The numbers are adding up for President Obama on Iran. It now appears that his nuclear deal - which he defended in a letter last week - will make it through Congress because it's "nearly mathematically impossible for GOP leaders to build a veto-proof majority in either the House or Senate," reports Politico .
Investors do due diligence on companies before putting their money where their mouth is. Do entrepreneurs do the same before accepting money? The situation is a trifle incestuous.
Qbox, a startup that operates a cloud-based version of the Elasticsearch open-source search database for use in other companies’ applications, in announcing today that it has raised $2.4 million.
Currently the startup makes it easier for companies to deploy and maintain services once they’ve chosen to use Elasticsearch. Developers no longer need to take time out to set up the database on top of server and storage infrastructure. Qbox has it ready to go in several locations within the Amazon Web Services, Microsoft Azure, IBM SoftLayer, and Rackspace clouds. Support is available, too. The service has attracted around 350 customers, including Nordstrom, Renault Nissan, and Yahoo Small Business.
And there is a market in hosted databases. Elastic, the company behind the Elasticsearch database, itself acquired a hosted Elasticsearch startup, Found, earlier this year. Startup Compose, which offered hosted Elasticsearch as well as hosted versions of other databases, got acquired by IBM last month. Other competitors include Bonsai and Rackspace’s ObjectRocket.
But Qbox wants to go further than just hosting Elasticsearch. The startup has set out on building “Project Gossamer” — a front end that admins will be able to use to configure search and analytics workloads for their applications.
“Right now, Elasticsearch is a back-end command-line technology,” Qbox cofounder and chief executive Mark Brandon told VentureBeat in a chat exchange today. “We want to make it more accessible to non-propeller heads.”
“Project Gossamer” will use machine learning to provide better search functionality.
“E-commerce search is the prototypical example,” Brandon wrote. Behavioral data is collected on the user from various sources (email, previous visits, click stream, etc.) and search results are instantly tailored to behavioral patterns, making conversions more likely. Right now, that’s a horrendous roll-your-own process for most merchants, if they roll it at all.”
Qbox, then, will become much more than a front end for Elasticsearch clusters. The system will be set up in such a way that companies can store the behavioral data in a database other than Elasticsearch, even if the search index stays in Elasticsearch, Brandon wrote.
The service could well help Qbox compete with the likes of Algolia and Swiftype. It should arrive in late October, Brandon wrote.
Qbox, formerly known as StackSearch, started in 2012 and has offices in San Francisco and Fayetteville, Ark. The startup participated in the Alchemist Accelerator program earlier this year. The startup employs 12 people, and the headcount should double in a year, Brandon wrote.
Vulcan Ventures led the round in Qbox. Flint Capital, Funders Club Salesforce chief technology officer Steve Tamm, and Docusign founder Steve King also participated.
A blog post has more on the news.
According to eMarketer, digital marketers have a “love affair with data.” Spend on data-driven campaigns continues to grow annually, along with profits attributable to those same initiatives. It’s undeniable that data intelligence has made marketing overall smarter, better, and more valuable than ever before.
But innovations that bring great improvement also spawn new challenges. As data has plugged gaps to optimize traditional ways of doing things, marketers must now navigate a new set of data-driven dilemmas to ensure success.
With this in mind, here are three ways data has elevated marketing, while also raising fresh challenges for marketers:
More efficient buying via programmatic
What is it: Advertising technology has advanced rapidly in recent years, with marketers now able to tap into the power of data to automate campaign planning and execution. With programmatic advertising, automated ad buying and selling platforms are able to do what no human can independently — swiftly sift through massive amounts of data to make connections that fuel better, faster, and smarter decisions.
Programmatic analysis might reveal that a brand’s target audience is watching only 12 of 800 cable channels; show that they’re most likely to convert on a desktop; or help ensure that a consumer isn’t served the same ad four times in a 30-minute period.
Why it’s important: Much like mechanized buying and selling technologies transformed the travel industry and Wall Street, modern marketing is quickly being reshaped by programmatic automation. Campaigns that used to be based on hunches and backward-looking analysis can now be fueled by data-driven insights and executed and optimized in real time, at scale and across screens. There’s no going back to manual.
Why it’s hard: Data integration has become the biggest hurdle to programmatic success. In the past three years, hundreds of automated point solutions have emerged to meet marketer demand. Typically, however, these individual solutions don’t fit together seamlessly. Each is its own silo. In part as a result, 61 percent of marketers have identified data integration — including identifying, vetting and integrating technology, platforms and vendors — as a “major or extreme challenge.” In the next year, vendors with the most open and flexible platforms will dominate the market.
People-centric targeting across all screens
What is it: Media consumption habits are more fragmented and diverse than ever, as consumers engage with multiple screens and devices. And in this multi-channel world, isolated, one-off marketing strategies and closed technologies no longer make sense. Advertisers don’t need a social plan or a mobile plan; they need to take full advantage of all available data to develop a people-centric plan that zeroes in on customers wherever they are and when they are most likely to engage. This requires platforms that can integrate data across channels and provide transparent, objective analysis of performance across every marketing touch point.
Why it’s important: People-centric marketing is even reaching the world of linear TV, where new streams of data from set-top boxes and connected televisions are making it possible for advertisers to programmatically buy specific audience segments rather than focusing only on shows and time slots, which is how TV has traditionally been bought and sold.
Data will continue to blur the lines between TV and digital, between mobile and display, and even between direct response and branding. It all comes down to the person — whether the screen they are looking at is on their wall, at their desk or in the palm of their hand.
Why it’s hard: Cross-channel engagement is the cornerstone of people-centric marketing. The challenge, though, lies in evaluating attribution — an understanding of how well channels performed in engaging targets. Several years ago, this degree of accountability was impossible to achieve because data analysis tools hadn’t yet innovated to the point of granular attribution. Today, however, multi-touch attribution platforms, driven by more recent advances in machine learning and predictive analytics, have made this a reality.
Optimized creative development and execution
What is it: Using data to bring more math and science to marketing strategy doesn’t mean there’s no longer room for any art. In fact, data can be used to help creatives refine, enhance, and experiment with their best ideas, opening the door to unprecedented personalization and engagement. Information about consumers’ behaviors, habits, and preferences provides powerful clues about the imagery and messages most likely to captivate their attention and prompt a response.
Why it’s important: In a broader sense, the automation of previously time-consuming and tedious tasks (like managing manual insertion orders and reviewing spreadsheets) frees the entire marketing department to focus on more creative and high-value activities across the board.
Why it’s hard: We’ve only scratched the surface on true data-driven creative. Given longstanding cultural tensions between data teams and creative teams, a true marriage here has stalled, particularly across creative ideation and conceptualization. However, as data has increasingly entered every aspect of the marketing process, normalizing its involvement at large, the relationship between the two has evolved to the point where data can more easily drive creative development as well as execution.
There’s no doubt: The breakthroughs in innovation that led us into a data-driven marketing landscape will continue to push the industry forward. As marketing teams rise to the challenge, we’ll see more and more flexible platforms, finely tuned attribution models, and creative processes that are inspired, not deterred, by rich sources of data.
As Senior Vice President of Enterprise-Platforms Solutions, Doug works with and leads a litany of teams within AOL’s advertising and advertising technology divisions. He leads the Enterprise Client, Platforms Sales and Agency Trading Desk sales teams, growing out AOL’s revenue streams. Additionally, he works with the Product, Engineering, Operations and Publisher services teams to continue building out ONE by AOL, the next generation of unified, holistic marketing and analytics platforms for advertisers and agencies.
Sony and Activision announced today that the Call of Duty: Black Ops III multiplayer beta test on the PlayStation 4 was the biggest ever for that platform.
Black Ops III is the second title to debut under the new three-team production strategy at Activision.
Call of Duty games come out every year. In the past, Treyarch and Infinity Ward took turns making each game on a two-year development cycle. But Activision added Sledgehammer Games to the mix, starting with last year’s Call of Duty: Advanced Warfare title. As a result, each team now has three years to make a game.
Treyarch, which is making Black Ops III, was able to work on this new title for three years, and so it was able to get most of the game done in time to launch a large-scale multiplayer beta test this summer.
Today Twitter revealed its latest staff diversity statistics and announced its intention to be slightly less white in 2016.
In 2015, U.S. Twitter employees are still predominantly white men. Since last year Twitter has managed to increase the number of women in its workforce by four percent, with women now comprising 34 percent of its U.S. workforce. Ladies represent only 13 percent of the company’s tech department and 22 percent of its leadership. Still, that’s a bump up from last year, when women only represented 10 percent of tech jobs and 21 percent of leadership.
In terms of ethnic background, Twitter employees are 59 percent White and 31 percent Asian. Overall, 10 percent of its staff identifies as Latino or Hispanic, biracial, Black or African American, and American Indian (those percentages breakdown to four percent, two percent, two percent, and less than one percent respectively). A majority of Twitter’s Black and Hispanic employees work in nontech positions, while tech and leadership positions are overwhelmingly comprised of White and Asian employees.
The percentage of minority ethnicities in leadership roles has actually decreased from last year. In 2014, Twitter reported that four percent of its staff identified as either Black, African American, or “other”. Though white leadership has remained steady at 72 percent, the number of Asian employees in leadership roles has risen four percent.
Next year the company wants to increase the number of underrepresented ethnicities in its U.S. ranks to 11 percent. It also plans on raising the number of women, both in the company at large and in tech roles, by one percent.
That may not seem like a lot, but it’s proving quite difficult for tech companies to infuse their workforce with women and more ethnically diverse employees.
To help diversify its staff, Twitter plans to tour historically black colleges and partner with organizations aimed at helping women and minority groups get the skills and experience they need to ascend in the tech industry.
Its 2016 goals include:
- Increase women overall to 35%
- Increase women in tech roles to 16%
- Increase women in leadership roles to 25%
- Increase underrepresented minorities overall to 11%*
- Increase underrepresented minorities in tech roles to 9%*
- Increase underrepresented minorities in leadership roles to 6%*
* US only