We are all aware of the fast-changing world around us. New technologies kept being introduced to help make our lives easier. The world of business is seeing the biggest changes with the introduction of AI and ML. Together, these technologies can increase operational efficiency using automation and other advanced tools.
That brings us to SaaS solutions that are redefining business practices by using data to improve operations. But how do SaaS business solutions work, and what makes them so popular? Continue reading and this article will tell you everything about SaaS and its implementation.
The State of SaaS Companies
The SaaS market is currently making up about 68% of the entire cloud market share. Even though SaaS has been around for years, it has been growing very slowly until 2020. The pandemic has pushed most businesses from traditional brick-and-mortar shops to online stores, leading to a sudden increase in the SaaS market.
Software as a service solution is now becoming a standard part of most business operations. With new SaaS solutions released all the time, the market is expected to keep growing in the following years. Most SaaS solutions use AI and ML to help businesses find bottlenecks and improve their operations.
In fact, Google, Amazon, Microsoft, and most other industry leaders use AI and ML to improve their operations. It's expected that new SaaS business solutions will include much more powerful AI and ML systems, which will speed up the market growth in the future.
AI and ML SaaS Solutions Use Cases
SaaS solutions use AI and ML to provide better results in business operations. Let's take a closer look at how SaaS can help improve a business.
1. Hyper-Personalization
No matter which type of business you're running, your customers will always prefer customized products. AI and ML can help you learn from customer interactions, allowing you to customize your product and offers according to their needs.
Every customer is unique, so how do you personalize your offer for every individual? Well, one option is to allow your customers to pick product features individually. AI can help you gather data and understand your customers better. You can segment customers into a few different groups and create multiple product versions for every group.
2. Automation
Most SaaS solutions have various automation features designed to improve business operations. Some use chatbots to improve customer support, others use automation to reduce operational costs, and for trainings also. AI-powered SaaS solutions are ideal for creating a stable customer support system and an impressive knowledge base. That takes the pressure off of your live agents and ensures that every customer gets the help they need as quickly as possible.
AI can provide high-quality help 24/7, allowing your customers to reach out and get answers at any time and in any language. Chatbots will take care of simpler issues, routing customers to live agents only in more complex cases.
3. Predictive Analytics
AI leverages data to find previously invisible connections between information. That leads to a better user experience and practices such as predictive analytics. After some time, machine learning models can learn how an operation works. Once it understands all details, the AI can make accurate predictions in many key business processes.
For example, ML can predict user behavior or preferences based on their previous activity. You can set it up to send automatic alerts in case a user is losing interest. Every customer counts, so predictive analytics is definitely one of the best features of using SaaS solutions.
4. Product Search
Whenever a customer searches for a product, you want to help them find exactly what they need. Product ranking depends on a few metrics including click-through rates or sell-through rates. When you add user behavior information to the mix, AI can identify the best product for each user. It analyzes query logs, allowing you to find correlations between queries and products.
Moreover, if you mine enough data, AI can help you figure out the intent behind a specific search query. Knowing the query intention helps understand the user and the semantics behind the search.
5. Release Management
SaaS solutions require a lot of coding and setting up before they can provide results. If you rush things, the integration can lead to errors and bugs in the system, which can be extremely expensive to correct. With that said, having the ability to set things up quickly is an advantage, especially in viable markets.
AI can fuse with SaaS developers' coding skills, leading to some incredible results. The AI can check every line of code and see if the coding is good or not. That means that you won't have to go through extensive testing phases, since the AI can do the same in a matter of minutes.
6. Marketing Improvement
Finding the best way to market your SaaS business product will have a huge impact on your revenue. If the current strategies aren't giving you the results you want, you have to improve your marketing by answering the following questions:
- How can you improve marketing campaign targeting?
- How can you use data to increase marketing value?
- Which methods increase your sales the most?
Again, AI and ML can help you find the answers to these questions. Together, they can improve your marketing efforts and make your campaigns more efficient. But how do AI and ML help you improve your marketing? Well, firstly, they can make all reporting and analytics efforts much faster. They can also identify user behavior patterns and segment your customers automatically. Lastly, ML helps increase lead nutrition strategies, leading to a longer customer lifecycle.
Final Words
When all things are considered, AI and ML work together to bring SaaS products to a new level. These technologies are extremely powerful and can help you generate valuable insights to get an edge over your competitors. That's exactly why more and more businesses are adopting SaaS in the past few years.
Most ML applications in SaaS focus on automation and improving efficiency while reducing costs. So, if you want your ML-based company to be successful, find an expensive business process and create a machine learning model that makes it affordable.
