Slack makes customer acquisition look easy.

The day we acquired our first Highspot customer, it was raining hard in Seattle. I was on my way to a startup event when I answered my cell phone and our prospect said, “We’re going with Highspot.” Relief, then excitement, hit me harder than the downpour outside. It was a milestone moment – one that came after a long journey of establishing product-market fit, developing a sustainable competitive advantage, and iterating repeatedly based on prospect feedback. In other words, it was anything but easy.

User-first products are driving rapid company growth in an era where individuals discover, adopt, and share software they like throughout their organizations. This is great if you’re a Slack, Shopify, or Dropbox, but what if your company doesn’t fit that profile?

Product-led growth is a strategy that works for the right technologies, but it’s not the end-all, be-all for B2B customer acquisition. For sophisticated enterprise software platforms designed to drive company-wide value, such as Marketo, ServiceNow and Workday, that value is realized when the product is adopted en masse by one or more large segments.

If you’re selling broad account value, rather than individual user or team value, acquisition boils down to two things: elevating account based-selling and revolutionizing the inside sales model. Done correctly, you lay a foundation capable of doubling revenue growth year-over-year, 95 percent company-wide retention, and more than 100 percent growth in new customer logos annually. Here are the steps you can take to build a model that realizes on-par results.

Work the account, not the deal

Account-based selling is not a new concept, but the availability of data today changes the game. Advanced analytics enable teams to develop comprehensive and personalized approaches that meet modern customers’ heightened expectations. And when 77 percent of business buyers feel that technology has significantly changed how companies should interact with them, you have no choice but to deliver.

Despite the multitude of products created to help sellers be more productive and personal, billions of cookie-cutter emails are still flooding the inboxes of a few decision makers. The market is loud. Competition is cut throat. It’s no wonder 40 percent of sales reps say getting a response from a prospect is more difficult than ever before. Even pioneers of sales engagement are recognizing the need for evolution – yesterday’s one-size-fits-all approach to outreach only widens the gap between today’s sellers and buyers.

Companies must radically change their approach to account-based selling by building trusted relationships over time from the first-touch onward. This requires that your entire sales force – from account development representatives to your head of sales – adds tailored, tangible value at every stage of the journey. Modern buyers don’t want to be sold. They want to be advised. But the majority of companies are still missing the mark, favoring spray-and-pray tactics over personalized guidance.

One reason spamming remains prevalent, despite growing awareness of the need for quality over quantity, is that implementing a tailored approach is hard work. However, companies can make great strides by doing just three things:

• Invest in personalization: Sales reps have quota, and sales leaders carry revenue targets. The pressure is as real as the numbers. But high velocity outreach tactics simply don’t work consistently. New research from Monetate and WBR Research found that 93% of businesses with advanced personalization strategies increased their revenue last year. And while scaling personalization may sound like an oxymoron, we now have artificial intelligence (AI) technology capable of doing just that. Of course, not all AI is created equal, so take the time to discern AI-powered platforms that deliver real value from the imposters. With a little research, you’ll find sales tools that discard  rinse-and-repeat prospecting methods in favor of intelligent guidance and actionable analytics.

  

Google Cloud today announced the launch of a new bare metal service, dubbed the Bare Metal Solution. We aren’t talking about bare metal servers offered directly by Google Cloud here, though. Instead, we’re talking about a solution that enterprises can use to run their specialized workloads on certified hardware that’s co-located in the Google Cloud data centers and directly connect them to Google Cloud’s suite of other services. The main workload that makes sense for this kind of setup is databases, Google notes, and specifically Oracle Database.

Bare Metal Solution is, as the name implies, a fully integrated and fully managed solution for setting up this kind of infrastructure. It involves a completely managed hardware infrastructure that includes servers and the rest of the data center facilities like power and cooling; support contracts with Google Cloud and billing are handled through Google’s systems, as well as an SLA. The software that’s deployed on those machines is managed by the customer — not Google.

The overall idea, though, is clearly to make it easier for enterprises with specialized workloads that can’t easily be migrated to the cloud to still benefit from the cloud-based services that need access to the data from these systems. Machine learning is an obvious example, but Google also notes that this provides these companies with a bridge to slowly modernize their tech infrastructure in general (where “modernize” tends to mean “move to the cloud”).

“These specialized workloads often require certified hardware and complicated licensing and support agreements,” Google writes. “This solution provides a path to modernize your application infrastructure landscape, while maintaining your existing investments and architecture. With Bare Metal Solution, you can bring your specialized workloads to Google Cloud, allowing you access and integration with GCP services with minimal latency.”

Because this service is co-located with Google Cloud, there are no separate ingress and egress charges for data that moves between Bare Metal Solution and Google Cloud in the same region.

The servers for this solution, which are certified to run a wide range of applications (including Oracle Database) range from dual-socket 16-core systems with 384 GB of RAM to quad-socket servers with 112 cores and 3072 GB of RAM. Pricing is on a monthly basis, with a preferred term length of 36 months.

Obviously, this isn’t the kind of solution that you self-provision, so the only way to get started — and get pricing information — is to talk to Google’s sales team. But this is clearly the kind of service that we should expect from Google Cloud, which is heavily focused on providing as many enterprise-ready services as possible.

Deep learning is all the rage these days in enterprise circles, and it isn’t hard to understand why. Whether it is optimizing ad spend, finding new drugs to cure cancer, or just offering better, more intelligent products to customers, machine learning — and particularly deep learning models — have the potential to massively improve a range of products and applications.

The key word though is ‘potential.’ While we have heard oodles of words sprayed across enterprise conferences the last few years about deep learning, there remain huge roadblocks to making these techniques widely available. Deep learning models are highly networked, with dense graphs of nodes that don’t “fit” well with the traditional ways computers process information. Plus, holding all of the information required for a deep learning model can take petabytes of storage and racks upon racks of processors in order to be usable.

There are lots of approaches underway right now to solve this next-generation compute problem, and Cerebras has to be among the most interesting.

As we talked about in August with the announcement of the company’s “Wafer Scale Engine” — the world’s largest silicon chip according to the company — Cerebras’ theory is that the way forward for deep learning is to essentially just get the entire machine learning model to fit on one massive chip. And so the company aimed to go big — really big.

Today, the company announced the launch of its end-user compute product, the Cerebras CS-1, and also announced its first customer of Argonne National Laboratory.

The CS-1 is a “complete solution” product designed to be added to a data center to handle AI workflows. It includes the Wafer Scale Engine (or WSE, i.e. the actual processing core) plus all the cooling, networking, storage, and other equipment required to operate and integrate the processor into the data center. It’s 26.25 inches tall (15 rack units), and includes 400,000 processing cores, 18 gigabytes of on-chip memory, 9 petabytes per second of on-die memory bandwidth, 12 gigabit ethernet connections to move data in and out of the CS-1 system, and sucks just 20 kilowatts of power.

A cross-section look at the CS-1. Photo via Cerebras

Cerebras claims that the CS-1 delivers the performance of more than 1,000 leading GPUs combined — a claim that TechCrunch hasn’t verified, although we are intently waiting for industry-standard benchmarks in the coming months when testers get their hands on these units.

In addition to the hardware itself, Cerebras also announced the release of a comprehensive software platform that allows developers to use popular ML libraries like TensorFlow and PyTorch to integrate their AI workflows with the CS-1 system.

In designing the system, CEO and co-founder Andrew Feldman said that “We’ve talked to more than 100 customers over the past year and a bit,“ in order to determine the needs for a new AI system and the software layer that should go on top of it. “What we’ve learned over the years is that you want to meet the software community where they are rather than asking them to move to you.”

I asked Feldman why the company was rebuilding so much of the hardware to power their system, rather than using already existing components. “If you were to build a Ferrari engine and put it in a Toyota, you cannot make a race car,” Feldman analogized. “Putting fast chips in Dell or [other] servers does not make fast compute. What it does is it moves the bottleneck.” Feldman explained that the CS-1 was meant to take the underlying WSE chip and give it the infrastructure required to allow it to perform to its full capability.

A diagram of the Cerebras CS-1 cooling system. Photo via Cerebras.

That infrastructure includes a high-performance water cooling system to keep this massive chip and platform operating at the right temperatures. I asked Feldman why Cerebras chose water, given that water cooling has traditionally been complicated in the data center. He said, “We looked at other technologies — freon. We looked at immersive solutions, we looked at phase-change solutions. And what we found was that water is extraordinary at moving heat.”

A side view of the CS-1 with its water and air cooling systems visible. Photo via Cerebras.

Why then make such a massive chip, which as we discussed back in August, has huge engineering requirements to operate compared to smaller chips that have better yield from wafers. Feldman said that “ it massively reduces communication time by using locality.”

In computer science, locality is placing data and compute in the right places within, let’s say a cloud, that minimizes delays and processing friction. By having a chip that can theoretically host an entire ML model on it, there’s no need for data to flow through multiple storage clusters or ethernet cables — everything that the chip needs to work with is available almost immediately.

According to a statement from Cerebras and Argonne National Laboratory, Cerebras is helping to power research in “cancer, traumatic brain injury and many other areas important to society today” at the lab. Feldman said that “It was very satisfying that right away customers were using this for things that are important and not for 17-year-old girls to find each other on Instagram or some shit like that.”

(Of course, one hopes that cancer research pays as well as influencer marketing when it comes to the value of deep learning models).

Cerebras itself has grown rapidly, reaching 181 engineers today according to the company. Feldman says that the company is hands down on customer sales and additional product development.

It has certainly been a busy time for startups in the next-generation artificial intelligence workflow space. Graphcore just announced this weekend that it was being installed in Microsoft’s Azure cloud, while I covered the funding of NUVIA, a startup led by the former lead chip designers from Apple who hope to apply their mobile backgrounds to solve the extreme power requirements these AI chips force on data centers.

Expect ever more announcements and activity in this space as deep learning continues to find new adherents in the enterprise.

What do you do when your startup idea doesn’t prove big enough? Run it as a scrawny but profitable lifestyle business? Or sell it to a competitor and take another swing at the fences? Social audience analytics startup SocialRank chose the latter and is going for glory.

Today, SocialRank announced it’s sold its business, brand, assets, and customers to influencer marketing campaign composer and distributor Trufan which will run it as a standalone product. But SocialRank’s team isn’t joining up. Instead, the full six-person staff is sticking together to work on a mobile-first professional social network called Upstream aiming to nip at LinkedIn.

SocialRank co-founder and CEO Alex Taub

Started in 2014 amidst a flurry of marketing analytics tools, SocialRank had raised $2.1 million from Rainfall Ventures and others before hitting profitability in 2017. But as the business plateaued, the team saw potential to use data science about people’s identity to get them better jobs.

“A few months ago we decided to start building a new product (what has become Upstream). And when we came to the conclusion to go all-in on Upstream, we knew we couldn’t run two businesses at the same time” SocialRank co-founder and CEO Alex Taub tells me. “We decided then to run a bit of a process. We ended up with a few offers but ultimately felt like Trufan was the best one to continue the business into the future.”

The move lets SocialRank avoid stranding its existing customers like the NFL, Netflix, and Samsung that rely on its audience segmentation software. Instead, they’ll continue to be supported by Trufan where Taub and fellow co-founder Michael Schonfeld will become advisors.

“While we built a sustainable business, we essentially knew that if we wanted to go real big, we would need to go to the drawing board” Taub explains.

SocialRank

Two-year-old Trufan has raised $1.8 million Canadian from Round13 Capital, local Toronto startup Clearbanc’s founders, and several NBA players. Trufan helps brands like Western Union and Kay Jewellers design marketing initiatives that engage their customer communities through social media. It’s raising an extra $400,000 USD in venture debt from Round13 to finance the acquisition, which should make Trufan cash-flow positive by the end of the year.

Why isn’t the SocialRank team going along for the ride? Taub said LinkedIn was leaving too much opportunity on the table. While it’s good for putting resumes online and searching for people, “All the social stuff are sort of bolt-ons that came after Facebook and Twitter arrived. People forget but LinkedIn is the oldest active social network out there”, Taub tells me, meaning it’s a bit outdated.

Trufan’s team

Rather than attack head-on, the newly forged Upstream plans to pick the Microsoft-owned professional network apart with better approaches to certain features. “I love the idea of ‘the unbundling of LinkedIn’, ala what’s been happening with Craigslist for the past few years” says Taub. “The first foundational piece we are building is a social professional network around giving and getting help. We’ll also be focused on the unbundling of the groups aspect of LinkedIn.”

Taub concludes that entrepreneurs can shackle themselves to impossible goals if they take too much venture capital for the wrong business. As we’ve seen with SoftBank, investors demand huge returns that can require pursuing risky and unsustainable expansion strategies.

“We realized that SocialRank had potential to be a few hundred million dollar in revenue business but venture growth wasn’t exactly the model for it” Taub says. “You need the potential of billions in revenue and a steep growth curve.” A professional network for the smartphone age has that kind of addressable market. And the team might feel better getting out of bed each day knowing they’re unlocking career paths for people instead of just getting them to click ads.