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In the data science industry, it is not enough to be a skilled data scientist — you need to sell your project and yourself. The first step of selling an idea or yourself is developing a solid elevator pitch that provides your value proposition and how your project is different from others. In this blog post, we will discuss how elevator pitches work, what goes into them, and examples of elevator pitches for data scientists.

1. What is an elevator pitch

You’ve been stuck in an elevator with an executive, and you only get 15 seconds to make a good first impression of yourself and your project. What do you say? You are at a networking event, and one of the industry leaders asks you to tell her about your project. Can you describe your project/product in a concise and compelling way?

In your mind, what you want to say is that “you are about to embark on an adventure with me that will change your life forever. I am the answer for all of those questions you’ve been asking yourself, and more importantly, you need a partner who understands exactly what you need”. Right? But we know that this doesn’t really come out right. You have 15 seconds to grab their attention. We need to be able to deliver a tightly developed elevator pitch.

2. Why do you need to know how to give a good elevator pitch

You should learn how to deliver an effective elevator pitch for your projects for two reasons. First, you may want to present a project at work or during an interview. Second, it’s good practice in writing concise descriptions of complicated subjects and ideas — which is exactly what data scientists do! You might not think that knowing how to sell yourself makes you any more effective at doing your job, but elevator pitches are great practice for giving presentations and making data science projects sound appealing to others.

3. The components of a successful elevator pitch

You will find endless advice and suggestions on how to develop your elevator pitch. However, I believe we should approach it systematically, meaning the formula should be based on evidence and research. Thus, this formula is from the Marketing research area…. they know how to market!!

You need two high-level components. You have to make Your Value Proposition and Your Differentiation.

Value Proposition:

1) Be specific about the targeted segment that your project is addressing. It’s hard to convince others that whatever that you are working on will help everyone. It also sets the context of the project.

2) Describe the problem by who is dissatisfied with the current environment.

3) Describe the product/service that you are developing and what specific problem that it is solving.

Your Differentiation:

1) Describe the alternative (perhaps your competition or currently available product/service)

2) Describe a specific (not all) functionality or feature that is different from the currently available product/service.

4. Elevator pitch formula

For [targeted segment of customers/users],

Who are dissatisfied with [currently available product/service]

Our product/service is a [new product category]

That provides a [key problem-solving capability]

Unlike [product/service alternative],

We have assembled [Key product/service features for your specific application]

5. Examples of how I use them

Example 1:

For political election campaign managers,

Who are dissatisfied with the traditional polling products,

Our application is a new type of polling product

That provides the ability to design, implement, and get results within 24 hours.

Unlike the other traditional polling products that take over 5 days to complete,

We have assembled a quick and more economic yet comparably accurate polling product.

Example 2:

For front line criminal investigators,

Who are dissatisfied with generic dashboards that display too much unnecessary information,

Our application is a new type of Intelligence product

That provides a highly customized and machine learning-enabled risk assessment tool that allows the investigator to uncover a hidden network of potential criminals.

Unlike the current dashboard that provides information that is often not very useful,

We have assembled an intelligence product that allows them to make associations between known and unknown actors of interest.

These are real-life examples that I have used. It works and certainly grabs their attention. Let me know what you think.