Optimization tips for your video campaigns on Facebook and...

Optimization tips for your Video campaigns on Facebook and Instagram | 1

Optimization tips for your video campaigns on Facebook and Instagram.

A piece of analytical work jointly executed by Publicis Media Group Spain,

MetrixLab and Facebook.

December 2019


Consumption of content has never been easier through mobile devices and faster through social media type of environment, offering more opportunities for marketers to get in touch with consumers. Consequently, marketers nowadays have many more options at their fingertips, to impact consumers. Doing it efficiently requires content assets to be tailored to each channel for a given brand.

In this context and given an objective of understanding in more depth advertising effectiveness in a mobile environment, Publicis Media Spain, MetrixLab (market research and consultancy independent company specialised in ad effectiveness and consumer research) and Facebook Marketing Science teams, have concluded a 6-month long collaboration. This project allows us to identify important components that maximise chances of improving the ad effectiveness of video campaigns on Facebook and Instagram on brands.

Practically speaking, the work undertaken allows us to deepen our understanding of ad effectiveness in a mobile environment for two key pillars:

• For creative assets, what are the important elements of these video-based ads that most likely drive better Ad, Brand or Campaign recall?

• From a campaign planning and set-up perspective, which strategy on the Facebook platform is most effective to maximise the impact of video campaigns on Facebook and Instagram? In other words, understanding the trade-off, if any between the cost per people reached, how much it took to impact these same people exposed to ads with a brand message and ensuring that the highest share of these exposed individuals consumed video ad content.

In the next steps below, we are going into details about how we structured this study, the main results, along with the learnings and a snapshot of the key conclusions. We would like to conclude with as much precision as possible what are the best way to maximise efficiency on the Facebook platform.

1. Creative assets recommendations

The first objective of this project consisted in identifying the creative elements who are likely to be the most important drivers of brand impact for marketers.

First, we partnered with MetrixLab which performed an in-depth analysis of a total of 57 creative assets from Facebook campaigns planned and set-up by Publicis Media Spain between January 2019 and May 2019. MetrixLab leveraged its post exposure creative test methodology in a mobile environment in order to do so. In this MetrixLab analysis 100 participants were recruited for each ad analysed. These participants were chosen in line with the target audience of the campaigns the ads pertain to. One of the ads chosen for the analysis was inserted in a recreated Facebook or Instagram news feed experience. The first phase consisted in observing participants’ behaviour with the feed-based content, without the participants being made aware of the type of content or ads they would be queried about.

The second part of the MetrixLab methodology consists in focusing on the ad itself. Participants are then surveyed as a second step, asked about specific brand questions for a specific ad, so that MetrixLab can estimate Ad or Brand recall.

Last, the third part of the MetrixLab analysis is a multi-variant regression analysis whose dependent variable is a brand impact metric and the independent variables are the ad creative elements



for which MetrixLab as a score for. The goal is to understand which creative element can have the most impact onto brand outcome.

Output derived from the above, allow us some insights on how to better optimize video-based campaigns for better results.

One of the very first piece of findings is that the potential for an ad or a brand to be recalled by consumers is not correlated with the length of that same ad. MetrixLab has compared, across all 57 videos, the relationship between video length or the share of video viewed and brand metrics. There was no correlation observed between these two dimensions, confirming what we knew empirically or through Facebook 1st party research.

Among the drivers of ad recall caused by the video-based campaigns, one of the main drivers is the presence of the brand in the first few seconds of the video. This piece of insights is well understood and accepted as best practice in the industry. In this piece of research only 9 out of the 57 ads mentioned did not have a brand presence in the first few seconds of each video ad tested, which corresponds to less than 20% of all video assets submitted.

MetrixLab conclusions are also clear about other important drivers. Brand presence in the first few seconds is a condition needed to generate impact, but it is, by no mean, sufficient or enough. MetrixLab findings called out 3 important additional drivers that are needed to maximise the marketers’ likelihood of success in a mobile environment:

1. How well the brand is embedded throughout the creative asset. It was demonstrated that inserting the brand at the end of the video as the reveal, like commonly done with TV based assets, is not enough. Brand reveals and brand cues have to be woven throughout the length of the video in order to maximise brand impact.

2. Product presence. Creative assets for which the product presence is more prominent or for which description of the product benefits, value proposition or product related features stand out, have a much better chance to generate brand value.

3. Simplifying the core message. The analysis demonstrated that creative assets that were able to communicate a clear, focused and concise message stand a much better chance to be successful.

MetrixLab analysis allows us to deepen our knowledge about consumers behaviour in a mobile environment along with validating some of the empirical hypothesis we, at Facebook, demonstrated over time. We also learned once more the need to adapt the creative assets to the device these will be displayed on. These conclusions help us maximising marketers’ likelihood to be successful from a creative standpoint.

2. Campaigns optimization recommendations

The second part of the study focuses on the most suitable optimization strategy on the Facebook platform. Marketers have on both Facebook and Instagram many optimization options depending upon their main campaign objective.

Practically speaking, often for video-based advertising campaigns, the trade-off can be challenging. The trade-off is between a large reach at a relatively lower CPM (i.e. cost per thousand impression delivered) on one side and a lower reach at a higher CPM but with higher portion of the audience consuming longer part of the content, on the other side.

For the next step of this study, we leveraged the Facebook Measurement solution called “Facebook Brand Lift”. The solution works by dividing the people into randomised test and control groups in an intent-to-treat test methodology. This solution will use marketers’ chosen audience to create its test and control groups and then poll individuals in both groups about topics such as ad recall, brand awareness and message association. There is no need for additional tagging as this solution is native to the Facebook platform and captures actual exposure on and off mobile devices.

Given the overall goal (i.e. comparison of optimization strategies) for this part of the study, we developed a multi-cell set-up for the “Facebook Brand Lift” solution. Such a set-up requires that campaigns are optimizing for two distinct strategies, every other campaigns’ elements being rigorous identical between the cells (frequency, budget, target audiences etc... are rigorous the same). The only difference between the cells being the campaign’s optimization objective which is exactly what we are trying to isolate and understand the impact of. Hence, each test group is subject to a campaign with a specific optimization objective and is compared to an equivalent control group as per the design below.

•

The campaign objective ‘Reach’ or ‘Brand Awareness’ available

on the Facebook platform would maximise on a relatively cheaper

basis large-scale ad exposure.

•

The campaign objective ‘video views’ would deliver ads to a

smaller subset of the intended audience, at a higher cost

but with a higher share of the audience consuming the video

for longer.


We proceeded with the multi-cell “Facebook Brand Lift” set-up in collaboration with 3 clients from the Publicis Group Spain for a total of 4 distinct brands who followed the rigorous multi-cell set-up described above. We analysed the results of these 4 distinct brand lifts through the lens of the following metrics:

• The reach: the overall volume of individuals exposed at least once to an ad that were part of the campaigns for which a brand lift was set-up for.

• Brand lift is the estimated difference in the number of people who gave the desired response to your poll between the test and control groups. Brand lift is calculated by multiplying the number of people you reached with your campaign by your brand lift percent.

• The cost of each additional person who gave a desired response to your poll in a brand lift test: Cost Per Brand Lift.

Campaigns’ Reach % lift of the campaigns • Brand lift (number of people)

• Cost per Brand Lift

Targ

et

Test TestControl Control

Cell A=Campaigns optimized for “Reach”

Cell B=Campaigns optimized for “Video Views”

Brands2 Campaign Objectives Reach3

Reach winning strategy

Adjusted Ad Recall Lift

(%)4

Brand lift (number of people). Ad Recall only Indexed to 100. 100 =

median

Ad Recall winning strategy

Cost per Brand Lift

(in US $ and Indexed

to. 100 = median)

Brand 1Reach 2.4 M 0.43% 123 37

Video Views 1.3 M 0.29% 46 82

Brand 2Reach 6.5 M 0.35% 274 73

Video Views 5.1 M 0.25% 152 135

Brand 3Reach 1.3 M 0.19% 30 137

Video Views 1.0 M 0.58% 70 58

Brand 4Reach 2.3 M 0.08% 22 221

Video Views 1.8 M 0.37% 83 58

3. Main Results1

1 Results for Ad Recall metric only for each of the 4 multi-cell brand lift set-up and executed in the Facebook platforms for advertising campaigns on Facebook and Instagram.

2 Each brand had campaigns and a study set-up that follow the corresponding start and end date: brand 1 for campaigns starting from 08/05/2019 onward, brand 2 for campaigns

starting from 26/02/2019 onward, brand 3 for campaigns starting 15/02/2019 onward, and for brand 4 for campaigns starting from 06/03/2019 onward.

3 Data extracted from lift study results available in Ads Manager or Business Manager

4 Figures were adjusted by the variance of results in the test and in the control group

in each cell of each brand lift. Variance is a function of the number of responses (in this case approximately 500 responses for Ad recall in the test and 500 responses in the control group) and the number of desired responses in both the test and the control. Variance is then squared in order to obtain a scaling factor that can be used as the adjustment factor in this table of results.

Based on the table of results above, we can conclude the following:

• The two different optimization strategies (‘Reach’ vs ‘Video Views’) imply different optimization criteria taken into account by the auction. It is reassuring to witness the fact that the Facebook auction delivers on the main optimization criteria chosen:

o A ‘Reach’ optimization objective helps the Facebook auction maximise its ability to reach as many individuals part of the defined target audience as possible, given a set budget.

o The ‘Video Views’ optimization objective limits to some extend the potential Reach, allowing the Facebook auction to serve ads to a smaller number of consumers who are more likely to watch ads for longer. It lowers the campaigns reach but improves video views related metrics.


Each brand aims at a different target audience with a different optimization objective, hence the actual delivery in terms of Reach and impressions can be different. A simple read of the % lift can be misleading and does not allow for rigorous comparisons between brands. It is important to equalise metrics and to focus on the number of people impacted which multiplies the lift % from the study with the overall campaigns reach delivered. It is also important to take into account the cost per Brand Lift as defined above.

These two metrics are crucial for concluding what works best.

In 4 out of 4 cases, a ‘Reach’ objective

consistently delivers a higher reach figure than a “Video Views’

objective.

A ‘Video Views’ optimization

objective in 2 out of 4 cases, delivers higher brand impact

to marketers in terms of the number of people impacted or the number of Ad recallers. In these two instances (i.e. Brand 3 and 4), the slightly lower

Reach generated is compensated by a much higher lift %

A ‘Video Views’ optimization

objective in 2 out of 4 cases, also

delivers better value to marketers as

the cost per brand lift is slightly lower than with a ‘Reach’

optimization objective.

There are certainly numerous factors both at the creative asset level (i.e. brand presence, product features etc..) and at the brand level (i.e. brand equity in the marketplace etc…) that are likely to contribute these split conclusions for these 4 brands tested on the Facebook and Instagram platforms.


Comparisons can only be done if the right metrics are considered. Although we can conclude on one of the two primary objectives of the 2nd part of this study, the following should be observed:

• Creative assets tailored for a mobile environment are very important to maximise marketers’ chances to deliver impact.

• Product and brand presence throughout the creative asset along with a precise and simple message are important.

• The ‘Reach’ campaign optimization objective delivers on its promises for the 4 brands tested: higher Reach.

• If the goal is to maximise brand impact, the choice between ‘Reach’ and ‘Video Views’ is non-trivial as demonstrated by these split conclusions for these 4 brands. We recommend a test and learn approach by which the optimisation objective is tested first on a small scale before the campaign is fully launched in the given market.

Authors:

Nicolas Arrivé, Facebook Marketing Science Agency PartnerSafia Dawood, Facebook Marketing Data ScientistAlfonso Calatrava, Facebook Marketing Science Iberia LeadVanesa Iglesias, Facebook Agency PartnerRebeca Benarroch, Data Sciences Lead, Publicis Media SpainEsther Pérez, Advanced Analytics Director, Publicis Media SpainBeatriz Calzada, Performance Media Director, Publicis Media SpainAlicia Macarro, Digital Senior Manager Data Sciences, Publicis Media SpainGines Ochoa, Social Supervisor, Publicis Media Spain

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