Auditing, Forecasting, and Market Research

How They Work Together

The triad of auditing, forecasting and market research are usually thought of as only attainable using sophisticated AI software algorithms and technology. The goal of Auditmetrics is to show that data science does not start with technology but how one approaches business data analytics with an understanding of the fundamental principles of statistical analysis.

 

Auditing, Forecasting, and Market Research

three components are interconnected in several ways:

 

1.  Data Integrity: Auditing ensures that the data used in forecasting is accurate and reliable. By verifying the quality of data sources through audits, organizations can improve the accuracy of their forecasts. For instance, if an audit reveals inconsistencies in sales data collection methods, adjustments can be made to enhance future forecasting efforts.

 

2.  Revenue Trends: Forecasting helps managers guide strategy and make informed decisions about critical business operations such as sales, expenses, revenue, and resource allocation. Forecasting adds a competitive advantage and can be the difference between successful and unsuccessful outcomes. Another benefit is that financial institutions will not put money in a business if it’s unable to provide a set of thoughtful forecasts. financial forecasts will also help develop operational and staffing plans that will make a business more efficient.

 

3. Informed Decision Making: Market research provides valuable insights that inform both auditing processes and forecasting models. By understanding consumer behavior and market dynamics through research, organizations can set realistic goals during audits and create more accurate forecasts that reflect current market conditions.

 

Continuous Improvement: The feedback loop created by these three components fosters continuous improvement. Audits can identify gaps in forecasting accuracy or market research methodologies. In turn, improved forecasting techniques can lead to better-informed market research initiatives that align with organizational goals.

Bringing it Together

In this section we will go over the transition from the audit to the forecast using a sample created by the Auditmetrics AI.

1.  Data Integrity

The audit is the starting point to assure that cashflow is performing with efficiency. A random sample is of upmost importance in conducting a valid audit. We will not go in to detail on this step. That is better handled in the “Getting Started” documentation.

 

Suppose a fiscal manager wants to do an audit of a sales account with a million transactions. The AI process guides the manager in obtaining an Excel statistical sample that can vary from 400 to 1000 records depending on the chosen precision, or as the pollsters term it “margin of error”. The AI process conforms to AICPA and IRS standards.

 

It would be valuable to review the case studies in chapter 2. Chapter 4 goes into more technical detail about the issue of statistical efficiency in the context of the sales tax audit. Spending time to understand statistical efficiency is well worth the effort. It is the pervasive underlying corner stone of all statistical inference processes for such diverse disciplines as medicine, economics, physics, opinion polling and social sciences. Take the time to understand it rather than pass it on to AI alone to do the thinking

 

2.  Revenue Trends

Transition data from audit to Forecast

Table 1 – Auditmetrics generated Random Sample that was used for an audit

 

Table 1 is a condensed exhibit of the sample (n= 1,158) using a 3% margin of error. In preparing the sample for forecasting two new variables are added. For first row the new cells are:

 

Year -   Excel function: =Year(C2)

Month- Excel function: =Month(C2)

 

The sample represents 30 months of data over a 2 ˝ year period. It is sorted by Year and Month by going to Data on the top menu and select sort. In the exhibit are the sort inputs:

 

The next step is to create a summary dataset that lists total revenue by month for the 2 ˝ years. The Excel tool is the Pivot Table. Select the dataset of interest and then select insert and then Pivot Table.

 

Figure 1- The pivot table set up and results

The resulting data table can now be used for a 30 month forecast of revenue. Table 2 is a restructuring of the data for using regression to create a prediction model.

 

Table 2 – Dataset for Regression Predictions

What is added is a new variable “Month_Count”. Does Revenue increase as we go from Month 1 to month 30?

 

 

 

 

A full discussion of regression modeling is in Springer Appendix I pp. 107-111. The details of how to use Excel’s ToolPak for Regression is on pp. 60-67.

Unfortunately, this regression model is not complete. So far, the basic model is a bivariate linear model, a dependent variable with only one predictor variable. Though we have a very good fit, there is a problem with the model.

 

The data is that of a wholesaler that supplies retail outlets. With this prediction model the next month will always be higher than the previous month. But business activity does have seasonal fluctuations. The fourth quarter of the year and its holiday activity will always be higher than the following first quarter of the following year. The model as it exists does not allow for seasonal fluctuations.

 

 

We need a new multiple regression model with two predictor variable types to perform monthly and quarterly adjustments. It is a statistical technique that uses several explanatory variables to predict the outcome of a dependent variable. In essence, multiple regression is an extension of our current regression model sometimes referred to as bivariate regression. Multiple regression involves more than one explanatory variable.  in the new forecasting model are added quarterly variable inputs to the pivot table. The model will now conduct forecasts that adjust for seasonal fluctuations.

 


 

Table 5 – Quarterly Variable Added

 

Table 5 adds the quarterly value for each month. Jan Feb March are all the first quarter of the year. Month 4 or April starts the second quarter.

 

The multiple regression dataset below takes into account seasonal fluctuations..

 

Table 6 – First Year Dataset with Quarterly Variables

 

 

The rationale and details of introducing the quarterly adjustment variables (Q1-Q3) is on pp 64-67 in the Springer Book.

 

The model as reported by toolpak is:

 

 

Coefficients

Intercept

28404.26

Month_ Count

1210.023

Q1

-8315.13

Q2

-10909.1

Q3

-8472.54

 

This model has a correlation of R =.78 which indicates a marked improvement of the model. It would be very useful to review again Appendix I regarding the concept of goodness of fit of the model.

 

The following discussion is excerpted from the Springer book. It deals with some methods of market research that can be readily implemented by small businesses.

3.  Informed Decision-Making

Regression and Local Market Area -Regression is very valuable in adjusting pre­dictions using categorical adjustments for various demographics factors such as geographic region and other characteristics such as gender. Geographic region can also be a surrogate for income distribution which is readily available from govern­ment published data. For example, there is available through census data sources that break down income tax collections or median income by zip code. Such geographic data combined with a company’s sales data with zip codes from its account record is an indicator of socio­economic characteristics of a business’ customer base.

 

The compilation of this type of socioeconomic dataset requires time, but gaining insight of the customer base is invaluable. The first step is to set up a dataset of sales accounts that include customer zip code.

 

 The first step is to group zip codes into broader zip code areas. This is where background research of examining census data to develop sociodemographic relevant zip code areas (Table 8.1).

 

Table 8.1 sales and sociodemographic research resulted in condensing the data into four geographic areas. This number was chosen to simplify the presentation of the concept of using regression to project sales by geographic area. It is most likely that many more areas would be of value, especially for larger businesses. From a statisti­cal data perspective, zip codes are categorical data.

 

Table 8.1 Sales Data by Zip Code and by Four Geographic Areas

Regression results:

R=.86

R2=.74

Sales = -$30,885 + $47,049 x Zip (Geographic Area number)

 

Pivot table to summarize sales by zip code area

As part of small business forecasting, it is key to get a picture of the possibilities for selling products or services in a local market. Looking at local markets will provide information about the types of individuals who might buy products or ser­vices and how extensive is the company’s geographic reach and what is the competi­tion within the various market areas.

Create a Customer Profile Next there is a need to determine who are the people who will buy products or services.

at what age are they?

What is their income level?

What is their education level?

What kind of jobs do they have?

What do they like to do for entertainment?

It may be too cumbersome and difficult for a small business to survey for such data. But a small demographically diverse focus group is a proven way to measure customer opinions. It is set up in guided or open discussions about new products or current views of the company to determine reactions that can be expected from a larger population. The use of focus groups is a market research method that is intended to collect data through interactive and directed discussions by an experienced interviewer. If there are issues with lagging sales that don’t respond to standard means of mar­keting, then arranging for a focus group may be what is needed.

 

Springer book pp. 81-86 discusses how to coordinate opinion Likert data with regression projections.

Constant Monitoring of Business Activities

 Regular timely random samples allow the business manager to deal with small workable subsets of account data representative of the total book. There is no need to use the gold standard of 3% margin of error with its sample size of 1,152 which should be reserved for official filings such as for tax agencies or fiscal year end assessments and reporting.

 

Regular routinely smaller samples in the 5% to 7%. range would require samples approximately 40% smaller reducing cost. Despite the reduced precision of a single point in time individual estimate, the random process trend over time will provide a fairly steady indication of trends.

 

Total Process Overview The overall process in conducting forecasting and market research is to:

1.    Start with a random sample of accounts.

2.    From there use regression to project revenue and expenses.

3.    Also add to the account data pertinent variables such as geographic and socio-demographic data.

4.    Set up a mechanism to obtain customer ratings using Likert scales.

5.    The total process from audit to market research cannot be done without also being closely connected to the personnel and operations of the business. The major benefit is creating an environment of administrative, technology  and employee cohesion.

 

Conclusion: A Synergistic Approach

In conclusion, auditing, forecasting, and market research work together synergistically to enhance organizational effectiveness. By ensuring data integrity through audits, leveraging insights from market research for informed decision-making, and utilizing accurate forecasts for strategic planning. Businesses can achieve greater success in their operations.

 

Other Auditmetrics Resources

Auditmetrics Small Business Power Series books available on Amazon:

Both Kindle and Paper Bound Available

       

Statistical Audit Automation

        Applying Artificial Intelligence Techniques

 

        ISBN: 9781973281016

  Forecasting Revenue and Expenses for Small Business Using Statistical Analytics

 

        ISBN: 9780578797250

 

Market Research for Small Business

Using Statistical Analytics

 

ISBN: 9780578813356

 

Value Added in Healthcare and Public Health

Value added is the extra value created over and above the original value of something

 • For private business it is usually the products sold to the consumer

 • It is the difference between a product final selling price and the direct and indirect

expenses incurred in providing that product

 

In healthcare and public health, the challenge is how to measure value added

 •Research into organizations that have achieved better health outcomes while often

lowering costs suggests a strategic framework for value-based public health and

healthcare implementation

 •Focusing on health outcomes aligns how patients experience their health with links to

the investment incurred

 •This is the basis of cost effectiveness and cost benefit analysis of public health and

healthcare programs

 

Also available on Amazon:

 

HealthLink Wellness: Science for the Individual

 

ISBN:9798365285866