As we know, consumer journeys are no longer a linear process; they have become a far more complicated process that consists of multiple channels and touch points emerging through.
With user behaviour continually changing, consumers are becoming more demanding and hungry to learn things.
Google is diving into various industries by solving their problems through machine learning. Now machine learning is not limited to a few industries, but instead is breaking through numerous industries.
At a recent event I attended, Academy on Air event by Google, the focus was on Growing with Machine Learning (ML) where I got more insights about the different uses of ML.
1. Imagery Recognition
Google helped companies with extensive real photo database such as the New York Times, in archiving their photo database through machine learning. Through this, they would have detailed information about the photos such as the descriptions, location, time, date and person who clicked them. And this database would be accessible to everyone who has been granted permission to view it.
2. Movie Prediction
If a production house is able to predict the kind of movies that would be well accepted by the audience, then it would help them immensely in producing the best kinds. Predicting the movie's performance before the shooting begins is one of the key issues faced by the production houses as they want to know if the movie will be accepted by the audience. And now through machine learning’s granular analysis (analysing the audience's' interest, intent and behaviour patterns), production houses are able to identify the number of people who would be interested in their movie. basis the audiences’ interest, intent and patterns.
3. Preventing Deforestation
Locals and the forest department face this major issue of deforestation especially due to loggers. Audio machine learning allows them to monitor the sounds coming from the forests and analyse the auditory real-time. With any difference spotted, then it would be reported to the respective authorities and this way deforestation can be prevented.
4. Smart Compose on Gmail
I am sure it’s not a surprise when I talk about this one because by now everyone would have noticed how Gmail has leveraged machine learning in helping their customers write emails. The Smart Compose suggests small and brief responses for your emails based on your previous user behaviour and content, and with just a single click, it will be added to your email.
For those of you who do not have technical backgrounds, you must be wondering what is ML? This is an alien term to me although I have seen and heard this being used almost everywhere.
ML is a simple way of letting the machine learn and apply its learning by giving us, the consumers the appropriate result.
Essentially, you don’t need to tell the computer how to perform a task, it will perform the tasks internally basis tons of data and provide you with the end result. And this will help solve problems, especially complex ones faster than before and reduce the time consumed previously on them.
For example, in Google Photos once you have tagged a person named ‘Kate’ in a particular photo, Google will identify ‘Kate’ across all other photos you upload which makes searching for all of ‘Kate’s photos much easier subsequently.
Google has a vast field in front of it to experiment ML in all its products. Google has already started using ML in Ads wherein they are trying to ease the brand advertiser's task for reaching out to the right audiences and driving valuable results. Below are some insights on ways to grow on Google Ads with ML:
Google is providing advertisers with various methods of targeting such as Keyword targeting, In-market audiences, Custom Intent Audiences. Using the best and the right type of targeting for your campaigns will help in achieving better results .
For example - Audiences looking for properties for rent or sale
Source: Google Ads
We at Happy Marketer, consider targeting as one of the important factors, as it helps us as an advertiser to be more accurate while choosing whom we want to show our ads. For example, Google Similar Audiences understands people with similar behaviour to the customer and helps us to target audiences when they are searching for our products. Similar audiences assist us in expanding our reach to more audiences who are looking for what we want to offer.
With machine learning and intent signals (data generated when people are performing behavioural actions online, such as visiting a page or making a booking), Google has made it easier for advertisers to show the right message to the right customer through various ad formats such as Responsive Search Ads, Dynamic Search Ads or YouTube MastHead .
For example - Responsive Search Ads
Source: Google Ads
- Bidding: Leveraging machine learning adjusts the bids in real-time as per the objective will help in every auction by setting the right bid for the right audience
- Attribution Model: Machine learning helps in evaluating the touch points that affect the consumer journey the most and change the attribution model which helps in optimizing the users' conversion path.
Happy Marketer also uses Smart Bidding which again works on ML in identifying how much we have to pay for each advertisement during the consumer journey. This strategy works on the machine’s intelligence and learnings basis the performance of the data
Machine learning is one of the fastest growing technologies and soon it will be an engine towards global growth by being a part of every industry. The faster we accept it and implement it in our business model, the better the results we would be able to achieve.