The precision of advertising analytics has thereby grown, not only thanks to the development of new platforms and the volume of data available, but also thanks to the measurement structures and standards that have been adopted around this data. As a result, the task is relatively simple when your key performance indicators (KPI) can be measured with precision through your data system, for example, a target number of transactions to attain, or an average cart value to maintain. But what if your KPI can’t be measured through your platform? How can you measure brand awareness or sentiment? Below you’ll find suggestions to guide your thinking around how to measure the effectiveness of a brand campaign.
As in any good analysis, the first step is to determine your methodology, and state it in a clear and precise way in your concluding report. There are a number of elements to take into account.
Although an enormous amount of data is available, you’ll have to verify its integrity and validate the official list of data sources you are going to use in your analysis. Pay attention to the scope of data you’re using: if you’re only using Google Analytics, your data will be limited to your site. If you want to know how your users are behaving beyond the borders of your own site, you’ll have to look to other tools, like your Facebook insights, for example.
Most of the time, analyses are done with Google Analytics (using last click – see below for more details), but if it’s available, try to broaden your analysis to include all sources of relevant data at your disposal:
These platforms regurgitate very precise data (impressions, clicks, etc.). But why limit yourself to this type of quantitative data? With survey tools like Google Surveys, you can ask your users relevant questions directly.
After having inventoried your data sources, you’ll need to choose the indicators you will be analyzing. Google has put together a very good model using the tools from its suite, here:
Here you can see that the methodology used relates the KPIs to the user’s purchase cycle. First contact (awareness, the user becomes aware that your brand exists), consideration (the user connects their need to your brand), then action (conversion). The analyst’s task here is to recreate the user journey, and attach the KPIs that will be best able to measure each step.
Finally, don’t forget to use Google Analytics’ multichannel reports. Most of the time, analyses are done with the Core Reporting API, which includes the majority of reports. The MCF API allows you to attribute a value to each point of contact in the user journey, not just the last one. Pay attention, too, to the logic of this API; it functions solely on conversions (goals and transactions), and not on the traditional indicators used by the Core Reporting API (sessions, users, page views, etc.).
Given that brand campaigns are intended to raise awareness and not to convert, an analysis using a first click attribution model or a linear model would be far more relevant in this situation than the last non-direct model used in the majority of Google Analytics reports.
Next, be sure to specify your date ranges, because they’ll need to be comparable. Moreover, if your data export isn’t automated, an error in your dates can result in you having to start all over again for no good reason. Here are a few criteria to keep in mind when determining your dates:
The first step is to compare how the contribution of the channels involved (organic and direct) has evolved in comparison to overall traffic, both in terms of volume (from 800 to 1,000 sessions, or +200 sessions) and contribution (from 5% to 10%, or an increase of 5 percentage points). This analysis is available directly in most platforms like Google Analytics shown here.
We can take the analysis further. The evolutionary analysis will give you the rate of variation between the two periods. But is this rate meaningful? Your traffic will naturally experience positive and negative variation over time. So, if you did see a variation in your traffic, was it greater than what you would normally see? We can measure that by calculating the variation coefficient:
The data needed to evaluate this is:
Easy to calculate with a spreadsheet like Google Sheet or Excel, here’s an example of a calculation of the variation coefficient:
You’ll then be able to compare your variation by subtracting the variation coefficient. Then you’ll see that just because the variation of the indicator is higher during the campaign, it doesn’t mean that it has a greater impact: although conversions increased more than sessions, the campaign made more of a difference in terms of reach than conversions.
Consider varying your data sources! The majority of users use only Google Analytics for these types of exercises, but other sources can be gold mines for this type of analysis. Among others, survey tools are easier to implement than ever. They put you in direct conversation with your users – not something you’ll want to miss out on.