Essential Market Analysis Techniques to Drive Key Insights | Symanto (2024)

Market research and competitive analysis are two of the main methods of analysing a market. With market research, you source data from customers and potential customers themselves, through surveys, interviews and focus groups. This data can then be used to understand customer needs and behaviours, market trends and the competition.

Competitive analysis, on the other hand, involves researching your competitors directly. This could involve observing their business practices, studying their marketing materials and tracking their sales figures. It can also entail sourcing customer feedback about your competitors, for example, through online reviews and social media comments.

Another common market analysis technique is field trials. This involves testing products or services in a real-world environment on a small scale, in order to gauge customer reaction and market demand. Field trials can be a useful way of assessing market potential without incurring the large costs associated with launching a product or service on a larger scale.

Field try customers are carefully selected to be as representative as possible of the target market. They are given a free trial of the product or service, in exchange for their feedback, which is then used to assess market potential.

Whichever market analysis techniques you use, you’ll need to be able to effectively manage the data that you collect to assess market potential.

A lot of valuable data collected from focus groups, interviews, customer reviews and social media comments is unstructured. This can make it difficult to analyse and draw insights from, so it’s important to find a way to structure the data to make sense of it.

Several statistical classification methods can help you identify patterns and trends in the data to assess market potential.

Factor analysis is a statistical method that is used to identify which factors and underlying variables are most important in explaining the variability in a data set.

For example, when analysing customer reviews for a restaurant, you might come across the following factors and variables:

• The quality of the food (freshness, taste, presentation)
• The service (speed, friendliness, knowledge)
• The price (value for money)
• The ambience (décor, atmosphere, cleanliness)

Once you have established the main factors you can identify which are the most important in terms of market potential.

Cluster analysis is a statistical method that groups data points together which are similar in terms of the variables that you are measuring. This can be useful for identifying market segments with similar characteristics, e.g. behavioural characteristics, values and preferences, or psychographic profiles.

Using the restaurant example, we can cluster customers into the following market segments:

• Foodies: those who love the food and are willing to pay a bit extra for quality,
• Budget-conscious: those who care about value for money,
• Ambience-seekers: those who place importance on the atmosphere and décor.

Cluster analysis is “exploratory”. It is used when you want to discover patterns, and no prior knowledge exists about the groups that might exist in the market.

Another widely used statistical market analysis technique used for market segmentation is discriminant analysis. Discriminant analysis differs from cluster analysis in that it requires knowledge of at least one market segment to be able to identify others.

So, if we wanted to use discriminant analysis to market segments for a new restaurant, we would need to have some data on a market segment that we could use as a ‘comparison group’. For example, if we wanted to target young professionals, we would need to have some data on this market segment in order to identify others like them.

Correlation analysis is a statistical method that measures the relationship between two variables. This market analysis technique is often used to assess market potential by identifying the relationship between different factors

For example, you could use correlation analysis to identify the relationship between the following variables:

• The price of a product and how likely people are to buy it,
• The quality of a product and how likely people are to recommend it to others,
• The satisfaction levels of customers and their likelihood to return in the future.

Data analysts for market research are increasingly turning to artificial intelligence (AI) to help them organise and make sense of the huge volume of data, both structured and unstructured, that is available to them.

There are several AI market analysis tools and technologies available that can help with market research tasks such as:

• Sourcing data from social media, customer reviews and other online sources,
• Analysing unstructured data to identify patterns and trends,
• Drawing insights and recommendations from the data.

Symanto offers a number of solutions combining advanced natural language processing, machine learning and psycho-linguistic profiling technology that can be applied across market research tasks such as competitive analysis, market segmentation, customer profiling, and market opportunity analysis.

Input your own market research data and/or let us source and collect data from social media, customer reviews, industry news sites and other online sources to assess your brand and key market competitors.

Our advanced NLP models convert unstructured, qualitative text feedback into unique insights into customer psychographics and other key factors driving consumer behaviour.

Get in touch to learn more about how we can help you with your market analysis.