Marketing Strategy and Plan

The product life cycle theory is the stepwise process through which products enter the market to when they disappear from the market (Weidema, Pizzol, Schmidt & Thoma, 2018). The theory is a four-stage process comprising introduction, growth, maturity, and decline. Products disappear or decline from the market due to several factors such as saturation, high competition, and low consumption or demand. While some products may last longer in the market, others may thrive for a short while. Companies ensure that their products last longer in the market by continually updating those using newer technologies (Markard, 2020). Product life cycle analysis is done to sustain a company’s product longevity. The product life cycle theory is an excellent tool used by companies and marketers to design product strategies. in other words, the theory ascertains the decisions of managers regarding the products throughout the various stages (Shaik & Abdul-Kader, 2014). However, the theory has faced a lot of criticism, although it is still useful, the product life cycle theory has many flaws. Below is a descriptive analysis of the many flaws in the product life cycle theory as well as an explanation as to why the theory is still very useful.

One of the major limitations of the product life cycle theory is that it does not apply to brands and services (Bakker, Wang, Huisman & Den Hollander, 2014). The strategy mainly relies on sales data thus foregoing other activities happening around the brand and its services. A critical example of this is Microsoft. The company has had quite a number of products that have been a hit in the market until they reached the decline stage. While some products or goods are thriving in the market, others are in the maturity or decline stage. However, a contradiction arises because this does not mean that the company itself is in either the maturity or decline stage. Another limitation of the product life cycle theory is that it does not apply to all cases (Li, Tao, Cheng & Zhao, 2015). The theory tends to assume that all products in the introduction phase will be successful or will in one way or another reach the maturity stage. Factually, this is not always the case as some products may not leave the first introductory phase. In this scenario, in the decision-making process, managers may be allured to over-invest in the product with the belief that it will be successful. This may lead to immense losses especially in a case where the product is overly expensive yet extremely under-performing. Summarily, most companies criticize the theory because not all products are high-growth products. On the same note, it is important to note that not all products will accurately adhere to the product life cycle pattern. Over the past, companies have witnessed deviations where some products follow different curves. This leaves marketers and managers with the proof-based criticism that the product life cycle theory is false. Companies and investors do not have security on the predictability of the theory on future products and sales. It is therefore advisable that companies and brands consider different patterns of the product life cycle theory before making decisions. As stated earlier, the product life cycle theory is overly reliant on sales data. Predictions on the rise and fall of products are purely based on the available sales data. The limitation of the theory arises when there are fluctuations in the sales data which result in a lack of accuracy in the prediction. In other words, when there are fluctuations, the product life cycle curve or pattern becomes pointless. Besides, market fluctuations are bound to happen due to several inevitable factors such as seasonal demand and product issues. The prediction of future sales in the product life cycle theory may be hindered by delays in the report and analysis of sales data. For a proper curve or pattern to be derived, the stepwise records of products from introduction and maturity to decline have to be timely recorded and analyzed. However, delays in product analytics are very common in most companies. The theory strictly requires records and analytics are performed immediately after every movement of products from one life cycle to the other. Delays in data sales consequently fail the theory (Lehner & Halliday, 2014). This is a risk that most companies are not willing to take. Other companies criticize the use of sales data by the product life cycle theory because of variations in market conditions. Due to the difference in consumption levels, sales data of one product will vary from one market to another. One product may thrive well in one market segment but fail in another. This raises a contradiction in the theory because other factors in the market are not put into consideration. The theory basically argues that a product will always move from the introductory, growth, maturity, and decline stages sequentially. The success or failure of a product is not only affected by the product itself, but by all the 4Ps of marketing. In addition to product, the marketing elements not included are price, promotion, and place. Other factors affect the outcome of a product in the market including packaging and people. Clearly, the life cycle of a product should not only be structured based on the product itself. For effectiveness and efficiency, the product life cycle theory should accommodate other critical factors such as marketing strategies, logistics, and pricing strategies.

All in all, the product life cycle theory still remains relevant in the market. Despite the criticisms and flaws, its advantages and benefits cannot be overruled. The theory is an excellent tool used by managers and strategists in planning and making key decisions affecting the company. First, the product life cycle theory presents both sales and data and the performance of products over time. These are the key bases in decision making especially when the decision-makers are torn between a number of multiple options. The use of well-summarized sales data and performance levels eases the decision-making process. For instance, when a product is in the growth stage of the life cycle, it means that consumers have already accepted it. It then follows that extensive marketing and promotion are needed for the product to reach the maturity stage. The decision-makers or managers know what direction to follow and a decision is comfortably made to invest more in the product. Besides, the strategization process is made easier because decision-makers can close accurately predict sales (Wang, Wang & Zhao, 2015). The product life cycle theory presents data in a visual representation such as a curve where the forecast is easy. Accurate and precise predictions require a good amount of experience but not necessarily heavy skills. When managers can tell what level of the growth curve a product is in, they can use the available data to predict its movement. A proper illustration of this is in the products of Samsung. Over the years, Samsung has understood the growth curve and life cycles of its products. For instance, when the company introduces a new smartphone in the market, it knows that the growth stage is within the first one and two months. Similarly, the product will attain maturity at three to six months. Finally, after the first six months, the product has reached its maturity level which takes about two to three years. At this level, the company may decide to launch a new model or product because consumers now begin to reach for newer models (Gmelin & Seuring, 2014). While the rough life cycle for most Samsung smartphones is two to three years, there are deviations for some products. Examples of the latter are Galaxy and Note which have survived longer in the market. Another key benefit and reason as to why the product life cycle theory is very helpful are its use in gaining competitive advantage. with the use of data sales, a company can analyze its competitor’s products and derive the life cycle of their products. The company can then use this to its own benefit by being a step ahead of its competitors. For instance, assuming a competitor’s product is in the introductory stage whereas the company’s product is in the maturity stage. The company can boost its sales over its competitors by overly investing in marketing, advertising, and promotions. Consumers will tend to incline more on the already existing product in the market and this will pull of customers from the competitor. Certainly, this will overpower the rival thus giving the company a competitive advantage. The company can alternatively opt to launch a new product to raise competition with the rival’s new product. Summarily, the product life cycle theory comes in handy in strategizing and planning a company’s operations towards its growth and development.


Bakker, C., Wang, F., Huisman, J. and Den Hollander, M., 2014. Products that go round: exploring product life extension through design. Journal of Cleaner Production69, pp.10-16.

Bilir, L.K., 2014. Patent laws, product life-cycle lengths, and multinational activity. American Economic Review104(7), pp.1979-2013.

Dayal, P. and Ferrara, A., 2018. Early galaxy formation and its large-scale effects. Physics Reports780, pp.1-64.

Evans, M., Dalkir, K. and Bidian, C., 2015. A holistic view of the knowledge life cycle: the knowledge management cycle (KMC) model. The Electronic Journal of Knowledge Management12(1), p.47.

Gmelin, H. and Seuring, S., 2014. Achieving sustainable new product development by integrating product lifecycle management capabilities. International Journal of Production Economics154, pp.166-177.

Gmelin, H. and Seuring, S., 2014. Determinants of sustainable new product development. Journal of Cleaner Production69, pp.1-9.

Igba, J., Alemzadeh, K., Gibbons, P.M. and Henningsen, K., 2015. A framework for optimizing product performance through feedback and reuse of in-service experience. Robotics and Computer-Integrated Manufacturing36, pp.2-12.

Lehner, M. and Halliday, S., 2014. Branding sustainability: Opportunity and risk behind a brand-based approach to sustainable markets. Ephemera: Theory and Politics in Organization.

Li, Q., Luo, H., Xie, P.X., Feng, X.Q. and Du, R.Y., 2015. Product whole life-cycle and Omni-channels data convergence oriented enterprise networks integration in a sensing environment. Computers in Industry70, pp.23-45.

Ma, J., Kwak, M. and Kim, H.M., 2014. Demand trend mining for predictive life cycle design. Journal of Cleaner Production68, pp.189-199.

Markard, J., 2020. The life cycle of technological innovation systems. Technological Forecasting and Social Change153, p.119407.

pro-Big data in product lifecycle management. The International Journal of Advanced Manufacturing Technology81(1), pp.667-684.

Purba, N.S. and Nooraeni, R., 2019, September. Forecasting of Quantum Dots Technology using Simple Logistic Growth Curve. In International Conference on Trade 2019 (ICOT 2019) (pp. 126-130). Atlantis Press.

San Gan, S., Pujawan, I.N. and Widodo, B., 2015. A pricing decision model for new and remanufactured short-life cycle products with time-dependent demand. Operations Research Perspectives2, pp.1-12.

Shaik, M.N. and Abdul-Kader, W., 2014. Comprehensive performance measurement and causal-effect decision-making model for reverse logistics enterprise. Computers & Industrial Engineering68, pp.87-103.

Tao, F., Wang, Y., Zuo, Y., Yang, H. and Zhang, M., 2016. Internet of Things in product life-cycle energy management. Journal of Industrial Information Integration1, pp.26-39.

Wang, Q., Wang, Z. and Zhao, X., 2015. Strategic orientations and mass customization capability: the moderating effect of the product life cycle. International Journal of Production Research53(17), pp.5278-5295.

Weidema, B.P., Pizzol, M., Schmidt, J. and Thoma, G., 2018. Attributional or consequential life cycle assessment: a matter of social responsibility. Journal of cleaner production174, pp.305-314.