Our clients are a main priority. Need proof? Read why we shut down an existing business to build a bigger and better one that will help brands grow.
The past year has been quite a journey in Latana land. We decided to change direction from our existing brand tracking business and launched our new data science-fuelled brand tracking software, paired with the new Latana brand.
But why did we move away from a proven business with existing clients and steady revenue streams? Because we realised that BrandTracker was not providing as much value to our clients as we wanted. We spent a lot of time thinking about what WOULD benefit our clients and therefore, devised a new way of brand tracking. Now, we want to tell you how we came to where we are today. So, if you want to better understand why we launched Latana, how we are building our brand tracking software, and why this is a big deal, please do read on.
Let’s start with the past and talk about BrandTracker. We launched the product in 2017 and offered brand tracking to small and medium-sized enterprises (SMEs) across Europe. This was in contrast to other brand trackers. See, brand tracking has been around for decades but is still mostly used by larger firms due to the prohibitively high cost. With BrandTracker, we offered a leaner, lower-cost version that focused on a set of standardised KPIs (around 5-10) and smaller sample sizes (usually 500). We delivered insights to our clients on a regular basis, usually monthly or quarterly, through an easy-to-use dashboard.
Some aspects of BrandTracker were received really well by our clients. The dashboard was intuitive and a big improvement over the industry-typical PowerPoint presentations or PDF documents.
Also, the speed of BrandTracker was a big plus. We were able to deliver results within 1-2 weeks, in an industry where clients often wait months to get the first insights. Lastly, our low-touch approach allowed us to keep the prices low. Our clients were surprised how much value they could get for their money, especially those that had previous experience with brand tracking.
BrandTracker worked well but we weren’t fully satisfied with what it provided. Since we wanted to build a product that truly solves our clients’ problems and is an irreplaceable part of their day-to-day workflow, we knew we would have to go further. See, despite giving our clients a high-level overview of how their brand was performing, our user tests showed that most of them did not act on the insights provided. And to our surprise, we found out that this is the norm, rather than the exception for most brand trackers. This is because brand tracking is typically done through quota sampling and in most cases, the methodology struggles to pick up real-world changes.
Another drawback is that entry-level services that run with a sample size of 500 have a margin-of-error of above 4% in the best case. If one wants to make decisions based on these insights, they should exceed this margin-of-error. However, this is unrealistic for most brands, as it requires reaching millions of additional consumers in a short period of time.
Most brands we work with do not actively market to everyone. Rather they have smaller target groups they focus on, be it in terms of age, gender, location, interest or other demographic or psychographic criteria. If one wants to boil it down to one of these target groups, the sample sizes become even smaller, the margin-of-error skyrockets out of control and the insights become entirely in-actionable.
After countless conversations with our clients, we concluded that we needed to fundamentally rethink our approach if we want to truly solve their problems and help them to build a thriving brand. Their problem is straightforward: they need reliable insights to understand how their brand is performing in the real world, for their various audiences, and how this is changing over time.
After months of conceptualising and prototyping, we concluded that recent innovation in data science, Multilevel Regression and Poststratification (MRP), could be a tool to solve this problem. It recently gained popularity within election predictions with great success so we decided to adapt and further develop it to the benefit of consumer brands.
MRP is a model-based method. It uses information from the entire sample size, which allows Latana to predict brand awareness based on a respondent’s characteristics. In other words, we use it to estimate the brand awareness of someone in any given target audience. Let’s use social media using, vegetarian Generation Z-ers. MRP looks at all social media using respondents, all vegetarian respondents and all Generation Z-ers in the sample. Because the amount of respondents that fit all three characteristics is likely to be very small, it combines the effects of these characteristics to produce an estimate for respondents who have all three characteristics - our target audience. As it uses the entire sample to build the model, the estimate for the target audience is actually very precise, even though there are only technically 20 respondents of the target audience in the sample.
You can read more about MRP but its key benefits, compared to traditional brand tracking, are the following:
Running MRP is a very complex exercise and only a few firms in the world have the ability to do so. Therefore, it helped that we started research in the field way before we decided to use it for Latana.
To make MRP work, large sample sizes are required, as the model gets better the more data it has. Here it helps that we are powered by Dalia’s survey engine and can access millions of respondents at the tap of a button. But data collection is only the first part and is followed by a computation-intense algorithm. When we started using MRP for Latana, some of the initial projects took us 3-4 days to compute, which is why we used to run them over weekends.
While we have solved the scalability issues, the resources required to model the insights are still immense and far from trivial to execute.
Given the novelty of the insights we now generate, we also decided to build a brand tracking platform from scratch that allows marketers to explore the large wealth of data, while maintaining usability and intuitiveness. The first version launched recently and it focuses on audiences. Users can build dozens of audiences to replicate their target groups and personas and track the performance of their brand.
This is an absolute novelty in the space of survey-based brand insights as users usually receive a static presentation and cannot explore the data themselves, let alone have the possibility to analyse dozens of audiences. Understanding how audiences vary in terms of brand awareness or brand associations allow marketers to tailor their messaging and advertising towards each of their audiences, maximising impact and brand growth.
While we are only at the beginning of our new journey, this platform already provides in-depth insights across audiences and helps marketers understand how their brand is performing.
We want to provide our clients with a similar depth and precision of insight for their brands, that they are used to from their digital marketing. Being able to understand target audiences, tracking changes and precisely measuring the impact of activities are only a few of the features we are adding to the realm of brand insights.
And while it tackles a similar problem, is a quantum leap compared to traditional brand tracking and the benefits for marketers are plentiful:
Moving away from quota sampling to MRP has opened up seemingly infinite possibilities and we have a lot of exciting features in the pipeline. Expect more cutting-edge innovation in a space that has been largely free from it for a long time.