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Use Algorithms to reach investors: 7 steps to introduce AI to Investor Marketing

Huan Koh provides a simplified look at how artificial intelligence (AI) is applied to automate investor relations programs from start to finish

· algorithms,investor relations,marketing,digital marketing

Artificial intelligence - once only accessible by big-name, ambitious projects like Google's self-driving cars and Netflix - is not finding its way into everyday business. In Investor Relations, Advertising and Marketing specifically, Companies might not be completely changing their public relations, marketing and corporate development programs to make from for AI just yet, however many are getting a feel for what it is by experimenting with Adtech and Technology-adaptable companies that handles isolated tasks like Online Advertising, Social Media Marketing, Investor Campaigns and Investor Recommendations.

The next wave of AI in Investor Relations and Marketing will be defined by automation of complex, multi-step processes - not just one-off aspects of a larger campaign, but the entire brand awareness and brand equity work, which many small-cap list-cos must do. For these companies, this will relinquish the control, trusting technology to come in and quickly understand processes comprised of numerous tasks, channels, people and procedures, an entire process that continuously and iteratively gets smarter and more accurate whilst being compliant.

Before handing over the reins, it's helpful to understand how AI in Public Relations and Investor Outreach works and how entire human thought processes can be converted into numbers and formulas. For all its complexity, here is a simple look at 7 steps to introduce AI in your online marketing campaign, one that can be automated entire and be holistic to a specific audience segment , from start to finish.

1. Continuously observe and unravel every step in the process

Creating artificial intelligence for "self-driving" investor outreach technology is not so different from creating AI for self-driving cars. In the case of the car, the machine must know how close it is to other cars, how and when to make a turn, how to ensure its in the right position after a turn, when to hit the gas pedal to go faster, what the road conditions are, and so on, all without telling the driver what to do.

Like driving a car, many of the thought processes that go into the day-to-day execution of the investor outreach programs also happen automatically and sometimes on a subconscious level. Transforming these subtle processes into tangible series of algorithms mean isolating logic and reasoning that humans often aren't even aware of when they are engaging in it.

This begins with the acute observation of Investor Relations Officers, Digital Marketers or Corporate Development Managers as they execute each step of a process over and over again. Often things seem quite trivial - like determining which content to put in today's social media post, which words work best for Twitter, what image to put in Facebook, how much budget to spend on each channel, when to spend the budget, which keywords do the target audience resonate with - all are critical parts of a larger process.

2. Understand why human IROs and Marketers make the decisions they do

The Machine doesn't need to know what steps to take; it needs to understand why each decision was made, whether its based on experience, logic, reasoning or simply knee-jerk instincts. This requires asking Digital Marketers and IROs to describe their decision-making process, which can be difficult considering that, as we already know, they sometimes don't know what motivated them to make that decision.

Why did you not target these keywords but those other ones? How did you decide your bid size? Say you see a set of audience engaging well and you increase the budget by 20% - why 20%? What is the best time of the day to send content out to this audience segment? Then what about the other audience segment?

3. Teach technology how to understand abstract information, such as Content creatives

Data in the form of words and numbers are unquestionably the domain of AI. So, what happens when technology is asked to process and make decisions that are more creative in nature?


For a human, understanding why certain images and text make more sense as a first interaction with a consumer rather than as a secondary or final interaction is almost second nature. A machine, on the other hand, needs to be told (or programmed) with this knowledge in order to be able to judge images and text, determine where they should appear along the journey, and not have to rely on humans to make these decisions for it.

4. Program the AI to do things humans can’t

AI can process millions of data points in a minute. And every minute, there are several variables — or combinations of variables — that will influence an exponential number of outcomes in any process. “If I do this, that will happen. But if I do that, this will happen.”

In an Investor Outreach campaign, maybe this is choosing which of the available headline options is best to use on a specific channel in this exact moment considering its specific audience, their known level of familiarity with the content and so on. The technology must be able to predict the performance of a single headline in relation to all others and specifically under these conditions.

5. Make individual tasks work together as a holistic system

One major problem with multi-step processes in businesses today is that more often than not, different parts of the process are handled by different people. Take an Investor Outreach Campaign where one person is responsible for Content Generation, another for research reports, another for Videos and GiFs, another person may be responsible for distribution to Social Media, another for online advertising, another for writing the newsletter emails, another to engage with the Investors. These people each receive different insights, which they use to calibrate their respective efforts.

It's very rare for digital marketing teams to use analytics from one channel to inform decisions on other others. Even if the intent was there, it will be difficult, if not impossible, for humans to keep up with and apply those insights across channels. AI, with the help of data visualisation, is suitable for using any data from any channel and applying it holistically, understanding the interplay of those discrete functions to properly relate and sequence the many moving parts.

6. Introduce checks and balances so the AI doesn’t go out-of-line

Perhaps one of the most important things for AI users to understand is that their technology isn’t going to go out of line on them. That’s why introducing checks and balances are so important. These built-in rules ensure that the AI doesn’t make decisions that are so out of sync with what a human would do in a given situation that the technology is at cross-purposes with the people or organization it’s serving. This is the imagined scenario that allows idiotic robots to take over the earth.

This is especially true when it comes to any decisions related to budget. It could be, for instance, that algorithms predict that you should triple your spend on a given ad campaign. In this case, checks and balances would kick in to reflect the fact that this is unusual behaviour and a choice that a person would approach with caution even if the rational logic is there from the machine.

A human in this position would want to understand the market conditions surrounding the suggestion, as well as the potential outcomes, so this layer of human precaution must be reflected in the machine too.

7. Make sure the AI works autonomously and its scalable

Finally, all of this must happen autonomously. Artificial intelligence is not interested in doing the same things as us flawed humans at the same pace and scale. Good AI understands everything that goes into a process and then does it way better and on a far greater (impossible for humans) scale. If a human can realistically manage a campaign for 3 listed companies using 10 different audience segments and monitoring 500 keywords, artificial intelligence wants to manage 30 companies, 100 different micro-audience-segments and 200,000 keywords.

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