The need for data-driven creatives

Up until a couple of decades ago, advertising and data were not closely associated terms.

However, as the internet became commonplace and year-by-year the use of handheld devices like smartphones boomed, marketers began to realize the potential of using technology to supplement their efforts.

Now, in the year 2020, it is quite common to use data for planning as well as analyzing marketing campaigns--and yet, the creative domain of marketing continues to lag behind in adopting data-driven advertising for delivering better results.

Programmatic advertising, i.e. the automated buying and selling of online advertising space, is a common practice nowadays.

It primarily involves using data to segment audiences so that advertisers only pay for ads delivered to the right people at the right time, instead of going the traditional route of sending out an ad to a random bunch of people and hoping for the best.

It is a proven fact that the technology used in programmatic advertising makes ad buying, placement, and optimization processes more efficient.

But, the current scenario is far from perfect. According to research by AppNexus, up to 97% of programmatic campaigns lack a targeted creative for each audience segment and this translates to a loss of about $6 billion for brands and advertisers.

The only way to minimize this loss is to move beyond audience segmentation and serve these segmented audiences a personalized advertising experience with data-driven creatives.

Data that can be used for developing data-driven creatives

In the process of developing data-driven ads you can use the following types of data:

1. Behavioral data which enables targeting based on a user’s previous web browsing behavior across the internet. This is generally collected by placing a cookie on a user’s browser.

2. Contextual data which enables targeting based on the content viewed by the user. This is most effective in aligning the messaging of your ad to the kind of content your customers are viewing.

3. Psychographic data which provides insight about the customers including their personalities, beliefs, values, interests, and lifestyles. This helps in painting a more detailed picture of who your target audiences are and what motivates them to buy something.

4. Demographic data which provides details such as age, gender, income, marital status, and household size of your customers.

5. Geographic data which provides information on where your customers are. It is useful targeting specific regions, especially for brands and businesses which have to tailor their ads keeping in mind the local sensibilities.

Approaches to using data in creatives

The exact way in which you use data while delivering your creatives may vary according to the goals you want to achieve with those creatives as well as the nuances in the nature of your business and your customers.

However, the following are a few ways in which businesses have used data-driven digital advertising successfully.

1. Dynamic Creative Optimization (DCO)

It involves using data feeds to generate a large number of creatives on-the-fly and can algorithmically optimize ad creatives based on multivariate testing.

Such an approach is commonly used for large direct response campaigns because of the ability to dynamically pull in content based on data within the impression.

It is particularly appealing when marketers require a very large number of impressions for remarketing campaigns.

2. Creative Management Platform (CMP)

Creative Management Platforms use both manual and automated features to build a high volume of highly variable static and dynamic creatives.

CMPs appeal to creative, production, and ad ops teams who are looking to increase the speed and efficiency in producing data-driven creatives.

CMP can be used to replace generic ads with personalized ones that are suited for match audience segments, and it can help find the best creative, media and data combinations.

3.The Right Timing

Having a data-driven approach to developing creatives can also help you with delivering the right creative at the right time.

This means that you can avoid the costs of developing entire new sets of creatives, and opt instead to use data for delivering the existing creatives at a time when they would have the maximum impact.

For instance, showing the ad for a cold cream to customers in tropical countries would have more impact in the months of December-January than in the months of May-June, while in the Nordic countries this difference in impact may not be as apparent.

Popularity, budget, seasons, etc. are a few factors that can help you decide the correct timing for your creatives.

4. Split Testing

Split Testing or A/B Testing is a common tool for using data to gain maximum gain out of creatives. It refers to the approach of using different sets of creatives with different sets of customers.

The data from these engagements is then analyzed to infer which set of the creatives was able to garner the best engagement from the customers. The ‘winning’ set of creatives is then used in the advertising campaigns for the larger customer base.

The video-streaming giant, Netflix, is a great example of this approach. Every Netflix video (suggestions thumbnails, trailers, announcements etc.) comes with multiple images that are tested with different subsets of the user base.

The images that yield the best results are then used with all of Netflix’s users. According to The Drum, by deploying A/B testing the company has achieved between 20 to 30 percent increase in video viewing.

By making use of the technological know-how available today in tandem with your own insights about your business and customers, you can use data-driven creatives to make your marketing more efficient.

This data-driven approach can help you make sure that in addition to reaching the maximum number of potential customers, you are also able to make a lasting and profitable impression on them.