Artificial intelligence (AI), predictive analytics, machine learning and other “smart” technologies are transforming the IR space. Next generation tools are redefining best practices for IROs to stay ahead of the Street, by improving their program’s efficiency and success. Q4 recently hosted a webinar on June 21st, exploring the technologies most likely to impact the IRO’s role over the next 12 months. Our forward-looking technologists discussed how today’s biggest brands are utilizing technology to streamline IR outreach and dramatically improve ROI. Moderated by Darrell Heaps (CEO and founder at Q4), the panel of experts included Adam Frederick (SVP of Intelligence at Q4) and Amit Sanghvi (Managing Director of Europe at Q4).
Recent events in the global market, like MiFID II (and not to mention GDPR), are driving the rapid adaptation of new technologies in IR. The changing dynamic between the buy and sell sides is clearly leaving corporates in the middle. Predictive analytics through AI and machine learning is becoming increasingly critical to the decision making process of both corporates and the buy-side.
According to our industry experts, here’s how technology is evolving to improve the IR function in the year ahead.
Machines are transforming shareholder targeting
With MiFID II, there’s a growing need to empower the IRO with actionable insights. AI technology is bridging the targeting and discovery gap left by the declining support of the sell-side. While the idea of machines replacing this functionality might sound a little scary, Adam assures, “it’s not about machines replacing humans, but adding automation and efficiencies to processes that have been stuck in the stone age.”
He continues, “It’s about putting IROs more in control, by understanding the right investors and what’s driving investment decisions. In the past, targets were sell-side driven and there was no real expectation for ROI on investor outreach.” With AI Targeting, IR professionals can quickly determine the investors most likely to buy/sell your stock, as well as the quality of these investors, in terms of the underlying reasons why they’re attracted to your stock.
Machine algorithms “learn” to predict targets
AI targeting predictions help IROs better understand best investor fit. According to Adam, “old-line technologies tried to fit your stock into a mould. If you were a GARP, you were matched generically with growth investors. What’s unique about AI targeting is that it takes into account not only company fundamentals versus your peers (such as investor history and tendencies over time) but it also takes into account live trading data. It’s able to accurately predict investors in your stock in the near term, because it combines fundamentals with real-time trading information and equities markets. This delivers an increasingly sophisticated understanding of the real drivers behind investment decisions.”
With machine learning, you can train an algorithm to crunch huge datasets and thousands of models. The algorithm gets smarter over time by learning the changing dynamics in pricing and markets, as well as evolving trends in investor behaviour. This output arms IROs with insights about who is most likely to buy your stock, integrating a variety of fundamental and trading data points, to rank the most important factors for each fund and firm. Adam clarifies, “not only are you able to put your senior management in front of the best investors at the most opportune times, you’ll also know the key factors driving their investments.”
The rise of pipeline management for IR
For companies based in both Europe and North America, the decline of the quality and quantity of sell-side research and the value of the broker has resulted in a tremendous increase in direct requests from the buy-side. IROs are now faced with streamlining, strategizing and facilitating roadshows and meetings, as well as covering the stocks.
It’s more critical than ever to leverage technology in the same way that enterprise sales teams use CRM functionality. Amit explains, “it’s all about your pipeline. From qualifying your targets to aligning yourself with investors, and determining if they want to continue the conversation.” He explains, “an automated pipeline is the best way to manage your connections, weeding out those of most interest to your company. It arms you with data-driven insights, such as how many meetings it takes to drive investors over the line and what issues most interest these investors at any given stage. Algorithmic analytics can also notify you if you haven’t spoken to a prospect in a while. And briefing books can be created and customized on the fly.”
With technology such as Q4’s recently released Pipeline, “IROs can nurture the investor relationship from identifying targets (whether they’re served-up by an AI engine or the traditional way), to organizing and following-up on meetings, and finally reporting investments made.” This incredibly powerful tool enables you to see where the investor is at each stage of the process, quantifying their probability of investing and their potential buying power.
Amit summarizes, “With the sell-side disappearing, technology is not only filling the gap, but actually improving the entire process.” He cites the recent release of Google Voice, a virtual assistant that organizes and makes appointments. He envisions that in concert with AI targeting and pipeline management, an AI-driven platform will recognize when a potential investor is in your neck of the woods and arrange a meeting on your behalf. This kind of technology will do your grunt work and deliver data-driven insights, so you can focus on proactively leading the conversation and nurturing your relationships.
The power of predictive analytics and alternative data
With the prevalence of predictive analytics to understand an investor’s behaviour and patterns, alongside the rise of passive investment strategies, it’s natural to wonder if robots will ultimately take over the role of the IRO. When it comes to the changing role of IR and its growing automation, Adam looks to the recent Alternative Data Conference in New York City, “the conference has grown in a year from 30 vendors to 153, and from 300 to over 700 attendees. This is a clear indication that machines aren’t taking our jobs away, but making everybody more efficient.” He explains, “we’re already using AI in our everyday lives, from SIRI to Google Assistant and smart refrigerators. In this same way, AI technology is enhancing the IRO’s ability to work more efficiently and quickly.”
“Alternative data” is another trend that’s impacting buy-side investment decisions. Satellite imagery is used to see how many cars are in a mega store’s parking lot, to help determine a brand’s popularity. Car sales can be predicted by looking at car insurance, because insurance is reported more frequently than actual sales. And Q4’s Activism Alarm can identify activists driving alpha, by considering sentiment in the marketplace.
Amit underscores the IRO’s new need to understand the variables that go into algorithms for communicating with the buy-side. He explains, “while algorithms are essentially machines, they’re still being driven by people and your evolving equity story.” Darrell adds, “corporates should be aware that they may be unintentionally putting data out there that’s affecting both consumer and trading decisions.”
According to Adam, “harnessing all of this data is key to understanding the drivers behind decision making. This is what corporates and the buy-side need to increasingly focus on.” He believes that in the near future IROs will make most decisions based on huge data sets, delivered by machines that can crunch vast amounts of information into actionable insights. He concludes, “this won’t replace IR roles, but allow you to focus on the relationship side of the business and let the data drive you to where you need to be, and when.”
Missed last week’s webinar? Watch it now, on demand.
Marla Hurov is the Content Marketing Manager at Q4 Inc and blogs regularly about trends in IR and digital communications.