Four Game-Changing Impact of AI and ML in the SaaS Industry
Tags :AIBig DataMLSaaS
Over the past few years, we have been observing the global evolution of the SaaS market. SaaS solutions are today spanning across horizontals and verticals, creating products for both individual and enterprise, and segmenting their markets to specific customer size. Analysts believe the market will grow at 12% every year and by 2022, the volume of investments will reach a staggering $141 billion.
However, there is but one common underlying factor – the foundation of all these solutions is built on Artificial Intelligence (AI) and Machine Learning (ML).
AI and ML have been the two most influential factors to impact SaaS businesses and have become the fundamental constituents of the SaaS landscape. With AI’s infusion, the industry today can see business categories that were non-existent earlier. AI has made several enhancements to the SaaS solutions that are currently driving the market growth.
Following are four areas, where we can see game-changing impact of AI and ML in the SaaS industry.
Consumers today are looking for personalized user experiences from any product or service – be it B2C or B2B. A one-size-fits-all template is an obsolete concept, especially when it comes to SaaS-usage. For instance, in general practices UIs are first designed taking into account the designer’s idea of the product.
If the design is not upgraded sooner or later, it will lead to the discord of options and redundancy. AI and its subset like ML and NPL (Natural Language Processing) is making it possible for solution providers to design tailor-made user interfaces and user experiences (UI/UX).
Applying AI, the customer’s usage history data will reveal patterns that will help the product teams to understand their usage behaviour and also their preferences. Consumers today have the choice of multiple providers to switch to, in case their needs are not specifically served by the solution.
The SaaS industry’s growth can be credited majorly to the accumulation of Big Data – a massive pool of untouched information for companies. But the data volumes are ever-increasing, and companies are struggling to manage the same. AI and ML together can enable a much more automated means of mass data processing.
With the help of automated data analyses, businesses can understand their product usages trends and identify areas that demand further improvement. This helps not only businesses to prevent and resolve any potential outages faster, but also to identify redundant features that can be either discontinued or changed. Businesses are better equipped to understand customer usage patterns and can use this data to give intelligent feedback.
SaaS offerings are also being popular because they provide a greater degree of automation, again largely enabled by AI. AI essentially aggregates large quantities of data and filters it into automatic processes. Automation allows companies to respond to customer needs automatically, with less reliance on human resources.
Chatbot is a great example here. A chatbot is a computer program designed to provide automated replies to preliminary queries of customers visiting a website or an app. It dismisses the need for a human assistant round the clock. Hence, companies can respond and troubleshoot customer inquiries automatically, making customer relations management faster and efficient. It can be an extension of the larger customer service team. An example could be, the banking services using AI to register the requirements of the customer before connecting them to the appropriate department, where they may have a person-to-person interaction for further services.
However, chatbots are not expected to replace human interactions completely. AI will add more value when it is used in conjunction with human-centric services. Together, they can drive the SaaS model further.
Marketing activities today are leveraging AI and ML techniques. AI principles and applications are directly applied to marketing concepts for targeting, acquiring and retaining customers. Here comes the term Marketing Technology (MarTech). MarTech can be a range of software and tools that assist in achieving marketing goals or objectives, and is a growing industry, both in size and scale. When MarTech software adopts AI applications in order to boost the RoI and effectiveness in SaaS and Cloud operations, we can safely term it AI Marketing.
For instance, while running cross-promotional activities or loyalty programs, large organizations will be collecting a fair amount of data for AI and ML to analyse. The analysis can help businesses to nail down their insight into potential customers.
The impact of ML and AI will be clearly visible practically in every application and industry, in the coming few years. Today, almost all modern business applications are delivered via the cloud. Having that settled, SaaS and cloud-based application providers should continue to deliver the competitive benefits of AI and ML to customers. Companies who want to stay relevant and updated do not have a choice but to adopt both the technologies while also ensuring full compliance with all regulatory requirements and keeping customer data fundamentally safe at all times.
(The author is CEO at Crayon Software Experts India and the views expressed in this article are his own)
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