Transform 2021

Elevate your enterprise data technology and strategy.

July 12-16

SugarCRM tweaked its SugarPredict AI engine this month to help marketing teams with automated, predictive lead scoring. The cloud-based marketing automation software, first released as a sales tool for customer relationship management (CRM), now also facilitates “rapid and reliable marketing lead qualification and prioritization,” the company said.

SugarPredict is available on the Cupertino, California-based company’s Sugar Market platform for automating and assisting with marketing tasks. It was first introduced in January for SugarCRM’s Sugar Sell users. SugarPredict for sales is billed as an AI-driven way to intelligently enrich the customer data people enter into CRM systems to ensure quality and consistency for sales team users.

Sugar Market is a customer lead-cultivating platform that helps marketing teams with tasks like quantifying how website visitors interact with digital marketing materials; assisting in the creation of emails, landing pages, and forms; qualifying leads with lead-scoring models; and aligning with sales via automated hand-offs of qualified leads.

Leave the lead scoring to the machine

“The new smart scoring engine in Sugar Market saves marketing teams time by automating the creation and maintenance of lead scoring logic,” SugarCRM GM Christian Wettre told VentureBeat.

“By continuously adjusting the lead-scoring rules based on customer behavior over time, SugarPredict helps marketing teams stay on top of changing markets. SugarPredict adds velocity of interest as one of many new factors of a lead score. By including more factors than just the sum of clicks, marketing teams deliver more intelligently scored leads to sales teams,” he said.

Describing the current state of CRM as a “crisis for companies,” Wettre cited a recent SugarCRM-commissioned survey of global enterprises that found more than half of respondents believe their current CRM systems are costing them revenue. Some 58% of those surveyed said their sales and marketing teams lacked the training and skills to customize their CRM to generate better results, while 53% percent said time consumed with administrative CRM and lead generation burdens was hurting productivity.

Automatic for the people

SugarPredict is intended to help alleviate these sales and marketing pain points with automated processes like augmenting incomplete customer profiles with relevant online data the AI engine discovers and pulls in, Wettre said.

He described SugarPredict helping SugarCRM customers in a number of ways:

• No blind spots: Instead of being limited to piecemeal views of the customer, you see all relevant information — past, present, and even future — instantly (with predictive insights).
• No busywork: Instead of having to manually enter endless details, you get a platform that automatically captures data and presents it in context to everyone who needs it.
• No roadblocks: Instead of all the standard limitations, you get a solution built around your needs and workflows.

SugarCRM was founded in 2004 and began as a developer of free open source CRM software. That was eventually discontinued as the company shifted to selling licensed CRM products. SugarCRM is owned by private equity firm Accel-KKR.

Transform 2021

Elevate your enterprise data technology and strategy.

July 12-16

This article is part of a VB Lab Insight series paid for by Plus.

The safest drivers are those with the most experience. Studies show it can take years of practice for automobile drivers to become careful and competent road users. Similarly, the more experience a truck driver has the less likely it is that they will cause a serious crash.

What holds true for human drivers holds true for autonomous driving systems — up to a point. The safest self-driving vehicle platforms are those that have accumulated the most experience.

Since driving experience is so important, how can technologists make sure computerized driving systems get the training they need to operate safely on the nation’s roads and highways?

Solving this challenge is the key to unlocking a fully autonomous future.

How computers learn to drive a semi-truck

Thanks to advances in sensor technology and artificial intelligence (AI), an automated truck is capable of analyzing many objects on the road and making a decision about how to respond.

This is accomplished in large part by training so-called “deep learning” algorithms. Repeatedly expose a self-driving system to all kinds of obstacles, from a cut-in vehicle to a construction site, and the system will start to understand how to react when an obstruction appears on the highway.

Here it is important to note that unlike people, machines lack common sense and don’t do well handling novel situations. Human drivers know to slow down in the face of an unexpected obstacle — a bear, say — because we can make decisions based on similar situations we have already encountered or extrapolate from other incidents.

Unlike humans, however, deep neural networks can only learn from data they have been trained on, whether from public roads, closed courses, or computer simulations.

So back to the original question: How do you train the machines so they are exposed to the full range of the driving experience?

Data, data, and more data

Plus’s goal is to help truck drivers on long-haul routes, where they encounter a variety of road and weather conditions. In addition to closed-road testing and computer simulations, the company’s PlusDrive system is learning on the open road, where the trucks can be exposed to real-world obstacles and situations. Junk flying from a pickup bed. Ice slicks. A wind turbine blade. A zigzagging motorcycle.

Though these so-called “long tail” phenomena comprise less than 1% of the time behind the wheel, knowing how to safely navigate them is critical for machines. Society expects that a computer-operated machine must be at least an order of magnitude safer than a human driver.

Billions of miles of on-road testing

Starting this summer, Plus will put its supervised automated driving system into factory production. It is also retrofitting existing trucks with the system. By this time next year, hundreds of automated trucks powered by PlusDrive will be on the road, hauling commercial cargo.

Human drivers will be behind the wheel. Like an experienced professional training a new recruit, Plus drivers will monitor the autonomous trucks while teaching them how to handle unexpected obstacles.

Plus estimates that its fleet will accumulate billions of collective miles before the company deploys fully driverless vehicles. Taking an evolutionary approach to full autonomy enables the company to rack up miles more quickly, with the assistance of on-board professional drivers who are training and validating the system.

To support its global deployment in the U.S., China, Europe, and other markets, Plus recently raised $420 million in new funding.

Truck driver retention and low-carbon solution

The drivers benefit too. The Plus supervised autonomous trucking solution elevates the role of the truck driver, upskilling them in preparation for an autonomous future. At the same time a digital co-pilot will ease driver exhaustion on long-haul routes, and fleets will spend less on the hiring process.

The system yields other gains. Fuel comprises about a third of a trucking company’s operating budget, by far the largest cost for heavy trucks. When an automated system understands the road, pulling in GPS and weather data too, they optimize shifting and braking. Plus has run pilot projects showing that  PlusDrive saves 10% of the tank compared to the most efficient drivers, a win for the bottom line and the environment.

The autonomous trucking future, now

Commercial space travel, solar-powered cities, autonomous vehicles — the first two visions of the future depend on specific economic inflection points, while the third is wholly dependent on the amount of data a system has accumulated.

Plus is building the necessary feedback loop of information today. Its trucks are accumulating the data. Its drivers, who are among the safest and most efficient Class A drivers, are training the system with their responses. Its engineers are fine-tuning PlusDrive’s algorithms and decisions. And eventually PlusDrive will be one of the safest and most experienced drivers on the road.

Plus is applying autonomous trucking technology to trucks today. For more information, please visit www.plus.ai.

VB Lab Insights content is created in collaboration with a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. Content produced by our editorial team is never influenced by advertisers or sponsors in any way. For more information, contact [email protected].