Data analytics technology is becoming a more important aspect of business models in all industries. SaaS companies are no exception. They need to leverage analytics strategically to maximize their revenue. The importance of customer loyalty and customer service has become increasingly well-known and companies have needed to adapt their business models accordingly to gain a competitive edge. One survey found 83% of customers attributed their brand loyalty to the ability to trust a company.

"Digital transformation" has been on the mind of many an organization in the last year: the pandemic and the shift it's brought to how we work are speeding up investments in new apps, infrastructure and work practices to improve productivity regardless of where we sit all day. Now, it looks like we're on to the next stage of that journey: actually figuring out how to adopt and run with all that new tech. In a sign of the times, today a startup called Whatfix -- which has built a platform that helps make better use of tech investments by giving chatbot-style guidance to users on how to use apps, with the option also to apply AI to understand what a person is doing to suggest what actions to take next -- is announcing $90 million in funding. It will use the money to continue expanding its tech platform and hiring more talent to meet demand, said CEO Khadim Batti, who co-founded the company with Vara Kumar (CTO), in an interview this week. Sources close to the company -- co-headquartered in San Jose and Bangalore -- confirmed that the Series D round was made at a valuation of around $600 million, triple Whatfix's value in its Series C round last year.

Building a B2B AI startup is hard enough between struggling to obtain training data and fighting with major tech companies to secure talent. Building a B2B AI startup held to the well-established software-as-a-service (SaaS) metrics is even harder. While many AI businesses deliver value via software monetized by a recurring subscription like their SaaS counterparts, the similarities between the two types of businesses end there. SaaS products built without data and AI offer generalized solutions to their customers. AI businesses more closely resemble a services business or consultancies because they provide solutions that become tailored to that customer's specific needs.

Small-business owners are always on the hunt for opportunities that can give their businesses a leg up. In the past, small business owners have eagerly adopted software-as-a-service products, transitioned to cloud infrastructure and embraced self-service digital advertising. Today, one of the most exciting opportunities is the potential to leverage machine learning (ML) to give your business a competitive advantage. ML solutions automate workflows, enhance data-driven decisions and facilitate interactions with customers. Two factors that are making ML accessible to small businesses are the commoditization of machine learning algorithms and the democratization of pre-trained ML models.

There is no one moment when SaaS – Software as a Service – was conceived, because SaaS as a concept has a host of components; all of which have had to come together in the right context in order to produce value for any sector or vertical market. Different sectors have moved towards SaaS models at different speeds. In technical terms, SaaS relies on cloud delivery at scale, a minimum degree of widely available connectivity, and enterprise-grade security. If any of these are weak, SaaS drops off the agenda. Then there's the business model and the attendant marketing around it – not only do SaaS businesses themselves have to sustain the transformation from a CapEx to an OpEx model, they also have to explain to their customers what's going on, and how they can benefit from it.

Big trends are not that hard to spot (they get talked and written about a lot), but they can be strangely hard for large organizations to embrace. We're just getting started in shaping up the next industrial revolution with AI. This post is a deep dive into the why and the how Analytics and Marketing SaaS products will use Artificial Intelligence (AI). Even if I didn't plan to correlate the two, this post almost comes as a natural follow-up to what I wrote last week. You can read my last post here: The Modern SaaS stack and the Unexploited Amount of data.