Overview of the role of data analytics in market research
A systematic approach to interpreting data is essential to maximising the insights that can be generated from it. Although data has can be highly informative, its uses in market research are limited unless it is analysed and presented clearly. For example, it is unlikely that the key trends and correlations of a large dataset can be discerned just from glancing at it. Therefore, data analytics play an important role in market research by condensing large and disorganised swathes of data into a series of concise insights that inform an understanding of a given market.
Why is data important?
For research to provide an accurate reflection of market trends and generate robust insights, it is critical to gather sufficient data to base analysis on. Without this step, findings tend to be built on gut-feelings or assumptions rather than evidence gleaned from real-world market data. Hence, it is critical for market research to contain at least some elements which are corroborated by data-driven insight.
Moreover, data provides an objective representation of the market. Statistics and figures are not swayed by emotion and instead simply represent current market sentiments through numbers. Larger datasets can also help precipitate accuracy by being more representative of the market. Having more respondents mitigates the effects of outliers skewing the data to not truly reflect what the actual state of the market is. Thus, leveraging data can help to remove some of the subjectivity which can cloud judgement when drawing conclusions about the situation in a certain market.
Methods of data analysis
Given how data is such a key resource in market research, Vox.Bio seeks to maximise its potential by using data analytics. Below are a few of the methods that can be used to harness the power of data.
Descriptive analysis makes data easier to understand by summarising it to capture the essence of multiple datapoints in a concise fashion. For example, a market research team might want a snapshot of how a certain product is perceived amongst a large group of respondents. Inevitably, as more perception scores are collected, the overall sentiment of the group of respondents will become less clear in the raw data as it will just be a large column of numbers. With descriptive analysis however, the set of scores can be broken down into an easy-to-follow set of metrics (such as the mean, median and mode) which provide a good overview of how the respondents perceive the product. The distribution of the scores is another descriptive analytic which can be represented in a simple diagram to show if the overall sentiment is skewed towards higher or lower scores. An additional breakdown of the percentage of the sample in each response band can further illuminate how respondents find the product as it shows where the majority of responses lie.
Furthermore, descriptive analysis can be used to discern trends amongst different response groups. If there are groups of interest in a dataset who might have different characteristics, these differences will not be apparent in the raw data. Descriptive analysis provides a simple solution by breaking down key metrics by the different response groups which should make the defining characteristics of these response groups more apparent. An example of this would be the mean and median score for a particular metric being far higher amongst groups of respondents from a particular country when compared to the rest of the sample.
Descriptive analytics are usually accompanied by significance testing which provides more depth to the analysis. Significance testing is used to discern if there is an actual difference between groups, or if the observed differences are purely coincidental. This is vital in market research since market researchers often want to know if there are different groups within a target market. One example where significance testing might be used is in the assessment of average Net Promoter Scores (NPS) (an often-used metric in market research that gauges how likely a respondent would be to recommend a particular product) amongst different response groups. Average NPS might be higher amongst a specific group of respondents (labelled ‘Group A’ for simplicity) compared to the rest of the sample. In this instance, significance testing can help to determine if ‘Group A’ respondents systematically have a higher NPS, or if the difference was merely a coincidence of sampling.
The main objective of regression analysis is to evaluate the relationship between two or more metrics. Regression analysis can reveal a directional relationship (does the value of a metric rise or fall as the value of another metric rises?) and even measure the strength of that relationship (how intensely do the values of two metrics correlate with each other?). Moreover, regression analysis can reveal the nature of this relationship by showing how much certain metrics affect each other (what effect does a 1-point increase in NPS score for a product have on the percentage market share that product has?).
Relationships between metrics are often of great interest to market researchers as they can reveal real drivers of choice. For example, regression analysis may reveal that the level of agreement with a certain marketing statement is positively and strongly correlated (the value of both metrics rises together closely) with the usage of a particular product. This would therefore indicate that an effective strategy to help maximize uptake of this product would be to prioritise that specific marketing statement in messaging (rather than other less strongly correlated ones).
In the field of market research, we often want insights to be data-driven since this means that conclusions reached are robust and evidence-based. However, data on its own cannot provide good insight. Therefore, it is imperative for market researchers to incorporate data analytics into their research to summarise data into more easily comprehensible forms. More advanced techniques can also be used to extract key trends which ultimately help to craft a coherent narrative from data as opposed to a collation of figures with no context.