As a marketing analyst, you are responsible for estimating the level of sales associated with different marketing mix allocation scenarios. You have historical sales
Review the important themes within the sub questions of each bullet point. The sub questions are designed to get you thinking about some of the important issues. Your response should provide a succinct synthesis of the key themes in a way that articulates a clear point, position, or conclusion supported by research.
As a marketing analyst, you are responsible for estimating the level of sales associated with different marketing mix allocation scenarios. You have historical sales data, as well as promotional response data, for each of the elements of the marketing mix.
- Describe the differences between the forecasting methods that can be used.
- Evaluate the forecasting methods in relation to the given scenario.
- Choose a forecasting method and justify your choice. If you make any assumptions, state them explicitly. Support your discussion with relevant examples, research, and rationale.
The final paragraph (three or four sentences) of your initial post should summarize the one or two key points that you are making in your initial response.
Submission Details
- Your posting should be the equivalent of 1 to 2 single-spaced pages (500–1000 words) in length.
- Since you are engaging in research, be sure to cite in the body of the post and add a reference list in APA format.
- respond to your 2 classmates responses
Week 5 Discussion
Kashisha Cunningham
As a marketing analyst, you are responsible for estimating the level of sales associated with different marketing mix allocation scenarios. You have historical sales data, as well as promotional response data, for each of the elements of the marketing mix.
· Describe the differences between the forecasting methods that can be used.
· Evaluate the forecasting methods in relation to the given scenario.
· Choose a forecasting method and justify your choice. If you make any assumptions, state them explicitly. Support your discussion with relevant examples, research, and rationale.
Marketing and Forecasting
The marketing mix has four components that a business can utilize to bring attention to their products and services. These four components are known as the 4 P’s, product, price, promotion, and place. Yasar (n.d.) says that the 4 P’s still holds a value on the marketing world because of their fundamental principles”, (Yasar, n.d., para.19).
Products – products and services that consumers desire and/or need.
Price – items sold at the consumers expectation.
Promotion – informing the consumers of products and servicing and informing them of how the product or service satisfies their needs.
Place – where the product or service can be acquired.
When it comes to marketing forecasting, businesses can use it to predict potential success when certain marketing efforts are used. The Adobe Cloud Team (2023) explains that the purpose is to ensure that the company focuses on the proper marketing and advertising techniques, (The Adobe Cloud Team, 2023, para.3). The benefits of marketing forecasting are:
Better Planning – you can predict the areas of failure and success. For example, poor performance can create better marketing strategies that will lead you into the direction of better performance.
Easier decision-making – the more data you have, the less time you will use trying to determine which strategy works best. For example, if you see that one product is selling better than the other, you have a choice to either discontinue that product or you can create another marketing strategy.
Better budgeting and scheduling – this allow you to move around resources to areas where they are needed more. For example, if you need more employees to help in one area that is lacking, you have the option to make changes and shift employees to offer additional help.
Healthier risk management – helps to prevent failures and setbacks. For example, if you conduct your research on a product and/or service, you will know beforehand the success of the product and/or service with the consumer before onboarding it.
Marketing forecast methods includes:
Delphi technique – which is more controllable and accurate. It questions anonymous experts rather than interviewing traditional groups.
Correlation technique – researches the correlation between the different variables. Its process compares the market factor against the performance. However, multiple trends can make the process more complicated.
Time series technique – utilizes several techniques to review historical data and applies it to future periods. However, the instability of the market and the numbers may have decelerated or accelerated.
Response model technique – utilizes direct consumer responses to help predict how they will response to future campaigns. This will allow a business to determine if consumers are willing to pay for a product and/or service.
In my opinion, I would utilize the time series technique because the data will allow me to adjust to marketing changes that may occur due to the market’s instability. For example, if I see an increase in online purchases versus in-store purchases, I can possibly determine that we will have more online purchases in the future. This means that the online marketing strategy is working and is more convenient for the consumer. I would then create more promotions for online to boost sales. For example, I could include free shipping for purchases over $25 or 20% off coupons for online purchases. Shopping from home is mor convenient and can save the consumer more money when there is a shift in the economy.
A great marketing strategies foundation is to make certain that you understand when and how to put the right products in the right place. This also includes providing them at the right price. When products and services are offered at an affordable price and performs at a high level, the business will have a chance to become competitive and obtain a loyal consumer base. This also means that a product and/or service must be created to target a particular group, sell it at a reasonable price, and do this during the time that the product is most needed. However, this needs to be done correctly or it will backfire. For example, you cannot wait to promote the sale of a 2024 vehicle in 2024, you need to start promotion in 2023 and promote it with a promotion such as free maintenance for the first 6 months or promote a rebate of $1000 cash back or off the sales price.
Kashisha C.
References
Adobe Experience Cloud Team. (2023). Marketing Forecasting – Definition, Components, and Best Methods. https://business.adobe.com/blog/basics/what-is-marketing-forcasting
Yasar, K. (n.d.). What is the Marketing Mix (4 Ps of Marketing). https://www.techtarget.com/whatis/definition/Four-Ps
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Week 5
Contains unread posts
Enrico Serpe posted Nov 23, 2023 6:51 AM
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Integrating Marketing Mix Elements into Sales Forecasting: A Strategic Approach
In the dynamic marketing field, the ability to accurately forecast sales is crucial for the success of any marketing strategy. As a marketing analyst, understanding and applying the right forecasting methods, especially in different marketing mix allocation scenarios, is essential. This discussion synthesizes key themes from recent research, articulating a clear position on effective sales forecasting methods supported by empirical evidence.
1. Understanding the Impact of Marketing Mix on Sales Volume
The study by Murdayani, Beti Nurbaiti, and Soehardi (2021) titled "The Effect of the Marketing Mix of MSME Products on Sales Volume During the Covid-19 Pandemic" provides clear insights into the relationship between various elements of the marketing mix and sales volume. Using Structural Equation Modeling (SEM), the research highlights that promotional variables have a significant positive effect on sales volume among the different elements of the marketing mix. I find this crucial for forecasting methods as it emphasizes the importance of weighing promotional response data heavily in sales predictions. A corresponding rise in sales volume can be anticipated in scenarios where promotional activities are increased.
2. The Comprehensive Approach of the 9P Marketing Mix Strategy
Further expanding on the complexity of the marketing mix, the research by Irkha Fitriana Jumari and S. Astutiningsih (2022), "Implementation of 9P Marketing Mix Strategy in Order to Increase Sales Volume in Abdul Ghoffar Rejotangan Tulungagung's Koi Fish Farming Business", demonstrates the effectiveness of a comprehensive 9P marketing mix strategy in increasing sales volume. This approach, which encompasses product, price, place, promotion, people, process, physical evidence, packaging, and positioning, offers a holistic view of the factors influencing sales. For a marketing analyst, this implies that forecasting methods should not be limited to traditional 4P elements but should also consider additional factors like packaging and positioning, which can significantly influence consumer response and sales.
What exactly is the 4P and 9P strategy? The 9P Marketing Mix Strategy is an expanded version of the traditional 4P Marketing Mix model. The original 4Ps – Product, Price, Place, and Promotion – were extended to include additional elements crucial in today's complex and customer-centric marketing landscape. The 9Ps typically include:
· Product: This refers to what is being sold, including its quality, features, branding, and any unique selling propositions.
· Price: This involves the pricing strategy for the product, considering factors like market demand, production costs, competitor pricing, and perceived value.
· Place: This is about distribution channels and how the product is delivered to the customer. It includes locations, logistics, and methods of distribution.
· Promotion: This encompasses all the marketing and communication strategies used to make the product known to the consumer, including advertising, sales promotions, public relations, and digital marketing.
· People: Recognizing the importance of human capital, this P refers to everyone involved in the production, marketing, and selling of the product, including customer service.
· Process: This relates to the systems and processes used to deliver the product or service to the customer, ensuring efficiency and quality of service.
· Physical Evidence: In service marketing, this refers to the environment in which the service is delivered and where the company and customer interact. It can also relate to any tangible components that facilitate the performance or communication of the service.
· Packaging: This is about how a product is presented to the consumer.
· Positioning: This involves how a brand is perceived in the context of competitive offerings. Positioning strategies can include aligning the brand with certain values, customer demographics, or lifestyle choices.
The 9P model is particularly relevant in the modern marketing environment, where customer experience and value-added services significantly differentiate products and services in a crowded market. This comprehensive approach helps businesses focus on the basic elements of marketing and consider broader aspects that contribute to the overall customer experience and brand perception.
3. Choosing and Justifying a Forecasting Method
Based on the insights from these studies, I propose a mixed-method approach for sales forecasting. This method combines quantitative data analysis, as seen in the SEM approach used by Murdayani et al., with a qualitative assessment of broader marketing mix elements, as suggested by Jumari and Astutiningsih. This approach allows for a comprehensive analysis of measurable data (like promotional response rates) and more nuanced factors (like consumer perception of packaging or positioning). The assumption here is that sales are a function of visible marketing efforts and subtler aspects of the marketing mix that collectively influence consumer behavior.
Conclusion
In summary, effective sales forecasting in marketing requires a good understanding of the various elements of the marketing mix and their impact on sales volume. The research by Murdayani et al. and Jumari and Astutiningsih provides a foundation for a mixed-method approach to forecasting, which balances quantitative data analysis with qualitative assessments of the broader marketing mix. This approach is well-suited to address the complexities of modern marketing strategies, ensuring more accurate and comprehensive sales forecasts and improving profitability.
References
Murdayani, Beti Nurbaiti, & Soehardi. (2021). The Effect of the Marketing Mix of MSME Products on Sales Volume During the Covid-19 Pandemic. Journal of Social and Government Science, 2(2), 1-12. Retrieved from https://scholarhub.ui.ac.id/cgi/viewcontent.cgi?article=1086&context=jsgs
Jumari, I. F., & Astutiningsih, S. (2022). Implementation of 9P Marketing Mix Strategy in Order to Increase Sales Volume in Abdul Ghoffar Rejotangan Tulungagung's Koi Fish Farming Business. International Journal of Journalism and Media (IJJM), 3(1), 1-10. Retrieved from https://www.ilomata.org/index.php/ijjm/article/download/447/257
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Goodness-of-Fit Tests.html
Goodness-of-Fit Tests
As suggested earlier, there are times when the nature of a statistical distribution has been hypothesized, and we wish to confirm the validity of the assumption that has been made. Goodness-of-fit tests are useful in allowing us to test the validity of the assumption under varying conditions.
Some goodness-of-fit tests are appropriate when the nature of the probabilities of some outcomes is already known (or is assumed to be known). For example, in a close three-way election (with two candidates backed by each of the two major political parties and a third, independent candidate), one might speculate that people will support these candidates in approximately equal numbers. Goodness-of-fit tests with specified probabilities will allow us to conclude whether it is true that all candidates enjoy approximately equal support.
In other situations, one might assume that a particular variable of interest (for example, household income) follows a normal distribution. Other goodness-of-fit tests, specifically designed to test situations in which the parameters of the population's distribution are not known (or are assumed to be unknown) can test whether or not the hypothesized distribution is consistent with the data that exists.
There are many nonparametric goodness-of-fit tests. These tests are uniquely suited to allow us to draw statistical conclusions about qualitative data (those falling into the nominal or ordinal categories discussed at the beginning of the course). In this introductory course, we will not attempt to address such nonparametric techniques. However, you are introduced to them here, in order to make you aware of their existence.
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Moving Average Forecasting.html
Moving Average Forecasting
Forecasting is the process of estimating future outcomes. Most business organizations engage in some type of forecasting for planning purposes, budgeting, and demand estimation. Forecasting can be as simple as making subjective estimates regarding the future or as complex as using mathematical models encompassing hundreds of different measures. (For subjective estimates, qualitative methodologies such as Delphian analysis are popular.)
Forecasting techniques are grouped into several categories:
- Judgmental or qualitative methods use opinions to develop forecasts with minimal reference to available historical data.
- Customer surveys identify factors that enhance relationships and brand perceptions, increase loyalty, and increase sales.
- Time-series methods make forecasts based on historical patterns in data and use time as an independent variable to estimate demand.
- Causal models use cause-and-effect logic to describe the behavior of a system in response to one or more factors.
A forecaster rarely chooses a specific method from among equally applicable alternatives. Often, the nature of the problem and data availability determine the optimal method to be used. For example, marketing problems tend to use marketing mix models, which are a class of causal models (often utilizing regression analysis). The reason is that many of these are routine problems, and there is a rich reservoir of historical data available for analysis. In such cases, it would be unwise to rely on techniques that do not take advantage of known relationships and available data.
One of the simplest forecasting techniques is known as moving average forecasting. Whether all pieces of historical data receive weights or remain unweighted, the prediction of the future based on past demand data is a common approach in business.
Some forecasting needs are not amenable to quantification. The problem of forecasting a terrorist attack disrupting the flow of gas through a pipeline would typically necessitate the use of judgmental methods because neither appropriate forecasting models nor the requisite quantitative data are readily available.
It is advantageous to make optimal use of available data. Judgmental methods are used where no reliable or sufficient data exists or when it is difficult to specify the relationship between variables. Consumer surveys tend to be used where customer preferences drive future outcomes. Time series analysis is used where historical outcomes are available and causal relationships are unspecified. Causal models are used when historical outcomes are available and causal relationships can be specified.
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Exponential Smoothing Forecasting.html
Exponential Smoothing Forecasting
Time series analysis is one of the most common forecasting techniques used in business forecasting. Another popular forecasting technique is exponential smoothing. Exponential smoothing involves selecting factors to define how quickly (or how slowly) a forecast responds to changes in demand data. If factors are given high values, then the forecasting model adjusts more quickly to changes in demand. However, if the factors are given low values, then the model does not adjust as quickly, and the forecasts are more “smoothed” (not as influenced by each individual observation).
The popularity of time series analysis is due to the fact that it requires limited data for analysis—outcome data captured across time. The limited data demands also mean potentially serious limitations. To illustrate, consider the use of time series data to forecast the sales of a particular brand.
A brand manager has sales data available for the last five years. The brand's sales have grown steadily over the past several years, as have promotional expenditures. The economy has also grown rapidly, and consumer confidence is high. The brand manager decides to use the sales data available for the last five years to construct a time series forecast for the upcoming year. Use of time series data would be appropriate only if conditions surrounding the past sales results remained constant in the future, i.e., marketing expenditures, economic growth, and consumer confidence continued to exert a favorable influence on sale levels.
Time series is a trend projection, which takes into account cyclical or seasonal effects but not extraneous factors, such as promotional spending levels, consumer sentiments, or general economic conditions. In the sales forecast example, if the economy is showing signs of slowing down, as reflected in a drop in consumer confidence, then time series analysis would result in an overestimation of future sales. If this sales forecast is used as the basis for promotional allocation, financially adverse outcomes might result. Time series forecasts would perform reasonably well if the external factors (i.e., the economy, consumer confidence) could be assumed to be similar to previous years.
Another aspect of time series forecasting to consider is the length of the forecast period. The further into the future the forecast, the less accurate it is likely to be. For example, in estimating weather projections, a forecast for tomorrow is generally more accurate than a forecast for two weeks from tomorrow.
Another issue to consider is how far back historical data should go when preparing a forecast. The answer depends on the nature of the forecast being prepared, how important seasonal or cyclic trends might be as opposed to events captured by recent data, and, of course, the availability and cost of data.
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