Factors that affects sales
Sales prediction or sales forecasting refers merely to the process of estimating future sales. One of the critical analysis of the companies, these reports help ascertain the performance of the company based on numerous factors. Forecasts use past numbers and statistics to predict the company’s current short term and long term performances. While it is necessary to have accuracy in these estimates, it should also be noted that various factors affect the sales and turnover of the company. Being a player in the market implies that the product is bound to be affected by several external forces that have a significant impact on the consumers’ mind. Some of the most common factors that directly affect sales include
- Increase in competition
- Changing industry trends
- Dynamics of political and global scenarios
- Product performance
- Regrouping of target audience
- Lack of product adaptability to current requirements
Why sales forecasts/predictions are essential?
A company cannot make-do with sales prediction. These reports are the anchor points for any business. From start-ups to multinational conglomerates, every business requires sales predictions to help ascertain their position in the market. The center of every business, sales forecasts have a pivotal role in the business plan. Since the ultimate aim of any company is to make sales, creating a benchmark seems a viable follow-up option. Being the lifeline of every business, sales predictions allows both marketers and data analysts to understand the market and alter the course of action based on the performance. These reports are taken on a quarterly basis to follow the market consistently. Some of the other important reasons why sales forecasts are the pivotal step of a business plan include.
- Role in financial planning
- Better supply chain management
- Defines marketing strategies
- Stabilises product pricing
- Improved inventory management
- Helps build investor relationships
Why is sales prediction inaccurate?
Sales prediction is a checkpoint used by managers to quantify the performance of the company. The benchmark set by these reports help in ascertaining the quality of work done by the company and the industry position as opposed to competitors. Given the pivotal role of sales prediction, it is necessary for analysts to predict with accuracy.
Inaccuracy in sales prediction implies that the company did not do proper market research to understand their viability and all the marketing activities were in vain. To avoid a situation that puts the manager and the company at stake, it is essential to keep to evaluate the reasons that could lead to such inaccuracy. Few factors could contribute to the discrepancies including
- Lack of sufficient knowledge of product viability
- Minimal research on current scenarios
- Missing sales milestones
- Inefficient sales process leading to drop in sales
The outlier in sales value that is abnormally high or low compared to the values in the sales time series. They can lead to inaccurate forecasts; we have to build a system that can automatically detect outliers and ignore them during forecast calculation.
Advantages of outliers analysis
- Improve data accuracy
- Identify trends quickly
- Build alerts only on essential changes in data
- Uncover the exact causes of increases and decreases in data patterns
How to evaluate sales prediction?
Sales prediction can be ascertained using two different sets of data. While both are different in numbers, a typical analysis method used to arrive at sales prediction is using outliers using IQR formula technique. The easiest method to calculate outliers using IQR focuses primarily on arranging the data in order. Once the same is done, calculating first quartile and third quartile will arrive at the value necessary to estimate the interquartile range which is IQR. The difference between third quartile and first quartile will give the IQR.
The two popularly used data points for calculating sale prediction are
Mathematically, identifying outliers data using time series data is cumbersome. Hence it is advisable to detect outliers using IQR formula technique. It is believed that if the IQR is above 1.5 interquartile range, it is the outlier.
While it is very hard to find the outlier in monthly time series sales data, one can identify outliers using IQR formula technique to ascertain the value.
Using the above example as a base, it can be concluded that there is a quick increase in the accuracy of sales prediction using simple outlier IQR techniques. The clever use of these analysis formulas helps in achieving accurate numbers that can be used as a parameter to measure sales. It should also be noted that there are more complicated outliers detection algorithms that help with multiple measures and attributes.
To use IQR technique appropriately, one must keep in mind these four focus areas or rules of thumb.
- Grasp of monthly sales data for a product
- Watch for absence of outliers in the time series data
- Detection of outliers in all the data points
- Comparing sales prediction before and after of outlier removal