It’s Autumn, But Predictive Software is Blooming with SalesforceIQ — Welcome to the AI Spring!
September 16, 2015
There’s a lot of conversation happening lately about predictive analytics, and there’s no question that it is starting to permeate the software industry. Yesterday’s SalesforceIQ announcement is just the latest example of an enterprise software company that’s sitting up and taking notice of the massive value data science can unlock. By leveraging relationship intelligence to help small businesses sell smarter, SalesforceIQ will help more and more companies begin the shift towards predictive-driven decision-making. This is one more way that machine learning and predictive modeling are increasing businesses’ appetite for actionable data.
The question is, what moves will the other big enterprise software players — like Microsoft, Oracle, Marketo and others — make during this ‘AI (artificial intelligence) spring’? If you look at how the predictive market is likely to play out, there are a few logical approaches the software giants will take as they look to bring predictive capabilities into their product portfolios.
Adding Basic Predictive Features
The first of these is to extend their existing apps with limited predictive functions – essentially playing it safe. As is the case with SalesforceIQ, these features will probably be based on data the vendor already controls and work with minimal customization. For example, SMBs using SalesforceIQ will see targeted recommendations on the next best follow up with a particular prospect, based on their email communications and calendars.
These improvements will help Salesforce with product adoption for a large swath of its customers. However it only scratches the surface of what predictive can do. For a model to be highly predictive, you need to go beyond the system of record and be vicious about acquiring data. Email communications and calendars are fairly consistent across companies, but there are many other data sources that can provide important clues. These signals that go into a model are different from one company to the next. Without a solid understanding of a company’s process, their data, and what outcome they’re trying to predict, it is difficult or even dangerous to build custom-fit models. You run the risk of setting bad targets, overfitting models, and ultimately making the wrong recommendations.
It is equivalent to Waze going awry and sending you an hour out of your way, only in this case your customer’s real revenue is at stake. That could create major issues for these automation companies that might even impact their renewals and recurring revenue.
Delivering Robust Predictive-as-a-Service
While the big software players could well build a deeper predictive service, it is more likely that they would move into it by acquiring a dedicated predictive vendor. There are a handful that have been working on this for years, and acquiring one of them would give the incumbents a jumpstart on the talent and technology front — as LinkedIn is aiming for with its acquisition of Fliptop. The challenge for other players is that building custom-fit predictive models is a very different business than selling an empty database or per-seat application like CRM. It requires a distinct technology architecture, new sales and support models, and a fundamentally different perspective on whether you should be able to actively see your customer’s data. When a company buys a database, they don’t want their vendor peeking in, but with predictive modeling that is a prerequisite (otherwise the vendor can’t monitor and tune its predictions).
Another hurdle that will likely keep some big providers on the sideline when it comes to full-fledged predictive solutions is that we’re not yet at a point where the revenue is material. Even Salesforce’s smallest product lines probably generate more revenue than all the vendors in the predictive space combined. That’s not to say that the predictive market won’t one day be substantial and strategic, but we’re still in the early days. Today, predictive acquisitions make the most sense as talent acquisitions to feed a strategy of adding basic predictive features.
Cultivating a Predictive Ecosystem
The third strategy the big players will inevitably pursue is to build their ecosystems, as Salesforce is doing to some extent with its Wave Analytics Platform. If a vendor has dozens of certified partners with different flavors of predictive, it can give customers more choice; all the while reaping the adoption benefits of AI. Predictive intelligence makes workflows, analytics and advertising that much more effective. By playing Switzerland, large software providers can invite in more innovation. And when it feels like the market is too big to ignore, they can make their move. This is very similar to the strategies we saw with the mobile CRM or marketing automation industries.
In the coming months, I expect that more of the marketing automation vendors will start building their own basic predictive capabilities. I think we’ll also see smaller talent acquisitions as some of the predictive vendors get squeezed out. And there will surely be pure play vendors who stay focused and separate themselves from the pack.
At the end of the day, most businesses will eventually want to adopt custom models that meet advanced predictive requirements. Many will bring in specialized solutions and expect their software vendors to plug in whatever predictive scores they want. By supporting this ecosystem, the Salesforces and Microsofts of the world can unlock more value for customers and add stickiness to their own products.
Whether vendors buy or build their own predictive features, I predict that they’ll also continue to partner with several different best-of-breed vendors in the predictive marketplace. This approach not only minimizes their risk, it offers their customers’ choice and drives innovation across the industry.
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