Good advertising has always been at the heart of successful companies. But it’s not just about the message you promote, it’s also about delivering it to the right people.
Only a few years back, advertising was something like throwing flyers off airplanes on top of a crowd, desperately trying to reach as many people possible to promote your products. But having many people read your flyers doesn’t necessarily mean they’re the right people, or that they’ll actually go ahead and buy anything. And on top of everything else, it’s also quite expensive.
What if you could make those flyers land only in the hands of those who are the most likely to buy your products, who actually have the income, availability, and interest to buy them? That’s what targeted advertising is, in an extremely metaphoric way of presenting it.
With the advance of machine learning and deep learning technology embedded into advertising platforms, companies can now target their ads with astonishing accuracy and speak directly to their future clients.
With today’s complex customer journey, a digital business may experience tens of interactions with a single person – across display, search, search, social, and on the site or app. And to make everything even more difficult, all these interactions take place on multiple devices, making it a pain to measure and to optimize.
Machine learning applications in advertising
Machine learning became critical for marketers and advertisers, who need to analyze endless signals in real time and deliver ads at the right moments to the right people. Machine learning is also essential in measuring consumer journeys that now span on multiple devices and channels across both the digital and physical worlds.
Last year, Google also started using machine learning in PPC advertising, with new solutions such as Google Attribution, or advanced machine learning features for AdWords. These new products made it possible for every advertiser to measure directly the impact of their campaigns across devices and across channels, and also to adapt instantly for maximum results.
If we keep looking at Google, the tech giant also pushed the pedal to the floor with machine learning, by adding two new bidding strategies for Adwords – for maximizing clicks and maximizing conversions, plus predicted click-through rate. Everything on top of ad rotation machine-powered learning algorithms.
The new Google Attribution solutions that integrate with AdWords, Google Analytics and DoubleClick Search make it easy to bring together data from all your marketing channels.
This means that advertisers have a complete view of their campaigns’ performance and can easily switch to data-driven attribution, all with the help of machine learning.
Adwords also got a long way since its early days. Smart bidding is now doing the heavy lifting for advertisers who embraced automation, and removed the guesswork out of setting bids to meeting performance goals.
Smart Bidding is basically a subset of automated bid strategies that use machine learning to optimize for conversions or conversion value in each and every auction—a feature known as “auction-time bidding”.
In bidding, machine learning algorithms train on data at a vast scale to help you make more accurate predictions across your account about how different bid amounts might impact conversions or conversion value.
These algorithms with a heavy impact on performance factor in a wider range of parameters than a single person or team could compute, so the sky’s the limit for advertisers.
Smart bidding also provides reporting tools that give you deeper insight into your bidding performance and help you quickly adjust your strategy to optimize your conversions.
Programmatic advertising represents an algorithmic purchase and sale of ads in real time, via software automation for everything from buying to placement and inventory optimization, by using a bidding system.
The new digital marketing algorithms allow marketers to deliver a targeted message to the right person, at the best time, within the most desirable context.
By leveraging advanced machine learning algorithms, advertisers can now identify potential customers by demographics, interests, time of day, device, weather, geography, on top of location, age, and gender. This helps companies target their clients with a lot more precision, by pinpointing them using a lot more coordinates than before.
A greater ad targeting accuracy will undoubtedly result in increased conversion and better campaign results.
Forecasts predict that more than 80% of Digital Display ads will be purchased programmatically in 2018 across the US. This means that 46 billion dollars are going through programmatic advertising this year, and the estimates are that by 2020, the number will skyrocket to 65 billion dollars.
Let’s take two real-life examples of brands using programmatic advertising to boost their sales. The first example: Audi. When the car manufacturer was preparing to launch their newest customizable vehicle – Audi Q2, the German producer wanted a data-driven personalized marketing. Audi worked with marketers, analysts and advertisers to analyze the key areas and the main touch points in relation to their users.
By adding a properly tracked car configurator on the website, Audi could see directly what their users’ dream car is. On top of insights about users preferences, such as model, color or trim, the German producer would also get information about the people who are actually interested in buying their cars.
The company started crafting and delivering dynamic creative ads, tailored according to the user’s dream car. This approach to advertising doubled the efficiency of traditional ads.
Another hall-of-fame example of brands using machine learning to boost their campaign results is the telecom company O2. The company wanted to cross-channel promote a TV commercial, by repurposing them and targeting mobile users as well.
The telecom company was collecting a lot of data about the users, from their in-store activity to their behavior in their operator’s accounts. By levering this data, as well as information such as users location and device, advertisers were able to deliver a personalized ad experience.
Marketers created more than 1.000 versions of the video during the campaign, targeting areas such as the user’s phone make and model, info regarding possible upgrades, plus branding info, such as the nearest O2 store. The campaign resulted in more than doubling their click-through rate.
Their results demonstrate that programmatic is not just efficient, but it can drastically increase targeting accuracy and the sophistication of your campaigns.