Decision Tree Case Study

Profit Computation and Analysis

Making strategic business decisions when you’re the sole owner of a small business can be tough. Sometimes it feels like you have to get everything right, or the whole business will fall apart. Part of DataDay’s job when working with our client partners is to make strategic decision making a bit less murky. We help our clients clearly understand the challenges they are confronting, and present what we think is the best path forward to help business owners avoid analysis paralysis. DataDay then collaborates with client teams to turn great plans into reality.

This case study is just an example of how we use strategic frameworks and first-hand experience to improve decision making. Here, we will be discussing maximizing revenues through customer segmentation and pricing strategy. Whether we are helping you improve your bottom line, launch a new brand concept, or simply providing project management support, our team delivers results with our organized, solution-based approach.

Imagine this example scenario: A few years ago, Greg, a marketing professional with years of experience in social media management and web design, started a side hustle doing digital marketing. He wasn’t sure if his company would ever get that big, so he was okay to start with a “pizza shop mentality”. No matter how many pizza shops a town might have, there’s usually room for one more. Despite the digital marketing space being quite crowded, there was so much demand as a result of COVID-19 and old businesses catching up with modern technology, that Greg was able to make a little cash from his side hustle for the first few years.

Greg has not tried to develop a competitive strategy and is generally apathetic towards his clients’ industry focus. While he has worked with a number of clients over the last few years, a lot of his work has been with individual athletes and personal trainers, rather than with traditional businesses. Despite the success of his early web and logo design projects, client churn has been high and revenues have been inconsistent. Despite these setbacks, Greg came to DataDay because he has been considering quitting his day job to scale his side hustle and hire a few more people. He asked us to help him develop a real competitive advantage and identify some ways to generate higher, more consistent revenues.

To ensure we could meet Greg’s expectations, we started by defining what success would look like. Developing a competitive advantage, to Greg, meant having a few reasons why clients would choose him over other digital marketing options, and continue to work with him as repeat clients. We needed to identify a few ways that Greg could provide unique value to his clients, that other digital marketing firms could not match. Additionally, Greg wanted to be able to consistently bring in at least $5k a month in profits for at least six months, before hiring anyone else.

When DataDay first met with Greg, he primarily attributed his unpredictable revenues and client churn to the one-off marketing needs of his staple clients – athletes and personal trainers. When we asked if Greg had tried to attract other types of clients, he said he put a lot of time into pitching small businesses on projects, but with limited success. He also said that he had already tried changing his service offerings and pricing to improve contract renewal/extension.

One thing that stood out to us immediately was that if Greg wanted to develop a competitive advantage and make his revenues more consistent, he might consider focusing on one client base. The thought behind this is that focusing just on small businesses or just on athletes/trainers would make the type of work Greg is doing, as well as the sales process, more standard. Specialization can lead to increases in service/product quality as a result. Committing to a particular customer segment can also reassure clients in that space that Greg deeply understands their needs and priorities. Ultimately, specialization may help deliver higher, more stable earnings. 

When we mentioned the idea to Greg, he agreed it made sense. Yet he wasn’t sure how we would come to any conclusion around which customers to specialize with. “Athletes have wanted to work with us more than small businesses in the past, so maybe we should focus our attention there. But small businesses always have more marketing needs and generate larger revenues for me when they commit to working with me.”

Hearing Greg’s internal conflict, and knowing that anecdotal evidence can often by misleading, we all agreed that it would be worth doing a data-based analysis. With some additional input from Greg, DataDay set out creating a decision tree to identify which customer segment is best for maximizing Greg’s long-term profit. Most of the assumptions that we made in our model came directly from Greg. 

Typically when he spent $2000 on an ad campaign, he would generate 100 warm leads, whether the ads focused on small businesses (“firms”), athletes, or both. He mentioned he was planning to run another campaign in the near future, once he finalized his competitive strategy. Analyzing Greg’s historical close rates, and understanding the impact that partnering with DataDay on marketing his “specialization” would have on his go-forward close rates, we put together the decision tree below. We looked at the close rate for each stage and each service offering separately (3 month vs. 6 month scope of work, $500 vs. $1500/mo). This allowed us to better understand the expected value of focusing on one customer segment, and the importance of closing different types of deals. For our purposes, “close rate” was the rate at which potential clients move to the next stage of Greg’s sales pipeline, with that particular offering. Once you know the close rate at each stage of a sales pipeline, it is quite easy to come up with overall “win rate,” for a particular service offering and client type. “Win rate” is he rate at which warm leads become new clients.

Decision Tree Case Study

In the above, you can clearly see that firms pay more for a given scope of work, yet athletes are 3x as likely to become Greg’s clients as small businesses. Once everything was clearly mapped out in the decision tree, we computed the expected profit for each of the two client groups over the next 6 months. This calculation was done using the expected value theory, as shown below. In this analysis, we assumed that the $2,000 ad spend is the only incremental cost that Greg will incur and he will not pursue new clients after those with 3-month contracts expire. We also assumed his costs to perform the various scopes of work would ultimately remain the same, due to Greg’s experience and smart use of automation. As already mentioned, we baked in some assumptions around increased close rates due to specializing on one client type, as well as some uplift from working with DataDay to improve the structure of Greg’s ad campaigns. As you can see below, the hands down winner is athletes and personal trainers.

EV (Athletes) = 100 * .3 {[.5*3 * [(.8*200) + (.2*500)]] + [.5*6 * [(.6*500) + (.4*1000)]]} – 2000 = 30 * (615 + 2100) – 2000 = $79,450 profit over next 6 months

EV (Firm) = 100 * .1 {[.5*3 * [(.7*500) + (.3*1500)]] + [.5*6 * [(.4*5000) + (.6*1500)]]} – 2000 = 10 * (1200 + 2900) – 2000 = $37,000 profit over next 6 months

Despite bringing in higher revenue on a per-contract basis, focusing on small businesses will bring in less money than focusing on athletes. This is primarily due to the 10% close rate on warm leads that are businesses, compared to the 30% closed rate on warm leads that are athletes. To maximize earnings, Greg should focus on the athlete/trainer segment going forward. Extrapolating from the decision tree, we can also say that if Greg wanted to specialize further in order to boost his revenues, he might do so by focusing on athletes with marketing needs that span a 6 month partnership, instead of 3 months, since the monthly contract values are higher.

Greg was thrilled to see our findings. What seemed like an uncertain decision was made easy, with the help of DataDay and one of our go-to strategic frameworks. Greg came to us wanting to increase his revenues and make them more consistent. He also wanted to establish a clear competitive advantage over the marketing firms he competes with. We helped him realize that specializing with one customer segment would provide a competitive advantage by making him stand out to potential clients. Focusing on one segment, has also allowed Greg to focus on the clients that generate greater overall revenues for his business and eliminate inefficiencies related to switching between different client types. After standardizing his sales process for pitching to athletes and trainers, he also saw his churn go down and his revenues become more consistent. Using strategic thinking and paying attention to the details of Greg’s business, DataDay was able to deliver the unique value proposition that Greg wanted, along with $5k a month in profit for more than six months in a row. Seeing these results, and the response from his clients, gave Greg the confidence he needed to hire his first employee (other than himself) earlier this year.

Why should you care about Greg’s experience working with DataDay? We all know that information is power, but only if you’re looking at the right information and analyzing it with the right lens. For Greg, partnering with DataDay helped him understand his own business more clearly. It took him from uncertainty to clarity. Because of our data-driven approach and structured attention to detail, Greg was confident in our findings and felt empowered to commit to this new competitive strategy. If you’re stuck in analysis paralysis, or are struggling to make odds and ends of what customers/scopes of work/geographic areas your business should focus on, a strategic framework like a decision tree may be able to help. If you don’t have the time to do this kind of analysis yourself, or need some extra support modifying your business processes to focus on what you have already identified as key priorities, DataDay may be able to help. 

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