A new business needs to do market research on potential customers as well as industry research to look at the existing market landscape to determine how its products will fit in.
Market Research looks at consumers: what they buy, who they are, what they value, which companies they buy from, etc.
Industry Research looks at companies operating in an industry: market size v. achievable market size, who makes the product or service, market share, competition, and how the industry operates.
The market research process is very similar to any research project. The steps of market research are:
Step #1: Define your population
Who, specifically, do you want to serve? This tells you, in general, who you need to study.
Step #1: Define the problem
What does the population need? What questions about the population of consumers or the industry do you need answered?
Step #3: Do background research
Determine what questions you can answer (or answer partially) using secondary sources. More strategies to conduct this background research appear on the "Data sources for market and industry research" tab of this guide.
Step #4: Design your direct research methodology
Determine whether you will use questionnaires, focus groups, interviews, ethnographic studies etc. Determine the sample population you will target, design your instruments to gather data and apply them to gather it.
Step #5: Analyze your results
Analyzing and interpreting the results is to look for meaning in the data. For quantitative data, apply an appropriate type of analysis, such as a regression analysis. For qualitative data, build an empathy map.
Step #6: Write the report
Don't get lost in the data - write a report that tells the story the data represents.
Step #7: Make decisions.
The fundamental methodology for market research is direct research. Direct research means that the business gathers data on their market themselves (or hires someone to do so). This data can be qualitative, meaning it is non-numerical and focuses on what people think and why. It measures consumers feelings, perceptions or values. Examples of qualitative research methodologies include:
Focus groups - a small group of people (often 6-10) who respond to online surveys or participate in an in-person series of questions. Survey questions may measure preferences or attitudes on a scale (ex. 1-5). Surveys may also include open-ended questions. In-person focus groups almost always employ open-ended questions.
Interviews - one-on-one interactions done in-person or electronically. They may be structured (where a specific set of questions is asked to each participant with no variation and little prompting), semi-structured (where specific questions are asked of each participant, but there is variation and prompting to allow more flexibility in the process) or in-depth (often described as a free conversation with a purpose).
Ethnographic studies/observation - this method studies people in their naturally occurring environment.
Direct market research may also gather quantitative, or numerical, data, such as by measuring sales, spending, clicks on a website. Quantitative research is useful for answering questions such as:
Secondary research uses information that is gathered by outside sources such as government agencies, media, chambers of commerce etc. This information is published in free government and non-governmental agency websites, commercial websites, and library databases. It is often quantitative and statistical in nature and may include demographic (who is the customer?) and psychographic (what does the customer value?).data.
Quantitative secondary research data is used by the "insights" industry or by companies to create (and sell) descriptive, diagnostic, prescriptive and predictive data analytics:
Descriptive analytics - what is going well / not going well in your business?
Diagnostic analytics - what is the root cause of something that is happening in a business, as identified in a descriptive analysis?
Prescriptive analytics - what should a business do to maximize positive or minimize negative business metrics?
Predictive analytics - what is likely to happen in the future, based on current trends?
There are several different analytical methods and techniques data analysts can use to process data and extract information. Some of the most popular methods are listed below.
Regression analysis entails analyzing the relationship between dependent variables to determine how a change in one may affect the change in another.
Factor analysis entails taking a large data set and shrinking it to a smaller data set. The goal of this maneuver is to attempt to discover hidden trends that would otherwise have been more difficult to see.
Cohort analysis is the process of breaking a data set into groups of similar data, often broken into a customer demographic. This allows data analysts and other users of data analytics to further dive into the numbers relating to a specific subset of data.
Monte Carlo simulations model the probability of different outcomes happening. Often used for risk mitigation and loss prevention, these simulations incorporate multiple values and variables and often have greater forecasting capabilities than other data analytics approaches.
Time series analysis tracks data over time and solidifies the relationship between the value of a data point and the occurrence of the data point. This data analysis technique is usually used to spot cyclical trends or to project financial forecasts.
