Big data continues to grow at an incredible rate, and the amount of data a business needs to capture, maintain, and analyze is estimated to double approximately every four years. Because of this, computer science has been forced to advance and come up with ways to deal with enormous data sets. Data science has, essentially, been the answer to this call. Data science is an interdisciplinary field that seeks to locate and extract patterns in data through a variety of scientific methods, including data mining, forecasting, predictive analytics, and machine learning, among others.
Fortunately, you don’t have to be an expert data scientist to take advantage of these methods. One of the best ways that the modern enterprise can utilize data science is in the field of market research. Most people know market research as testing the viability of new products and services through interviews, focus groups, and other methods to find the right target audience (if there is one). This is just part of the puzzle, however. While such primary research performed by the business itself is important, organizations also need to consider secondary market research. This involves pulling data from outside sources, such as market reports, financial journals, or even a thorough competitive analysis. No matter the goal of your market research, here are some of the best data science methods to make your research a success.
Break down your business problems.
You can’t conduct effective research without first knowing the problems you’re trying to solve in detail. Data scientists specialize in breaking a seemingly simple question down into more specific ones that address the true roots of the problem. Businesses typically have quarterly goals that they seek to meet, and they collect a lot of data to track how they’re doing. If your business goals are to increase sales this quarter, for example, you’d likely set a sales goal and track key performance indicators (KPIs) each month to see how you’re doing.
If you really want to attack the problem of increasing sales efficiently, however, you’ll need to collect more data than that. Take a look at your existing sales data to determine what you most popular products/services are, which demographics buy them, and if there are any seasonal correlations to higher sales. These insights will help you come up with more effective promotions, develop products that are relevant to your best customers, and even predict future market trends through a combination of historical and current data. Of course, predictive analytics have other uses as well, such as in maintenance.
Integrate your data sources.
Data is difficult to monetize properly unless you’re constantly keeping it accurate and current, and the easiest way to do this is with intelligence integrations, so all of your data sets are collected in a single source of truth. This is one of the primary features of a reliable market research information system, and it makes it much easier to deal with a huge amount of data. Thanks to advancements in machine learning and artificial intelligence, these research systems can analyze big data sets, find patterns, and even simplify them in visualizations far more quickly than any human ever could.
Best of all, your data will be constantly updated in real-time, so you’ll never have to question if you have the best information available to inform your decision-making.
Prepare for the inevitable business changes.
A good data scientist will always have a hypothesis for how things will change once more efficient data science techniques are deployed, and business leaders need to consider the same. Data science tools are a natural part of the modern digital transformation, and while they’re necessary to help your business thrive in a constantly changing environment, you’ll need to have the best talent available to present these changes to employees and help them adapt to new processes.