Marketing becomes new revenue arm

Traditionally, CMOs have dealt with the “soft skills” of marketing. They headed up cost centers filled with branding, advertising and campaigns that were expensive endeavors, producing benefits that were often difficult to measure. In this current shift, CMOs might not bring CIOs to their knees, but if corporate budgets could talk they would certainly favor the CMO. To wit: Gartner predicts that by 2017 the CMO will spend more on IT than the CIO. Why? Mobile, big data and real-time analytics are transforming the modern CMO’s organization from a cost center to a critical revenue-driving arm to reach and engage the customer base.

Realizing that CIOs and CMOs probably hate stories of a battle raging between them, there is actually a peaceful, and logical, middle ground in which the two work together to harness the vastness of big data to create real-time – and importantly, actionable – analytics. While the CMO brings the marketing skills to the table, the CIO has the technical chops to deal with capturing, processing and integrating data to make it useful.

Mobile transition requires new tools

The CMO is in a particularly prime position in the increasingly important mobile channel, as the feedback loop is uniquely personal and immediate. The ability to measure, predict and act upon an end-user action has never been more precise.

However, mobile is a different beast and legacy systems designed for the web rely on cookies to track user interaction. As we go more mobile, we enter a world devoid of cookies. New tools are required to match mobile users to their actions across multiple channels. Along with new tools, there needs to be a new partnership – the CMO and CIO united as a Dynamic Duo – working diligently together to deliver the cloud-based and back-office infrastructure required to pull actionable information from big data across all channels: bricks and mortar, web and mobile.

New focus on data-driven outreach

To get a view into what makes the CMO so potentially powerful, let’s turn to another battle, the one that happens on a quarterly basis across corporate America and beyond: The fight to make the quarterly earnings number. Typically, a CEO faced with a sales or earnings shortfall will turn to the CFO and the head of sales and say: “What can you do?”

The honest answer is: short term, not much. They can jam the channel, which will make the next quarter’s number even harder to achieve. Or shovel product out the door at prices that wouldn’t otherwise make sense. But by the time the word gets to the field and actions kick into gear, there can be quite a lag in results. And, again, these results may have value only for the short-term.

In the very near future – one that has already arrived for some organizations – the CEO turns to the CMO, who looks up from his or her laptop and says: “No problem. We should make that number by noon.”

The CFO and head of sales turn and say in unison: “How?”

“While you were talking I found an overstock of our Zing42 and sent an offer to 234,000 customers who have purchased this in the past, and statistically are ready to buy again, and to another 341,000 customers who haven’t purchased this yet, but show a high probability of doing so at the price point we are offering.”

No hurried sales calls to the field asking them to stuff the channel. No broadcasting of sales and discounts that can tarnish an image or weaken a brand. But rather, an analytically-driven offer to a curated digital audience delivered via a simple mobile notification, or in-application offer, to thousands of people who are analyzed and selected to be eager buyers.

This is the look of the CMO ascending: Powered by a cloud of mobile, big data and real-time analytics.

Rob Lilleness is the CEO of Medio, a Seattle-based provider of analytics solutions for mobile computing. Follow him on Twitter @Robmedio.

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Of course, as Om and others have already mentioned, there’s no guarantee this deal will actually be consummated: it could fall apart on valuation, as so many deals do — or Facebook could swoop in with a much higher offer and snatch Tumblr out of Yahoo’s clutches, the same way it did when it stole Instagram away from Twitter last year for close to $1 billion.

Update: According to the Wall Street Journal, the Yahoo board of directors has approved a $1.1-billion all-cash bid to acquire Tumblr.

It makes Yahoo look desperate — because it is

Even if the deal does get done, one of the risks for Mayer and Yahoo is that the company could look desperate by paying more than $1 billion for a site that had revenues of less than $15 million last year (although CEO David Karp has said that figure should hit $100 million this year). That’s an almost bubble-like multiple for a company, and there will likely be plenty of criticism from investors who believe that $1 billion could be better spent elsewhere — in other words, on businesses that would make Yahoo a better return.

But the painful fact is that Yahoo doesn’t just look desperate — in many ways it is desperate. Mayer has made some changes since she took over the ailing former web portal, including the acquisition of Summly and a number of other mobile-focused startups and services, but the company still needs to make some aggressive moves if it is going to jump-start any growth at all. And since Yahoo has about $4 billion in cash on hand, it can arguably afford to make a big bet.

Yahoo buying Tumblr makes sense. Tumblr is only big, cool, newish social platform that Yahoo can afford.—
Henry Blodget (@hblodget) May 17, 2013

For Yahoo, the addition of Tumblr would do a number of things: because of the size and profile of the deal, it would make a major statement about Mayer’s intention to do whatever it takes to revitalize the company, and it would also send a signal to Facebook and Google — and even Apple — that Yahoo is a potential force to be reckoned with when it comes to potential acquisitions. Is doing that worth $1 billion? That’s for Yahoo’s investors and board of directors to decide.

Just as important, it would inject some much-needed life and energy into the somewhat stale lineup of content that the company currently relies on, which caters more to the over-50 set than it does to anyone in the much-desired 18 to 25 demographic. More than any other network, Tumblr is the platform of choice for media-obsessed teens and 20-somethings, who spend massive amounts of time sharing photos and videos and animated GIFs on the site — an engine of potential value that Yahoo desperately needs.

Tumblr gets a massive exit

This doesn’t come without its own risks, of course: As a number of observers have noted, Tumblr’s content contains a large quantity of not only mature or arguably offensive content but outright pornography, which many argue is the source of its massive traffic numbers. How Yahoo (or Facebook for that matter) would deal with this kind of content remains to be seen.

3 q's for Yahoo: 1) Can you convert Tumblr users to Yahoo products? 2) Can you monetize Tumblr PVs? 3) What to do w/ all that Tumblr porn?—
Mark Zohar (@markzohar) May 18, 2013

For Tumblr, meanwhile, being acquired would solve a number of problems — the main one being that the company has gone well beyond the “we’re a startup so we don’t really have to make money” stage, and is facing increasing pressure from the investors who have given CEO David Karp more than $125 million in venture financing, an investment that values the company at about $800 million. Accepting a giant check from Yahoo would take care of that problem in one fell swoop, especially if it was all in cash.

With a major company like Yahoo as a partner, Tumblr could connect its massive audience of users to the firehose of ads and other monetization methods the giant web portal has, and potentially generate much more revenue than it could have by itself. The only lingering question at that point is whether Tumblr fans decide that Yahoo is poisoning the well of social content and community on the site, and decide to flee for greener pastures. In other words, does Yahoo make Tumblr into YouTube — a successful standalone network that can grow and prosper on its own — or does it become MySpace?

The only scenario where a Yahoo-Tumblr combo works is if Yahoo keeps Tumblr separate in the same way Google managed YouTube.—
Mark Birch (@marksbirch) May 18, 2013

Post and thumbnail photos courtesy of Shutterstock / ollyy and Albert Chau

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“We’re thrilled,” Tableau co-founder and CEO Christian Chabot told me during a call after the market closed. One should hope so.

Chabot and his fellow co-founders stand to make a lot of money if today’s closing price holds up, as does its sole investor NEA. The firm put $15 million into Tableau since it launched in 2003, and has rode that sum to profitability and more than $127 million in annual revenue.

Here’s a quick chart (made using Tableau Public) showing who owns how many share and what they’re potentially worth.

The company didn’t really need more capital to operate, Chabot said, but one of the primary drivers was to raise awareness of the company. It has about 12,000 customers, he said, but there are millions more possible users. As part of attracting them, the company is going to expand globally and is working to improve its reach across mobile devices, the cloud and the Mac operating system.

“I don’t believe in the this whole ‘or’ philosophy with computers,” Chabot said. “It’s ‘and’” — meaning people will use desktops and tablets and smartphones.

More prominence and more users singing its praises might also dispel the notion that Tableau is just about visualization. It has some fairly advanced features under the covers (as a commenter to my earlier post about the company’s influence pointed out), even if they’re hidden by the relatively simple user experience.

“Tableau is not a visualization company, per se, it’s really an analytics company,” Chabot said.

However, if the company really wants to expand its reach to everyone one who wants to gain knowledge from data — something Chabot calls a “timeless human need” — it might actually need to get simpler. More marketing can let potential business users know about new features like forecasting and data-extraction, but it won’t make a dentist is Des Moines better at formatting his data.

After raising $254 million in its IPO, though, Tableau is in a good place to do whatever it has to.

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The company’s product, Spire, which provided full SQL support on top of the HBase NoSQL database, was one of the first products to try to blend Hadoop’s scalability with the robustness and familiarity of SQL. That’s now an increasingly crowded space (and has grown since that linked graphic was created). In March, Drawn to Scale expanded its support to MongoDB, as well.

I wasn’t shocked when Stephens told me the news — questions about the four-year-old company’s financial health had been swirling for a while — but to hear of its financial woes was a bit surprising. His account in the post pretty much echoes what I had heard from others:

“It seemed we had everything going for us — paid customers such as American Express, Orange Telecom, Flurry, and 4 others. Our technology worked brilliantly, we had a big hiring pipeline, and we had great media presence against our competitors who raised 10-100x more cash.”

He added:

“Yet five days before we signed term sheets for a big A round or sold the company, we started getting hit by a series of black swans — and we just didn’t have what we needed to recover. I’ll leave the public detail at that level, but I will say that paying employees’ health insurance out of your meager savings is a powerful incentive to change course.”

Up to this point, the company had raised $925,000 from RTP Ventures, IA Ventures and SK Ventures. There’s no word yet on what will come of the company’s intellectual property.

As Stephens — who’s now doing an entrepreneur-in-residence gig at Ping Identity and helping out other startups (including popular wardrobe app Cloth) — succinctly put it during a phone discussion, “We just don’t have the horsepower to keep running the company.”

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Parakweet is a company that’s working to use natural language processing to cull through your tweets and make smart, targeted suggestions based on the data. On Friday, the company plans to announce the launch of two products. One is Bookvi.be, a consumer-oriented book recommendation engine, and TrendFinder For Movies, which is a social media dashboard primarily for entertainment companies to monitor conversations around movies. The latter is a paid product that provides the company with revenue, and the former is free for consumers.

“It’s a very hard problem we’ve tackled, which is accurately identifying sentiments,” CEO Ramesh Haridas said. “With 400 million tweets a day, there are 700,000 a day discussing movies, and if you tried text-matching techniques you’d come back with 40 million results. Many movies and books have very common titles, so you’d just drown in data.”

Both products use natural language processing to figure out how common a title is on Twitter, but also how a consumer is tweeting about a particular product, and they make recommendations based on those tweets. For instance, if I tweeted that a particular book is terrible and no one should ever read it, it would look ridiculous for a book recommendation engine to suggest that book to people. So Bookvi.be is structured to recognize the words I’m using in my tweet and know not to recommend that book. Users can choose to have a weekly email send to them with book suggestions, and they can type in their Twitter username to get book suggestions based on the people they follow.

“The bar on accuracy is very high,” Haridas said. “Especially if it’s sent via email, the precision needs to be intact.”

I’ve looked at a good number of social recommendation tools, and this one definitely stood out. For one, it was incredibly accurate — all the books it suggested were books I would actually read. But most importantly, it didn’t require me to create a new social network, or depend on friends for reviews, so you could get a lot of value from it right away. This is the obvious benefit of using someone else’s social graph, but Twitter seems perfectly suited to making content recommendations for things like books. Because unlike my Facebook friends, the people I follow on Twitter tend to accurately reflect my intellectual interests.

Of course, there are the obvious potential pitfalls of building a product around someone else’s platform, although Haridas said they support Facebook and are adding other platforms. But there’s a good deal of money to be made in accurately processing and understanding the words people are tweeting, as evidenced by Twitter’s acquisition of Lucky Sort this week, a similar company that also tries to figure out what people are talking about on social media.  As I’ve written before, as Twitter ramps up its advertising products it’s more important than ever for the company to be able to provide brands with more accurate ad targeting which hinges on the words people are tweeting and searching.

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Without a doubt, the panel, at Google I/O, was an apt forum for that question. If any company is innovating in a big way, it’s Google, with recent advancements in voice recognition, wearable technology, quantum computing and other realms. So it wouldn’t be surprising to see some of the Google luminaries’ ideas actually come into being. Here’s what they had to say:

“Speech recognition and vision are showing dramatic improvements over the last few years. We just need to scale them up and make them work better. … They’re (mobile devices) going to vanish into much smaller devices that you carry around and aren’t full-size laptops.” — Jeff Dean

“We’re getting more contextualized. The computer is not what you go to to use. It’s something that’s around you all the time and sort of more integrated into your life, rather than a separate thing.” — Peter Norvig

“I would argue that we’re currently living the singularity, where the tool stops and the mind begins will start becoming blurry.” — Thad Starner

So there you have it, folks — the computer as a smaller and more natural extension of the human brain. Now, let’s set the kitchen timer for 10 years and see what actually happens.

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For example, rural Kenyan farmers can easily spend 30 percent of their income on fertilizer, but 80 percent of that fertilizer can be wasted because there is little data collected about the best places and times to use it. The lack of info isn’t just from the farmer’s perspective. Aramburu says when he met the CEO of a major fertilizer company recently, he asked him what he knew about his customers — his response: “very little.”

Re:char CTO Luke Iseman (L) and founder/CEO Jason Aramburu (R) showing off an early prototype of SoilIQ

A soil data cloud in the sky

The two young entrepreneurs latest project emerged from this black hole of information. Working within French telco giant Orange’s first accelerator program, called Orange Fab, Re:char plans to build a $5 plug-in device, called SoilIQ, for an Android phone that can read the moisture levels in soil. During our interview, Iseman takes a very early prototype of the device out of a pouch and shows me how it plugs into the Android phone and taps into the phone’s computing power to detect moisture levels between two screws.

Down the road such a gadget could be developed to pick up other soil data, like fertilizer-level readings. Iseman, an avid gardener, schools me on NPK — nitrogen, phosphorus, and potassium — which he says are the holy grail of fertilizer readings. When SoilIQ is a commercial product, farmers could buy the gadget to take these types of readings, and enhance their farming productivity and the efficiency of their fertilizer use.

But the real value of such a system will likely be in the data collection, and the data analytics and services. The hourly and daily micro readings, which are coded with GPS data, could be used to create a cloud-based location data map of the quality and details of the land. This information could be used to launch data-focused services for both farmers, fertilizers companies, government groups, and others.

Re:char envisions using the data to launch a subscription service for farmers that can alert the farmers to the most fertile land, or even if there’s the danger of crop disease anywhere in the region (maize rot is a huge problem in rural Kenya). Fertilizer companies could use such data to offer better products to farmers, and potentially learn more about the end farmer customer (fertilizer can change hands ten times in Kenya until it reaches the person who will use it, says Aramburu).

Such land data could even be valuable outside of the farming communities. A massive data soil map in the cloud could help determine things like land values, or land ownership issues.

SoilIQ is part internet of things, part sustainability, and part data analytics. The entrepreneurs are actually very focused on developing tools that encourage the more efficient use of resources, which could (and should in my opinion) be the next-generation of cleantech focus.

The core concept also reminds me of what Safecast has been trying to do with its grass roots nuclear radiation sensors and data mapping in Japan. Safecast is bringing that concept to air pollution in L.A., too.

Orange interest

For telco Orange, which has a substantial presence in Kenya, such a system could help them increase mobile data use among customers. The Android devices obviously send the data to the cloud over the cellular networks. And Android phones — as Google announced this week at Google.io — are being used by 900 million people globally.

But potentially even more important is the branding involved. As Orange’s Executive Director of Business Services, Vivek Badrinath, told me in an interview at the Orange Fab event on Monday night, the mobile phone is often times the first branded product that a customer in a developing country has. That brand in turn has a unique ability to transition into offering core services, like mobile banking, and credit.

Orange has a mobile payment system, Orange Money, that is growing nicely and Re:char could plug into it for its planned services. And if SoilIQ becomes a killer app for the bottom of the pyramid, Orange would have a key position in it. Orange is interested enough in Re:char’s new idea that it not only brought the company into its accelerator program, but is investing in its angel round.

Re:char hopes to close an angel round by the end of the program, and later this year raise a series A round. By the end of the three months, Re:char also plans to have its soil moisture-reading gadget developed enough to move it into production.

If you’re a backyard composter or gardener — like Re:charge CTO Iseman — you’re probably wondering if you can get your hands on SoilIQ one day. Aramburu and Iseman tell me that they’d like to make it available in the U.S., too, so the gardening hobbyist could collect their own data.

Clearly, the team is in the very early stages of making SoilIQ, so a lot of their plans will hinge on these next few months. They plan to keep running the kiln bio charcoal business, and think SoilIQ could even help grow that business, too. They also might split the businesses in two down the road.

Ultimately if they can deliver a simple, easy to use, and cheap device, and convince Kenyan farmers to start using it, they could be on their way to unleashing data from Kenya’s rural landscape.

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That’s an apt ticker symbol for a company that is in some ways a bellwether for the current fascination with all things data. Tableau isn’t a big data company, per se, but its visualization software breathes life into many big data calculations. Its focus on making software that’s easy to use and that creates visually captivating charts has turned people from numerous professions into amateur data analysts. (I’ve even used it in the past, including for the first time in 2011.)

Christian Chabot

As Chabot told me during a conversation in 2011, “In any field of human endeavor … there are a hundred to a thousand more people who understand the data of that field more than they understand reporting and analytics.”

Anytime you read about a hot new visualization or analytics startup promising the moon, you’re also seeing the results of what Tableau has sown in terms of the user experience. Many of those same companies will be quick to tell you how limited Tableau’s capabilities are. It’s memory-bound, it doesn’t have a database, it’s not available in the cloud (or on the Mac operating system), it can’t do predictive analytics. All true.

Of course, if it raises the kind of capital it expects to by going public, it can build and buy a lot of those capabilities. If pricing stays flat all day Friday, Tableau stands to make $155 million from its 5 million shares. Previous estimates had Tableau’s market cap at around $1.7 billion at a price of $29 per share (the company’s S-1 filing is available here).

If investors have really bought into the company and the concept of a data-driven world, then who knows. Machine-data expert Splunk wnet public in 2012, flying the big data banner, and saw shares peak at 91 percent above its original asking price of $17.

I’m not suggesting Tableau is the biggest name in data, or even that it will some day become it. This next-generation analytics field is very young, with startups and larger vendors alike sometimes competing against themselves to win wholly new accounts than trying to displace legacy vendors within large enterprises. And every month, it seems, I come across some new startup that was built with the same principles in mind as Tableau, but with the advantage of having today’s best practices baked into its software.

But Tableau definitely commands a lot of the mindshare. How it fares as a public company could be a strong indicator of just how powerful the data movement is, and how well it capitalizes on a new influx of cash will determine how long it stays on the top of customers’ minds.

This post was updated at 7:01 p.m. to include previous estimates of the company’s market capitalization and a link to its S-1 filing.

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Blind trust in black box, or click-and-run, software is a growing problem in science, according to a commentary published Thursday in the journal Science, and the concern extends beyond formal research to other domains that use high performance computing.

The researchers who addressed the “troubling trend in scientific software use” were motivated by a growing unease that the abundance of powerful software is letting scientists derive answers without a thorough understanding of what the software is doing. Software snafus have been responsible for some high-profile data misinterpretations and retractions.

This wouldn’t normally cause a blip on the average citizen’s radar, but now a lot of these scientific conclusions have real-world implications, from climate modeling and weather forecasting to high volume financial trading. In any domain using big data, misplaced trust in the power of software can be problematic, particularly when the decision makers don’t know what the software they are using is doing, said lead author Lucas Joppa of Microsoft Research.

So what does ecology have to do with any of this? Joppa is an ecologist by training, and works on computational techniques in that field that may also have applications for big data more broadly. He and his colleagues surveyed scientists in a sub-field of ecology — species distribution modeling (SDM) — to find out how they choose software and how well they understand its inner workings.

“Lots of SDM techniques are only available as computational methods, but there is a lot of discourse going on in the literature about whether the methods themselves are correct,” said Joppa. Scientists use SDM to forecast where plants and animals will be in the future given current numbers, known habitats, and climate change. It’s a niche area of research, but the disquieting survey results should be noted in any domain where forecasting is done by plugging data into software.

Only 8 percent of the more than 400 scientists who responded had validated their modeling software against other methods. “The number speaks for itself,” said Joppa. “The real crux of the problem is the results from software being published in a peer-reviewed journal, versus the software itself having been peer-reviewed,” which is rare. Software packages, whether proprietary or not, are often black box systems that can’t be opened and inspected. Even if you can get under the proverbial hood, like with open source software, said Joppa, most people will still have no idea what they are looking at, or how to judge its quality.

To top it all off, having confidence in what your software is doing results in a massive computational catch-22: how do you know the software is giving you the right answer, if you can’t get the answer without running the software? The level of confusion over what algorithms are doing in the SDM field is illustrated by a debate over which of two statistical techniques is superior. It turns out, Joppa explained, that the two techniques were mathematically equivalent, but the ways they were implemented in software resulted in big predictive differences.

This sort of mix-up isn’t surprising given the messy nature of software development (if you can even call it that) in research environments. Joppa lauded efforts like Software Carpentry that teach scientists basic software fundamentals for better programming, and said the days of getting a doctorate by merely pushing a button are over.

“Scientists themselves can learn a bare minimum of software engineering,” said Joppa. On the flip side, he said computer science students should have more exposure to scientific methods. “People with traditional software engineering training become uncomfortable with the way scientists want to work with software, where the design and specs are constantly changing. The way that scientific software is built is fundamentally different from consumer apps.”

Developers of scientific software, like MathWorks or SAS, may want to watch this space. If Joppa’s suggestions are implemented, journals may start requiring that even proprietary software be opened up for inspection and peer-review. Nearly half of the surveyed ecologists report using free statistical language R as their primary software, so maybe there is hope yet, both for open, inspectable code, and for computational science becoming more accessible while yielding trustworthy, high impact results.

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