Big data has become a crucial tool for marketers. Targeting customers has never been so easy, quick, and most importantly, accurate. A quick search on the internet, an article read on the web, a message sent to a friend and the magic happens; our “action” is analysed, we are categorised and we start to see related and pertinent adverts.  

Because of this, there’s a perception that brands have never been so close to their customers – but is this really the case? Aren’t statistical information and algorithms the opposite of human behaviour, which is most of the time triggered by emotion, social context or culture?

Feeling close to someone, and by extension, to a brand, means you have built a relationship: you understand the emotions and reasons behind a behaviour. While big data can give you a picture of what your customers do, it can’t tell you why they do it. So, what can brands do to humanise data, so they can really understand consumers and ensure they evolve to meet their expectations?

We believe primary research is one of the answers. It can help to fill in the gaps, uncover trends and bring customers to life. So how do you use primary research to complement and humanise big data?

 1. Focus on more longitudinal methodologies

Big data is based on a series of actions. To understand the “why” and to humanise the data, we need to be able to deep dive to understand the behaviour as it happens. So you might want to steer clear of more traditional methodologies like focus groups that rely on consumers recalling past experiences that could have been months or weeks ago. Longitudinal methodologies are more appropriate for investigating big data trends.

One longitudinal methodology you can use is passive tracking. By installing an app on a respondent’s phone, we can passively track their activity to understand their digital behaviours. Almost immediately after, we follow this with a short telephone interview to dig into the reasons behind that behaviour. In this way, we’re able to have an almost immediate conversation to understand the subtleties and motivations surrounding consumer behaviour that might have been difficult to gather through other more traditional methodologies. For example, when a focus group is run 6 months after an event, the recall process for the respondents is much more difficult and less accurate, and the facts and emotions recalled are going to be closer to perception than reality.

Another option is to use an online community, setting consumers missions to understand their behaviours and then probing around motivations.  Using activities that ask respondents to take pictures and videos that capture an event as it happens, we can not only deep dive into motivations but also gather contextual information necessary for understanding the full picture surrounding behaviours.

 2. Use quantitative research to fill in the gaps

If a qualitative methodology is the natural answer for uncovering the ‘whys’, quantitative research can also help to fill some big data gaps.

If you only analyse your own customers using big data, the risk is that you’ll be led by biased conclusions. Why? Because big data only reflects consumer behaviour in relation to your own brand.  

When you look at big data, you’re often missing information about broader consumer behaviour. This will allow you to establish hypotheses, but they’ll be based on a limited set of information. To get the big picture easily and simply, use these gaps and your hypotheses to set the objectives and questions for further research. A quantitative survey, for example, can help you to confirm or invalidate your hypotheses, and will give you an opportunity to get a bigger picture of the market as you will be able to ask questions about competitors too.

Quantitative research can also be a useful tool for humanising a segmentation that’s based on big data. Too many companies rely on big data for their segmentation and take decisions without having the full picture. If you are solely relying on your own data, you’ll only understand consumers from your own point of view, rather than that of the customer. A quantitative survey can help you complement demographic information with information on attitudes and motivations, adding rich information to your segmentation.

3. Research doesn’t just help you to understand. It can bring customers to life.

Another way that market research can humanise data is in bringing your customers to life. Some methodologies, like online communities allow you to deep dive into consumers’ lives. We can collect pictures and videos that allow us to visualise their daily routine. Your customer is no longer an anonymous target. It becomes someone you know, understand and can have a relationship with, creating a stronger and more memorable connection, since what you know about them is no longer narrowed to a cold and emotionless dataset or a CRM record.

You can use these visual outputs to create personas with details that bring to life a segment’s behaviours or expectations. This can help you align your organisations behind a segment. You can visualise that person and remember their motivations, so that when you make a decision, you’re doing it with your customers in mind.

Use primary research to humanise big data

While big data is an amazing tool to track behaviours and create a segmentation, primary research is a great complement to humanise that data and help you understand who the people behind the data are. You will uncover the reasons behind behaviours, understand their motivations and attitudes, and bring your customers to life, helping you to make better decisions, than if you acted off the data alone.