A report from Oracle suggests that 80% of the companies are planning to adopt Artificial Intelligence (AI) by 2020 for customer service. Today, AI and Machine Learning (ML) has completely revolutionized the SaaS by turning masses of fuzzy data into a money-making factory. The use is lesser on the scientific, but more on the business side.
A recent post from Mentionlytics explains how AI consistently comes with a myriad of offshoots that influences business decision making. The technology is not only impacting the software industry, but it has expanded its scope of services across a wide spectrum of industries such as healthcare, automobile, manufacturing, entertainment, agriculture, and many more.
Well, this is not the first time Machine Learning has surprised the world with its technological advancements. It had changed the entire phase since its inception in 1784 when the first steam engine was introduced, followed by electricity in 1870, information technology in 1969, and finally artificial intelligence in 2000.
With the widespread use of AI, it has become a game-changer with a potential contribution of 15.7 trillion. Now, the future of artificial intelligence holds more inventions that will bring us closer to an unparalleled future. Let’s look at a few ways the software industry can utilize this revolutionary change to their advantage:
Personalization helps to navigate the competitive market for which 73% of the companies are planning to switch to SaaS by 2020.
AI supports personalization in SaaS and serves as a platform for mass data processing, which helps marketers to shape their strategies. The personalization targets and segments the massive data that allows marketers to identify their leads from high-value customers, locations, and channels.
Moreover, marketers can send contextual and personalized information to these leads based on their interest, and the channels they use to communicate. Personalization through machine learning plays a crucial role in gaining knowledgeable insights from the data gathered to recognize trends and draw conclusions.
Automation is another revolutionary development in SaaS. It helps scale SaaS companies by managing multiple marketing campaigns across different channels. The entire process helps in generating more leads segmenting, lead scoring, and customer retention.
The best example of marketing automation is using chatbots that serves as a great source to qualify the visitors of your website. Moreover, you can recognize his pattern and understand on which stage he is by analyzing the set of questions he asks through chatbots. This way, your marketing team can get the most sales-ready qualified leads.
The predictive analysis describes a set of techniques using statistical data that aims to predict the future by analyzing the patterns in the past. A combination of machine learning in user-friendly SaaS models can give more access to predictive analysis. It helps build customer personas by analyzing their behavior, and these predictions get better over time.
When it comes to using AI for product searches, then SaaS companies mine data to understand the intent behind the query. For instance, if a user searches for a car, but what is the purpose? Is he looking for a new car or the spare parts of the vehicle?
The behavioral understanding of the users helps to analyze the intent behind a search when the user searches for a product these companies tend to provide with the best results they could.
User click through rates and product sell-through rates play a crucial role in the entire process and act as a significant factor to rank a product. Through the data, they can create graphs between different products and related queries.
Release management is a scheduled deployment of any program code changes for software applications. It offers a distinctive advantage that includes bug fixes, upgrades, and enhancements. Well, SaaS release management can be done manually also, but it will be quite expensive for organizations.
AI and automation provide a better solution in terms of operational efficiency and effectiveness. It can impact SaaS offerings and can revolutionize the entire process.
Moreover, it also allows us to avoid the pitfalls of poor quality software that usually happens during manual release management. An automated release management software helps to excel in the competitive marketplace and prevent application downtime.
Apart from this, this automation also reduces the complexity of software release management. It helps in reducing the workload on IT professionals.
IT experts consider security as a top criterion with 47% to buy SaaS apps. It indicates the need for enhanced security where the use of AI is obvious.
As the modern SaaS requires a robust solution that provides for cloud computing at scale along with connectivity, cloud security is a significant issue. All thanks to AI and its security services that facilitate threat detection.
The combination of SaaS in cloud applications and machine learning paves the way for enhanced security as it protects the platforms, applications, networks, operating systems, and physical infrastructure.
It also restricts access to customer data to safeguard intellectual property and sensitive data in cloud applications.
Pricing Model Disruption
Do you know that 50% of SaaS companies choose user-based pricing?
B2C companies are utilizing big data to determine price gaps and manage price discrimination. This takes you far away from the traditional set of experimentation and brings you to a new world where you can easily track buying trends to determine competitive prices for your products.
Machine learning will help to develop a dynamic pricing solution to adjust product prices for customers based on their buying habits.
What are the Attributes of Companies to Invest In?
- Gain exclusive access to data – the algorithms are accessible to everyone. To gain a better competitive advantage, you can create proprietary access to data through event-driven SaaS products.
- End to end applications, rather than platforms – SaaS companies should invest in the end to end applications and not platforms as it helps to lower the cost of infrastructure. Moreover, it also increases revenue, which is beneficial, especially for startups.
- Investing in strong GTM enabled by Machine Learning – As a SaaS startup, if you tend to invest in the go-to-market operations enabled by AI will inevitably change the way a buyer perceives a software. Moreover, it will also reduce the cost of acquiring a customer.
- Invest in experts in the field – The entire system of SaaS is widely applicable that can be leveraged to gain better results. However, this will not suffice as you cannot manage everything on your own. You will surely need experts to deliver exceptional results. You must hire experts in speech recognition, natural language processing, or any other specialized area that you will need to grow your SaaS company.
- Algorithmic advances – Everyone invests in fundamental algorithmic advances to create something fresh with a unique idea. You must spend some time and money on algorithmic advances to create something distinct that it can’t be replicated anywhere else.
The practice of collecting data to understand the demographics has become the thing of the past, and machine learning seems to hold the future. Businesses have started adopting artificial intelligence and machine learning technologies to stay ahead in the game of marketing. When are you planning to jump into the mainstream?
George Mastorakis is co-founder of Mentionlytics, a social media monitoring tool. George and his team provide brands the customized social intelligence tools and technology needed to maximize their social footprint.