I recently had room service delivered by a robot. And not just any old robot. This was no face-less assembly line machine. She–yes, she–had a name, was wearing a neck scarf, and presumably knew how to use an elevator.
While my run-in with artificial intelligence was both bemusing and slightly unnerving, it represents the innovative approaches that surround us. Automation is now a staple of organizations that provide services in the supply chain, health care, or air travel industries in many countries. Our global network relies on it. And so does the world of SaaS.
You’re almost certainly familiar with some level of automation in your SaaS business already. But what about AI? And one step further, how about machine learning and deep learning? These sound like terms from a sci-fi movie but in reality, this technology is here. And it’s not just knocking on the front door. It’s breaking it down.
The important question is how you can adapt to prepare for this radical and ever-changing landscape, and what the role of automation will be in the future of your business.
I’ve gathered the thoughts and opinions of 6 prominent SaaS leaders to try and give you an answer.
1. Automation shouldn’t feel like a tool, it should feel like a business partner
At least that’s what Jason VandeBoom thinks. He’s the founder and CEO of ActiveCampaign, a global SaaS unicorn and leader in Customer Experience Automation (CXA). ActiveCampaign is an icon in innovation and is built upon automation being at the very core of the SaaS industry.
So what does he see as the biggest challenge in the future of automation?
The future of marketing automation is about individualization
In the past, automation was focused on time-saving at the sacrifice of personalized experiences. For example, the benefit of being able to send thousands of emails at once with little human input overshadowed the desire to individualize them—at least any further than the contact’s name.
Yes, you could run split tests. Or customize the workflow, timing between emails, and content according to customer segments. But even then, we’re still talking about hundreds or thousands of people getting the same experience.
This is where AI comes in. It can make every customer experience truly unique.
Instead of someone needing to build out thousands—or tens of thousands—of different workflows to get that granularity of personalization, AI can do it at scale by using machine learning (ML) at the contact level to analyze consumer behavior and individual attributes.
So, what’s the impact of AI-assisted personalization on a SaaS business?
Well, it’s huge. These new technologies foster a shift in thinking—from static to dynamic. From optimizing for one overall workflow or funnel that performs best, to something that’s individual to every customer.
From a marketing perspective, this helps you optimize for transformative events and conversions through things like predictive sending, cross-channel predictive content, dynamic routing, and recommending timings for human touch. Among other benefits, conversion rate optimization (CRO) increases revenue.
2. We’re already in a new era of AI-centered SaaS
Pekka Koskinen is the CEO & Founder of Leadfeeder, a B2B lead generation software. He points out 2 ways that automation, artificial intelligence, and machine learning are the present and future of SaaS.
First and foremost, let’s really drive home the importance of individualization.
With the sheer amount of “big data” businesses collect, we assume they’ll offer us personalized products, experiences, and content. In fact, research by Salesforce shows 3 out of 4 B2B customers expect suppliers to predict and pre-empt their needs before they initiate contact. Talk about having a crystal ball to see into the future.
Traditional information application software may not have the gusto to make use of big data. But AI uses extremely powerful pattern recognition and analysis to help your SaaS business meet those expectations.
Efficiency and support
To scale your SaaS business, certain processes must be automated. Things like billing, subscription management, and accounting will quickly generate enough work for a small army if you try to handle them manually. Software solutions that automate these areas reduce operational friction, enable scalability, and are a vital part of your digital technology stack.
The best example of AI in customer support is the rise of chatbots. By handling huge volumes of inquiries, they reduce support team headcounts and save SaaS businesses a lot of money. They can even qualify leads and optimize the sales process.
According to business leaders, chatbots have increased sales by an average of 67%! Still not sure whether you should implement chatbot technology? You should also know more than 65% of those that adopted chatbots are SaaS businesses.
3. Breakthroughs in AI have a transformational impact on SaaS
Artificial intelligence is so intrinsically linked to SaaS that it’s not just a part of its future: it is shaping it. Advances in technologies like AI and machine learning are a catalyst for some of the most disruptive SaaS startups.
One such startup is Tractable, a software company that uses AI to assess vehicle and property damage. Tractable’s incredible growth (from foundation to Unicorn in just 6 years) can be attributed to its ability to scale human expertise—in this case, AI that mirrors the role of human insurance appraisers.
Its CEO and co-founder is Alex Dalyac. He says, “Thanks to our academic breakthrough-driven timing, we were still the only ones in the industry to master deep learning. That gave us a huge advantage when it came to getting our first training data partnerships because we were the only ones who could wield this technical magic.”
The world of SaaS is changing fast and those that can’t adapt will fall by the wayside. If you want to exist in the digital future, AI and machine learning must be an integral part of your technology stack. You must be ready to harness the power of new and emerging technologies.
4. Predictive analytics unlocks the value from your existing goldmine of data
“SaaS businesses have no lack of data about their customers,” explains Craig Soules, Founder & CEO of Natero (now Freshworks Customer Success). “The challenge is making use of this data to identify customers who are not realizing the value they expect from a solution, as well as those that are ripe to convert from trial or buy more.”
To make things even more complicated, the volume of data that comes flying in is huge. Thousands of data points are constantly being created across multiple systems, like your CRM system, billing system, and support ticketing system. Not to mention customers’ usage data.
Instead of relying on specific and singular pre-determined triggers, such as 14 days’ account inactivity, machine learning evaluates thousands of factors to determine things like churn probability. This helps the customer success team be more targeted, proactive, and effective in their efforts which creates a better user experience and maximizes retention, engagement, and lifetime value (LTV).
5. AI is integral to release management and could avert disaster
Given the competitiveness of the SaaS industry and the rampant pace at which it moves, there’s often a lot of pressure to deploy software releases… like, yesterday. But rushing through code might mean errors go undetected. This could cause bugs, crashes, or other bad news further down the line.
Perhaps most famous is the case of Knight Capital. A release was rushed through, and a simple human error in the code caused the market maker to purchase stocks worth around $7 billion within an hour. Yikes.
Luckily, the lessons learned there led to developments in AI that are changing the way we code. Microsoft has even created AI that writes code by itself! But in a more practical sense, its tool Code Defect AI uses machine learning (ML) to analyze code, spot potential problems, and suggest how to fix the errors.
As Deloitte analysts (and authors of “AI is helping to make better software“) David Schatsky and Sourabh Bumb explain, AI can “spot bugs while code is being written, while they can also automate as many as half of the tests needed to confirm the quality of software.” Watertight checks like this slash deployment times, let you sleep at night, and give your DevOps teams more time to focus on other tasks.
6. Automated self-healing security could be the answer to SaaS security problem
Avoiding, identifying, and resolving any possible outages or disruptions quickly is paramount to preventing downtime and security leaks as Thomas Donnelly, chief information officer of BetterCloud explains. But for many years, cloud security has been a persisting issue in SaaS.
It seems automated solutions and AI promise a brighter future: security services that can replicate and learn from new threats automatically. This technology finds a way to heal itself—firstly, it detects vulnerabilities. Then, it remediates them and checks them. This is an automated cycle that requires multiple platforms to work together.
For example, Oracle’s Anomaly Detection helps to flag critical incidents, resulting in faster time to detection and resolution.
Automation is instrumental in running a successful SaaS business. It reduces high-volume manual processes down to faster, simplified automated ones, saving time and money. For example, you could save $22k or more every single month by implementing automated billing software.
And despite a healthy dose of robot mistrust, artificial intelligence isn’t intended to replace people. The opposite is more true: it enables employees to offload data-handling tasks so they can use their skills on creative work.
Plus, AI can help you gain a market edge by deploying faster than ever and personalizing marketing and sales efforts on a more granular level than seen before. Smart business leaders throughout the SaaS global network recognize automation, AI, and machine learning as intrinsic to growth—not just for the next decade and years ahead, but into the future as far as we can see.