Using data science assess the market when launching a new product

When you're launching a new product or service, it's easy to get caught up in the excitement without taking the time to intelligently assess the market to see if you can compete.

You need to be able to answer the following questions:

• Will people be interested in my new product or service?

• What's the competitive landscape like?

• How much money and energy will this new venture take?

• What are the underserved niches, and can I fill one?

If someone wanted to start a new restaurant back in the 1990s, they would have to physically stake out a few potential local eateries and tally up how many people walked in through the front door for lunch. Now, we have the internet to do a lot of the data collection for us. But the internet provides the data in a raw and unfiltered form—we need a way to collect, analyze, and make sense of it. 

Enter data science.

At Witmer Group, a Dallas marketing agency, we sat down with our in-house data scientist Joo Ann Lee. She provided us with a few tips for using data science to make a more informed decision.

What is data science?

Data science is a rapidly evolving field that seeks to uncover intelligent insights hidden in a vast treasure trove of data. 

Data scientists identify questions that don't currently have answers ("how will the market respond to my new product?"), locate where the data to answer that question resides (forums, social media, customer surveys, etc.), and then extract and analyze that data to determine an answer.

Bigger companies have teams of data scientists working in their marketing department and using a combination of artificial intelligence (AI), complex mathematical modeling, and computer programs to help uncover hidden insights. But you don't need a multimillion-dollar annual budget to use data science. Let's look at how you can do this for your own business.

3 tiers of assessing competitiveness

There are three main pillars of data that you'll need to gather:

• Industry saturation

• Sentiment analysis

• Customer research

Keep in mind that you need to have the right kind of data—if it's incomplete or biased, you can get misleading results. To avoid this, always make sure that the data source is coming from a resource relevant to the business. For example, if you're looking to open a brick-and-mortar shop, you should focus on compiling reviews from local people instead of travelers. If you focus too much on the tastes and preferences of out-of-towners, you might lose the interest of locals. Similarly, as you collect your data, be sure it's all up to date. You don't want to be relying on five-year-old customer reviews.

Industry saturation

First, you'll identify the saturation of the industry. How many companies are out there selling similar products and services? 

For example, if you're considering starting a vegan and gluten-free pizza restaurant, look to see how many of those exist in your geographical area. You can use Google, Uber Eats, Yelp, and other apps; then tally up the search and see how many competitors are within your chosen radius. 

Once you know the number, here are some of the questions you might ask about these businesses to understand how tough the competition will be:

• How many years have they been in business?

• How many online reviews do they have?

• How many products do they have?

• How big is the company? 

Sentiment analysis

After you've got a good idea of the lay of the proverbial competitive landscape, it's time to get more into the nitty-gritty. Sentiment analysis is what people are saying about the product or company.

Comb through forums and review sites to find the best feature reviews. Try to find similarities among what various people are saying about the product. The more people who mention a specific feature or benefit, the greater confidence you can have in the accuracy of the data. For example, if several of the reviews you find mention that the product breaks within one month of ownership, this might provide you with manufacturing insights and a competitive advantage opportunity. Knowing what your potential competitors do and don't do well will help you understand if there's an opening for you.

Keep in mind that there will always be a lot of bad reviews that aren't meaningful—for example, they're complaining about something out of the business's control. Don't include these data types because they don't provide valuable information.

Customer research

Now that you know who the competition is and how people feel about them, it's time to look at the people leaving the reviews. This will allow you to more precisely pinpoint and serve the customers that matter most to your business. You're looking for things like:

• Geographic similarities

• Demographic similarities

• Personal tastes (e.g., prefer spicy food)

• Purchasing power

Once you've compiled a list, you can laser-target those individuals in your marketing. But more importantly, knowing your target audience can also help you sell the product better or provide new business opportunities. 

Here's an example. There's a company that makes and sells small pipes for industrial applications. They decided to set up a shop on Amazon and sell their products. They experienced a considerable amount of sales, but almost everyone who bought their product left a one-star review on Amazon claiming the pipe wasn't "food safe," despite the product being clearly labeled as "industrial."

The owner couldn't figure out why so many people hated his products—yet they continued to buy them. After looking at the data, the owner realized what was going on: the homebrew community (people who craft beer at home) couldn't get a specific size of pipe they needed at Lowes or Home Depot. Amazon was the only place, but the pipe needed to be food safe.

Knowing who (homebrewers) said what (pipe is not food safe) provided the business owner with a huge opportunity to develop the same pipe that was safe for food. 

Ideas for data discovery

There are many ways that you can go about finding data. Here are a few that we've found have worked the best for our clients.

• Facebook Viewpoints. Facebook Viewpoints has you pay to have people provide you with their opinions, including countless filters that will enable you to laser-target a particular niche or demographic.

• Google Ads research. By looking at exactly what keywords your competitors are targeting, you can better understand their business and their customers. It can also clue you into features and benefits you might not have considered.

• Physically scope out a location. Several years ago, Witmer Group was hired by a client who was launching his third restaurant in the Dallas area. We worked with him to physically scout out the location and report back trends in potential site visibility from roadside traffic, time of day, and peak business hours. This allowed him to narrow his choices down to two different locations based on lunchtime traffic. He successfully repeated this formula over the next 24 months as he opened a chain of restaurants around the Dallas area.

• Poll your online network. Social media polls let you do quick data collection. Try to limit the survey to the most important questions to increase the chances of a response (and an accurate one).

• Online forums and product reviews. These kinds of sites offer authentic insights into how competitors' users respond to the existing products and services. 

The whole process in action

To give you a better idea of what the whole process looks like, here's an example. A managed IT company that wanted to expand its offerings approached Witmer Group and asked us to determine what the market wanted in terms of computer security. 

Joo Ann first compiled a list of all companies that sell cybersecurity products. Sites like CNET and G2 were a gold mine of honest thoughts and concerns from users and potential users of these products. She then went to various forums and collected all three data tiers (industry saturation, sentiment analysis, and customer research). She looked for recurring themes and consistent similarities—both what was good and what was lacking in the product. 

From her research, she found that the most common pain points were speed and licensing issues. Joo Ann presented her findings to the client, which allowed them to design and market a very popular solution that spoke to those pain points.

The skillset of a data scientist who can write algorithms and perform the complex math necessary to extrapolate intelligent insights is a huge boon. But by using some basic data science principles, the average small business owner can discover enough information to make an informed decision.