|Rosella Machine Intelligence & Data Mining|
Sales trend analysis and Sales forecasting
Timely identification of newly emerging trends is very important to businesses. Sales patterns of customer segments may indicate market trends. Upward and downward trends in sales signify new market trends. Time-series predictive modeling can be used to identify market trends embedded in changes of sales revenues. Understanding of sales trends is important for marketing as well as for customer retention. Typical sales trend analysis includes;
Segmentation methods and techniques for Sales trend analysis and Sales trend forecasting
Market segmentation is a process that divides a market into smaller sub-markets called segments. Normally, market is segmented in such as way that customers of a segment have the same attributes. Commonly used segmentation methods and techniques for sales forecasting include the followings;
Why customer segmentation? People with similar attributes tend to exhibit similar purchasing patterns. This fact is particularly important in customer relationship management, marketing, and risk management. For example, people with certain life-styles tend to buy certain-types of products. Promoting products particularly targeted towards the demographic group can lead to successful marketing. Customers are segmented along the following demographic and psychographic attributes, and time-series trend analysis is performed;
PSM is a simple sales trend analysis method to manage your most valuable business resources: customers and markets. Profile your customers and markets as suggested in Customer Profiling. Based on profiling, develop customer and market segments. Finally, monitor the following trends;
Sales forecasting methods and techniques: Time-series Regression
Regression is an analytic technique used in developing predictive models for numerical data. It automatically derives mathematical functions that summarize trends embedded in past historical data, in such a way that minimizes the errors between actual input data and predicted values by the models. Regression can be applied to sales time-series data. A time-series consists of a set of observations which are measured at specific time intervals, say, monthly, quarterly, yearly, etc. Observations we are interested are sales revenues.
Customer (or market) segments have different sales trends. Some segments may be growing, while others are declining. Segment-by-segment sales forecasting can produce very useful information. Forecasting can be short term, mid term and long term. Long term forecasting may not produce accurate predictions. However it is very useful in understanding market trends.
Sales forecasting with Seasonal Adjustment
Sales time-series data often contain seasonal patterns. For example, clothing and fruits sales can fluctuate based on seasons. This hides underlying sales patterns and makes it difficult to project sales figures accurately. Seasonal adjustment is used to overcome this problem. It removes seasonal factors. Time-series regression on seasonally adjusted data can capture hidden patterns. Predicted values on seasonally adjusted data are then converted back to actual values. This process can significantly improve accuracy of predictions. It is noted that to make seasonally adjusted sales forecasting works, multi-year series data is required. At least three years data is recommended.
Sales Trend Analysis and Sales Forecasting Tool: Group-by Excel Add-in Tool
Rosella Group-by Excel Add-in provides powerful simple-to-use tools for trend analysis. It combines groupby aggregation with time-series predictive modeling. It employs powerful non-linear regression. Currently it supports 16 different mathematical functions using advanced function fitting algorithms. The following figure shows an example of Rosella Groupby Add-in reports with time-series analysis;
Columns "MTH1" to "MTH7" indicate recent actual trend values. Columns "Next 1" to "Next 3" show projections for the coming months. The "Chart" column displays them in charts. Notice that charts show that projections are not necessarily linear! The Addin automatically detects a most suitable function for you. Actual function description can be viewed from the notes. Prognostics columns provide information showing fitness and trends. The "Signal" column indicates which row items to pay attentions in three colors: green (for good), red (for bad), and yellow (for warning).
The following figure shows highlighted cells. It is based on column-wise high- and low-values. Red indicates high-values and blue shows low-value cells. Color intensity is based on relative cell-values.
Time-series Regression Function for Excel Formula with Seasonal Adjustment
Rosella Groupby Excel Add-in tool also provides the following time-series regression function that can be used in cell formula specification;
=RGTSREGRES(1, $D$1:$D$45, "01D",,4 ...)
This incorporates time-series regression with seasonal adjustment and smoothing (moving average and exponential) with the following parameters;
Examples Of Long Term Forecasting with Seasonal Adjustment
The following codes can be used to produce long term forecasting work sheet with seasonal adjustment. The screen image shows example output of these formulas. It's very easy to do!
Download: Groupby Excel Addin Tool for Trend Analysis and Forecasting
For one year free license copy of this Excel Addin, click Download Rosella Groupby Excel Addin. This features groupby aggregation, time-series regression, moving average smoothing, seasonal adjustment, etc. Note that this was derived from advanced multivariate general regression modeling program developed for CMSR Data Miner. (Last updated: March 12, 2018)