How the Data Kinetics consumer Insights engine weights & combines multiple sources

Hello everyone.

This month im going to go into a little detail about the sources of information we use to power our consumer insight engine Data Kinetics, and how we weight those sources.

I have spoken before about what I think one of the key differences between Data kinetics and most of the other marketing Insight systems out there is, that we that start from the basis that all human opinion is ‘kinetic’, or fluid, that is, opinions shift depending on who we humans are in front of and the culture we are in at that time, we can have different opinions, and express and them differently, more or less at the same time.

Most traditional research methods ignore those contradictions, they are too hard to make sense of.

Our approach is to ingest them, and then weight them and seek the most powerful path through those divergent motivations.

This enables us to work with multiple sources of consumer insight and data. Below are the key sources we sources we work. As you can see they are many and varied.

While multiple sources are excellent for getting us that ‘Total Human Insight, you need to understand that each has source has different strengths and weaknesses, accuracies and inaccuracies - which we will go into in a minute.

Obviously they also have different levels of volume. For example, the source of ‘ethnography’, that is observing consumers in real time, delivers accurate data about product use in real time, however it is low in volume and can miss trends due to lack of scale - also people know they are observed, so behaviour does change.

Survey data has scale, but the answers are skewed by the necessity to use consumers who have the inclination and time to fill out surveys. The act of payment also encourages panels to give answers – even if they don’t have an opinion - and again they know they are being judged - and nothing changes behaviour more than the knowledge we are being judged by our peers.

Here is an overviewof the levels of volume we seek to obtain from each source.

Strengths an weaknesses of consumer research data sources.

When it comes to different strengths and weaknesses of sources, we understand these and weight the results from different sources based on those strengths and weaknesses. For example, there are sources where consumers do not understand they are being monitored or reviewed - so they ask questions that perhaps they are embarrassed to ask on social, or highlight fears or barriers that they wont talk about in public. These include links that individuals click on, but also questions that are asked in Google and other search services.

When it comes to understanding trends, social media and social listening are a good source to understand the direction of travel, but you have to understand that whilst the loudest voices on social media are important and tend to define the general public perception of a product of service, they are still only one persons opinion. In other words one person saying the same thing 100 times to thousands of people, is still only one persons opinion, most social listening systems are not built to take this into account, Data Kinetics does. Here are the strengths - as we see them - of different sources.

And here are the weaknesses.

Weighting the data

In the end however you still need to weight these sources. This weighting can change per project, but generally our weighting is as below.

And this is where it starts to come together.

Sources and their strengths and weaknesses.

Social media as a source: for example you can see how we explain that social media is excellent for consumers identifying themselves as category participants, enabling us to find them and extract them. With AI then able to give a highly accurate analysis of these consumers affinities, psychology, opportunities and issues and channel usage – income level is however inferred and not accurate.

When it comes to Ethnography, it provides strong observational and direct analysis but it is not relied on for trend as volume inhibits accurate pattern growth prediction.

Survey enables strong answers to specific questions, but it is skewed by the knowledge the survey giver is being evaluated, so it is used as overlay to support / detract from Social & Ethnography sources.

Google Trends and Adwords are not used as primary sources, but provide colour on hidden motivations and reweight opportunities and Issues found from Social / Ethnography / Focus group studies.

Advertising data - showing who clicks on, has an interest in, and engages with certain ads, provided by Meta, is highly accurate for Demographic, some affinities and in particular income and interest data, however Meta controls all validation and does all the language analysis, so this is second order extraction, first order analysis based on primary data.

Reviews and forum such as Reddit and Quora discuss Issues and Opportunities in volume and these affect public perception due to ‘influencers’, however it is difficult to know if all participants are in category, as such data is used to validate trends only and colour opportunities / issues.

I hope that provides a clearer understanding of how we put these sources together.

Conclusion

Its still a work in progress and in the end I Think that consumer research is still 50% up and 50% data but I think we are getting better at combining the art and the science and the sources helping us gain a clearer understanding of the complexity but also the general direction of travel of consumer opinion.