Source: information-age.com

There is no single moment when SaaS (Software-as-a-Service) arrived, because SaaS is a concept that involves numerous components. SaaS has evolved to the point where vendors and suppliers manage their own software and no installation is required because software is distributed instantaneously over the internet (via the cloud). Cloud computing allows businesses to consume computing resources over the internet as a utility — in much the same way they consume water or electricity. The SaaS-based cloud model now offers businesses significant efficiencies and cost savings, although different sectors have moved towards SaaS models at different speeds. In technical terms, SaaS relies on cloud delivery at scale, a minimum degree of widely available connectivity and enterprise-grade security.

And SaaS doesn’t sit still. As part of this continuous evolution, both artificial intelligence and machine learning are playing their respective roles as they become an integral part of the SaaS landscape.

Data

Historically, you distributed software to the consumers and customers, but you didn’t get the insight regarding how they leverage your software — which features are in use and those that are not. When providing your Software-as-a-Service, you also have lots of data and insights that can help you improve your service. You can therefore use this information to provide insight to your customers, you are better able to understand usage patterns and, ultimately, be able to use this data to give intelligent feedback. The SaaS era coincides with the almost ubiquitous concept of big data. And SaaS, which is able to leverage AI and ML techniques, has a distinct advantage over the software provision days of old — the software provider now has access to aggregated data from different customers, which he can leverage to build a better service.

Companies now hold significant volumes of data from customers all in one place. Artificial intelligence and machine learning enables a more automated means of mass data processing. Gartner defines big data by not only volume, but also by its variety and velocity.

Variety refers to the different media we use to represent data (beyond simple figures) and velocity is determined by the speed at which data is collected, analysed and implemented. The ultimate reality is that IT teams are dealing with increasing amounts of data and a variety of tools to monitor that data – which can mean significant delays in identifying and solving issues. And with the whole area of IT operations being challenged by this rapid data growth (that must be captured, analysed and acted on), many businesses are turning to AI solutions to help prevent, identify and resolve any potential outages more quickly.

Marketing is particularly well placed to leverage AI and ML techniques. The data SaaS companies collect need to be relevant and recent. The more up to date the data is the more efficiently it can be implemented. Large corporations can access data collected through loyalty programs and cross-promotional activities, and smaller businesses can acquire data through customer surveys, online tracking or by competitor analysis. AI/ML solutions can certainly be a golden opportunity for businesses to broaden their perspective on potential customers.

Automation

For B2B customer-centric businesses, AI allows more functions which may previously have had a manual component to be automated – for example, it enables them to automate many customer experience processes, such as training and onboarding, marketing campaigns and ongoing customer service. Artificial intelligence essentially aggregates large quantities of data for example, customer data and filters it into automatic processes. Customer service AI platforms like chatbots, which respond to and troubleshoot customer inquiries automatically, enable customer service departments to take on additional inquiries. That’s great news for revenue retention and churn reduction, as customers tend to show a heightened interest in a purchase, following a positive customer service experience.

Likewise, negative customer service experience is a good way to get rid of customers. Supplementing AI technology with your customer service team can target the seamless cross-section between convenience, problem-solving and human experience — a typical example here could include using machine learning to automate aspects of customer service (especially self-serve).

The main UX challenge of SaaS is remoteness. Artificial intelligence can help to alleviate that sense of remoteness whilst delivering a more satisfying experience to the customer. There has been a lot of scaremongering with respect to machines taking jobs from humans, and that AI will bring about automation in practically all walks of working life — however, the more likely scenario is that AI will deliver most value when it is deployed in conjunction with human beings. SaaS can, and should, manage those interactions which can be handled automatically (classic SaaS) and those which require human intervention. AI-augmented human interactions can drive SaaS interactions too.

Personalisation

Natural language processing and machine learning allow SaaS companies to massively boost personalisation. And, let’s face it, consumers have become much more demanding, wanting personal experiences that are tailored to their specific needs. AI can help here by providing analysis of your users’ previous actions, giving you actionable insights into their preferences and interests. This allows you to configure user interfaces providing that all-important tailored experience. Remember, If they don’t receive it from your business, they’ll go elsewhere to find it.

AI also helps consumers during the adoption of new features – a big help if you are installing more complexity on your consumer app or interface. And, in addition to helping businesses deliver more individualised email campaigns for example, AI can support features such as voice control in order to more accurately track user behaviour (back to that personal UX).

Artificial intelligence and machine learning have disrupted, and will continue to disrupt, industries up and down. Their long-term application has endless opportunities. It’s no different for the SaaS industry as it looks toward the future.