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How Artificial Intelligence is disrupting the SaaS market

How Artificial Intelligence is disrupting the SaaS market

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Article by: Kyle Shamorian, Content Marketing Manager, Cleverbridge

The fervent push from enterprise to Software-as-a-Service (SaaS) within the last decade has enabled end-users to sidestep some of the key hurdles associated with software maintenance and implementation. Chief among them include ease of installation and upgrades, streamlined testing and training, and minimising an otherwise large upfront cost.

As SaaS trends evolve further still, Artificial Intelligence (AI) and Machine Learning (ML) have staked their claim among the topics dominating the SaaS conversation, as many analysts peg AI as the next big shift in the market.

As AI becomes a more integral part of that evolution, let’s explore a few ways in which SaaS companies can take advantage of and in some cases prepare for, market disruption in the months and years to come.

SaaS automation

AI essentially aggregates large quantities of data – in this case customer data – and distils it into automatic processes normally accomplished by humans.

As any SaaS company decision-maker knows, it requires an immense amount of intel, effort and manpower to keep customers engaged with their product, especially over time as their needs change. AI enables companies to refine and automate many of these customer experience processes, such as training and onboarding, marketing campaigns, upsells and most importantly, ongoing customer service.

Customer service AI platforms like chatbots, which respond to and troubleshoot customer inquiries automatically, enable customer service departments to take on between 30-40% of additional inquiries, according to experts.

That’s great news for revenue retention and churn reduction, as some 42% of customers show a heightened interest in a purchase following a positive customer service experience, according to a Zendesk study. Likewise, 52% say even one negative customer service experience will send them packing.

Supplementing AI technology with your customer service team can target the seamless cross-section between convenience, problem-solving and human experience.

Customer personalisation

Consumers demand personal experiences tailored to their unique needs. If they don’t receive it from your business, they’ll go elsewhere. And let’s face it, simply developing and installing a more complex series of features on your consumer app or interface may do little else but muddle the customer experience with extraneous options.

In addition to more individualised email campaigns and other customer communication, AI supports such features as voice control and natural language processing and can acutely track user behaviour to better customise functionality to their particular set of preferences. In turn, this hyper-targeting can support customer loyalty in the face of growing competition.

Predictive analytics

Predictive analytics may be the most vital of all AI capabilities, as ML empowers an enterprise to identify and analyse not just what customers are doing now, but what they will do in the future.
The combination of historical data working in tandem with advanced analytics can track and form patterns to identify what a consumer’s next step will likely be: open an email, renew their subscription, buy a new product, or disengage from your brand altogether, for example.

This depth of data can help you better personalise your marketing communications, segment and refine your customer database and further customise the user experience before they make their next decision in the buying cycle. This active-instead-of-reactive approach can ostensibly help you identify customer needs even before they do.

Pricing model disruption

The traditional B2B SaaS pricing model operates on a per-seat basis, meaning the more users you have registered on the account, the more revenue you ultimately accrue.

Investing in AI capabilities, however, is designed to streamline and automate much of the end-user’s experience with the software, likely requiring fewer people needing access to it. This may improve your end-user experience and save your client money, but as a software provider, you’re working against your own pricing model.

This may require a rapid move from a per-seat pricing model, to one that is more value- or outcome-based.

‘To succeed in any market, B2B marketing leaders must pivot from selling products to delivering outcomes’ according to a recent Forrester report. ‘The greater the digital content of your service, the greater the opportunity to migrate from asset rental to value-based pricing’ it said.

Ultimately, it’s an advantage for your business to enhance its technology using AI and to benefit the end-user’s goals. But in terms of revenue growth, adapting your pricing model should be customised to your value proposition.

One model may charge based on actual usage of the product, or a sales- or marketing-focused platform may charge based on leads or conversions.

“There is no perfect model and each one has its pluses and minuses,” said Forrester Analyst, Duncan Jones. “It’s about understanding the complexity and ROI of your offering and aligning your pricing to that end.”

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