An op-ed I wrote for TechCrunch:
There are several enterprise players that want a share of Salesforce’s business, but just aren’t making headway by knuckling up against the company’s dominant, entrenched SaaS CRM offerings. Rather than competing head on, a smarter approach for these businesses is to “front door” Salesforce, instead.
By acquiring Marketo, a competitor could get into Salesforce’s accounts, then, over time, work themselves down the funnel and leverage better integrations with Marketo in order to eventually displace Salesforce. Marketo’s strategic foothold in the enterprise and its current market value relative to potential acquirers like IBM, Microsoft, Oracle, SAP and even Salesforce make this a great time to buy the leading marketing automation vendor.
Many industry watchers overlook the mission-critical role Marketo plays in its customers’ go-to-market operations. The majority of Marketo’s 4,000 customers also use Salesforce, but the marketing automation system has access to more data about the funnel than its CRM counterpart. Marketo can sync bi-directionally with Salesforce, capturing all the data stored there, while also holding top-of-the-funnel lead behavior data that doesn’t get stored in CRM. Hence, it has access to an invaluable superset of data about a company’s potential and existing customers.
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An op-ed I wrote for VentureBeat:
The line between marketing and sales is getting blurrier by the minute. Sales reps are leveraging new sales acceleration tools like Tout, Yesware, Sidekick, and Outreach, and it feels like a new one comes out every quarter.
These specialized apps have become so sophisticated that they’re enabling sales to run their own campaigns and sidestep marketing automation. They help teams increase response rates through more personalization and control, a 1:1 touch, simple plain text messages, and more follow up vs. blanket general marketing blasts.
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An op-ed I wrote with Tomasz Tunguz for Techcrunch:
One of the most critical metrics for software companies — but also one of the most difficult to measure — is the lifetime value of their customers (LTV). The lifetime value dictates how a company should spend its marketing and sales dollars.
Unfortunately, many early stage startups struggle to measure LTV, because they haven’t been around very long and, consequently, haven’t seen a large number of customers through their lifespans with the product.
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An op-ed I wrote for TechCrunch:
According to industry expert David Raab, almost 70 percent of marketers are either unhappy or only marginally happy with their marketing automation software. According to Bluewolf’s new “State of Salesforce” study, only 7 percent are seeing good, measurable ROI from those investments. There’s a lot of fragmentation and dissatisfaction in this category despite the huge potential benefits of automating marketing.
A key contributor to the current state of marketing automation is the fact that its roots stem from email blasting. As these systems layered in landing pages and forms, web activity data, triggers, etc. over time, they became bloated from trying to do too much and began to over-promise and under-deliver.
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An op-ed I wrote for TechCrunch:
Today’s B2B sales and marketing folks struggle with the overwhelming number of channels for finding and reaching new leads. The customer “funnel” continues to expand as buyers do more of their own research before raising their hand to connect with a sales rep. But imagine if you could make the funnel taller by identifying leads when they’re just browsing your site and haven’t yet filled out your “contact me” form, or leads who haven’t yet visited but are likely to be a good fit for your product? That’s hard to do with the primitive tools that are available for sales and marketers today, unless you bring together some very rare assets – which just so happen to all exist at LinkedIn.
LinkedIn is the only company with fairly clean, accurate details on pretty much every contact that matters in the business world (unfortunately, most other data providers’ contact info contains 80% garbage, and they can’t really improve it without violating CAN-SPAM laws). LinkedIn also reflects the direction sales is heading with strong channels for thought leadership. Via LinkedIn, you can educate and advocate for your customers vs. just selling to them.
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An op-ed I wrote for VentureBeat on why Salesforce launched Wave and the impact that will have on the analytics industry at large:
At Salesforce.com’s Dreamforce conference in San Francisco on Monday, Marc Benioff unveiled his company’s much anticipated Wave Analytics Cloud product. Marketed as “analytics for everyone” with a focus on mobility and slick visualizations inspired by video games, Wave aims to bring more analytics to decision makers more quickly.
Wave is great news for Salesforce’s massive customer base. Current customers will gain the ability to easily attain valuable insight via modern dashboards on any device, and to even execute advanced analytical operations on all types of business data. This business-intelligence (BI) approach, which appears to treat customer analytics (sales, customer support, and marketing) as a first-class citizen, is quite a departure from current horizontal BI tools like GoodData, Birst, and Tableau that focus more on performing analytics across a myriad of functions. This Salesforce Analytics Cloud is sure to deliver real business value by offering a platform that’s more specialized for customer needs, which makes sense since most use cases for BI are related to sales and customer analytics.
Why analytics is a great move for Salesforce
Anyone close to Salesforce knows that Analytics Cloud is a huge step beyond the company’s standard reporting capabilities, which have historically been rather limited. Until now, the system really just scratched the surface of a business’ sales data (try to report on something like your sales cycle lengths by lead source over time, and you’ll see what I mean). That said, it was smart of Salesforce to leave analytics up to its AppExchange partners in the beginning, because the company was busy building the SaaS world we all play in today, starting with its customer-relationship management platform. Tackling analytics at that time would have been like running two entirely different companies.
However, over the past couple years, the data needs of modern sales and marketing leaders have grown dramatically with the rise of big data. Customers are hungry for insight, and have been asking why Salesforce doesn’t offer seamless, built-in, advanced analytics. Most companies just don’t want to move data between multiple services, especially if they have rigorous security policies or huge data sets. In this data-driven environment, Salesforce’s customer satisfaction has become heavily dependent on partners it can’t control, making it increasingly important for the company to shift toward a hands-on approach to analytics and meet customers’ needs directly.
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A guest piece I wrote for TechCrunch on how predictive-first (like “mobile-first”) applications will change the software game:
Over the past several decades, enterprise technology has consistently followed a trail that’s been blazed by top consumer tech brands. This has certainly been true of delivery models – first there were software CDs, then the cloud, and now all kinds of mobile apps. In tandem with this shift, the way we build applications has changed and we’re increasingly learning the benefits of taking a mobile-first approach to software development.
Case in point: Facebook, which of course began as a desktop app, struggled to keep up with emerging mobile-first experiences like Instagram and WhatsApp, and ended up acquiring them for billions of dollars to play catch up.
The Predictive-First Revolution
Recent events like the acquisition of RelateIQ by Salesforce demonstrate that we’re at the beginning of another shift toward a new age of predictive-first applications. The value of data science and predictive analytics has been proven again and again in the consumer landscape by products like Siri, Waze and Pandora.
Big consumer brands are going even deeper, investing in artificial intelligence (AI) models such as “deep learning.” Earlier this year, Google spent $400 million to snap up AI company DeepMind, and just a few weeks ago, Twitter bought another sophisticated machine-learning startup called MadBits. Even Microsoft is jumping on the bandwagon, with claims that its “Project Adam” network is faster than the leading AI system, Google Brain, and that its Cortana virtual personal assistant is smarter than Apple’s Siri.
The battle for the best data science is clearly underway. Expect even more data-intelligent applications to emerge beyond the ones you use every day like Google web search. In fact, this shift is long overdue for enterprise software. (Read More)