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This is a guest post written by Claude Johnson, a Lead Site Reliability Engineer at salesforce.com.

The following is an architectural overview of salesforce.com’s core platform and applications. Other systems such as Heroku's Dyno architecture or the subsystems of other products such as work.com and do.com are specifically not covered by this material, although database.com is. The idea is to share with the technology community some insight about how salesforce.com does what it does. Any mistakes or omissions are mine.

This is by no means comprehensive but if there is interest, the author would be happy to tackle other areas of how salesforce.com works. Salesforce.com is interested in being more open with the technology communities that we have not previously interacted with. Here’s to the start of “Opening the Kimono” about how we work.

Since 1999, salesforce.com has been singularly focused on building technologies for business that are delivered over the Internet, displacing traditional enterprise software. Our customers pay via monthly subscription to access our services anywhere, anytime through a web browser. We hope this exploration of the core salesforce.com architecture will be the first of many contributions to the community.

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Original author: 
Alyson Shontell

Corey Capasso is only 26, but he's already founded five companies. He founded his first startup, a plastic retainer business in Connecticut, when he was 18. He noticed classmates chewing on pen caps, and thought the experience would be better if the caps tasted like fruit. So he and a partner invented flavored plastic.

That business is still up and running, but Capasso went on to another venture. Capasso helped create Spinback, a startup that was acquired by Buddy Media in a mostly-stock deal. That acquisition was fruitful when Buddy Media was acquired by Salesforce one year later for nearly $700 million.

We spoke to the startup-addicted twenty-something about what motivates him in business, and how 15 failures led to ultimate success.

 

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SEE ALSO: Why Physical Retail Stores Are Not Going To Die — At Least Not Until 2020

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 AlbumIn the business of selling stuff, there’s a lot of managing. Sales reps usually have a boss they check in with on the status of deals in the pipeline, maybe to get some advice on how to close a deal when there’s stiff competition from another company, or to go over how an important customer was reeled in, so that others can learn from it.

These check-ins are sometimes referred to as coaching, and there is data to show that coaching can boost sales performance. A study by the Sales Executive Council suggests that reps who received three or more hours of coaching per month outsold those who received two hours or less of coaching per month, by as much as 17 percent.

Getting that coaching done can be kind of a hassle. But it’s the sort of hassle that Salesforce.com has often sought to understand intimately, and then create products within its suite of cloud software tools.

Today is one of those days. The company is announcing a trial of a new feature that closely ties its traditional Sales Cloud with its Work.com product. The point is to do a few things: Speed up the review portion that has always tended to be a big consumer of time and attention in pretty much any organization, and also to make it easier for sales managers to find ways to motivate their teams to, you know, sell more stuff, which is basically the point of sales in the first place.

Through a combination of Salesforce services including the Sales Cloud, its social enterprise platform Chatter and Work.com, an HR software outfit that includes the Rypple acquisition it made last year, sales teams will see each other’s goals, will learn about big deals coming in, and know about each other’s expertise.

The new tools will also give managers a way to provide instant feedback and public recognition to those sales people who are doing well. Remember “gamification”? It’s not my favorite word, but apparently it works to some extent, especially with sales people who have monthly, quarterly and annual targets to make.

There is research to back up the assertion that when people leave sales jobs they do so in part because they don’t think they’re getting enough recognition from above. Now, on those occasions when a rep lands a big customer in a competitive deal, the manager can publicly pat them on the back with a “thanks in Chatter” feature, and give them a “sales Ninja” badge, or something like it, that everyone can see in their Chatter feeds.

Think it all sounds hokey? Maybe it is, but there’s a lot of evidence that these things have a way to making sales people happier on the job. And happy sales reps are sales reps who close deals, or least that’s the theory. We’ve come a long way since Alec Baldwin’s memorable (and profanity-laced) monologue in “Glengarry Glen Ross.”

The new features are coming in early 2013, and are available for certain Salesforce customers on a pilot basis starting today.

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hal380The advent of Salesforce Marketing Cloud and Adobe Marketing Cloud demonstrates the need for enterprises to develop new ways of harnessing the vast potential of big data. Yet these marketing clouds beg the question of who will help marketers, the frontline of businesses, maximize marketing spending and ROI and help their brands win in the end. Simply moving software from onsite to hosted servers does not change the capabilities marketers require — real competitive advantage stems from intelligent use of big data.

Marc Benioff, who is famous for declaring that “Software Is Dead,” may face a similar fate with his recent bets on Buddy Media and Radian6. These applications provide data to people who must then analyze, prioritize and act — often at a pace much slower than the digital world. Data, content and platform insights are too massive for mere mortals to handle without costing a fortune. Solutions that leverage big data are poised to win — freeing up people to do the strategy and content creation that is best done by humans, not machines.

Big data is too big for humans to work with, at least in the all-important analytical construct of responding to opportunities in real time — formulating efficient and timely responses to opportunities generated from your marketing cloud, or pursuing the never-ending quest for perfecting search engine optimization (SEO) and search engine marketing (SEM). The volume, velocity and veracity of raw, unstructured data is overwhelming. Big data pioneers such as Facebook and eBay have moved to massive Hadoop clusters to process their petabytes of information.

In recent years, we’ve gone from analyzing megabytes of data to working with gigabytes, and then terabytes, and then petabytes and exabytes, and beyond. Two years ago, James Rogers, writing in The Street, wrote: “It’s estimated that 1 Petabyte is equal to 20 million four-door filing cabinets full of text.” We’ve become jaded to seeing such figures. But 20 million filing cabinets? If those filing cabinets were a standard 15 inches wide, you could line them up, side by side, all the way from Seattle to New York — and back again. One would need a lot of coffee to peruse so much information, one cabinet at a time. And, a lot of marketing staff.

Of course, we have computers that do the perusing for us, but as big data gets bigger, and as analysts, marketers and others seek to do more with the massive intelligence that can be pulled from big data, we risk running into a human bottleneck. Just how much can one person — or a cubicle farm of persons — accomplish in a timely manner from the dashboard of their marketing cloud? While marketing clouds do a fine job of gathering data, it still comes down to expecting analysts and marketers to interpret and act on it — often with data that has gone out of date by the time they work with it.

Hence, big data solutions leveraging machine learning, language models and prediction, in the form of self-learning solutions that go from using algorithms for harvesting information from big data, to using algorithms to initiate actions based on the data.

Yes, this may sound a bit frightful: Removing the human from the loop. Marketers indeed need to automate some decision-making. But the human touch will still be there, doing what only people can do — creating great content that evokes emotions from consumers — and then monitoring and fine-tuning the overall performance of a system designed to take actions on the basis of big data.

This isn’t a radical idea. Programmed trading algorithms already drive significant activity across stock markets. And, of course, Amazon, eBay and Facebook have become generators of — and consummate users of — big data. Others are jumping on the bandwagon as well. RocketFuel uses big data about consumers, sites, ads and prior ad performance to optimize display advertising. Turn.com uses big data from consumer Web behavior, on-site behaviors and publisher content to create, optimize and buy advertising across the Web for display advertisers.

The big data revolution is just beginning as it moves beyond analytics. If we were building CRM again, we wouldn’t just track sales-force productivity; we’d recommend how you’re doing versus your competitors based on data across the industry. If we were building marketing automation software, we wouldn’t just capture and nurture leads generated by our clients, we’d find and attract more leads for them from across the Web. If we were building a financial application, it wouldn’t just track the financials of your company, it would compare them to public filings in your category so you could benchmark yourself and act on best practices.

Benioff is correct that there’s an undeniable trend that most marketing budgets today are betting more on social and mobile. The ability to manage social, mobile and Web analysis for better marketing has quickly become a real focus — and a big data marketing cloud is needed to do it. However, the real value and ROI comes from the ability to turn big data analysis into action, automatically. There’s clearly big value in big data, but it’s not cost-effective for any company to interpret and act on it before the trend changes or is over. Some reports find that 70 percent of marketers are concerned with making sense of the data and more than 91 percent are concerned with extracting marketing ROI from it. Incorporating big data technologies that create action means that your organization’s marketing can get smarter even while you sleep.

Raj De Datta founded BloomReach with 10 years of enterprise and entrepreneurial experience behind him. Most recently, he was an Entrepreneur-In-Residence at Mohr-Davidow Ventures. Previously, he was a Director of Product Marketing at Cisco. Raj also worked in technology investment banking at Lazard Freres. He holds a BSE in Electrical Engineering from Princeton and an MBA from Harvard Business School.

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zuckerberg money

Everyone remembers when Facebook bought Instagram for a headline-friendly billion dollars last April. But what about the 10 companies it acquired after that? Or the 29 prior?

The social media landscape is in such a constant state of flux that it's sometimes hard to remember who did what when.

The Interactive Advertising Bureau (IAB) and The Jordan, Edmiston Group Inc (JEGI) teamed up to take snapshots of what the social media ecosystem looks like right now.

Among other things, the report breaks down the acquisition timelines for major digital players, including Facebook, Twitter, LinkedIn, Salesforce, and Google.

Facebook's acquisition timeline:

According to the report, "Facebook has acqui‐hired talented staff from a number of companies in addition to its traditional M&A, focusing on enhanced content sharing/discovery (FriendFeed, Face.com), IP (Tagtile), location awareness (Gowalla), ecommerce/gifts (Karma) and mobile (Glancee, Lightbox)." 

Furthermore, Facebook is known to scoop up its competition for hefty chunks of change. When threatened by Instagram in the image and mobile space, Facebook bought the start-up for a cool billion dollars.

Twitter's acquisition timeline:

Twitter's acquisitions help boost user experience and its ad-based business model.

The report notes that "areas of interest include keyword bidding, social marketing automation, geo‐targeted ads, and social analytics," which is reflected by recent buys.

LinkedIn's acquisition timeline:

LinkedIn acquired SlideShare, a company that allows users to easily share business documents and presentations, for $119 in May 2012. Apart from that, LinkedIn's acquisitions "have typically focused on tuck‐ins and tend to acquire businesses that are pre‐revenue."

See the rest of the story at Business Insider

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