Over the past decade, the feverish push from enterprise to software-as-a-service (SaaS) has allowed end users to sidestep some of the key hurdles associated with software maintenance and implementation. These include ease of installation and upgrades, streamlined testing and training, and minimizing otherwise large upfront costs.
As the SaaS trend further develops, artificial intelligence (AI) and machine learning (ML) have become topics dominating the SaaS conversation, with many analysts considering AI as a market the next big shift.
With artificial intelligence playing an increasingly important role in this evolution, let’s explore some SaaS companies that can leverage and, in some cases, prepare for market disruption in the months and years ahead. Method of preparation.
SaaS Automation
Artificial intelligence essentially aggregates large amounts of data – in this case, customer data – and extracts it into automated processes that are typically done by humans.
Decision makers at any SaaS company know that keeping customers interested in a product requires a lot of knowledge, effort, and manpower, especially as customer needs change over time. AI enables companies to optimize and automate many customer experience processes, such as training and onboarding, marketing campaigns, upselling, and most importantly, ongoing customer service.
According to experts, customer service AI platforms such as chatbots can automatically respond to and resolve customer inquiries, allowing customer service departments to handle 30-40% of additional inquiries.
This is good news for maintaining revenue and reducing churn. According to a study by Zendesk, about 42% of customers will show higher interest in making a purchase after having a positive customer service experience. And 52% of customers say even one negative customer service experience causes them to leave.
Complementing AI technology with customer service teams can achieve a seamless intersection between convenience, problem solving, and human experience.
Customer Personalization
Consumers demand personal experiences tailored to their unique needs. If they don't experience it and will choose another company. Businesses need to face reality. Simply developing and installing a set of more complex features on your own consumer application or interface will only disrupt the customer experience.
In addition to more personalized email campaigns and other customer communications, AI enables features such as voice control and natural language processing, and can keenly track user behavior to better tailor it to a user’s specific preferences Custom features. In turn, this hyper-targeting can support customer loyalty in the face of increasing competition.
Predictive Analytics
Predictive analytics may be the most important of all AI capabilities because ML allows businesses to identify and analyze not only what customers are doing now, but what they will do in the future What.
Historical data combined with advanced analytics can be tracked and formed into patterns to determine what consumers are likely to do next: such as opening an email, renewing a subscription, purchasing a new product or choosing a different brand.
This depth of data can help companies better personalize their marketing communications, segment and optimize their customer databases, and further customize the user experience before customers make their next purchasing decision. This proactive rather than reactive approach can ostensibly help identify customer needs before they even need them.
Pricing Model Disruption
The traditional B2B SaaS pricing model is based on seat pricing, which means that the more users a company registers for its account, the more revenue it ultimately earns.
However, the purpose of investing in AI capabilities is to simplify and automate much of the end-user experience with the software, potentially requiring fewer people to access it. This could potentially improve the end-user experience for businesses and save customers money. But as a software supplier, it goes against its own pricing model.
This may require a rapid shift from a per-seat pricing model to a more value- or outcome-focused model.
To succeed in any market, B2B marketing leaders must move from selling products to delivering results, according to a recent Forrester report. The more digital content is served, the greater the opportunity for a shift from asset leasing to value-based pricing.
Ultimately, it is an advantage for businesses to use artificial intelligence to enhance their technology and benefit end-user goals. But in terms of revenue growth, adapting your pricing model should be tailored to your own value proposition.
One model might charge based on actual usage of the product, or a sales- or marketing-focused platform might charge based on leads or conversions.
Forrester analyst Duncan Jones said: "There is no perfect model, and each model has advantages and disadvantages. It is about understanding the complexity of the product and the return on investment, and adjusting pricing accordingly."
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