When you think of hot startup communities, it is too easy to start the list at Silicon Valley and stop right there. But then you’d be missing out on a whole world of opportunity.

Denmark has a community of over 240 budding “next big thing” candidates, and some incredible recent success stories, including Unity Technologies — which has captured 45 percent of the game engine market — and Podio, which was acquired for around $50 million by Citrix.

And today, “Silicon Viking” startup — EASI’R — has launched its bid to disrupt the old, antiquated car sales industry with an intelligent algorithm that predicts when customers will buy new vehicles, even before the customers know it themselves.

Located in Denmark’s second-largest city, Aarhus, with a second office in Chemnitz, Germany, EASI’R has spent the last decade quietly building an impressive client base for its automotive customer relationship management (CRM) solution — clients that include Audi, VW, and Toyota. It is this foundation that has allowed the company to launch its new predictive sales solution without the need for a training phase (the period within which a machine learning solution acquires data and discovers patterns).

In fact, EASI’R’s new algorithm works within its CRM from the first second it is switched on, thanks to over 20 million customer interactions collected over a ten-year period.

Here’s how it works.

EASI'R- hi_res

The solution analyzes patterns in customer behavior, then clusters those customers by demographic data, online search behavior, and transactional data, even drawing on sales interaction records held in the CRM. Using that information, and its understanding of millions of customer interactions, it predicts the most promising next steps for each customer.

And it does this throughout the buying cycle, guiding salespeople to take the next steps and helping them send the right information at the right time.

For example, EASI’R can predict when a customer is going to buy a new car, a trigger every car salesperson wants to understand. Of course, that kind of insight is ideal for the “ambulance-chasing” sellers of the world, but the program also offers a range of subtle buying triggers that can help salespeople gain additional business in a less — well — secondhand-car-salesperson way.

If a customer has rejected an offer, for example, the algorithm predicts when the ideal time would be to contact them again, and with which counteroffer, to increase the likelihood of closing an alternate deal.

With its knowledge of car-buying customer patterns, the algorithm can tell the automotive dealers when to send content, what specifically should be sent, and which content delivery channel would be most effective at that particular time.

Predictive analytics and machine learning seem to be finding a happy home within the consultative sales industry.

“Within the consultative sales industry, the selling process is typically longer than in other industries and includes multiple interactions,” Mikael Moeslund, cofounder at EASI’R told me. “Intelligent algorithms learn what drives results and find patterns not obvious to any salespeople. By equipping salespeople with these insights and enabling them to base their daily decisions on what actually drives sales, their closure rate will increase. Salespeople are no longer dependent on personal talent, guesswork, or sheer luck.”

But that isn’t the only reason systems like this are helpful. VB Insight’s recent research report indicates that somewhere between 57 percent and 98 percent of your visitors are unknown, and consumers are not identifying themselves until very late in the buying cycle. That identification process is made more difficult by today’s privacy-conscious mobile customer — a subject we’ll be talking about in depth with the likes of GrubHub, Intuit, Pandora, and Tinder during Mobile Summit 2016.

“The framework conditions for the consultative sales industry have changed,” Moeslund said. “The direct contact with a salesperson happens later and later in the sales process now, with customers getting ever more tech-savvy and handling their pre-purchase research increasingly online. Years ago, the direct consultation with a salesperson, in a car showroom, was decisive for a purchase decision. Today, customers make their purchase decisions much earlier online. Salespeople need to regain their influence and know what their customers want and how they want to be addressed.”

But is there a danger that predictive sales solutions will replace salespeople entirely? Moeslund thinks not.

“An algorithm does not sell cars; salespeople do,” he said. “However, salespeople will find that algorithms can be their wingman, guiding decisions on how to prioritize opportunities and help to select the most profitable relation path in each case. Salespeople will also find that they are able to manage more leads and customers than before and achieve better conversion rates. In addition, they are able to focus much more targeted and efficient on the customer interactions that really count, and deliver more targeted what their customers demand.”

EASI’R is currently being used in 2,600 car dealerships across Denmark and Germany, and its latest deal brings another disruptive car sales solution into play — eBay Motors. EASI’R is initially launching to 1,400 dealers within eBay’s network.

EASI’R has plans to roll out its solution beyond Germany and the Nordic region, but that will present additional hurdles. Localization and personalization are important here, but so is an understanding of cultural purchasing decisions, something we discussed at length in a recent webinar.

Will the program have to go through a new training phase in order to seed the system with data that takes those difference into account?

“We will continuously seed the EASI’R intelligent algorithm as we expand,” Moeslund said. “It will learn from any geographical and brand-specific buying differences.”

EASI’R’s new predictive sales algorithms are live today for all existing customers.

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