What is Quantitative Market Research?
Many people have some idea of what quantitative research is already – its something to do with numbers, surveys and statistics. How boring!
At Ruby Cha Cha we believe quantitative research isn’t quite as cut and dry that. These days it is much more than surveys, analytics and endless charts. From the basic building blocks to more complex outputs like market segmentations, NPS and market sizing we are about “the consumer story in the numbers” alongside the need for robustness, representative samples, great data, significance testing amongst other things. Ultimately, we seek the measurement of consumer truth which leads to better business decision making.
Quantitative Market Research is Evolving
We’re living in a time where businesses are able to understand people using multiple data sets and therefore the definition of what is included in quantitative market research is changing in the era of “Big Data”. It’s not just about survey data now, but how survey data helps to compliment other data sets and vice versa. We need to use different data sets so we can quantitatively understand the ‘multiple consumer truths’ in order to paint the full picture of the consumer, market or phenomena we are studying.
The Fit with Big Data
Quantitative research can also incorporate data analytics. Many businesses have lots of data these days, so much so that the term ‘Big Data’ was coined. Quantitative research tends to be research that is what we call primary research – customised surveys to understand specific business questions such as who is my customer and what price to go to market with and how people think and feel. Big data is behavioural only – what people have spent, where they have shopped and what they have bought. Fixed data in other words.
Big data is collected by companies from sales data and everything from loyalty cards through to subscription data. The holy grail is to link actual behavioural data to what people think and feel. And that’s how the world of quant is changing. Its more collaborative and complex than ever before, and businesses need to use a variety of quantitative sources in order to get a robust picture of the market for sound decision making.
Data Quality and Quantitative Research
Data quality is essential to answer the strategic business questions. But, in the age of shorter attention spans and more time pressures it’s important that we be creative in our use of survey design to get to this quality data. Gone are the days where we can expect respondents to fill in a 30-minute survey (or longer!). As researchers, we must ‘unlearn’ the way we’ve been asking the typical market research survey questions since the 1950s.
Whilst we are not saying goodbye to surveys, a focus on timing, rigour and audience breadth is critical to overcome present trust issues with survey data. Think about how wrong political polling has been for major elections and referendums. Of course, hindsight is 20/20, but imagine the embarrassment felt by those in charge of calling the wrong result. Marketers cannot afford to call the wrong shots based on poor data, as the repercussions can cost millions of dollars.
How to get to the truth in quantitative research
As researchers we don’t always make it easy on survey participants – sometimes we bore, sometimes we ask the impossible and sometimes we confuse. Factor in human nature and this leads to several barriers to getting to the truth:
- Right answers, wrong people: findings from surveys must represent the profile of the target audience. For example, if a survey was designed to reflect the views of Australians aged 18 and over, yet it achieved a gender mix of 70% to 30%, it would be presenting a false truth of how the country feels.
- A poor brief: spend time on ensuring you have distilled what you need into a clear market research brief. Long winded sets of objectives lead to poor survey design and poor survey outcomes
- It’s a chore: long surveys are a burden and people rush their way through, paying less attention to the questions as they progress.
- Right questions, wrong options: if a question doesn’t provide the most relevant answer options the truth is never uncovered.
- The ‘think so’: for some questions, people don’t mean to lie. They believe they are answering correctly – such in cases of as remembering past events or predicting future actions. In the words of George Costanza, “It’s not a lie… if you believe it.” But these types of questions can lead to levels of ‘overclaim’ and ‘underclaim’.
- There’s ego: people want to build their self-worth and appear to be better than they are, leading to overclaim.
- The Open Self: people lean in to their Open Self– (the public self which is known by others) leading to answers they think are more socially desirable.
- The Hidden Self: people don’t always want to reveal their Hidden Self (the self-others don’t know about) even with the anonymity of online surveys.
But none of this means that you shouldn’t use survey data!
Key Takeaways
- Survey data is a powerful asset as it models your market, provides rigorous competitor analysis and categorically tells you which ideas to pursue for market success.
- Quantitative research is not just about survey data, but how survey data helps to compliment other data sets , such as big data and google analytics.
- With more and more data at our fingertips, Quantitative Research is essential for segmenting audiences and understanding drivers and market forces.
- The key to getting to the truth, though, lies in a fine balancing act of study, design and interpretation. This delivers reliable consumer data – to help marketers make better decisions, to measure ROI of new initiatives and to talk to trade.
The culmination of this ensures your brand has the best chance of winning.
