How To Reduce Data Collection Cost For Brand Tracking By Over 90%
Tracking how people perceive a brand can be very expensive, running into the hundreds of thousands of USD per year and market! Data collection typically accounts for at least half of the total cost of a brand tracker. This is not just a problem for the brand tracking providers, but also for the brands who wish to use the service, too.
Therefore, when launching Latana for the brand professional masses, reducing data collection costs was the key to making brand tracking more affordable and accessible. This article explains just how we did that while retaining accurate and reliable data.
What makes collecting brand tracking data so expensive?
To understand how we can reduce data collection costs, let’s first look at what makes collecting brand tracking data so expensive.
The length and complexity of a survey
There are lots of potential metrics to track, from the classic funnel metrics (brand awareness, brand consideration, brand preference) to “unaided awareness”, usage metrics, ad-recall, NPS scores, and perception metrics (e.g. whether people “love” a brand).
Researchers and marketers often find it difficult to choose which KPI to track and thus go for “all of the above”, which - if tracked for a dozen or more competitor brands - leads to long and often boring surveys that few people want to complete. Getting enough people to accurately complete poorly designed surveys can be more than twice as expensive as getting people to accurately complete short and easy surveys.
Audience segmentation
Getting reliable data on how “men between the age of 18 and 39 who regularly drive a car and live in a city” (a group that is less than 5% of the general population in many countries) view electric car brands is tricky. It often requires complex quota management in classic survey research to ensure that the final sample doesn’t just include 37 people who fit that criteria, thus making it difficult to generate meaningful insights from it. The granularity of audience segmentation can increase the cost of data collection by a factor of three or more.
The frequency of data delivery
How often do we need updates on our brand performance? This is a question that many brand managers struggle with, and rightly so. Each “wave” comes with a fixed cost for data collection, so having quarterly updates is four times as expensive as having annual updates, and having monthly updates is three times as expensive as quarterly ones.
Combined, these three drivers can have an enormous impact on sampling costs. Here’s an illustrative overview of data collection costs for different setups of a brand tracking study:
The costs of data collection pose a dilemma for many brands: either they buy a cheap version of brand tracking that provides only high-level insights with often limited value, or they invest quite substantial sums to get reliable data at a sufficient frequency. Often, only the world’s largest brands can afford the latter for all their markets.
So how can brand tracking become more accessible to brands?
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Reducing data collection costs in brand tracking
By taking the right steps, it is possible to reduce data collection costs for brand tracking by over 90%. Here’s how.
Focus on what matters and make surveys user-friendly
Some KPIs like unaided awareness require open questions that often lead to inconsistent data and require lots of manual data cleaning and large sample sizes to deliver reliable results on changes. In addition, other common KPIs rarely change significantly (e.g. usage data), are campaign specific (eg ad-recall), or are difficult to answer adequately (e.g. most image statements) and thus don’t belong to a general tracker.
At Latana, we focus only on the metrics that matter: brand awareness, brand understanding, positive and negative brand perception, purchase drivers, brand consideration, and brand preference.
We also split up longer surveys into smaller modules that can be individually answered in one-to-two minutes to maximize engagement. This alone enables us to reduce data collection costs by over 50% while increasing response quality significantly.
Use Bayesian statistics to get reliable estimates on niche audiences
Segmenting on simple demographics like age and gender, or on key segments like “car drivers”, or “living in a city”, is often easy with a sample size of 1,000 interviews. But when it comes to drilling down on combinations of those segments (e.g. “men between the age of 18 and 39 who regularly drive a car and live in a city”), things get tricky. Results either come with huge margins of errors or the gross sample size needs to be increased significantly.
We employ Bayesian statistics to be able to segment niche audiences while maintaining high precision and a low margin of error. This enables us to provide a detailed view of the results at less than half the sample size and costs that would be required with non-Bayesian analytical approaches.
Syndicate data collection
Most KPIs are the same between brands, so instead of running the same setup for multiple clients, we collect data on the shared KPIs in a “syndicated” setup (i.e. generate standard data sets that can be accessed by different brands at shared costs). This enables us to collect a high volume of data and deliver more frequent updates without increasing the cost for our clients.
Conclusion
By following the three steps above, Latana can offer a brand tracking solution that delivers the value of a large and sophisticated tracker at a fraction of the price. Our standard setup includes:
If you’re keen to explore a more affordable way to get high-quality brand insights, or if you’re looking for more frequent, timely, and reliable brand insights, book a call with our team today.