Latte Larry's Coffee Shop

Tools used in this project
Latte Larry's Coffee Shop

About this project

Project Context

This project involved the following scope of analysis and report building:

You've been asked to share an explanatory report providing a data-driven strategy for opening their first coffee shop. The investors expressed interest in the following areas, but are open to any additional insights and recommendations you can provide:

  • Target audience: What type of customer should we target, and what are their preferences?
  • Product offering: What types of coffee beans and drinks should we offer?
  • Pricing strategy: How can we align prices with customer value perception?

What stands out here is the need for an explanatory (static) report, using data to develop a strategy for a coffee shop. I take this as being the equivalent of a cafe or somewhere you can either drink a coffee in house, or buy and take away, akin to a Starbucks, or equivalent.

So, I see it as a single page report, no exploratory features like slicers or filters, just a simple, straight forward business analysis report.


The data brings some additional context to the insights we are requested to consider. The data is from "The Great American Coffee Taste Test", which appears to be a survey given to approximately 4,000 coffee lovers. Some may have felt there were bias in the data, but the data is inherently biased, as it only considers people who will have had sufficient interest and love of coffee to volunteer and take part in this taste test, and these are the type of people our clients want to attract....people who "know coffee".

However, although the data is from coffee lovers likes and dislikes with respect to various aspects of coffee consumption, I decided I only needed to focus on a selected portion of the data, which aligned with our target audience.

Selected Data

As our analysis was centred around developing a strategy for a coffee shop, I decided to focus in on survey respondents who answered the multiple choice question of where they drank their coffee as "in a cafe" or "on the go". Therefore people who exclusively drank their coffee either at home or in the office were omitted from my analysis, as their responses would be less relevant.

I also decided to focus on disparities between male and females. I omitted data from respondents who either didn't provide a gender or were non-binary, as the sample size was quite small.


I developed a model in power query/power bi to create a main data table, which was linked unpivoted tables containing answers to the multichoice questions. I put together a video showing this methodology. It enabled the easier analysis of multichoice answers, without worrying about applied filters.


As a fan of Curb Your Enthusiasm, I couldn't resist using the Latte Larry's name and colour scheme. I stopped short of adopting the logo of "Come for the coffee, stay for the spite"!


My structure was quite similar to my previous entry for the Pizza challenge. Due to limited time, and a lack of creativity I opted for a similar layout, but as they say, "if it isn't broken, why fix it?"undefinedI separated out each section, with a header and then a key take-away at the top which briefly summarised the contents of the section., which could help develop the overall strategy.

I then included an explanatory title and paragraph for each visual, to provide that reinforcing context.

I also decided to use the same visual type for each section for familiarity and ease of understanding.....


For the visuals, I decided to use a proportional dot plot to display the % values for each section, with sizing and colour affected by the % value, and size values provided. I felt this provided an easy to understand comparison between both the categories and genders.

Consistent structure and colour palette in the visual means that the end user only has to be familiar with one type of visual in order to understand all the visuals in the report.



Preferences & Consumption

I decided to focus in on coffee strength, roast level, monthly spend and daily coffee consumption. I thought this would give a good overview of general coffee style preferences and market for the average coffee lover, and set some potential overarching strategy points.

Beans and Coffee

I looked at the data related to coffee bean and drink preferences, and found key differences between men and women. I would suggest that combining these two insights could help you decide on which coffee beans to match with the most popular drinks.


Again, looking at costs, I examined the breakdown between what the most people have spent in the past versus what they would be willing to spend in the future for a cup of coffee to look at potential prices that could be applied in the new shop. And again, this could be linked to the analysis on the beans and drinks, where certain drinks and more likely to be ordered by women, who in turn have a specific price range when they consider what they would like to pay.

Additional project images

Discussion and feedback(2 comments)
2 months ago
This is extraordinary

Luis Rolando Triminio Castro
Luis Rolando Triminio Castro
2 months ago
2000 characters remaining