It’s not easy being a digital marketer. The results of the job challenges are reflected by the fact that the median length of a CMO’s tenure dropped sharply to 26.5 months, and, from personal experience, I can say that I have seen how digital marketers are changing jobs even faster.
Pressure to generate results quickly and to prove ROI, in conjunction with the amount of technologies that need to be managed, often create a “marketing vertigo”. Marketing departments are today expected to manage:
- Social listening platforms.
- Analytics platforms.
- Advertising platforms.
- Multi-channel communication and distribution platforms.
Added to the complexity is choosing the right partner, connecting all the data points together – you know the drill. The point is that the world of digital marketing is getting more complex, especially with the increase of data collected and number of channels to be unified.
Back in January, I wrote a column predicting that artificial intelligence will be a hot topic in the tech space and start to be applied in digital marketing.
Are Campaigns and Journeys Really Automated?
Let’s admit it: often (very often), the way we start journeys and campaigns is by having an idea. We read about it, we see what the competitors are doing, and then we start setting rules that will automate it.
To refute the hypothesis (a communication idea from the marketer, which is being used to create a journey) and ensure that the campaign resonates well with audiences, there are two main ways we go about starting a campaign journey:
- Split test the campaigns (be it advertising or retention communication).
- Use control groups that will not be included in the communication and will enable us to know if the campaigns yielded real uplift.
Because there are so many data points, channels, and ever-changing user preferences, this process can take forever to reach an optimum result, if it ever does!
My questions to you are: 1) Can you really tell how long to wait between communication with the customer? 2) Can you tell which incentive works the best and will bring a customer back? How about, which channel will work best for my customer (not customers) and at what time of the day?
If you’re nodding your head, you’re not alone. The world of marketing today is very challenging. The point I would like to make, is that the campaigns are not fully automated and always need to be optimized based on the results of the split tests and control groups assigned to the campaigns. Driving results quicker in a fast-moving world is extremely challenging.
5 Examples of AI Marketing Already in Play
It is exactly in this complexity that machine learning technologies are coming to our aid. To drive this point home, here are five examples where algorithms and machine learning enables marketers to overcome the complexity, and drive better experiences to their customers:
- Content automation and monetization
In most online publications and newspapers you read today, content, both free and paid, will automatically be recommended to you based on your previous behaviors. This works automatically by analyzing aggregate historical user behavior and similarities between audiences in real time.
- Product recommendations
The browsing experience has changed dramatically since automatic product recommendations were introduced to the mass market. Today, this end-to-end solution is a reality on every e-commerce website that makes real-time web recommendations and follows up with customers via email and targeted ads. Can you really find what you like among Amazon’s hundreds of thousands of SKU’s without product recommendations?
- Calls to action:
Emerging technologies today allow marketers to automate calls to action. This becomes possible by analysis of huge data sets, where algorithms look for what worked best for customers in the past.
- Geolocation on emails:
It is already a common practice to let a dynamic email solution populate an email with a location map, add local weather and traffic information without the need to segment, and decide which content to send, this allows the software to decide on the right content in the right context.
- Real-time bidding and programmatic advertising:
Real-time bidding and programmatic advertising are fantastic examples of how software automates and applies rules to execute automatically, without human intervention. This allows for increased efficiency because ad buyers do not have to work directly with ad networks or publishers to negotiate ad prices.
And there are even more examples you can find here.
As it stands, there are already many resources that help marketers do their jobs more efficiently and drive higher revenues and engagement. However, there are still many more that need to be automated, and only by using some serious computation power, and thus, artificial intelligence, will marketers be able to solve these challenges. Soon, decisions such as time of sending, device of choice, which incentives to offer (if at all), which copy resonates best with customers, how long to wait between communication, and many more, will be automated with the help of cognitive-infused artificial intelligence.
The future of digital marketing is bright. Some of yesterday’s challenges were already automated with the assistance of algorithms, which were widely adopted by marketers around the world. Other processes that were once manual (and mostly guesswork) are being solved now by AI automation.
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