Go Automatic In A World Of Stick Shifts.

If we go by what Mr. Arnold Schwarzenegger once said, way back in 1984, machines are going to always keep coming back into the picture. Whether we harness the fear of that classic line “I’ll be back” or make it work for us, machines are here to stay, no matter what and today we are living in a world that was only imagined back then!

We have come a long way since the invention of the worldwide network of computers and the exponential explosion in data storage and computation. Today, thanks to Artificial Intelligence (AI) and Machine Learning (ML), programs/machines can learn from each other.

As of February 2020, MIT stated that one of their research teams have identified, with the help of artificial intelligence, a new drug that can kill many of the antibiotic-resistant bacterias that have been plaguing us for decades.

Looking at the bigger picture, the global machine learning market is projected to reach a total market value of US $1.3 Bn by 2026 which translates to a CAGR of around 22.4% from 2017 (US $2.5 Bn). (Source PR, MarketWatch)

In all the splash and uproar created by machine learning, the digital industry is the one that is enjoying an immediate win. Companies are moving into collecting (either primarily or from secondary sources) millions of relevant datasets.

They feed these data sets to teach machines, like we teach a three year old child. In fact, it might just be relatively simpler. They are introducing huge chunks of data over a long period of time to algorithms that have the capability of viewing, understanding, analyzing and interpreting the data to bring out appropriate and beneficial outputs.

Focusing on digital advertising, the industry is slowly understanding the criticality of machine learning in their day-to-day activities. Currently, a lot of business functions are being run manually.

The ever increasing demand to understand marketing activities on a granular level has made manually operable activities redundant.

The diversity of teams and workflows such as inter-department communication, thousands of datasets, requests and instructions, etc. it is becoming extremely difficult to cope with the dynamic demands of sales, operations, financial aspects of the digital advertising business. Right now, all stakeholders are running operations on disparate tech and managing multiple sources of revenue.

1 – Identifying User Segments

The one challenge faced in the advertising industry is to understand who should be seeing our ads along with figuring out the other parameters like time, frequency, etc.

Machine learning has the ability to understand the requirement and direct you in the best direction using relevant data collected over years. Usually, manual effort is required to understand inventory and set specific targets for your campaign that you think works best.

Machine learning can be used here to override the manual effort required to suggest placements, delivery method, targeting and a lot of the other parameters that are ideal for the campaign. This will ensure successful implementation of campaigns, and you will be able to gauge all configurations and extract maximum returns with minimum effort. 

As a former Conversion Rate Optimizer, I realized early on that this is a very important aspect of monetization in the digital industry. Although I worked mainly with ecommerce businesses, the logic really applies to almost all digital operations.

Understanding the right kind of audience was always an important way of structuring optimization plans to make sure we are headed in the right direction. With machine learning, the work of a human to identify huge datasets, analyze and conclude on the best audience to target is going to come down to efficiently using data and removing manual effort.

2 – Identifying The Right Buyer

A vital element of a successful ad campaign is the placement of ads itself. It is important to have the ability to target the right people for the right things. You could have the best marketing ideas and the best creatives but if you end up showing an Igloo to an Eskimo, you won’t be able to close that deal. Well, unless you are already optimizing that sales pipeline!

3 – Reporting Intelligently

Once your ad campaigns are LIVE, you need to collect, view and analyze reports that can give you actionable insights on further optimizing your campaigns. Even though the analyzing is mostly done manually, collecting the data sets all in one place is a huge task considering the possibilities of multiple teams being a part of one string of operations at a time. ML’s work here is mainly to conduct reporting in a way that can be injected back into the system to optimize the way we work and gradually reduce effort and increase returns.

4 – Metric Based Suggestions

Each of your campaigns always have a specific purpose. Every campaign’s end objective is to generate optimal returns for the crucial metrics selected for the campaign.

The universal truth is that campaign performance is dependent on your inventory, ad placements and metrics. Machine learning can help you identify the most optimal method of gauging results.

Hence, instead of making changes or regretting in retrospect, machine learning will enable you to identify and set the right metrics for the right kind of campaigns from the beginning.

It isn’t wrong to assume that businesses are slowly going to incorporate machine learning in their daily operations to drive efficiency. As organizations acquire larger sets of relevant data, their processes and results will become refined.

I believe that holding a repository of all ad operations is proving to be difficult today. But the industry is seeing trail blazers starting to work in a way that wasn’t thought of before making the jobs a little easier.

Machine learning has long made its move from research to practical application today. I think it is safe to say that content might just have been dethroned by this ‘artificial heir’ that is the powerhouse of algorithms. 

What do you think?