The convergence of advertising and marketing technologies is increasing the way data can be used to improve the efficiency of display ads.
A large percentage of marketing budgets (75%-80% by some accounts) are directed towards the acquisition of customers, while much of the rest is dedicated to retaining customers.
Being a long-term advocate of retention marketing, I am in constant search of how retention and acquisition can work together. And now I think I have found my answer.
The history of display in a nutshell
We all know that display is expensive, but it is often seen as the front line in acquiring customers. The ineffectiveness of current display standards however, has forced ad tech firms, and advertising in general, to become smarter.
Smarter in the way that the marketer is enabled to implant a cookie on the user’s computer or mobile device and then serve them with dynamic ads attributed often to actions the users would have taken anyway.
Programmatic ad buying and real-time bidding technologies now allow marketers to buy smartly and target the consumer at the right time. The challenges here are few, but include:
- Unidentified users across devices
- Ads that may be related to actions the user has already taken
- Measurability of the true attribution
Email is the new cookie
The latest revolution in the display industry was made by Facebook (closely followed by Google) to allow marketers to find and segment users based on their email addresses.
Email lists of users who haven’t opened, clicked, purchased, and/or visited us, can be uploaded to Facebook or Google and we can find those users within these two sites. Hurray! This, in essence, now allows marketers to personalize in scale. In other words, old technologies assist in retargeting, while new technologies provide the ability to cross-sell and up-sell using email – first party data.
Here’s a graphic courtesy of Luma Partners to illustrate this.
Datasplay: where the fun really starts
Are you wondering what datasplay is? Don’t worry, this term doesn’t exist – I have made it up, but I think it conveys well the act of relying on data for display advertising.
Before I elaborate on datasplay, it is important marketers are up to speed on this landscape. Here’s an overview:
To generate sustainable, lifetime revenues from true 1:1 marketing, automated and at scale, you’ll need your display and marketing cloud platform to work together.
While mar tech is about understanding the customer and the ability to communicate with them on different channels, ad tech is mostly about branding (copy and creative) and traffic/acquisition. Ad tech relies on the data management platform (DMP) that is used to store and analyze data – mostly cookies. The demand-side platform (DSP) is used to buy advertising based on that information.
What is clear is that 1:1 personalization cannot be achieved when ad tech and mar tech do not talk to one another and for you, my marketing friends, this means a harder life connecting systems and data sets together. The good news is that these two tectonic plates are moving towards each other, and the immediate future looks promising.
Here are three steps to an integrated data and display strategy:
1. Lead with data: choose your segment
Find your gold buyers – the ones that spend the most money with you (use eRFM if available).
Filter out regular buyers and focus on those who stopped giving you regular business.
Use display only if they have not responded on cheaper channels: email->mobile push/in-app->web (they visit your website but do not buy).
2. Set a campaign: using first and third party data
Upload this segment to Facebook using email as the identifier, and set a time limitation to your campaign.
Do the same on Google.
Don’t forget to offer a good incentive and preferably your display (if your technology allows it) will be focusing on recommended products similar to the ones already purchased.
Buy third-party data from your DMP, and look for these users all over the web and mobile world.
3. Acquire: search for high-valued lookalikes
Target lookalikes from Facebook and Google. These have a very targeted likelihood to purchase your products. In similar campaigns we have run for our clients we have found three-digit ROI.
Run an automatic program that filters out the best buyers in your database, and then periodically searches for lookalikes with the same profile on Facebook and Google.
Recreating the flow, it would look like this:
While the tips above will create a highly contextual campaign, it still requires you to do some extra work. In pursuit of retailers who want to execute automated, omnichannel, contextual campaigns, technologies in the market will soon enable companies to do just that.
This in essence will allow marketers to be much more targeted and efficient when using their display budget to acquire leads with a very high likelihood to purchase, and generate very efficient and targeted cross-sell and upsell campaigns to their existing clients based on the customer life cycle.
Tech-enhanced customer experiences have always had a very receptive audience in China, and as a result, VR technologies are being widely embraced by both brands and platforms.
Header bidding is a programmatic technique that allows publishers to offer their inventory through multiple ad exchanges before they serve up ads from their ad server.
Marketers need to know what’s in their data and trim out the filler to provide continuous, data-driven ROI for their brands.
All top Chinese retailers, banks and internet companies share mobile data in earning releases. None of the top 10 US retailers do, nor does Google. US banks and Facebook are better.