Nate Patel and Chuck Lopez met while studying together at The University of Melbourne. After graduation Patel had accepted a position with the largest independent marketing research firm in Sydney, and Lopez went to work for an insurance company as an actuary in Canberra. At their tenth class reunion in 2015 they met again. Each was somewhat unhappy in their current job, and both wanted to return to the Melbourne area to live. A casual conversation turned to something more serious as they discussed leaving their jobs to start a market research survey consultancy in the Melbourne area.

Data Intelligence was founded as a partnership early in 2016. The business plan Nate and Chuck had written called for the new company to offer specialized surveys to companies that did not have internal marketing research staff. Through hard work and some goodluck in the strong economies of 2017 and 2018, the company had grown to employ ten professionals including the partners by the end of 2018. Revenues were approaching #3 million and income for 2018 was $600,000. Loans taken to fund the early days of operations had all been repaid.

The company focused on conducting surveys of consumers of existing and new products and services sold by manufacturers, distributors, and consultants. Data was collected and analyzed to provide information about demand, pricing, design, distribution channels, and delivery systems. A typical project ended with a report prepared for and delivered to the client who had contracted for the work with Survey Masters. In 2018, the company had completed 120 projects. An income statement in rounded numbers for 2018 is shown in Exhibit 1. 

The survey process

Data Intelligence typically used a three-phase process in designing and conducting surveys for clients.

•   Survey  proposal  and  design:  Clients  requested  services  or  Data  Intelligence  identified potential clients through the expected need for customer preferences or information. Proposals and survey design were perfected in conferences with prospective clients by company teams that  traveled  to  the  client’s  location  to  study  needs  and  the  information  sought  by  the prospective client.

•   Data collection: The survey instrument, developed during and following the design process, consisted  of  questions  about  the  product  or  service  was  administered  to  customers  or purchasing agents. Telephone surveys were common, but in-person visits were also used. Much of the data collection was done by temporary workers hired specifically for a given project, usually for about $10 per hour or less. The company professionals were seldom used in this phase.

•   Analysis and report preparation: Data were analyzed using a variety of statistical and analytical techniques, and a final report was prepared and presented to the client that had contracted for the survey.

Management

Chuck Lopez was the first to raise questions about the future direction for the company. Some  surveys were small projects done quickly, but a larger project might require several weeks to design, collect information, and prepare the final analysis and report. In the early days Data Intelligence  had been taking on any and all projects that they could find. But now that 2018 had gone as well  as it had, he raised with Nate the question of whether they should begin to be more selective or  more directive in the kinds and sizes of the projects that the company should undertake. Although  they had kept careful records of each project from the beginning, they really did not know which  projects were most profitable. The records assigned salaries and data collection costs to each  project by the phase and kind of work that was done.

As a first step in their evaluation, Patel and Lopez met with their accountant, Erica Evans, and raised with her the question about whether large projects or small projects were likely to be more profitable for Data Intelligence in the future. Within hours Evans sent the partners two summaries of her estimates of the profitability of each type of project (see Exhibit 2). The cover memo that she had attached described the basis for her analyses. It read in part:

I listed all of the projects by project revenue from the largest to the smallest.  Without a clear  criterion for separating large from small projects, I first used project salaries as the basis for  defining the two categories. Our total project salaries for 120 projects in 2018 were $800,000.  Half of that amount was spent on what I first called large projects (of which there were 20), and  the other half went into the 100 smaller projects. I used salaries as the basis for dividing the  $1,400,000 in overhead expenses.  This  analysis  led to a conclusion  that of the  $600,000  in  estimated income for 2018, $200,000 was generated from the largest 20 projects, and $400,000  was generated from the smallest 100 projects. The results of this analysis are shown in Panel A of Exhibit 2.

This was a little hard for me to believe, so I did a second analysis, shown in Panel B. For the second analysis, I looked at the largest 60 projects and the smallest 60 projects ranked by project revenue. This points to a conclusion that the larger and smaller projects are equally profitable, which seems more plausible than the results in Panel A.

But I have a nagging suspicion that neither of these analyses actually presents the information you need to set a direction for Data Intelligence in the future. The smaller projects require many more trips (shorter in time and fewer people), more days of data collection, and more pages of analysis and report than the larger projects. I have summarized these data in Exhibit 3, and activity analysis taken from our records.

I have to be away for a few days to care for my mother, who is ill, and I have not had time to complete an analysis based on the activities related to larger and smaller projects. I will do so immediately on my return, but you may want to do such an analysis yourselves so that you can proceed to think about whether you want to continue to take all projects or focus on larger or smaller projects in the future.

 Nate and Chuck decided they could work with Erica’s information and agreed to start the next day in the conference room and see if the data would help them figure out the direction that they should take for their future business.

Requirements:

1.   Comment on the analyses prepared by Erica Evans in Exhibit 2. Why do smaller projects appear to be more or at least equally profitable as larger projects? (15 marks)

2.   Use the information in Exhibit 3 on overhead spending and activities to prepare analyses of the profitability of larger and smaller projects undertaken by Data Intelligence in 2018. What are your conclusions about the relationship between project size and project profitability? (20 marks)

3.   Should Data Intelligence continue to take all projects offered to the company? Why or why not? On which size of project should they focus their sales efforts? Should they refuse to take on larger or smaller projects in the future? What should be their strategy in selecting future projects to undertake with clients? (20 marks)

4.   Data Intelligence wonders about the effect of the proposed strategy (based on response 3) on profitability. Suppose that Data Intelligence decides to only take on larger client projects, but does not enlarge its professional staff. Prepare a projection of income for 2019 for the strategy that you proposed in requirement 3 above. Comment on the effectiveness of the strategy and outline the actions you will consider taking. (20 marks)

5.   The assumption underlying the above analyses is a linear relationship between project size and profitability. However, the assumption is  questionable and deserves  further investigation. Utilize the existing data, classify projects by size into three groups (small, medium, and large), and calculate their profitability. Comment on the linearity assumption and discuss how you would revise your response to requirement 3. (25 marks)

Exhibit 1

Data Intelligence Pro Forma Income Statement for the Year 2018 (Rounded)