Whenever you see an article about unlocking something, what do you picture? It’s highly likely you see a lock and a key. And here at DCMN we work closely with a key, namely the one that unlocks the potential of your TV results.
Meet TV attribution. This guide, written by a member of our data science team, will take a look into how TV attribution solves your problems and the tools and tricks that help you get there.
Before measuring your TVC, you need to know how to optimise it
TV marketing is a tried and tested way to send your reach to the stars. But you want more than just eyeballs. You want campaigns that drive traction to your website or app – with measurable results.
What is TV attribution?
Let’s imagine you just produced a cool new product commercial ready to be launched on TV.
Now once that commercial airs, you would like to find out the return of this investment. In fact, you would probably like to know specifically how much online foot traffic that particular commercial brought to your product, website or mobile app.The answer to this is TV attribution.
It’s a way to compare the traffic before and after the commercial (also called a ‘spot’, if we’re being strictly technical) airs. That difference of traffic between the before (called baseline traffic) and after is called the uplift. The net uplift is how we measure the true impact of your commercial.
upliftnet = post_campaign_traffic – baseline_traffic
Of course, this equation above is an oversimplification, since it usually involves a number of steps and processes which lead to the uplift net. Regardless, TV attribution is a path for attributing the uplift traffic to the spot time (the time which the commercial / spot was aired on TV, in minutes).
How do you choose a TV attribution tool?
TV attribution isn’t so new anymore. In fact, it’s around a decade old now, meaning most of us are now well accustomed to measuring TV campaigns. Today, there are a plethora of TV attribution tools that offer these services, bringing us to the important question: how do you choose the best tool or service platform for your campaign performance analysis?
Most of these tools will help you compute the traffic uplift and attribute them to your spots. The uplifts are usually measured in units of goals, whether it’s conversions, purchases, installs or whatever metric is most relevant for you.
DCMN’s attribution tool DC Analytics is one such tool that can help you track and optimise your TV campaigns. Using our very own custom TrackingSDK technology, DC Analytics enables clients to tap into the true potential of their TV marketing campaigns by providing them with relevant insights and actionable reports.
Given that these platforms are essentially yardsticks for measuring the efficacy of each campaign, a key factor to look for in selecting a tool is their level of measurement. More specifically, it’s the granularity of the scale which matters. How low can you go?
The finer the granularity, the better it is for measuring campaign performance and comparing different campaigns to a more precise value.
Why does granularity matter?
Imagine trying to measure the length of a caterpillar with a standard 30cm wooden ruler. Grab a willing caterpillar and measure its length with the ruler. Here you can see our cute little green friend helping us out as a visual aid.
Now we have a measurement, which is great. But there’s a catch! We would like to measure its growth each day. So every day at an agreed upon time – after coordinating with our little green friend’s calendar of course – she joins us by the ruler and we measure her. Oddly enough, for the first week, the measurements are almost the same, despite that fact that caterpillars are meant to grow a lot during the first two weeks. So what’s going on? The answer is this: the smallest units of our ruler are actually too big for what we are trying to measure! With a 30cm ruler, we simply don’t have a finer grained scale to measure those tiny changes in length. Hence the need for a finer grained unit of measurement.
What does Google Analytics offer?
Before we dive into this, it is important to note that Google Analytics comes in two different service packages. The first is the free version Google Analytics Standard. This is the one we will refer to in the remainder of this article as Google Analytics.
The second version is the paid Google Analytics 360, which comes with a boosted feature set, higher data capabilities and more as can be seen in their documentation here.
Now, in terms of granularity, Google Analytics Standard only allows us access to raw traffic data up to the hour mark. This means that the most granular data we can get from Google Analytics is the raw traffic for each hour of the day.
Why is hourly data not good enough?
Here’s the crux of the issue: we have free raw traffic data from Google Analytics but it’s not enough. Why? Well, it’s the same reason we couldn’t measure the caterpillar’s daily growth with the wooden ruler — the smallest units of measurement are not granular enough.
If you think about the commercial that started all this, you will remember that it aired at a particular time on TV. That spot time was in minutes (that is, one level of granularity higher than hours). In order to have an accurate analysis, we would need raw traffic data with a higher level of granularity (in minutes) which the Google Analytics Reporting API unfortunately does not offer.
What is the solution then?
As DCMN is always solution-driven, we have found a way to unlock the greater potential of these analytics by going a level deeper into the granularity of raw traffic data. With our in-house TrackingSDK technology, we are able to extract raw traffic data from your website and mobile platforms up to the granularity level of minutes!
This allows us to present to you campaign performance results on a much deeper level by being able to attribute post campaign traffic to the very minute your spot aired on TV. Here is an example of TV attribution results, made possible by our finer-grained TrackingSDK technology.
As you can see from the graph, the top row displays the raw input traffic data (in terms of sessions, or the periods of time that the user interacted with your webpages). This is the input data that comes from our in-house trackingSDK technology embedded in your website html.
Of course, you would have a baseline traffic, which is the regular amount of traffic your website would experience if you were not running a campaign. We consider this while calculating your net uplift. Then there’s the bottom row which shows you the final output, i.e. the net uplift (upliftnet) which has been attributed to its corresponding spot time in minutes. This enables you to identify exactly which spots have done well down to the very minute they were aired and offers a level of attribution and analysis that is much more fine-grained and accurate than those attempting to match hourly data with minute-level spot timings. Whether it’s a caterpillar or a TV campaign, the onus is on the details — and this is precisely what DC Analytics offers. To find out more about DC Analytics and its features, check out our website here.
Reach out to us if you would like to unlock the true potential of your post-campaign traffic analysis!