Capturing the Right Data from Your Advertising Campaigns

Picture this. You are about to launch a major advertising campaign. The creative team makes three different videos to use. Your boss likes video 1 the best. But based on past audience reactions, you think video 2 is better. The campaign launches. The data starts rolling in, but is it the right data to tell you if your campaign is effective?

Key points:

• Nearly 30 different metrics measure the effectiveness of ads
• Marketers spend nearly 60% on reporting tasks as compared to 10 years ago
• Attention and emotion-based data create a powerful, measurable metric that marketers can use to test and predict true campaign effectiveness

A study conducted by the media agency, PHD, reveals that time spent on reporting tasks has grown by nearly 60% over the last 10 years. Reporting is also the number one time-consuming task for marketers today. Even more shocking, the same study mentions that marketers “…spend just 18% of their time thinking creatively and coming up with new ideas.”

What’s driving marketers to spend so much time on reporting? According to the Adverity Marketing Analytics State of Play 2022 Report, 41% of marketers struggle with manual data wrangling. Other key challenges marketers face include:

• Low level of trust in data due to inaccuracies and mistakes
• Inability to measure ROI on marketing spend
• Lack of data-driven insights to drive marketing strategy
• Inability to demonstrate the business impact of marketing efforts

So how does a marketer ensure they are capturing the right data from their advertising? How can you measure what works and doesn’t work in an ad?

Marketers use several methods to track the effectiveness of ads.

	Quantitative methods involve data collection and analysis using metrics that are either built into the ad platform or can be calculated manually or through other tools.
	Qualitative methods involve other types of activities such as audience analysis through ad testing or surveys.

When combined, quantitative and qualitative methods help provide context about what’s working and not working in advertising campaigns. Let’s take a look at these methods in more detail. 

Tracking common advertising metrics

In this past blog post, we discussed three different types of marketing campaign metrics:

• Pre-campaign metrics to test content relevance
• During campaign metrics to measure current data flowing in
• Post-campaign metrics to confirm campaign effectiveness 

Advertising platforms typically have several built-in advertising metrics, depending on the media type. But Databox lists 28 different metrics that a marketer might typically track on an advertising campaign, including:

• Return on Ad Spend (ROAS): calculated by dividing revenue from ads by the ad cost
• Cost per Acquisition (CPA): the amount you pay for action or goal such as a form submission or phone call
• Customer Acquisition Cost (CAC): measures the cost per paying customer acquired
• Click-through Rate (CTR): the number of times your ad was clicked per views
• Cost per Click: the cost associated which each click on an ad
• Earnings per Click: calculated by taking your conversion rate and multiplying the average amount of money earned by one customer
• Cost per Lead: the amount you pay for each qualified lead
• Engagement: this metric is usually calculated by the platform you are using and Databox recommends landing between 3% to 5% 

Tracking through other qualitative methods

Surveys provide another method to measure ad effectiveness. Surveys typically happen before the official campaign launches. GWI recommends taking an active and passive data approach, which involves surveying audiences who have and haven’t seen the campaign. Surveying both audiences allows you to obtain unbiased feedback to which aspects of the advertisements are most effective. However, surveys still don’t provide the full story. 

The ad testing survey asks participants questions about their thoughts on the content. But studies show that people lose attention after a few seconds. That’s the type of data you should be capturing to measure undeniable campaign effectiveness.

Predictive attention and emotions analysis provides important information, such as:

• The point in time the audience feels specific 
• How different emotions interact with each other
• The attention levels
• Whether attention grows over time
• Whether the engagement is changing in response to specific stimuli

The following attention and emotion-based KPIs provide more details on campaign effectiveness:

• Global index: summarizes the test results carried out on a piece of content
• Attention threshold: helps measure the effectiveness of marketing campaigns
• Predictive click: data collected through the attentive and emotional responses analysis of the target to know whether the content will be interacted with or not

>>You Might Like: Predictive Click Vs. Click-Through Rate

Let’s return back to our campaign example from the beginning. Using attention and emotion-based KPIs, you can confidently identify the best performing creative based on real-time data. Make decisions based on quantitative and qualitative data and not just on personal hunches.  

Collecting accurate data about your advertising campaigns and the channels that generate the highest engagement can help you reallocate your marketing budget more effectively. Learn more about how to obtain valuable user behavior data based on attention and emotional analysis.