Auditing, Forecasting, and Market Research

How They Work Together

1. Understanding the Roles of Each Component

Auditing involves a systematic review of an organization’s processes, strategies, and performance metrics to ensure compliance with established standards and best practices. It helps identify areas of strength and weakness within marketing functions or forecasting methods.

 

Forecasting is the process of predicting future trends based on historical data and analysis. It plays a crucial role in planning by providing insights into potential market conditions, customer behavior, and resource needs.

 

Market Research gathers information about consumer preferences, market trends, and competitive dynamics. This data is essential for making informed decisions regarding product development, marketing strategies, and overall business direction.

 

2. Auditing, Forecasting, and Market Research Relationship

three components are interconnected in several ways:

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.

 

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.

 3. Practical Application of Their Integration

Integrating auditing, forecasting, and market research allows organizations to:

Develop comprehensive marketing strategies that are grounded in solid data.

Allocate resources more effectively by understanding projected demand through accurate forecasts.

Monitor performance against established benchmarks using insights gained from audits.

For example, a company may conduct a marketing audit to assess its current strategies’ effectiveness while simultaneously analyzing forecasted sales figures derived from recent market research findings. This holistic approach enables decision-makers to adjust their tactics proactively based on empirical evidence rather than assumptions.

4. Examples of Bringing it Together (excerpted from Springer Book)

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 data combined with a company’s sales data is a good indicator distribution of the 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 from a sales account with links to customers’ zip code. A summary of sales data by indi­vidual zip code would be very unyielding to interpret. The first step is to group zip code into broader zip code areas. This is where background research of census data and a map to develop relevant zip code areas (Table 8.1).

The 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 Zio Code and by Four Geographic Areas

Regression results:

 

 

R=.86

R2=.74

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

Used Excel 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.

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 a researcher. 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.

Obtaining Customer Ratings A Likert scale is a scale commonly involved in research that employs questionnaires. It is the most widely used approach to scaling responses in survey research, such that it is often used interchangeably with rating scale, although there are other types of rating scales.

The scale is in a format in which responses are scored along a range. When responding to a Likert item, respondents specify their level of agreement or dis­agreement on a symmetric agree-disagree scale for a series of statements. Thus, the range captures the intensity of one’s feelings for a given item. The Likert scale has found widespread use in business and marketing, primarily because of its simplicity.

A scale can be created as the simple sum or average of questionnaire responses over the set of individual items. Likert scaling assumes distances between each choice on the sale are equal. The design of a set of scale items is such that they are highly correlated but also that together will capture a full range of customer preferences.

A Likert item is a statement that a customer respondent is asked to evaluate by giving it a quantitative value based on a level of agreement/disagreement being the dimension most commonly used.

The format of a typical five-level Likert item, for example, could be:

·      Strongly disagree

·      Disagree

·      Neither agree nor disagree

·      Agree

·      Strongly agree

It is a bipolar scaling method, measuring either positive or negative response to a statement. Sometimes an even-point scale is used, where the middle option of “neither agree nor disagree” is not available. This is sometimes called a “forced choice” method, since the neutral option is removed. The neutral option can be seen as an easy option to take when a customer is unsure, but there is much discussion if it is a true neutral option or just when the respondent is confused. It has been dis­cussed that there is not a significant difference in the use of forced neutral or not.

Likert scales may be subject to distortion from several causes. Respondents may:

·      Avoid using extreme response categories (central tendency bias), especially out of a desire to avoid being perceived as having extremist views.

·      Agree with statements as presented (acquiescence bias), who may by an eager­ness to please.

·      Disagree with sentences as presented out of a defensive desire to avoid making erroneous statements and/or avoid negative consequences that respondents may fear will result from their answers being used against them.

·      Try to portray themselves in a light that they believe the examiner or society to consider more favorable than their true beliefs.

The biases listed become paramount when questioning individuals regarding socially or highly personal issues. But for the small business manager, the dimen­sions are primarily three dimensions of primary concern:

1.    Overall customer satisfaction with interaction with the business.

2.    Satisfaction of value of goods or services based on price and other per­ceived values.

3.    Likelihood of repeat business and recommend to others.

For the small business, these scales are a straightforward indicator of customer satisfaction with the business. They do not deal with more highly charged social and personal assessments. The issue of bias is not a major concern.

Question Design After the questionnaire is completed, below are things to keep in mind when formulating individual questions:

·      Make the questions very specific. Notwithstanding the importance of brevity and simplicity, there are occasions when it is advisable to lengthen the question by adding clarification. For example, it is good practice to be specific with time periods.

·      Avoid jargon or shorthand. It cannot be assumed that respondents will under­stand words commonly used by people in the business. Trade jargon, acronyms, and initials should be avoided unless they are in everyday use.

·      Steer clear of sophisticated or uncommon words. A question is not a place to score literary points, so only use words in common parlance. Colloquialisms are acceptable if they will be understood by everybody.

·      Avoid ambiguous words. Words such as “usually” or “frequently” have no spe­cific meaning and need qualifying. Avoid questions with a negative in them. Questions are more difficult to under­stand if they are asked in a negative sense. It is better to say “Do you ever ...?”, as opposed to “Do you never ...?Avoid hypothetical questions. It is difficult to answer questions on imaginary situations. Answers may be given but they cannot necessarily be trusted.

·    Do not use words which could be misheard. This is especially important when the question is administered over the telephone. For example, fifteen and fifty can sound very similar.

·    Desensitize questions by using response bands. Questions which ask about age is best presented as a range of response bands. This softens the question by indi­cating that precision isn’t necessary and only a broad answer is needed.

Forecasting Economic Potential There is a treasure trove of economic informa­tion for market research that is contained in business accounting systems. But to unlock that, information requires careful planning. Auditmetrics experience is that standard accounting system reports are useful, but the level of detail may not be sufficient. In a business assessment concerning the lack of growth of a new product, the immediate response of the business manager is usually “let’s increase the adver­tising budget,” a typical marketing research response. A review of product sales data for the period of time before and after the initiation of the product was conducted. A random sample was selected to pull records to interview employees and custom­ers. The results indicated staff were not familiar with the requirements of the new product that unfortunately led to customers to be confused. In an audit it was found that there was an unusually high number of product returns. More training of employees is what helped in increasing sales. Fortunately, this assessment was done quickly enough to avoid the company having an image problem.

Routinely Sample Accounts to Monitor Business Activities Many business man­agers complain that selecting account samples takes a lot of time. It is better to sell rather than sample. There is nothing wrong with pushing for more sales as long as there are not some unforeseen barriers.

What helps is to make inroads on the time issue. 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% which would require a fairly substantial random sample. The other time saver is the Auditmetrics AI assistance software feature. This makes it possible to rapidly gen­erate a random sample by simply deciding on the desired margin of error and test different number of strata. The business manager can make more rapid forecasts using MS Excel in effect putting the business on a monitor.

Account Data and Customer Input In this section Likert customer rating is linked to customer sales data. It was designed as a preliminary small sample looking at linking customer ratings on price and satisfaction and whether they would recom­mend the business to others. The random sample was derived from a QuickBooks report that list total sales by customer (Table 8.2):

The variables are:

Zip_Area—That is the customers’ zip code aggregated by areas based on census data that delineate areas of different socioeconomic characteristics based on median family income.

Table 8.2 Likert average Likert scale rating and total sales by zip area

Lprice—Average Likert customer rating evaluating price and value of the product LSatisfied—Average rating for satisfaction with interaction with business staff LrRecommend—Average rating for repeat business and recommend to others

Average Likert rating is used even though there are scaling issues discussed pre­viously. This assessment is intended to be a quick look at consumer opinions because time is also of value to the customer. A more detailed analysis can be done with a focus group recruiting customers for a more detailed assessment. It usually involves offering some sort of compensation not necessarily cash but product or service discounts as an alternative.

The next step in the analysis is to conduct regression analysis with zip area as the dependent variable and the Likert ratings as the independent variables with the fol­lowing results:

Multiple R = 0.46

N =60

The correlation coefficient is .46. As expected, data variables that measure people’s attitudes are not as predictable as the prior regressions involving account data. So as an ongoing prediction model good­ness of fit, this provides a preliminary look. Below is the table that gives each inde­pendent variable’s alpha error. The decision is if the observed coefficients occur by random chance alone is less than 5%, then one can assume there is a measurable effect of that scale by zip area. The Likert rating for price does differ among the different zip areas. Though this is a preliminary snapshot, the observed alpha error (p-value) is so small that it should require further analysis (Table 8.3).

Zip Area 1 is a geographic area which is in the lowest end of median family income based on census data. A breakdown of average sales by that zip area is also at the low end. It may be wise to have an advertising campaign specifically tailored

Table 8.3 Statistical significance of Likert ratings

Table 8.4 Average sales by zip area

to those families in Area 1. That is the area that may increase demand if price dis­counts are offered. There can be repercussions if sociodemographic data is used, so further assessment should be conducted. If it happens that this geographic area has an older population, mostly retirees on fixed incomes, then a senior discount for all customers is appropriate (Table 8.4).

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.

Step 4 should be part of a total package to obtain customer loyalty. To truly build this loyalty, companies need to move from transaction interaction with their customers to building company customer relationships. The first step in building these relation­ships is engaging with customers beyond basic one-way dialog. Customers don’t feel valued when it takes undo time to contact the business they patronize. At the same time, sending out mass text messages without a prompt response will also not give customers a satisfying feeling either. Correct proactive outreach can help organizations maximize productivity, customer satisfaction, and contributions to the bottom line.

Though much discussion in this book involved quantitative measurements struc­tured to act as part of a business performance monitoring process. Is it worth it? The quantitative methods will expand control of day-to-day operations. Also, when seeking funding for current operations and new business plans, the quantitative methods discussed follow both AICPA and IRS standards at a level of statistical sophistication that usually is available only to large corporations.

5. 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.

 

6. Resources for this Discussion

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

 

7. 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