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Insights That Work

How to Choose the Right Data Source

Collecting data from multiple touchpoints and using different approaches yields more holistic results. The future of insights is being able to data into one insight ecosystem, to better evaluate consumers.

Editor’s Note: I’ve felt for a long time that there was much business value to be gained by combining data sets.  No one data set should be forced into trying to answer all of a client’s business questions.  It is gratifying to see progress being made on this front, and that combining data sets is becoming more of a regular practice than an exception.

In medieval times people sought a universal elixir. They failed, just as modern insights professionals fail if we proselytize one approach is ‘the future’. We search for truth through data – but history tells us there is no universal solution.

Rather, we believe research, tech, insight entrepreneurs and intrapreneurs should join forces to unlock new truths and create a stronger ecosystem – where each collaborates to create a whole that is greater than the sum of its parts.

Marketers are awash with data. Which data sources should we use? Traditional metrics seem like old, blunt tools.

But what should we put in their place? How do we deal with the increasingly systemic limitations of survey work and integrate new tech methods whilst recognizing their synergies and limitations?

Having data sources that look through the eyes of the customer to see how they encounter brands in the real world – and the real world is different to being forced to view an ad on a computer for research analysis. It is crucial to help marketers optimize their marketing and media budgets.  All the better is these data sources are longitudinal as the nature of the data means we can pick out what brand encounters lead to other encounters. Using our own Real-time Experience Tracking (RET) data source, recently, we were investigating online purchases for a client and on many occasions, people were buying brands they had never mentioned they were considering. In one instance the online purchase was prompted by seeing the brand in-store six days earlier.

Using one data source to illuminate other data sources

We have always combined this Real-time Experience Tracking (RET) data with other sources, such as media spend to look at the cost per experience to ascertain media effectiveness. However, only recently are we seeing, firstly how this data source can illuminate many others in an ecosystem and, secondly how AI can unlock insights never seen previously.

When you look at a data source it becomes the focus, which is both a good and a bad thing. For years, because we focused on measuring the effectiveness of TV advertising, we assumed that this was the key marketing lever for success. And, often, that our brand metrics, like consideration, were driven by advertising. We know now, that other paid, owned and earned brand encounters can have a more profound impact. Personal interaction with branch staff or trying a new drink in an on-premise environment can be the key to changing behavior and attitudes. Sometimes it can be tempting to look at social media data, like Twitter, to assume this is how everyone is experiencing the brand.

But perhaps social media only accounts for 5% of people’s experiences with your brand and encountering the brand in real life accounts for 25%. We saw how people were encountering Heineken and its competitors. The TV was important, but so was In-Store, On-Premise and At Home.

By taking the Real-time Experience Tracking data, which looks through the customers’ eyes, we can put all our other marketing data streams into context. We can see that TV experiences account for about a quarter of experiences and paid TV slightly less than this (with owned accounting for some too – say 5%). Within paid, there is the manufacturer ad (e.g. Heineken) and the retailer ad featuring the brand (e.g. Tesco – say 5% of experiences). What’s more, we might know (and we have seen this across studies) that seeing the brand featured in a retailer ad can have a significant impact on brand consideration. From the quarter of TV experiences, about 15% are for the manufacturer’s TV ad. This means that we can look at the detailed data, such as GRPs, ad recall, in this context.

Using AI to unlock new insight within data sources

We have thousands of comments that relate to each experience in any MESH study. Whilst we have always used these for qualitative analysis, we can now use AI to uncover new insight. Working with Signoi, we discovered for one client that the topics (such as picture quality) have very different levels of positivity (The experience made me feel Very or Fairly Positive).

The fact that we already have participants’ own evaluations of the experiences is helpful to quickly see which topics are most positive and persuasive. This evaluation can also help to validate the coding by the AI. Negative experiences, as coded by our participants, come out as Sad, Fear, Disgust, etc. through Signoi.

We can also look at the AI topics by touchpoint to see if some of the most engaging topics are being communicated through certain touchpoints. And by applying logistic regression we can see which topics are changing people’s perceptions because we know a participant’s brand consideration before and after they reported their experiences.

AI can completely refresh an existing data set.

Understanding how data sources fit into an insight ecosystem

The way we see this data source fitting into the ecosystem is different from the Real-time Experience Tracking. There are already many techniques for evaluating display in-store and technology is bringing in many more – from beacons on trolleys to virtual reality testing of material. We see different techniques working in a complementary way along the path to purchase.

We are also combining the camera ethnography data with other data sources, such as sales. For one client we saw a very strong correlation between sales and the numbers of people viewing the display.

However, the data doesn’t answer everything! We are also combining observation and exit interviews with the camera ethnography. Someone may have stopped at the display and purchased the product, but how has this changed their perception of the brand? Survey data via exit interviews can help to answer this.

We are right at the beginning of this exciting journey using algorithms and they are being continually improved. We are making assumptions that may not be correct and will certainly be refined over time. There has been much research to suggest that positive emotions lead to positive brand outcomes, so we look for advertising to create a positive/happy impression. The algorithm we use picks up emotion and we count the number of people who exhibit a happy emotion to report on. However, over time, I am sure that we will uncover the optimum emotional signature – the sequence and combination of emotions that lead to a sale or the change in perception of a brand.

In Summary

  • Speed
  • Use new data sources
  • Learn from other data sources
  • Blend and fuse

In many ways, artificial intelligence is surpassing human beings – particularly when it comes to speed.  We have been blending research methodologies for some time now. However, the data blending and fusion that we will see in the next five years will make what we have done in the last five years seem like child’s play.

This article was originally published on ASMRS

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