Today, there is an abundance of research options available to marketers, from the more traditional techniques to automated options like ours, but there’s more to consumer insights than cycle of test and learn.

The best research helps businesses make better decisions; ones that fuel their development, advertising, and promotional campaigns.

What are we calling advanced analytics? When we combine human and machine insights, tagging, and data sciences, our intelligent platform can pull the most revealing insights from across multiple surveys.

This blog post outlines the importance of data standardization in achieving success through advanced analysis, as well as the benefits of embracing a wider-scale, bird’s eye view of historic research projects – rather than on a survey-by-survey basis.

Stop being reactive and start making better business decisions

The road to advanced analytics is one of patience, perseverance, and consistency. As an organization begins conducting new research projects, it should learn how to:

  • store all research data in one place and in one format
  • tag concepts and media by relevant components and themes
  • test more frequently and establish far-ranging analyses

These practices are a prelude to achieving deeper analytical learnings accessible via occasional one-off tests. Let’s take a closer look at these three key areas.

1. Standardization

Marketers have always harbored a desire to compare results over time. For decades, norms-building has been the standard industry approach (comparing your results with an agency’s aggregate score) but comparisons with norms and averages aren’t always easy to decipher.

They largely depend on the service provider; if your business is conducting research via one agency and later moves to another, their norms databases will differ – they’ll use different methodologies and, more than likely, silo their data in different formats and on different platforms.

Opening up the ability to conduct cross-project analysis is a crucial next step. That’s only possible when everything looks, sounds, and feels the same: our advanced analysis platform collects a) consistent, b) reliable, and c) representative data – for every project.

Interestingly, we talk of data standardization in a similar way to shipping containers: huge variety, shifted, sorted, and maneuvered in different locations for different purposes, through one, universally agreeable, cohesive design.

2. Tagging

By tagging concepts and media uploads, users integrate what they know about the creative, their brand, and its category, into the dataset. This means that when the time comes to look back on 100+ tested ads, they can be reviewed and scrutinized from a range of perspectives.

  • Do ads featuring celebrities perform better than those without?
  • Are colorful ads more effective than those in black and white?
  • Were your Christmas ads as well-received as your summer ads?

Integrating smart-thinking like this teaches our platform which actions or traits most commonly result in a certain consequence. Therefore, the more consistently a brand tests, and the more cohesively an organization agrees to tag its creatives, the more they will learn.

Rather than overspending on a risky ad, brands can ask ahead of time: will the inclusion of a celebrity in a black and white Christmas ad positively impact our ROI? The answer is obtained by running historic, tagged test results through an algorithm. As the data is already structured in the right way, this process becomes seamless.

3. Advanced Analysis

Because we collect data in a standardized way and encourage users to tag their creatives, we can break down data silos. The questions you ask today will teach you important information about the future.

It has always been cheaper for brands to just keep asking disconnected questions in unrelated platforms – but this is a scruffier, more shallow approach to finding out about your audience. What’s the use of uncovering this data if it cannot be synthesized and continually inform brand strategy?

However, embracing advanced analysis isn’t something you can start tomorrow with little or no planning. Full organizational behavior change is a prerequisite to getting the best out of advanced analysis.

You might recognize this as a sort of Zappi mantra by now: test and learn.

If an organization doesn’t test thoroughly throughout the development cycle, these powerful features are ineffective. Deeper analysis isn’t accessible for businesses with fewer than 30 historic tests.

Rally together and combine your efforts: different departments should stop conducting similar tests and start testing (and tagging) using one service provider. This creates genuine institutional knowledge.

The Road Beyond Advanced Analysis: What We’re Working Towards

What will help us gain an even richer understanding of what consumers think about your brand? Steve Perianen from our data science team says his focus is on deciphering the true meaning behind open-ended responses:

Beyond their answers to scale questions, knowing what respondents are trying to say is important. Open-ended responses give context as to why your stimuli performs the way it does and Zappi is striving to offer a more in-depth understanding. We want to answer clients’ lingering questions around consumers’ reason and judgment by leveraging the hidden insight behind open-end text. – Steven Perianen, Data Science Team

Obtaining a broader understanding of nuance and context? That kind of knowledge opens a new world of possibilities in layering insights learnings over time.

With the work we’re doing, we can measure the accuracy of our coding and, hopefully, produce more consistent and reliable coded data. This could make a huge difference and really change the way we survey respondents.

So, a back-catalog of answers to scale questions is a powerful informant in predictive insights, but once we grapple with the true meaning behind more complex text-based answers and long-form feedback, we’ll unlock an even greater understanding of consumers’ overall advertising experience.


Zappi tested 100 of 2017’s most-loved ads using its powerful market research automation platform. The final report analyzes these ads based on audience reaction, demographic, and contents. Download it here.

Katie O'Connor

Posted by

Katie O'Connor

Loading Disqus Comments ...