Answer Feedback Question

My submission:

RE: Week 5 Discussion
COLLAPSE
Forecasting

As you think about your company’s ability to forecast future sales and profit, what are two or three of the most significant variables that are difficult to predict?
Forecasting, in my opinion, is more than simply a guess based on a few facts and verification of growth activity. It is, in my opinion, a science. It is the correct assimilation of facts of an organization that allows for future company performance. It aids in the understanding of particular patterns, as well as deficits and fluctuations in firm performance, in addition to future planning. It can also aid in recruiting prospective talent and investment. Forecasting, however, has limits due to the apparently infinite number of factors that might affect its accuracy. Forecasting can be influenced by any element that affects the industry, enterprise, or community. Budgeting, marketing spending, political situations, economic conditions, consumer happiness, and weather are the most prevalent causes. Among these variables, the most difficult to anticipate are political situations, economic conditions, and weather. All of the criteria stated above must be taken into account in order to make an accurate forecast. Aside from predicting, these aspects aid in understanding the shortcomings of any forecast.

What information and data would you use to improve the forecast accuracy?
Forecasting, in my opinion, is fundamentally pointless if it is not correct. It is simple to acquire data and draw conclusions from it, but when it comes to precise forecasting, we need solid, good data from trusted sources. Furthermore, the data must be thoroughly analyzed, cultivated, and included in the forecasting process (Veganzones & Severin, 2020). The accuracy of the data is important in predicting and is a major cause of bad forecasting. The forecast data that I will use is based on industry trends, historical data, and the present sales pipeline. However, there are other elements that I will examine when collecting data, such as seasonal fluctuations, corporate development, and regulatory changes, the arrival of new rivals, or the loss of old ones. Taking these aspects into account allows me to narrow down my acquired data and nurture for precise forecasts.

How can you go about collecting and leveraging this data?
I think that data gathering is the collecting of facts, events, objects, figures, and symbols from many sources. Better decision-making and forecasting are dependent on accurate data collection (Veganzones & Severin, 2020). Based on the type of data, there are two basic categories of data collection methods: quantitative methods and qualitative methods. Statistical techniques are commonly used in quantitative approaches. For long-term projections, they are quite dependable. These collections are built on historical data, which is then analyzed using time series analysis, barometer methods, and smoothing techniques. However, when historical data is unavailable, qualitative procedures are appropriate for data collection. This data frequently represents non-quantifiable aspects such as words, sentiments, gestures, colors, and so on. Surveys, interviews, and polls are used to acquire this information.
Data collection is not difficult, especially with contemporary technology that can search across several sources in a split second. Filtering out strong data sets that give actionable information, on the other hand, is a time-consuming effort (Veganzones & Severin, 2020). In this context, I should first reduce the number of duplicates in the acquired data that contribute repetitious information to the workflow. Because it is difficult to digest data from many sources, the acquired data is often structured and arranged for complete usage. Time-sensitive prediction necessitates the use of real-time data. As a result, data collecting based on response time aids in swift decision-making. I also verify that new data is easily integrated with existing data. By integrating old systems with the new, and improved workflow with fewer mistakes is provided. These data levering techniques will aid in the transformation of raw data into meaningful insight for accurate prediction.

References
Veganzones, D., & Severin, E. (2020). Corporate failure prediction models in the twenty-first century: A review. European Business Review, 33(2), 204–226. https://doi.org/10.1108/ebr-12-2018-0209
 

Feedback Question from Progessor:

You laid many perspectives on forecasting more accurately by being artistic about analyzing various pieces of data including budget allocations, economic conditions, political conditions, customer satisfaction, even the weather. Central to these concepts is the notion of how customer demand will respond.  In our current environment, it is hard to predict when consumer demand is going to come back.  A lot of it is predicated on the vaccine rollouts, when stimulus bleeds out, when states loosen up restrictions for businesses, and when consumer sentiment returns to some sense of normalcy in terms of buying behavior. This seems to be coming back more recently.

Jack tells us in his video this week to never bet on volume.  Having insight or relative insight into where a business cycle sits or is headed is an important piece of information that can help us understand where volume might be headed (at least relative to the current year or where the cycle currently sits) so we do not have to be overly aggressive in betting on volume.
I saw this indicator come across my LinkedIn feed a while back and it intrigued me — it is a Buffett indicator that measures overall equity market cap to GDP.  It suggests from a valuation standpoint that investors believe in the future earnings power of equity markets.  This suggests that in 2021/2022, consumer demand is going to come roaring back and sales growth will be big.

What do you think about this indicator as a measure of the business cycle?

Is it enough to bet on volume for the coming year or are you more bearish?  Interested in your thoughts?