In this new series, we chat with leading app owners, creators and marketers to get their insights into what app success looks like and how they're engaging users. Plus: what do they see working when it comes to app messaging tactics, and how do they use analytics to inform marketing? If you want to participate, please email email@example.com.
For this installment we asked Tim Koschella, the CEO & Co-founder of AppLift, about the latest mobile adtech trends, new ad formats, and tips for planning your budget.
At the end of last year, we released our top 11 mobile adtech predictions for 2016. To take the top 3:
All sources predict it, and we strongly agree: programmatic spending is on the rise, especially for mobile. According to eMarketer, programmatic will make up 78% of total mobile display ad spending as well as 76% of total programmatic digital display ad spending by 2017. If you haven’t yet taken the programmatic turn, it’s time to start seriously thinking about it.
Since the social behemoth started making its successful entry into mobile advertising, Facebook has mostly focused on performance players, because brands did not trust the platform due to its lack of visibility. However, with their latest acquisitions (Atlas, Liverail) and the expansion of their advertising stack to services such as advanced analytics, Facebook is offering tools likely to seduce brand advertisers and convince them to finally start spending. This should push prices upwards on the platform, which in turn is likely to drive performance advertisers towards RTB, where supply is (almost) unlimited.
Since the end of SMS and content distribution, Telco companies have been trying to find a way to monetize their huge user bases. They now understand that the deterministic data they own on their users, such as demographic data (from the contract information), or usage data, is extremely valuable to advertisers. We’ve already seen a few deals happen last year (think Verizon’s acquisition of AOL and Millennial Media) and can expect more of them in 2016.
There is currently no clear-cut definition of programmatic advertising and people tend to put a lot of different things into it. A very broad definition could be that programmatic advertising is a process whereby the decision of the media purchase does not require any human interaction. Oftentimes, real-time bidding (RTB) is understood as a synonym of programmatic, but the truth is that RTB is a only a subset of programmatic. There are other types of programmatic trading, such as programmatic direct, or the APIs of the various social networks turned advertising networks (Facebook’s Marketing API, Twitter’s API, etc.).
One of the great things with RTB is that app advertisers can leverage the data they own about their users (so-called “first-party data”, such as in-app behavioral data) to improve their targeting and, down the line, maximize the performance of their campaigns.
We can structure our best practices in three stages stages: Strategy and Creation, Data Gathering, and Data Reading & Creative Optimisation:
First of all, you need to align the creative strategy with the campaign goals, understand the type of offer being promoted. The creatives have to be appealing to the audience you are trying to reach, using previous knowledge on which type of creatives work best for a certain type of offer. Then you obviously can enrich it by adding new elements. The data you have on audience groups also enables the possibility of targeting specific creatives to specific groups. We recommend to have more than one set of creatives with several simple variations. This goes for animation, copy, call-to-action button, color, etc.
Programmatic creatives can be a time-saver for creating ad units at a bigger scale, thereby saving time and costs. However there is still a need for a human eye for the creative part and to make sure the assets and campaign specifications are well combined.
Once the campaign has been set up, you can start running creatives and gather substantial data which will enable you to make first assumptions on performance and user experience. This should be done by A/B testing multiple creatives; having more than one creative is crucial to understanding what works and what doesn’t.
Creativity and data actually complement each other. Even though we create ad units based on previous knowledge, ultimately only data can confirm what actually performs. With Dynamic Creative Optimization (DCO), creative performance is analyzed in real-time and the design elements of the creatives are optimized programmatically based on these results. It can also be helpful to gather results on user experience, such as loading time, to understand how the user interacted with the ad (time spent, actions etc).
Overall, data can actually be a trigger for creativity in the sense that it offers direct results of the ad unit’s design qualities and creative strategy that can foster new creative ideas and later be used to design new creatives.
In a nutshell, 3D mobile ads can create a new dynamic because they are more visually attractive, they bring the app, product or service closer to users, and because they provide a highly engaging experience. 3D ads offer a more engaging and immersive experience, in the sense that:
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You also have to remember that 3D integrations happen within a 2D context, which means that they also stand out by contrast with their environment. It should be noted that 3D ads have until now primarily been used by brands, but they can still be tested in a performance context, such as an app install campaign.
Generally, the biggest trend in APAC is obviously the use of “over-the-top” (OTT) messaging apps as gateway apps to access all kinds of mobile content, from games to utilities such as taxi services, banking, travel services, etc. We’re mostly talking about WeChat in China, Kakao in South Korea, and Line in Japan. For instance, it remains extremely difficult to get in the top 10 ranking of the Korean app store without being published by Kakao.
In China more specifically, we’ve seen a trend where important utility apps start heavily monetizing their traffic by building their own ad platforms. The best example is Cheetah Mobile: they started with a few launchers, anti-virus and cleaner apps and they are now a listed company with over 1,000 employees. This is a natural evolution: large, successful publishers that have acquired a lot of expertise in in-app monetization and server-side mechanics leverage their know-how to expand into the supply side of adtech.
In Korea, we’ve observed bigger brands transition to apps and resort to performance marketing for their mobile strategy. The fact is that brands are still hesitant in their mobile advertising strategies and many are reluctant to engage in display advertising as they lack visibility over the quality of the inventory. Creating branded apps is then one of the safest way to reach users while making sure that the experience around the brand remains premium at all times.
The main piece of advice I would give app developers is to commit enough resources and headcount allocation to their mobile operations. We recently visited a major stock-listed company in London where mobile efforts were managed by a “digital” team. This effectively means that one person is dedicating about three hours a week to mobile UA. That’s just not sufficient. Also, that person was not focused on mobile. Desktop and mobile are two very different beasts. You need dedicated people with a mobile mindset, otherwise you end up selecting the wrong KPIs and comparing apples to oranges.
Then, developers should refrain from putting all their eggs in one basket and instead work with “silos” of budgets and constantly test new channels to see what works and what doesn’t. Mobile advertising is a highly empirical discipline: you should probably spend 5% of your time planning, but the rest of the time should be devoted to relentless testing.
Finally, make sure to keep some buffer in your campaign budget in case your app takes off beyond expectations or one channel proves extremely successful; it would be a shame to see your app’s success cut short by a lack of UA budget.
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