Blog: Intelligent Automation in Marketing
When you think of the term “automation”, large industrial machinery and robots carrying out repetitive tasks come to mind. It is basically a hack that humans discovered in order to get out of doing mundane tasks. With time, humans began pushing the boundaries of automation — can it do more things? Can these tools be trained to think like humans too? This is when the term artificial intelligence started becoming a part of our lexicon. Machines that were initially designed to process large amounts of data and infer patterns, started to make predictions based on these patterns and are now moving towards making decisions for us as well.
How has marketing benefitted from automation?
One of the largest applications of automation has been marketing. This includes a suite of tools that lets you execute your media strategy without having to click “publish” on all your posts or “send” on each of your emails. When a business grows, maintaining a 1-to-1 relationship with all customers is not just mundane or boring, it’s impossible. In the same vein, artificial intelligence in media automation is revolutionising digital marketing as we know it — ushering in the era of intelligent automation in marketing.
Right now, our digital lives are divided into distinct silos — Facebook, Twitter, Instagram and the like — all have their own unique algorithms and are largely insulated from each other.
There is no information exchange between them, but consumer behaviours are starkly different on each of these platforms — resulting in isolated decision-making on these platforms. Although there has been a fair amount of automation, these efforts have mainly been on the media buying side — buying space on channels for product placement to ensure that the product gets the most eyeballs. There is still much to be desired in terms of automation on the media planning side where marketers optimize each of their channels to come up with the best strategy to place their products. This is especially important now when there is a tremendous amount of data being created outside of these digital platforms. There are now too many touchpoints where data about consumer behaviour is being generated and simplistic rule-based automation cannot handle this. As the world and our behaviour shifts away from simple black and white, a more heuristic approach needs to be formulated to navigate the grey.
To illustrate this, let’s take the example of an artisanal burger joint.
Traditional wisdom tells us that people eat three meals a day — breakfast, lunch and dinner. Simple marketing automation based on this data would dictate that the media planning be done around this — increase the frequency of advertisements around meal times. However, in today’s world, people post pictures of their food on social media platforms — the most popular being Instagram. So, when people are browsing their Instagram feeds, usually after work hours between 5 pm and 8 pm, they come across these photos. The burger joint sees a surge in social media engagement and food orders, despite this being outside of traditional meal time. It would be impossible to imagine this and all other scenarios, and then hard code rules for all of them. This just one example of behaviour that is outside of the norm — imagine all the different people and habits that we could discover and take action based on using intelligent automation. Training a machine to think like us and teach it the logic of why we make certain rules would not only help companies deal with the sheer volume of such consumer behaviour data but also predict some of it to an extent.
This is just one of the use cases of intelligent automation and the full extent of its power is seemingly boundless.
Larger companies have already started employing these practices and while, initially, there are many learning curves to navigate, we are well on way to seeing large scale adoption. While the capabilities of this technology are currently at its nascent stages, it is well worth the effort considering the world is moving ahead at breakneck speed. In order to keep up, we don’t just need faster machines anymore — we need smarter ones.