The Evolution of Digital Marketing
Digital marketing has come a long way in the past decade. As consumers have access to more information, sales funnels have evolved, and we can now identify more touchpoints in any given transaction. This evolution in consumer psychology comes with a lot more data. In order to successfully leverage digital marketing, strategists today need to be able to make decisions based on a large quantity of numbers in a short period of time. This particular increase in data volume has opened the doors for one of the biggest trends this year: machine learning and automation.
With this increased volume of data and lower prices in data storage and management, industry leaders have invested in machine learning, to understand customers and create new opportunities for advertisers. As technology continues to evolve, we will see smarter networks that will deliver custom experiences for potential clients.
The Year of Machine Learning
According to Google, “machine learning is a new way of problem-solving. Rather than spending hundreds of hours manually coding computers to answer specific questions, we can save time by teaching them to learn on their own. To do that, we give the computer examples until it starts to learn from them – identifying patterns, like the difference between a cat and a dog.”
A recent survey by Andrew Park, at QuanticMind, revealed that 97% of marketing influencers believe the future of digital marketing will be heavily influenced by machine learning. Both Google and Bing have invested time and resources in developing their machine learning programs, to improve the overall advertising experience. An improved advertising experience means doing one thing a lot better (and faster): growing businesses.
Image courtesy of Search Engine Journal
This will be the year where we see the results of all the beta programs, and we’ll see smarter campaigns. From Data Attribution to bots and automated bidding, machine learning will soon evolve into a standard practice for most advertisers. This will give advertisers time to focus on the bigger picture: overall strategy and uniqueness in an extremely competitive market. Here are some of the trends and tools we will be seeing more often this year:
So far, both Google and Bing have fallen short in this category when compared to Facebook. After flirting with new In-Market Audiences in 2017, both major channels will likely roll out custom audience features, allowing more effective targeting for new campaigns. Machine learning will help build these audiences, based on consumer psychology, and the constantly evolving customer journey, opening the door to new strategies and campaigns.
For instance, according to Google’s director audience, conversion rates for in-market audiences could be up to ten times higher than other audiences thanks to smarter algorithms and audience segmentation. Frederick Vallaeys, a guru in AdWords automation, wrote a great article discussing the future of in-market audiences, and the impact of machine learning in audience segmentation. According to Vallaeys, features like life-event targeting or the use of keywords for in-market audience creation should significantly boost our probabilities or targeting the right customers with the right ads. An audience based on machine learning helps build a custom advertising experience: the right ad in front of the right crowd.
After years of research, Google decided to launch a new attribution model that would improve accuracy based on more complex customer journeys. These new models feed on data mining and machine learning and were created to measure the impact of the marketing efforts across multiple devices and channels. According to Google, this data-driven attribution uses machine learning to determine how much credit to assign to each step in the consumer journey – from the first time they engage with your brand for early research down to the final click before purchase.
This change alone had a tremendous impact on the way we measure performance. After doing some initial tests myself, I found a lot of relevant information that gives me a clearer picture of what AdWords brings to the table in terms of performance and ROI. This change alone allows account managers to optimize based on more accurate results, making it easier to improve campaign performance. This is a perfect example of how machine-learning helps us adapt to the changes in consumer behavior, and gives us the tools to generate better results.
Universal Shopping Campaigns
2018 will also be the year where we will see an upgrade in automation tools for shopping campaigns. Even though Dynamic Search Ads are a great alternative for eCommerce clients, traditional shopping campaigns are the most powerful Google product to advertise a large number of eCommerce products. However, creating shopping campaigns from scratch can be time consuming and overwhelming. This is where machine learning comes into play.
Google will be launching a new type of campaign – Universal Shopping Campaigns. The idea is to replicate the workflow from Dynamic Search Ads for the shopping network. Campaigns created under this format will have automated ads, focused on increasing the total number of transactions and ROI. This will not only help account managers save time, but it will also help improve performance by optimizing the campaigns based on relevant consumer data.
Another new feature that will be launched this year will be the automated ad suggestions, that will allow account managers to immediately create new ad variations at the click of a button. As Google outlines, writing ads can be time-consuming and challenging. To help simplify the process, Google will now provide ad suggestions which combine the best of machine learning and a human review. This automated variations will launch automatically unless account managers decide to opt out of the program.
These new ad variations will be built taking into account top performing search terms, landing page content, and keywords within the ad group. Furthermore, these automated variations will be driven by performance, and will automatically perform A/B testing to optimize for conversions. This should give campaigns a boost in terms of quality of content and overall performance.
Bing has been a key player in the automation battle, by launching bots for local business in 2017. These bots, fed from machine learning and complex algorithms, have the capability of interacting with potential customers and complete tasks such as booking appointments or answering basic questions. Even though this started as a beta program for certain industries, we can expect for this feature to be rolled out across several industries in 2018. The automotive industry has already seen great success from this beta, by allowing bots to book test drives for potential customers. The level of personalized, real-time interaction is greatly enhanced by these bots, and we can expect this process to improve as the machine learning process improves.
Assistants and Voice Advertising
Advertisers are finding a new market for potential customers with the rise of voice technology and assistants. Google, Apple, Amazon, and Microsoft have all invested a lot of resources in developing personalized assistants capable of using machine learning to have conversations with potential customers. Amazon has taken the lead with the Echo Home speakers, and the rise of Alexa as the standard for voice assistants. However, Google made its 2018 CES presentation all about voice and the enablement of Google Assistant products.
Image courtesy of Geeks of Technology
How will this impact digital marketing? By leveraging these conversations with assistants as tools for customer enablement. We have already seen some initial tests where Google will be rolling out ads for Local Services through its Google Assistant program. Customers will now be able to ask Google Assistant for recommendations on specific services, and will also able to make calls directly from their smartphones or speakers. As the assistants interact with each user, machine learning will be able to personalize the experience to the point where it will be able to provide specific suggestions depending on the user asking questions. This provides tremendous potential for advertisers, and will definitely have a strong impact on digital advertising the coming years.
One of the areas where Google has made great progress through machine learning is automated bidding. According to Google, “Machine learning offers an automated, real-time way to optimize your bids to achieve more conversions or higher conversion values. It simply puts the power of data to work for you. Choose the metrics that are most important to you – things like target CPA or ROAS or maximum conversions – and machine learning can help you make the best bid for every auction.”
Image courtesy of Think With Google
Bing has also made great progress in its automated bidding system, and will soon provide a similar experience to the one provided by Google. Both advertising giants have identified this to be a key to improving the advertiser experience, and a quick win for account managers.
Conclusion: Embrace the Machines
Machine learning will continue to evolve, adapting to changing markets and consumer behaviors. Understanding the potential these tools have will allow account managers to deliver better results, provide them with resources to expand their marketing efforts, and the ability to identify new areas of opportunity for their clients.
However, it is important to highlight that even though machine learning will have a huge role in digital marketing this year, it is still necessary to have a human behind the wheel. Algorithms and systems can fail, especially if the data we are tracking is not accurate. The ideal digital marketing strategy will be a hybrid of highly-skilled PPC managers and machine learning doing the heavy lifting in the background. These constant changes in automation algorithms will allow account managers to spend more time evaluating strategy and creating content that no machine will ever be able to do. 2018 should be the year where we see a boost in digital marketing performance, thanks to the power tools that crunch large amounts of data to deliver better results.