Artificial Intelligence (AI), powered by machine learning principles has long been a driver of fiction. But with recent breakthrough in computing power and availability of massive data have made some of those fiction ideas into a reality today.
AI and Machine Learning are one of the big trends and it should be no surprise that SaaS is very much a part of this “big trend.” A couple of weeks ago, Google CEO Sundar Pichai told an audience at a Recode-sponsored event that for humanity, the impact of artificial intelligence could be “more profound than, I dunno, electricity or fire”. In this article, I will explore how SaaS companies can use AI/ML going forward.
The SaaS Market today:
A report from IDC shows that the SaaS segment, which makes up 68.7 percent of overall cloud market share, was the slowest-growing segment of the cloud market with a 22.9 percent year-over-year growth rate last year.
Venture capital funding, usually an indicator of how “hot” investors view a market to be, has been found to be on the decline when it comes to SaaS startups. TechCrunch attributed this in large part to market saturation and the fact that those newbies seeking funding are often trying to compete with large, established players.
While the SaaS market is still expected to show some growth, it’s expected to be at a lower rate than we’ve seen previously. SaaS markets are maturing and it seems that those who are going to come out as winners will need to be onto the next “big thing.”
AI and machine learning can help SaaS create a more strategic position
All big player like Amazon, Google and Microsoft in the market are announcing offerings that integrate AI. Oracle, another big player in SaaS market said it is placing a big bet on AI and Machine learning to overtake salesforce in SaaS. These are a solid indicator that that AI and machine learning could be the next step in differentiating a SaaS and helping it to carve out space in the market.
How SaaS use AI and machine learning
SaaS is picking up on the trend for AI and machine learning and investment in this area is on a continuous rise. Below are some of the SaaS solutions where machine learning is playing a strategic role.
AI can bring the option of hyper-personalization to SaaS, something which we have already seen in mobile apps in particular (looks at Starbucks’ “My Starbucks Barista”). In SaaS, natural language processing and the ability of AI to learn from user’s previous interactions can help configure user interfaces so that they cater to the individual.
For example, if you think about any SaaS without AI capability, adding more functions or features tends to cram the user interface and add complexity for the user. AI can help not only with personalization but with the easier adoption of features.
Automation is demonstrated in many different ways in SaaS with AI baked in. It can take over where previously manual functions were required, for example, in the case of chatbots that help to provide users with answers to basic questions.
Automation reduces costs because they eliminate the need to hire additional people to handle more work. A bot replies to login reset questions with an automated response in a link to a knowledge base, freeing customer support reps to focus on more challenging questions.
One of the challenges for SaaS is always keeping an engaged customer base from a remotely operated perspective. It can be difficult staying on top of customer service requests and ensuring that every customer gets a good experience. AI can help with that by reducing that remoteness and stepping in to supplement human effort.
For example, there are already several examples of apps (Verizon, banking apps etc.) where chatbots answer questions up to a point, but refer users to human operators where necessary.
3. Predictive analytics
There are many potential ways in which AI built into SaaS can leverage predictive analytics to create a better user experience and/or help to arrest churn for SaaS. For example, machine learning can help to predict user preferences or behavior, then perhaps trigger alerts or actions when it appears the user is disengaging.
4. Product Search
When the user searches for a product, how do we find the best results for the user? One factor used in product ranking is user click-through rates or product sell-through rates. In addition, user behavioural data gives the link from a query, to a product page view, all the way to the purchase event. Through large-scale data analysis of query logs, we can create graphs between queries and products, and between different products.
We can also mine data to understand user query intent. When a user search for “Toyota Prius”, are they searching for a new car, or just repair parts of the car? Query intent detection comes from understanding the user, other users’ searches, and the semantics of query terms.
5. Release Management
The consequences for a SaaS rushing through the code and deploying early, only to have a crash or bug that affects all users can be very costly. Reputation and potential liability issues abound, yet being able to deploy quickly can be a distinct advantage. If you’re in a competitive market, the difference between leading or lagging can be if you are first to reach people.
AI is a game-changer for SaaS developers because it can augment their own coding abilities by providing the necessary checks that the coding is good. Deployment can be cut down from months to a very short time when AI can verify that the SaaS is built to scale to thousands of users.
Docker is an example of this, checking and testing code for quick deployment. Look out for further developments in this area — Microsoft and the University of Cambridge are working on teaching AI to write the code itself.
6. Enhanced Security
Cloud security issues are always a hot topic among SaaS, and traditional security measures tend to be static, perimeter devices which require human input to update for new threats. AI gives SaaS the possibility of security services that can replicate and learn from new security threats automatically. Oracle has recently added machine learning and AI to their cloud security services, facilitating automated threat detection.
AI represents a new generation of SaaS products and the opportunity to embrace new ways to gain a market edge. We are seeing many of the bigger players move into this space already and industry experts predict it will continue to grow.
By and large, the most frequent applications of machine learning in SaaS today are efficiency applications — automating the high-volume manual processes and reducing costs. Consequently, if you looking to build a machine learning based SaaS company, find a really expensive internal process and automate it.
Are AI and machine learning considerations for your SaaS?