What Are the 10 Ways to Use AI SaaS to 10x Revenue?
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The following guide shows 10 ways of integrating AI SaaS platform to increase your business revenue.
1. Personalization using AI SaaS Platform
Modern-day businesses can’t cater to all their customers with a cookie-cutter approach. Each customer is a unique individual, therefore, modern businesses must treat them as such. Personalization is important. SaaS businesses need to use personalization as well.
How do you introduce personalization though? If you have many customers, how do you analyze their interactions and behavior? You would find it hard to commit a huge amount of resources for this!
What if you go ahead with offering more features without analyzing individual customer behavior? You may not find many takers for such features, and the new features will probably just clutter your UI!
You can use AI SaaS to analyze the massive amount of data generated by customer interactions with your product. This helps you to find the intentions of your individual customers. You could then categorize your customers into more meaningful segments.
The insights you gather from personalization can help you to offer targeted features. Read “Personalization at scale with artificial intelligence” to learn how artificial intelligence helps with personalization.
2. Customer service using chatbots
You need to prioritize customer service in your business, and SaaS businesses aren’t exceptions. Customers now demand much more than earlier, thanks to the choices they have. As a result of these factors, the customer service function has seen a steep rise in complexity.
Your SaaS business probably sees more complexity vis-à-vis customer support. By their very nature, SaaS businesses operate remotely. You probably serve customers from all over the world, therefore, you get customer support requests from all geographies.
If you have managed to grow your SaaS business, then you have many customers. This brings in a higher volume of customer support requests. You can find it hard to build a large enough team of competent customer support representatives to handle such a volume. Volume and time-zone differences can stretch your customer support team thin.
You can use artificial intelligence-powered chatbots to manage this. Train these chatbots with the help of your customer support incident database so that they can solve simpler tickets.
Take the example of password-reset requests. A chatbot can quickly point the user to the relevant knowledge base article. Chatbots are available 24×7, therefore, customers won’t see a delay.
Train your chatbots using natural language processing techniques to route more complex queries to your experienced customer support team. Keep your customers happy and simultaneously achieve better utilization of your support team! This is one of the benefits of AI for SaaS.
Read “How chatbots for customer service are redefining customer engagement with AI” for more insights.
3. Improve customer engagement with machine learning in SaaS
Every business needs to engage customers, and your SaaS business isn’t an exception. You need to track customers that are disengaging. Unless you take corrective actions, the disengaged customers could switch to another provider.
In the case of a SaaS business, you could find it hard to detect when a customer is disengaging. Your interaction with customers is mainly through your product. If a customer doesn’t find value in your product anymore, then you won’t know that quickly enough.
You need to analyze how customers use your product and whether they are reducing usage. Use machine learning here to incorporate predictive analytics in this exercise. Machine learning can help you to predict the future behavior of your customers, which will tell you if they are disengaging.
You can then take steps to address whatever concerns your customers may have. Thanks to the insights derived from machine learning in SaaS, you engage your customers before a churn.
If you want to understand how machine learning and data science can help with predictive analytics, then read “AI for predictive analytics: everything you need to know”.
4. Use intelligent automation to improve customer-facing functionalities
As a leader in your SaaS organization, you are constantly trying to improve your customer-facing functionalities. You could have a wide range of such customer-facing functionalities, e.g., onboarding, training, etc.
You must have considered automation to improve customer-facing functionalities. Now, traditional rule-based automation has its limitations. This kind of automation can complete simple tasks. However, it required human intervention in the form of explicit coding to perform complex tasks.
The SaaS landscape is dynamic, and you need to innovate quickly to stay ahead of the curve. This applies to your customer-facing functionalities too. You need intelligent automation, and that’s what artificial intelligence can deliver.
Artificial intelligence can “learn” from your existing datasets and deliver automation far beyond the scope of rule-based automation. This can deliver a better user experience by improving customer-facing functionalities.
Take the example of Saxo Bank, a Danish investment bank. Using “Robotic Process Automation” (RPA), Saxo Bank has reduced the customer onboarding time from 5 days to 1 hour for non-complex customers. Read “Saxo Bank reduces customer onboarding to one hour using machine learning” to learn more about its experience.
Your SaaS business involves customer onboarding and similar processes too. Look to simplify these processes using AI in SaaS.
5. Lead your AI-powered SaaS business to an outcome-based direction
You are probably selling your SaaS product based on the seat-based pricing. This means that an organization “X” buys your product for Y number of its team members, and you get paid for Y number of “seats”.
Does “X” use your product optimally? This question might remain open, however, you still get paid for Y number of seats! You can see how you are going the commodity way, and you don’t want that!
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Focus on value instead of letting your SaaS product become commoditized. When you introduce AI and ML into your SaaS product, you will deliver better efficiency to your customers. They will need to buy fewer seats since they now use a more efficient product. The focus has shifted from the number of users to the outcome!
You just transitioned yourself from seat-based pricing to outcome-based pricing and avoided the trap of commoditization!
Read “How AI will disrupt SaaS business models” to learn how AI-powered SaaS can help you to move to an outcome-based pricing model.
6. Secure your SaaS product
While you are innovating to improve your SaaS product, cyber-attackers aren’t idle! You deliver your SaaS product to your customers via the Internet, and cybercriminals are always targeting such products.
Traditional cybersecurity approaches have been reactive. These approaches involve studying threats that have already materialized. If you are following such an approach, then your cybersecurity team is strengthening your security against attacks that someone else has already faced!
You aren’t preparing for the emerging cybersecurity threats though. Cybercriminals are creating new malware every day, however, your security solution might not even be aware of them.
You can transition from this reactive approach to a proactive approach with the help of AI. Cybersecurity experts are “training” AI-powered systems to detect suspicious behavior patterns online.
These AI systems can now detect new malware or other cybersecurity threats. Such systems can also isolate such malware before it enters the infrastructure of your SaaS product. This has emerged as one of the benefits of AI for SaaS.
Using predictive analytics, these AI systems can also prepare you for threats that might emerge in the future. Read “The impact of artificial intelligence on cyber security” for more insights.
7. Improve your marketing
Marketing your SaaS product effectively can increase your revenue significantly. You must have invested in marketing platforms and strategies already, however, you need to enhance it continuously.
You need to consider various questions when you try to improve your marketing, e.g.:
- How can you target your marketing campaigns most effectively?
- How can you derive more value from your marketing data?
- Which incentives can increase your sales the most?
AI and ML can give you insights to address these questions. They can also make your marketing effort more efficient in the following ways:
- With the help of AI and ML, you can reduce the time spent on reporting and analysis.
- ML can identify patterns in user behavior and arrive at smarter auto-segmentation of your customer base.
- Machine learning in SaaS can find the best lead nurturing strategies.
Read “What is AI marketing and how it impacts SaaS cloud industry?” for more insights.
8. Use AI SaaS Platform to recommend user actions
Your users buy your SaaS product to fulfill certain specific needs. Typically, SaaS products have workflows that users will need to follow. These workflows aren’t all uniform since they depend on the functionalities of the product. Such workflows also have branches depending on sub-functionalities and the various courses of user-actions.
Despite your best efforts to design your UI effectively, certain SaaS products can be complex. Again, despite your efforts to design the workflows in a simple manner, some of the workflows can be complex. Some SaaS products do have inherently complex features!
You need to make life easier for your users, and AI-powered recommendation engines can help. You already have vast datasets that contain the history of how users interact with your product. Now, you can “train” an AI recommendation engine with the help of these datasets.
These AI recommendation engines can recommend the next course of action to your users when they interact with your SaaS product. They will derive these recommendations based on user preference and historical data. Read “A simple way to explain the recommendation engine in AI” to learn more about this.
9. AI-powered SaaS can operate using new forms of data
Would you find any notable use of pictures of damaged cars if your business is powered by traditional technologies? You probably won’t.
AI capabilities have changed that. They now enable computer systems to process various forms of data that these systems couldn’t process earlier.
Tractable AI is an AI-powered SaaS solution. It uses AI-powered computer vision algorithms. The company has trained these machine learning algorithms using large datasets that contain pictures of damaged cars. Tractable AI can quickly assess damages to cars based on this training. The assessments have a high degree of accuracy, which helps in auto insurance claim settlement.
10. Improved predictive analysis models using machine learning in SaaS
Some SaaS businesses offer high-stakes predictive models, and that’s the key value they deliver. Naturally, they would need to create accurate models!
Take the example of Invenia, a SaaS company. This company uses data from energy grids to derive insights on energy usage and grid operations. It also uses weather data.
Its predictive models are its sources of revenue, moreover, these models are critical to companies that operate energy grids. Grid operators use predictive models from Invenia to avoid overproduction and blackouts. By their very nature, these predictive models are complex.
Companies like Invenia use ML to build such predictive models accurately. Read “SaaS is evolving: introducing the new wave of AI-first enterprise solutions” to learn more about this.
Looking for Ways to Improve Your Revenue Using AI?
We just reviewed 10 ways in which AI in SaaS can be a game-changer and give you a competitive edge in the market. Are you trying to transform your SaaS business with the help of AI and Machine Learning?
Contact us at DevTeam.Space via this form, if you need help. DevTeam.Space has a community of field-expert software developers and data scientists to assist you in building an AI SaaS platform that will help you increase your business profits many folds.
Frequently Asked Questions
AI can benefit SaaS businesses in the following areas:
Personalize to serve customers better;
Transform customer service by using chatbots;
Improve customer engagement;
Improve customer-facing functions with intelligent automation;
Transition to an outcome-based business model from a seat-based one;
Strengthen cybersecurity solutions;
Improve content management;
Improve recommendation actions to users;
Improve business data management;
Create better predictive models using big data.
The following kinds of SaaS businesses can realize significant benefits from AI:
SaaS businesses that operate in very complex environments like the energy sector can take advantage of AI to process complex datasets.
SaaS businesses that deal with the form of data that computers couldn’t identify earlier can benefit from AI.
The following are a few examples of SaaS companies that use AI:
Invenia, which uses AI to create predictive models from energy-related data.
Tractable AI, which uses AI to assess automobile damages accurately for automobile insurance companies.