Category: Tools

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With a decision tree output, its beauty is in the simplicity of it – it really helps you and your stakeholders make sense of all of it at a glance. As you go down the diagram, you discover new and deeper info and you begin to understand how consumers discern between products, even if it doesn’t fully align with what is on the shelf or your ingoing assumptions. Of course, the most important output is the behavioral decision tree which shows the behavioral importance of the product attributes.

Another valuable output that can be included is one I’m almost as excited about as the behavioral decision treemap itself. Our Brand Gain & Loss Analysis can tell you the proportion who switched from your brand to Competitive Brand X, etc., and when or who switched from Brand X to your brand and when. We can outline the 4 different types of shoppers, but this is even more granular and can show you where and to whom repertoire or loyalties lie. We also include the stated importance of the attributes, brand recall, dominant occasions, and missions, as well as ease of shopping and shelf organization.

So, you get a 360-degree view of the shopper and the shelf by using both implicit and explicit methods in a single study.

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One of the impactful advertising tools that has been recently launched on Amazon product list pages is enhanced Branded Content that adds features to the customer experience with interactive design and innovative features.

Customers are staying longer on the page, and the attention span is longer due to the interactive elements – they focus more on the features that help them understand a bit more about the product before they decide about purchasing it. More customers reach the end of the page with this EBC++ content than when looking through static images and text, as on the regular PDPs. Also, we have found that EBC++ drives sales up to 44% –

adding to the basket is more likely to happen when interactive content is present on the PDP.

Another important tool is interactive Q&A and comparison table. These features drive the highest interaction, likability, and future usage. On average 1 in 10 users interact with any of the features. However, if there are more different types of interactive features, the number goes up.

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In any complex study, the organization is key, of course. But I think it’s also important to know that ingoing hypotheses are just that – hypotheses.

As researchers, we need to remember to take a step back and let the consumer data tell us the story.

It sounds trite, but we represent the voice of the consumer. Of course, we need to add the layer of our research and business expertise to the consumer’s voice for our stakeholders, but the basis of what we do is consumer-led insights.

Ensuring we’ve done due diligence in setting up complex studies in areas of discovery or during times of discovery is critical – garbage in, garbage out – and that may mean a couple of extra steps. Things like desk research, talking with counterparts in other countries or categories, connecting dots from other research initiatives are all ways we make sure we’re at the top of our game at EyeSee to help our clients navigate complexity.

E-commerce is different from FMCG, but while it is sometimes more complex than packaging, it is at least as exciting. And with solutions such as the online path to purchase, we simplify that complexity for our clients and their stakeholders.

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The first step was to recreate an existing laptop area in 3D. We also modelized our 3 different table setups to incorporate them into the 3D store.

Everything was reconstructed in detail to recreate the full in-store experience – from the table shapes to each laptop and even stickers.

In the second step, we recruited respondents to a 25-minute experience on their own laptop, and thanks to the virtual stores, we were able to follow them during the whole in-store purchase experience: First, they began with the eye-tracking part. Respondents were invited to watch the video of the 3D store and to look anywhere they would like as if they were in the real store. The aim was to measure where they looked at, for how long, and in which order to drive conclusions about Chromebook brand visibility. We then asked them questions to measure spontaneous brand awareness & recall. Then, they entered the click tracking task. Respondents were invited to interact with the tables. They were asked to click on anything they found interesting on the table. As everything has been modelized in 3D, they could explore each communication element, such as product stickers or even play videos. Clicks were recorded to measure interaction during this experience. In the end, they were exposed again to each communication element and then answered a regular questionnaire about Chromebook and table elements. It allowed us to measure product understanding.

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I would say the ability to build a lifelike experience for the respondents and have a fully experimental design that you can control is the greatest advantage of using behavioral, contextual studies. They provide very high ROI since they allow for so much experimentation, testing iterations, validating your assumptions with real respondents in a natural setting while capturing the implicit unbiased behavioral measurements.
We have a team that works hard to create cutting-edge environments and protocols for any kind of testing you can imagine – new industries, new consumer experiences, but also new ways of testing staples like packaging, advertising, and NPDs. Another thing I’d like to point out here is that

we love to build behavioral versions of things that were usually tested without this essential dimension.

As we grow, tech will continue to be one of the areas on which we spend the most
time focused on as a company.

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This is where EyeSee comes to life for me – I love the fact that we could look at where the people’s eyeballs were looking on the page, we could get their engagement of what they click on as areas of interest, and then tie all of that together with a customer survey that goes on top of that. To be able to really utilize these three big bodies of information, we can understand on any given page are consumers even seeing the pieces of real estate that are on the page; if they see it, do they even care about it. We actually had one element on the page that was really passionate for us on our product description page, and we put a lot of value.

We found that only 34% of people saw what we were putting on the page, and of those, only 2-4% were clicking on it.

So it was a huge revelation for us as well. We were able to target some of those quantitative questions to gauge do consumers even care about this, maybe that’s why they are not looking for it, perhaps that’s why they are not seeing it. Being able to go at the research from those three areas is super valuable.

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I would say we really work to understand consumers’ unmet needs and then try to figure out what are the products that fit those needs. But, that isn’t perfect because, for example, when Apple introduced the iPhone, they introduced a product that consumers didn’t even know they needed – that obviously became a gigantic success.

So, along with just trying to understand what our consumers’ unmet needs are, we are trying to understand what are their needs they don’t even know that they have.

At a high level, it really involves a lot of both quantitative and qualitative research.

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The biggest thing is talking to a wide variety of stakeholders: working in our field teams, directly with our partners, corporate teams that design the corporate strategy, and the Go-to-market team. It can seem really disparate when you think about the complexity and what each of those individual teams is looking for.

So it’s really important when I’m designing a study that I understand each of those cohorts.

I want to learn more about what are the things they are thinking about this year or the next few months. So what I typically will do is write a research brief, and I’ll typically send that to each of the stakeholders as well, and make sure that I have alignment. Once I have it, that’s when I reach out to the vendor.

Getting everyone aligned not only helps me get a very clear and concise view of what I am looking for but also helps me ensure I have a good partnership with my vendors right off the gate.

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The biggest single long-term change that data and research provides should embrace is a move from explicit to implicit data.

Relying on or putting faith in claimed purchase intent is like a habit that the industry won’t shake off. Those metrics are so poor and so poorly validated that even as directional metrics, they are of such little use and so outdated. I understand

it is not easy to move every metric to implicit right now, but I am a big fan of those that are trying to do

and of clients that embrace the effort to move forward with methodologies where consumers don’t know what you are asking them and are not preprogrammed and biased to a certain way, which you see all the time in sorts of explicit research.

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We conducted a meta-analysis to understand how different are the results for virtual shopping and behavioral methodology compared to a traditional approach of a Purchase Intent Scale. Based on this, we wanted to validate recommendations based on a simulated environment that has better use for our clients. The simulated environment for us means that our respondents are not in isolation but in a clear social media feed.

The way we will rate packaging is not only in a standalone mode but in a very close to real shelf situation in-store, with its competitors surrounding it.

Finally, our respondents are not only answering survey questions but have to make a shopping decision on the spot. That is the essence of the way we do research. Knowing this, virtual shopping can also help us satisfy our researcher’s curiosity and answer broader questions, for example, about the different KPIs we use in other methods. It is a type of reality check, a way to compare other findings or behavior that is strongly grounded in reality.