It is estimated that the global SaaS and IaaS (opens in new tab) industry will be worth over $130 billion by next year. In the wake of the success of companies such as Salesforce, the industry has now evolved to the point where vendors and suppliers manage their own software and no installation is required (software is distributed via the cloud).
This SaaS-based cloud services (opens in new tab) model offers businesses significant efficiencies and cost savings and relies on cloud delivery at scale. Perhaps the most recent influential factors to impact SaaS businesses, and will likely continue to be, are Artificial Intelligence (AI) (opens in new tab) and Machine Learning (ML) (opens in new tab) respectively - they are set to become fundamental constituents of the SaaS landscape.
Yair Green is the CTO of GlobalDots.
A key area where AI is driving SaaS (opens in new tab) businesses involves the concept of personalized services. Natural Language Processing and machine learning have allowed SaaS companies to advance personalization massively - for example, user interfaces can be customized based on the customer’s history and how they’ve previously used the platform.
Without personalization, these interfaces can be a cacophony of options and redundancy. Correctly utilizing user data allows SaaS companies to set up interfaces to be highly personalized. Consumers have become much more demanding, wanting personal experiences that are tailored to their specific needs - if you do not offer this type of service to your customers, then they will seek it from your competitors.
Automation is another key area for customer-centric businesses, and AI enables a much higher degree of automation. Artificial Intelligence essentially aggregates large quantities of data and filters it into automatic processes. The main benefit of automation is that it enables businesses to respond to customer needs with less reliance on human resources.
A great example of this are Chatbots - a feature where common questions can be answered by a machine rather than a person. This type of customer service initiative responds to, and troubleshoots, customer inquiries automatically, making customer relations management (opens in new tab) much more efficient. You can also integrate AI technology with your physical customer service team, bringing the problem-solving and human experience together; a good example of this is using AI to automate aspects of customer service within retail stores at self-service tills.
This also serves as a neat rebuff to those who paint a gloomy picture with respect to machines taking jobs from humans - that AI will bring about automation in all walks of working life. The more likely scenario, however, is that AI will deliver more value when it is used in conjunction with human resources (opens in new tab) - AI-augmented human interactions can drive SaaS interactions too.
The SaaS industry has grown alongside the concept of big data (opens in new tab). As businesses now hold significant volumes of data from customers all in one place, AI, along with ML, enables a much more automated means of mass data processing. With IT management teams (opens in new tab) dealing with an ever-increasing volume of data (along with a variety of tools to monitor that data), this can mean significant delays in identifying and solving issues. Data must be captured, analysed and acted on, therefore many businesses have turned to AI solutions to help prevent and resolve any potential outages in a more expedient fashion.
The evolution of SaaS has also brought about clearer insight into data usage and analytics (opens in new tab). In the past, software was distributed to consumers and customers without the insight regarding how they leverage your software - which features are they using? What features are redundant? With the SaaS model (leveraging both AI and ML techniques), the advantage is that you get tonnes of data and insight that can help you improve your service. Businesses are therefore better able to understand customer usage patterns and are able to use this data to give intelligent feedback.
Marketing (opens in new tab) is particularly well placed to leverage AI and ML techniques. Specifically, AI Marketing is a combination of AI principles and applications directly applied to Marketing concepts to target, acquire and retain customers. Marketing Technology (MarTech) is certainly growing in size and scale. When MarTech stacks begin to adopt AI applications to boost the ROI and effectiveness in SaaS and Cloud operations, then we see the true face of AI Marketing.
Assuming that SaaS companies are collecting relevant and recent data then we witness efficient implementation - large corporations accessing data collected via loyalty programs or cross-promotional activities - Artificial Intelligence and Machine Learning solutions can be an ideal opportunity for businesses to nail down their insight into potential customers.
Machine Learning and Artificial Intelligence will impact practically every application in every industry in the coming few years. Modern business applications (opens in new tab) are almost all delivered via the cloud which can only positively impact Software-as-a-Service and cloud-based application vendors who should continue to deliver the competitive benefits of AI and ML to customers. Indeed, companies who want to stay relevant and up-to-date must adopt these new ML and AI techniques whilst ensuring full compliance with all regulatory requirements and keeping customer data fundamentally safe at all times.
Yair Green is the CTO of GlobalDots (opens in new tab).
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