Software as a Service (SaaS)

Go Faster: chases faster sales… and more

In mid-January picked up another $50m in funding to cement its reputation as one of the most interesting companies in business software today. The interview reported here was set up to replace a piece we were forced to spike after Jim Steele, the erstwhile InsideSales president left the company for personal reasons recently. But my phone call with CEO and founder Dave Elkington turned out to be good value; the more I learn about this very 21st-century company, the more interested I am.

First, some background.

InsideSales is one of the hottest companies in what some call “sales acceleration”, a better way for sales execs to get deals done, from initial contact to closing and renewal. The company was founded in 2004 but has itself accelerated in recent years and has raised about $250m on the back of a promise that machine-learning algorithms and records of how previous sales deals developed will lead to a smarter and more rapid sales cycle. The Provo, Utah company’s cloud based service suggests approaches that have worked in the past for similar relationships and transactions based on a vast database of interactions, buyer profiles and contextual data.

InsideSales is in many ways the logical extension of cloud CRM pioneer, putting data to work rather than treating it as part of a journal of record. Indeed, Steele himself was in the leadership team at Salesforce and incoming president Lindsey Armstrong was also high up at the company which is also a major investor in InsideSales and a customer. A watershed moment in the company’s history came with a demo at Salesforce showed InsideSales could have provided a boost in accuracy that would have led to an $50m sales bump for Salesforce.


Smarter sales

The service sits between the sales rep and the customer, pulling out the data that helps customers find patterns and prescribing the right sales actions. About 70 per cent of the data is sucked in from interactions logged on Salesforce but other sources are always being added. That intelligence is a boon at a time when big companies eschew traditional sales training and buyers rely largely on the internet, media and word of mouth for core information rather than speaking to a parade of sales types and their cohorts.

Just as Google has a good chance of guessing what you’re really searching for and Amazon of knowing what you might want to buy, InsideSales wants to apply that model to business sales and, as we shall see, beyond.


State of the game

There’s been plenty of progress at InsideSales with sales growing sharply, the company claims, and annual revenues in the $50-100m range. There’s also a growing relationship with Microsoft (another investor), more than 500 staff and closing in on 3,000 customers, each of which has to provide its anonymised data to enhance the algorithms and service.

Europe is also moving quickly from nigh on zero in sales a year-and-a-half ago to almost 15 per cent of revenue today and Elkington says that he foresees especially big opportunities from the EMEA region. One investor in the most recent funding round was the Irish government and InsideSales is investing heavily in Reland, seeing the country and her universities as a happy hunting ground for machine learning expertise.

“Ireland to me is not about tax advantages but about access to talent; most people don’t know there’s a burgeoning presence in AI,” he adds. This will create longer-term, higher-value jobs than the type created by companies setting up local headquarters or call centres, he argues.

InsideSales already serves customers such as Thomson Reuters, Dyson and CSC but Elkington says that every large company is going through a sales transformation or modernisation process.

“It’s the very beginning of a market,” he says, comparing its stage with that of Salesforce, Siebel and VMware in their early days. “It’s not just a company but a market that’s emerging: the digital disruption of sales where field sales and old school ‘get on the golf course’” approaches are being edged out by millennials. Just as AI helps us find Netflix movies, tell us when it’s time to go for a run and recommend music or food, it will also make sales smarter and the sales cycle less lossy.


Not just selling

But the future might also include scenarios that go way beyond today’s core of sales acceleration. How much further, I ask, up to 50 per cent perhaps?

“Way beyond that,” Elkington says. “We seeded the market with our sales platform because I needed the data crowdsourcing engine.”

(In that case, surely the company name will be a bum steer to its true focus, I suggest. That branding issue, he hints, is currently being addressed.)

There are already customers that fit that profile, including a datacentre service provider business that’s trying to figure out how to be more predictable consumption-wise and an entertainment company trying to target VIP customers.

Elkington says that the real competition might be IBM Watson, Google DeepMind and the other projects intended to unpick trends and patterns that will in turn unlock some of the world’s biggest challenges. But while others focus on algorithms and maths, Elkington argues that having access to unique engagement data will be his USP.

“At some point someone will build a learning platform [that becomes the de facto leader],” he says. “We’re in the Wild West of AI and people don’t know how to win, but we have a hypothesis that we think is the best.”

Of course, rivals like IBM are also acquiring scads of data, for example through the acquisition of the Weather Company but Elkington suggests they might be missing a trick. “We buy huge amounts of data too and use all kinds of data but the value is the exclusiveness, the uniqueness,” he says.

This, he suggests, is why LinkedIn keeps its data close to its vest and why maybe Twitter, with its oceans of mostly public data, is less valuable.

Data science, machine learning and so on aren’t new, he adds, namechecking IBM’s Arthur Samuel who created software to win at checkers in 1959. “What’s different,” he says, “is data”, and the chance to capitalise on the deluge of information at our fingertips if we can only capture it and interpret it. But that data also has to be parsed because data ages quickly and people use terms that disguise the truth - who tells social media they’re getting divorced or have been fired, for example?

Salesforce, Amazon and others have foregone profitability in order to build hugely ambitious platforms but Elkington feels that the best route is to reach a situation where profitability could quickly be switched on. The plan, he says, is to aim high but not lose sight of common sense or business planning.

Unusually, he does not definitively see an IPO being the obvious way forward, although it would provide obvious benefits: and he is not dead set against the idea that InsideSales could one day be acquired by a company that could help it realise its towering plans. But he would only be willing if that epic mission would be served by that process, he says.

Of course there are always risks. Could InsideSales be snared by legal changes on data protection, for example? Privacy Shield registration has helped it and it uses AWS datacentres which should provide some sort of umbrella protection, but a future curve ball in the shape of draconian new rules could have an impact.

But for now InsideSales is a hugely intriguing proposition.


Also read:
The brightest of Salesforce alumni?
IBM Watson, Bob Dylan and the limits of machine learning
IBM’s Big Blue hope: Watson


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Martin Veitch

Martin Veitch is Contributing Editor for IDG Connect

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