression Model uses direct marketing. Assumptions Behind the Linear Regression Model directly emails potential customers- especially its B2B consumers for detailing its product offerings and features. Assumptions Behind the Linear Regression Model uses personalized messages and captures new clients and customers for the business. In addition to direct emailing, the Assumptions Behind the Linear Regression Model also makes use of telemarketing and direct mail for targeting audiences through direct marketing (Chernev, 2018; Sahaf, 2019).

In-store promotion

Assumptions Behind the Linear Regression Model also focuses on in-store promotions for appealing to the customers, and boosting sales as well as raising brand awareness and profile of its offerings (Baines, Fill, & Rosengren, 2017). For Assumptions Behind the Linear Regression Model, the in-store promotions include offering price discounts, loyalty points, and flash sales for its products. In addition, the company also invests in building up the POS within the store locations (Stead & Hastings, 2018; Groucutt & Hopkins, 2015).

Social media marketing

One of the more contemporary forms of marketing and promotion for Assumptions Behind the Linear Regression Model includes social media marketing. The company has an official presence and profiles on social media platforms such as Facebook and Instagram, and regularly uses these platforms to promote its offerings, and product features and characteristics (Stead & Hastings, 2018). In addition, these platforms are also used by Assumptions Behind the Linear Regression Model to inform consumers about using sales and discounts to increase in-store footfall.

Traditional advertising

The Assumptions Behind the Linear Regression Model continues to use traditional marketing tactics and promotional platforms as well – largely for mass marketing purposes. The company especially focuses on TV advertisements, ad print media advertising for this purpose (Išoraitė, 2016; Iacobucci, 2021).

TV

TV advertisements are generally placed in prime time for higher visibility and reach by Assumptions Behind the Linear Regression Model. The TV advertisements use functional as well as emotional appeals to communicate the message of the Assumptions Behind the Linear Regression Model to the audiences (Iacobucci, 2021; Stead & Hastings, 2018).

Print

Print media and advertisements are published in newspapers and magazines – both of which are generally consumed in high proportion by the broader target audience of the Assumptions Behind the Linear Regression Model (Chernev, 2018; Iacobucci, 2021; Stead & Hastings, 2018).

Radio

The Assumptions Behind the Linear Regression Model also places advertisements on the radio to appeal to a segment of the target population. The radio communications by the Assumptions Behind the Linear Regression Model are usually shorter and focus on functional appeal only (Park, 2020; Išoraitė, 2016; Groucutt & Hopkins, 2015).

Integrated marketing communications

The advertisement and promotional messages by Assumptions Behind the Linear Regression Model for all mediums and channels however are built on an integrated plan, and ensure that they reflect messages and communication that is similar to the overall campaign to void confusion and discrepancies (Gillespie & Swan, 2021; Kotler & Keller, 2021). The use of integrated marketing and integrated media has allowed the Assumptions Behind the Linear Regression Model to build strong relations with the consumers through prompting conversations and discussions directly with them (Deepak & Jeyakumar, 2019; Sahaf, 2019; Stead & Hastings, 2018).

Conclusion

The 4p model or the marketing mix is an important aspect of brand building and development for the Assumptions Behind the Linear Regression Model and significantly guides the company in the chalking out of its strategic marketing goals and plans. The marketing mix model or the 4P model has helped the Assumptions Behind the Linear Regression Model in increasing its products’ and services’ reach and penetration and witness high levels of expansion and growth. The model has also led Assumptions Behind the Linear Regression Model towards a better understanding of its target audience and consumers. This understanding, in turn, has fostered strong emotional relations and increased loyalty on part of consumers towards the company – leading to an overall increase in the brand value and brand equity, as well as higher levels of brand affiliation, brand awareness, and brand recall. Together, the marketing mix has helped the company boost its sales and revenue by aligning its offerings with the needs and demands of the consumers, and the market more effectively and efficiently.

References

Abratt, R., & Bendixen, M. (2018). Strategic marketing: Concepts and cases. New York, United States: Routledge.

Baines, P., Fill, C., & Rosengren, S. (2017). Marketing. New York, United States: Oxford University Press.

Chernev, A. (2018). Strategic marketing management. Berlin/Heidelberg, Germany: Cerebellum Press.

Deepak, R., & Jeyakumar, S. (2019). Marketing management. New Delhi, India: Educreation Publishing.

Gillespie, K., & Swan, K. (2021). Global marketing. New York, United States: Routledge.

Grewal, D., & Levy, M. (2021). M: marketing. New York, United States: McGraw-Hill Education.

Groucutt, J., & Hopkins, C. (2015). Marketing. London: Macmillan International Higher Education.

Iacobucci, D. (2021). Marketing management. Boston, Massachusetts, United States: Cengage Learning.

Išoraitė, M. (2016). Marketing mix theoretical aspects. International Journal of Research-Granthaalayah, 4(6), 25-37.

Kareh, A. (2018). Evolution of the four Ps: Revisiting the marketing mix. Retrieved June 2022, from https://www.forbes.com/sites/forbesagencycouncil/2018/01/03/evolution-of-the-four-ps-revisiting-the-marketing-mix/

Khan, M. (2014). The concept of ‘marketing mix’and its elements. International journal of information, business and management, 6(2), 95-107.

Kotabe, M., & Helsen, K. (2020). Global marketing management. Hoboken, New Jersey, United States: John Wiley & Sons.

Kotler, P., & Keller, K. (2021). Marketing Management (15th global edition). London, United Kingdom: Pearson Education Limited.

Kucuk, S. (2017). Marketing and Marketing Mix. In Visualizing Marketing (pp. 3-7). London, United Kingdom: Palgrave Macmillan.

Park, S. (2020). Marketing management (Vol. 3). Retrieved June 2022, from https://books.google.com.pk/books/about/Marketing_Management.html?id=p6v7DwAAQBAJ&redir_esc=y

Sahaf, A. (2019). Strategic marketing: Making decisions for strategic advantage. New Delhi, India: PHI Learning Pvt. Ltd.

Stead, M., & Hastings, G. (2018). Advertising in the social marketing mix: getting the balance right. In Social Marketing (pp. 29-43). London, England: Psychology Press.

Varadarajan, R. (2015). Strategic marketing, marketing strategy and market strategy. AMS review , 5(3), 78-90.

Wu, Y., & Li, E. (2018). Marketing mix, customer value, and customer loyalty in social commerce: A stimulus-organism-response perspective. Internet Research., 28(1), 74-104.