Bringing Data Analytics Communities Together


With outburst of Big Data, Machine Learning, Deep Learning, Cloud platforms and startups, market is ripe for a storm on tools to address data analytics problems and provide immense power and capabilities to address various analytical issues.

While on one side it has produced a lot of options for businesses and professionals to pursue, on the other hand it has made market convoluted, complex and cluttered for businesses to indulge. This is the right time for data analytics ecosystem to drink its own coolaid and decrypt the ecosystem and make it functional for its members.

@AnalyticsWeek is on a mission to help businesses and professionals use it’s platform to create a clear bridge between demand and supply in data analytics. Within less than an year, @AnalyticsWeek has gained the support of 45 businesses, 2500 supporters, 10 industries (primarily 3 clusters): Consulting Services, Analytics Capabilities Providers, and Consumers of Analytics. @AnalyticsWeek partners include Big Enterprises, Mid-size companies and startups.

This is the right time for @AnalyticsWeek to pave ways to help data analytics driven businesses and professionals by providing them a targeted and adaptive environment, which listens to their problems and connects them to opportunities.

While on a mission, @AnalyticsWeek is determined to make a huge network of data analytics businesses and professionals and help them:

1. Recruit better tool, talent, technique and technology
2. Create effective partnerships faster for optimal impact
3. Build deeper community engagement for collaborative growth

Through it’s online and offline channels, @AnalyticsWeek is providing safe haven for data analytics professionals to mingle and grow collaboratively.

Source: Bringing Data Analytics Communities Together

The Big List: 80 Of The Hottest SEO, Social Media & Digital Analytics Tools For Marketers


In online marketing, having the right analytics tools and software can make a major difference. A good program can save you time and help demonstrate the value of your work to supervisors or clients.

Over the last couple of months, while preparing the syllabus for my next UC San Diego class and doing research for clients, I compiled and reviewed an extensive list of analytics programs for SEO and social media. I’m sharing what resulted in this column.

(Editor’s note: be sure to check out the 2015 Martech landscape for the latest developments in software for marketing.)

A few notes:

  • This is not a comprehensive list, but it is one of the biggest lists online and represents a solid chunk of what’s available out there. The entries aren’t in any particular order.
  • Many of the analytics programs also have some type of tool component — so you can take action on what you learn through analytics — and I included those, as well. There seems to be a natural progression in which tools add analytics elements and analytics programs add tools.
  • I did my best to include everything relevant to SEO and Social Media. If I missed a good one, make sure to let us all know in the comments.
  • I was unable to include every feature each of these analytic tools offered. The descriptions are brief and mostly come from the information offered at face value on the website, so if you’re a user and want to share info about an important feature (or about something that doesn’t work as described) please do so in a comment.
  • I don’t have any financial relationships with any of the companies mentioned here.

SEO Analytics Tools

1. BuzzSumo: BuzzSumo allows you to find key influencers to promote your content by analyzing content types and determining which topics receive the most attention, so you can get the best performance results. Simply enter a topic or domain to find a topic or influencer to reach out to, in hopes of getting them to promote your marketing or SEO campaign.

Pricing: Free, Pro $99, Agency $2299, Enterprise $499+ (per month)

2. HubSpot: HubSpot allows users to integrate marketing tools to build a marketing and sales platform. Offerings include analytics, CRM, blogging, social media, landing page creation, and call-t0-action testing.

Pricing: Basic $200, Pro $800, Enterprise $2,400 (per month).

3. Rio SEO: Local SEO, mobile search, social sharing analytics, retargeting software, and an automated SEO platform. A global automated SEO platform aimed at helping marketers to increase ROI.

Pricing: Determined upon needs. Must contact specialist.

4. Conductor: Conductor for SEO promises to help marketers reach out and grab their customers with compelling content. The web presence management tool helps you create a platform using unpaid channels like organic search, content, and social. The technology also gathers the data aimed at helping marketers transform their brand.

Pricing: Starter $1,995, Standard $3,750 (per month). Pricing for Premium and Elite packages available by request.

5. SEMrush: Discover competitive data from users, keywords, domains, and databases. SEMrush says it will help you drive your projects to the next level as massive amounts of SERP data is collected, allowing you to capitalize on search trends and competition to find relevant and real-world solutions. The comprehensive analysis fine tunes your SEO efforts.

Pricing: Pro Recurring $69.95, Pro 1 month $79.95, Guru Recurring $149.95 (per month).

6. Search Metrics: Search Metrics offers software, API, and SEO services for content, marketing, extensive digital solutions and web optimization. Review customized recommendations for on page and off page SEO using intelligence forecasts to define and achieve your business goals. Create a solid foundation with cross-channel campaigns to develop targeted strategies.

Pricing: User account Free, Essentials $69, Starter $449, Business price upon request (per month).

7. AuthorityLabs: Offers search engine ranking data with daily SEO ranking reports. Automate your marketing campaign, local rank tracking, and competition tracking. Then, share the data with your team by reporting to unlimited users.

Pricing: Plus $49, Pro $99, Enterprise $450 (per month)

8. Raven Tools: Manage and report all of your online marketing with Raven Tools. The company promises you’ll work smarter, not harder, with the SEO, content marketing, social media, and PPC tools you need. Raven says it will do the tedious work while you manage and report. Custom branding, campaign management, Google integrations, and more.

Pricing: Raven Pro $99, Raven Agency $249 (per month).

9. Spy Fu: The company promises you can expose the marketing formula of your competitors using Spy Fu to unlock their keywords, organic ranking, and ad variations for the past 6 years. Find the most profitable keywords and eliminate the poor to create the best ad copy by leveraging your competitors. Additionally includes SEO sales reporting and AdWords advice.

Pricing: Basic $79, Professional $139, Agency $999 (per month).

10. ProSEOTools: The company promises to combine big data analytics with actionable insights to help marketers maximize SEO performance. Discover the ranking of all keywords with instant ranking data, daily keyword and URL tracking, white label PDF reports, and the health of your SEO efforts.

Pricing: Pro Lite $49.95, Pro Medium, $89.95, Pro Large $199.95 (per month), Enterprise Custom pricing available upon request.

11. MOZ: Moz’s internet marketing software aims to simplify ranking, links, social, brand, content, and traffic– all in one place. It promises to help marketers find errors, missed conversions, and SEO opportunities with custom reporting and research tools. You can also create and manage your online business listings with a single click to boost your local search results and prevent your business from getting lost.

Pricing: Standard $99, Medium $149, Large $249, Premium $599 (per month).

12. Advanced Web Ranking: This online SEO software aims to help marketers thrive by providing local and mobile rank tracking, white label reports, competition monitoring, and data privacy. The company promises you will spend less time monitoring and more time optimizing as Advanced Web Ranking grows your business and revenue with pinpoint accuracy reports and monitoring, on demand ranking updates, and more.

Pricing for Lifetime Licenses: Standard $199, Professional $399, Enterprise $799, Server $2999.

13. SEO PowerSuite: The company says its 4 powerful tools will deliver everything you need for SEO functionality in one package. SEO PowerSuite says it can help marketers find the perfect whole website optimization campaign with specific automated SEO tasks to push their websites to the top. It also says you can accelerate your SEO efficiency by personalizing your SEO campaign by analyzing top competitors in a particular niche.

Pricing: Varies upon products and bundles

14. Whitespark: Whitespark’s tools and services allow you to manage and optimize your search presence and reach with a local citation finder, local rank tracker, and a link prospector. The company says it helps you make it easy for customers to find you with a review generator, building local search visibility that lasts.

Pricing: Varies upon product/service.

15. The company promises to help marketers track and prove their SEO with accurate daily keyword rankings. Test and monitor SEO experiments, understand charts and SEO data, and access daily reports with this SEO software. Access everything you need, including conversions, rankings, and traffic, from one dashboard, the company says.

Pricing: Solo $99, Pro $299, Enterprise $1999 (per month).

16. Positionly: Use this SEO software to monitor daily changes, measure SEO performance, and track competitors to improve your search engine ranking. The company says its tools will help you generate more organic traffic using pro-level tracking tools that feature a simple, usable interface. Plus, it says you can monitor media from around the web to react to mentions and monitor incoming links.

Pricing:$19, Silver $49, Gold $99, (per month). Platinum pricing available upon request.

17. RankRanger: The tool maker promises you can gain a competitive edge with this comprehensive SEO platform that provides daily keyword rank, tracking reports, and website insights to reach traffic and marketing achievements. Report and analyze, conduct market research, monitor social signals, and use a link manager to increase your site’s performance with the team-based platform.

Pricing: 250 Keywords $49, 500 keywords $99, 1500 keywords $249 (per month). 30 day free trials also available.

18. MySEOTool: Manage and monitor rankings, analytics, backlinks, social media, and PPC with simplified SEO tools with a traditional SEO interface. Automatically gather SEO metrics with automated PDF repots to take control over SEO. My SEOTool promises that with in-depth reporting, you can manage all aspects of SEO for better results and performance by staying organized with custom management features.

Pricing: Freelancer $49, Agency $99, Enterprise $249, Unlimited $499 (per month).

19. SheerSEO: SheerSEO is an online software that automates SEO efforts with rank tracking and link building. According to the company, you can create SEO reports for your website with SERP tracking, historical graphics, directory submission software, link analysis, social mentions, and alerts. You’ll receive data from Google Analytics to increase search volume.

Pricing: Varies from $7-$40 depending on needs. 2 months free also available.

20. WebPosition: The company promises competitive keyword optimization, inbound link analysis, and online rank tracking aimed to help marketers build an audience. It says you may use the data to get to know your competition to evaluate for inbound link opportunities, benefit from white label search ranking reports, and view an SEO summary in a single report.

Pricing: Standard $29, Premium $69 (per month). Free 30-day trial also available.

21. SEO Rank Monitor: SEO Rank Monitor, according to its makers, is a complete SEO ranking tool used to boost rankings, track competitors, and monitor SEO performance with comprehensive rank tracking reports. It promises to take the stress out of tracking with a user-friendly dashboard that makes ranking quick and simple.

Pricing: Personal $19, Pro $49, ProPlus $99, Enterprise $199 (per month).

22. Analytics SEO: This search engine optimization software offering promises to help you improve your search engine ranking to gain more website traffic, leads, and customers. It says whether you’re a small business or an agency, you can explore keyword data and create custom SEO campaigns with automated keyword research to discover the keywords that matter the most.

Pricing: Startup $29, Growing $59, Established $169 (per month). Free limited starter package available.

23. GeoRanker: Benefit from local ranking reports for cities and countries with custom heat maps to monitor and track rankings. Find local citation sources with indexation secrets to reveal the top sources in a specific city and industry. GeoRanker claims to have a powerful API that offers the data you’re hungry for and allows you to analyze white label reports to boost traffic.

Pricing: Pro $99, Agencies $249, Enterprises $490 (per month). Free account also available.

24. Microsite Masters: The company promises its offerings will provide you with reliable and accurate SERP tracking with ranking and performance reports. Advanced tracking metrics increase campaign efficiency by reducing guess work, the company says. Then, the tools allow marketers to share results, including multiple keyword sets and domains, while keeping sensitive data private.

Pricing: More information on site

25. Sistrix: The company promises an easy to use SEO tool that helps you create a SEO strategy in one place. Evaluate your link risk, learn from competition, and improve your on page optimization, while recognizing Google penalties, Sistrix says.

Pricing: Varies between 100-400 Euros per month. A 14 day free trial is available.

26. BrightEdge: The company says it offers a complete platform for content ROI with the latest SEO technology. It promises to help you align and optimize your content to improve results with increased conversions and revenue.  BrightEdge says it allows you to discover your competitor’s strategy so you can beat your competition.

Pricing: Available upon request.

27. Marketing Samurai: Marketing Samurai aims to help marketers automatically find more customers. Get your customers to buy more with increased traffic and leads through highly efficient SEO courses. You’ll sell more and sell faster as you climb in search engine ranking, turning more leads into actual revenue.

Pricing: Varies by needs.

28. GetSTAT: The company promises to help marketers exceed the ordinary with unlimited keywords from any location in the world. It says you’ll see unlimited SERP tracking from anywhere at any time with always fresh metrics and custom alerts. GetSTAT says it can help you stay ahead of your competitors with automated insights and full-scale competitor tracking results.

Pricing: Available upon request.

29. SEO Diver: The company says you can benefit from daily monitoring and analysis of competitors with keyword research and analytic backlink research. It claims it will help you optimize your website for more targeted and efficient results.

Pricing: Varies upon subscription length and needs.

30. ZoomRank: Supercharge your search marketing with the best tools and data with ZoomRank, the company claims. ZoomRank says it allows marketers to view accurate organic rankings, mobile rankings, local rankings, and raw rank results. It also tracks social metrics.

Pricing: Available upon request.

31. Rank Tracker: Discover how your search engine rankings are tied directly to your bottom line, Rank Tracker promises. You’ll see just where your site ranks on Google and Yahoo within minutes, the company claims. Then, view the best keywords to optimize your site for maximum performance.

Pricing: Range from $15-$499 per month. A free package is also available.

32. SERPFox: The company promises to help marketers leverage a simple, powerful, and automated search engine tracking system with advanced reporting, multiple timeframes, and consistent data. It says you can visualize SERP changes in a new light as you’re able to track daily, weekly, and monthly changes and also receive customized alerts.

Pricing: Starter $10, Intermediate $24, Pro $55, Corporate $99, Agency $200 (per month).

33. Tiny Rocket Lab: Track your onpage SEO efforts with the combined power of the rank trackers and onpage optimizer through Tiny Rocket Lab, the company says. It says you can track keywords, get suggestions, and follow results from one location. Create and assign tasks and experiments to team members with visuals to tell what’s working and what isn’t.

Pricing: Available upon request. Free account is available.

34. Rankinity: Measure your internet visibility with visual representations of your performance, Rankinity promises. It says it allows marketers to check hundreds of keywords on multiple search engines, including local searches, to estimate standings to reveal weaknesses. Compare your positions to your competitors and reveal new players on the market to enhance your promotion strategy.

Pricing: Varies upon number of keywords and checking interval.

35. Web CEO: Web CEO says its SEO tools and training allow marketers to research their niche and promote their website with a comprehensive SEO checklist. Create optimized content and monitor your site ranking across local and global search engines, according to the company. Listen to what people are saying about your company to analyze visitor traffic, conversions, and ROI.

Pricing: Varies upon needs.

36. SERPBook: Utilize accurate tracking with automated and white label SEO reports, SERPBook says it offers. Gather keyword information then view it on sophisticated charts and through powerful notes, so you can  see how your keyword has progressed throughout time, the company claims.

Pricing: Varies between $4.95 per month for 25 keywords/domains, to $329.95 for 500 keywords/domains.

37. CognitiveSEO: The company claims it offers cutting edge SEO tools to benchmark and outrank SEO competition. It promises a fast backlink checker, daily ranking tracker, and refined link analysis for better insight. Unique SEO features and integrated tools simplify management, tracking, and optimization, says CognitiveSEO.

Pricing: Professional $99, Premium $199, Elite $499 (per month). Custom Plans also available starting at $999/month.

38. Ezee Rank Tracker: Get accurate reports of your website’s ranking with unlimited website and keyword searches for regional and leading search engine results, Ezee Rank Tracker promises. Track social signals from 9 platforms and YouTube and receive up-to-date notifications to maintain your site’s performance with quick change reports, according to the company. Track and save your results to monitor performance.

Pricing: Varies upon product

39. SERPWoo: Monitor all of the top 20 results for your keywords with ORM and SERP analysis, SERPWoo claims. The niche tracker, according to the company, offers a quick glance as to how the top keywords are performing for a better understanding of your site. With integrated data, you’ll spot new competition, view SERP archives, discover new platforms, ORM monitoring, and view multiple data feeds.

Pricing: $19.95 per month.

40. SpyRanks: Improve your SEO with real-time position details, monitoring competitors, and API Google analytics, according to SpyRanks. The company says its tool will help you understand your global visibility better than ever before. The report configuration will allow you to plan, customize, and monitor your SEO campaigns with an easy to use interface.

Pricing: Is dependent upon subscription length and professional or individual status. One month and one year subscriptions available.

41. GinzaMetrics: GinzaMetrics aims to help marketers use marketing intelligence that’s designed to get you found with thousands of marketing strategies, search terms, content creations, and daily metrics to improve brand visibility. The company says its scalable SEO platform lets you view marketing channel insights, perform daily SERP tracking, do keyword and topic discovery, and gain content recommendations, while checking out competitor intelligence.

Pricing: Professional $1000, Premium $2000, Portfolio $3000 (per month). Enterprise package available upon request.

42. AlloRank: AlloRank says it allows marketers to view daily Google SERP reports with multiple keyword positions on several websites. Set the frequency to view results hourly, daily, or weekly. The personalized dashboard offers a free SEO tool, which is directly accessible through your web browser so there’s no need to install any software.

Pricing: Varies between 29-449 euros per month depending on plan. A free plan is also available.

43. Colibri.IO: Maximize brand visibility with social media and SEO monitoring in one, claims Colibri.IO. Tap into conversions, competitor info, collect leads, and improve your search engine ranking with SEO monitoring.

Pricing: Standard $95, Large $295, Agency $995 (per month).

44. SERPScan: Track your search engine rankings with SERPScan. The company says its tool will reveal actionable insights with daily reports and alerts for any drastic change. SERPScan claims to help you find out more about your local ranking, track your competitors, and track over 150 search engines from around the world with query tracking and real time updates.

Pricing: Consultant $99.99, Agency $249.99, orporate $399.99 (per month).

45. EZ Website Monitoring: Keep tabs on your website and your competitors with one easy to read report, says EZ Website Monitoring. Monitor and track the popularity of keywords, view historical graphs, and receive instant notifications. With Uptime monitoring, your website will be checked every 5 minutes from multiple locations to ensure it’s performing its best, the company claims.

Pricing: Site is currently in Beta, so it’s free.

46. Fresh Web Explorer: Research and compare mentions and links with analytics of your brand, competitors, and keywords with Fresh Web Explorer, the company claims. Fresh Web Explorer says it helps marketers index and find the latest mentions from over 3 million RSS feeds and more than 72 million URLs to discover the most relevant content on the web.

Pricing: Available with Moz packages. 30-day free trial also available.

47. SERP mojo: Track your rankings on an Android phone with this cool tracking application, say the makers of SERP mojo. There is a free version that is easy to install and use. With over 10,000 downloads and 788 largely positive reviews, this app helps marketers to track rankings on the go. You can also upgrade to Pro for daily data refresh and additional analytic insights.

Pricing: Free version and upgrade to pro for $3.99.

Social Media Analytic Tools

There are a lot of social media tools out there, but here are some to watch.

48. Iconosquare: Become more familiar with your Instagram account and how it’s performing with key metrics offered by Iconosquare, the company says. The company says it helps improve audience engagement via sponsored photo and video contests, while allowing marketers to discover their total “likes,” the origination of responses, and how followers are finding them. Iconosquare can be connected to Hootsuite.

Pricing: Available upon request.

49. Curalate: Curalate offers a smarter means to measure, monitor, and grow a brand using visuals, the company claims. According to Curalate, marketers can use the visual marketing platform to connect Pinterest, Instagram, Tumblr, and Facebook to run contests, analyze social images, and schedule and publish posts.

Pricing: Available upon request.

50. Sprout Social: The company claims it offers world-class engagement tools allow you to engage, publish, and analyze to create an exceptional brand experience . The tool, according to the company, allows marketers to publish to Google+, Facebook, LinkedIn, and Twitter, while engaging their audience right from their desktop or mobile device. Integrated analytics allow users to monitor their social media efforts with in-depth reports.

Pricing: Packages range from $59/month – $500/month

51. Brandwatch: Brandwatch is a business platform that allows advanced social media monitoring and analytics, according to the company. Searches pinpoint every discussion about your brand or product, allowing you to “listen” in 27 different languages to what people are saying. The data analytics offer customizable solutions to share data with your entire organization.

Pricing: Packages range from $800/month – $3200/month.

52. Radian6: Known as the industry’s social pioneer, Radian6 says it lets marketers quickly and efficiently track, monitor, and react to comments, questions, and complaints in real-time. Listen and engage more than 650 million sources, including Facebook, Twitter, and YouTube.

Pricing: Available upon request.

53. Woobox: The company urges marketers to join more than 3 million brands by creating engaging social media promotions with powerful contests, sweepstakes, coupons, polls, and more. The promotional marketing and Facebook apps allow you to engage your fans to grow your fan base by spreading your message, while keeping fans happy. Although Woobox is more of a social platform for contests and engagement, they do offer some Facebook analytics.

Pricing: Packages range from free to $249/month.

54. North Social*: Offers fully integrated Facebook apps aimed at improving marketing and PR. The software, according to its makers, allows you to attract and engage to capture and maintain leads to drive better conversion and retention rates. Then, get a snapshot or in-depth report with easy-to-read analytics to measure your results to get more from your marketing.

*Note: Now a division of Vocus.

Pricing: Available upon request.

55. Hootsuite: Manage and measure more than 35 social media platforms from what the company calls the leading social media dashboard. Manage multiple social media accounts, schedule social media posts, track mentions, and analyze social media traffic to track conversions and measure campaign results. Join more than 10 million satisfied users to simplify managing your social networks, Hootsuite urges marketers.

Pricing: Varies upon needs. Contact for more information.

56. Twitter Feed: Feed your blog to Twitter, Facebook, LinkedIn, and many other social networks, Twitter Feed promises. With real-time tracking, monitor stats for each of your feeds to see which content in generating the most engagement and which is generating the least. Use the data to adjust your blogging for increased success.

Pricing: Varies upon needs. Contact for more information.

57. Followerwonk: Followerwonk, a Twitter analytics tool from Moz, allows you to find, analyze, and optimize to increase your social growth. The company promises the tool helps you explore your social graph by connecting with new influencers, sort your current followers, understand your social network with actionable visuals, and share your reports to engage your followers with niche specific tweets.

Pricing: Starting at $99/month (included in Moz Pro Subscription – includes other tools as well).

58. Peek Analytics*: Peek Analytics is the ultimate Twitter audience measurement tool, the company claims, offering unmatched demographics and psychographic insights to consumer data. Gather data from more than 60 social networking sites and every major blog platform to understand your social customers. The in-depth social audience reports offer aggregate level insights, including demographics, career, education, interests, and more.

*Note: This site is now StatSocial.”

Pricing: Available upon request.

59. Simply Measured: Simply Measured says it provides social media analytics that are specifically designed with serious marketers in mind, featuring visuals of your efforts and those of your competitors. The company claims that, with its tools, you’ll get the most complete view of your social activities from top social networks to analyze owned, earned, and paid social media, as well as audience insights with cross-channel analysis.

Pricing: Social $500, Professional $800 (per month). For Enterprise package contact Simply Measured.

60. Social Mention: Social Mention is a real time social media search engine that looks for mentions of whatever keyword you put in. One of the best parts is, you can do a search for just about anything in real time and get results. Social Mention gives insight into the sentiment of the term, based on keywords used in social media posts.

Pricing: Available upon request.

61. ViralTag: Publish your visual content to multiple platforms at once with ViralTag. The company says its platform already has over 40,000 users, both individuals and business, and is geared largely towards Pinterest.

Pricing: You can sign up for free. There is a pricing page, but it was not working at the moment.

62. Piquora: Geared towards Pinterest and Instagram marketing, Piquora is currently used by over 400 brands to get more out of the visual web, according to the company. They have both a Pinterest and Instagram marketing tool and analytics set. On the Pinterest side, you can see things like most pinned, repinned, etc. On the Instagram side, see most liked, most commented, etc.

Pricing: Get a demo is your only option.

63. Tailwind: Trusted by over 25,000 brands and agencies, the company says, Tailwind is a Pinterest management and analytics tool that lets you schedule pins and repins, track comments, likes and traffic on pins over time and helps you determine influencers in each niche.

Pricing: Free trial option, $9.99 option, $149 option, $399 option and enterprise option.

64. Pin Alerts: This simple tracking tool tells you when someone pins a URL from your website. According to PinAlerts, “Setup free PinAlerts in seconds, and receive email notifications whenever someone pins something from your website.”

Pricing: Free.

65. ViralWoot: Over 39,000 individuals and businesses use ViralWoot for their Pinterest management and analytics, according to the company. The platform allows you to gain followers by being displayed in the ViralWoot database, promote pins, create pin alerts, manage multiple accounts and get analytics on each of these items.

Pricing: Sign up for free, no pricing listed.

Honorable Mentions

These analytics programs are not necessarily social media or SEO focused. Some are big data, others are mobile- or marketing-focused.

66. KeenIO: According to the company, Keen IO takes the pain out of custom analytics with powerful APIs to gather the data you want to find the answers you need, which is stored in the Cloud. Transform your data into answers to strengthen your network with scalable information in minutes. Build and collect huge amounts of event data whenever you’d like.

Pricing: Packages range from Free to $2,000/month.

67. MixPanel: MixPanel says it has created the most advanced platform for mobile and web to measure engagement metrics, not just page views. The segmentation tool simplifies queries by allowing marketers to visualize data in less than 10 minutes. With in-depth data, MixPanel claims, you’re able to customize your social approach to reach more relevant social engagement.

Pricing: Packages range from Free to $2,000/month.

68. SimilarWeb: Receive insights from any website or app with SimilarWeb — the secret weapon of marketing teams around the world, the company claims. Integrate tools you’re already using to sort data from your databases using traffic and engagement factors. Use your insights to boost your intelligence capabilities directly from your browser with instant traffic stats.

Pricing: Basic $199, Advanced $499, Ultimate $799 (per month).

69. Voodoo Alerts: Voodoo Alerts offers 24/7 automated conversion and funnel monitoring to identify conversion problems with alerts, according to the company. Voodoo Alerts claims it makes it simple to pinpoint website problems and broken segments to visualize online conversions.

Pricing: From $25 – $500 per month. Discounted if you sign up for a year.

70. Lucky Orange: See how your visitors actually use your website with specific heat maps and polls using Lucky Orange. The company claims you can learn directly from your customers with interactive user chat and instant customizable polls to form comprehensive analytics to understand your audience. Use the heat maps and visitor recordings to strengthen weak areas for increased engagement.

Pricing: From $10 – $100 per month. 10% off if sign up for one year, 30% off if sign up for two years

71. Optimizely: Optimizely for websites and mobile apps offers testing tools that allow marketers to experiment with different versions of pages and choose the one that’s best at reaching their business goals. The company says it has a code-free visual editor that provides a powerful experience that’s fast, scalable, secure and integrates with best-in-class solutions.

Pricing: Starter Package is Free, Enterprise package available with pricing upon request.

72. Crazy Egg: See where visitors click to improve conversions and understand user behavior with Crazy Egg’s visual website analytics and heat maps. Over 200,000 businesses are producing better conversions with heat mapping technology to target and engage website visitors, the company claims. Receive immediate insights to understand your visitors’ behavior.

Pricing: Packages range from $9 -$99/month (paid annually).

73. Megalytic: The company claims its tools simplify analytic reports to enable marketers to gain faster insights and deliver the data you need to the right people exactly when you want it. Streamline the monthly client reporting process to produce better reports faster with insightful digital analytics, Megalytic says.

Pricing: Packages range from $4.99 – $179.99 per month.

74. Cyfe All-In-One Dashboard: With Cyfe all-in-one dashboard, you’re able to monitor all of your business’s date from one location. View social media, analytics, marketing, sales, and even your infrastructure with pre-built widgets for custom data sources. Find worldwide information about your business or manage projects from one location. The company urges you to join more than 70,000 satisfied users by trying the dashboard app.

Pricing: $19 per month ($14 per month if paid annually).

75. Tableau Software: Tableau Software provides one of the simplest methods to use and access your data by providing real answers to your data needs, the company claims. The simple interface allows you to analyze your data without the need for canned reports, dashboard widgets, or templates. With the interactive data features and the creation of custom charts, you’re able to easily share analytics in the Cloud.

Pricing: Ranges from $500 – $1,999.

76. IBM Watson Analytics: Watson Analytics by IBM gives you the ability to improve, manage and sort data in advanced ways in order to make better business decisions. With Watson Analytics, you can create clear infographics (not the marketing kind, the big data display kind). According to the company, 22,000 people have signed up for this platform since it launched on December 14th. The three main areas offered are explore, predict and assemble.

Pricing: Free Trial, No Pricing Information.

The Good Old Boys

Don’t forget about the core programs out there, of course. I think of these as some of the most important and haven’t mentioned them above because they’re practically a given. We love those too!

  1. Kiss Metrics
  2. Google Analytics
  3. Webmaster Tools
  4. Omniture
  5. Etc.

Summing It Up

I hope you enjoyed my list and hopefully found a new analytics program that got you excited. There are so many programs out there, it can be hard to keep up. If I missed one of your favorites let me know. Please keep in mind, I tried to stay away from programs that only have a tool component like Tweet Adder,  Screaming Frog andXenu Link Sleuth. This was more of an analytics list.

Creating this list fascinated me. I really feel we are in the infant stages of Internet marketing analytics. Watching these programs and tools evolve is going to be very exciting. Special thanks to @DannyAtIgnite and @alyssa_ast for your help on some of the pricing research and write ups.

Note: This article originally appeared in Marketing Land. Click for link here.

Originally Posted at: The Big List: 80 Of The Hottest SEO, Social Media & Digital Analytics Tools For Marketers by analyticsweekpick

Slow progress forces Navy to change strategies for cloud, data centers

The Department of the Navy isn’t making as much progress on data center consolidation and moving to the cloud as it wants to. So the Navy is moving the initiatives under a new owner and coming down hard on those who are standing in the way.

“Later this year, we will make an organizational change to our approach to data center consolidation. The Data Center and Application Optimization (DCAO) program office will move from under Space and Naval Warfare Systems Command (SPAWAR) headquarters to under Program Executive Office-Enterprise Information Systems (PEO-EIS) as a separate entity or program office,” said John Zangardi, the Navy’s deputy assistant secretary for command, control, computers, intelligence, information operations and space and acting chief information officer. “This will better align consolidation efforts with network efforts and more fully leverage the Next Generation Enterprise Network (NGEN) contract.

So we will build on their application experience. The DCAO will be responsible for establishing a working model for Navy cloud hosting service brokerage. This will be for the delivery of application hosting via commercial and federal agencies. Culturally, we have to make this shift from a mistaken belief that all our data has to be near us and somewhere where I can do and hug the server, instead of someplace where I don’t know in the cloud. This is a big shift for many within the department. It’s not going to be an easy transition.”

Since 2012, the Navy has made some progress. Zangardi, who spoke at the 14th annual Naval IT Day sponsored by AFCEA’s Northern Virginia chapter, said over the last three years, the Navy has consolidated 290 systems and apps across 45 sites. But overall, he said getting bases and commands to move faster just isn’t happening.

The Navy plans to officially move the data center consolidation office into the PEO-EIS office in July.

Testing the cloud access point

Knowing the difficulties and challenges over the past few years, Zangardi said he’s taking several steps to help ease the pain.

First, he said his office picked three data centers that are lagging behind and required them to develop a plan to consolidate and move their data to a centralized data center.

Second, the Navy is rationalizing large scale apps. Zangardi said too often people hold their applications and servers close.

“I spend a lot of time thinking about the cloud access point (CAP) and our data centers. My objective is to move stuff as quickly as possible. The applications we are looking at right now to move to our cloud access point, the ones I’m most interested in moving right now, would come out of the N4 world, so we are talking about things like maintenance or aviation type of stuff so think logistics,” he said. “We’re also looking at enterprise resource planning (ERP). Can we move our ERP to a cloud type of solution to drive in more efficiencies? I think most of the things we are looking at, at least upfront, would be business sort of applications.”

The third way to ease the pain is by using pilot programs to get commands and bases comfortable with the idea of letting go of their servers and data.

“PEO-EIS and SPAWAR Systems Center Atlantic are piloting a cloud access point in conjunction with the commercial cloud service provider. It’s currently operating under an interim authority to test,” Zangardi said. “These organizations have the right expertise to develop the approach for the department to leverage the cloud. However, the CAP pilot is in its early stages. Essentially right now we are doing table top testing.

Our objective over the next year is to move from a pilot effort to what I would term a productionized commercial cloud. What do I mean by productionized? Simply to me it means an industry leveraged approach that can scale to demand from users. This capability should be secure, provide lower costs for storage and data and facilitate mobility.”

One big question about this consolidation effort is how to break out the 17 or 19 data centers that fall under the NGEN Intranet, and put them under the PEO- EIS team with the other data centers.

Zangardi said the Navy is considering an approach to this, but it’s still in the early stages.

Private cloud works just fine

While the Navy is open to using commercial or public clouds, the Marine Corps is going its own way.

Several Marine Corps IT executives seemed signal that the organization will follow closely to what the Navy is doing, but put their own twist on the initiative.

One often talked about example of this is the Marines decision to not move to the Joint Regional Security Stacks (JRSS) that is part of the Joint Information Environment (JIE) until at least version 2 comes online in 2017. Marine Corps CIO Gen. Kevin Nally said the decision not use the initial versions of JRSS is because Marine Corps’ current security set up is better and cheaper than version 1 or 1.5.

More see –

Source: Slow progress forces Navy to change strategies for cloud, data centers

The Horizontal Impact of Advanced Machine Learning: Network Optimization

A substantial amount of literature has been dedicated to Artificial Intelligence’s analytic capabilities. Recurring use cases include the machine learning potential of its algorithms for fraud detection, churn reduction, recommendation engines, and personalization of individual preferences via digital assistants.

What is less talked about, yet perhaps even more influential, is its potential for network optimization. Whereas the aforementioned use cases are specific to particular verticals, the optimization capabilities of advanced machine learning and deep learning have a horizontal effect that reinforces business value regardless of an organization’s area of specialization.

According to One Network founder and CEO Greg Brady, with traditional network planning tools for the supply chain industry “the user has to be involved and decide what the planning tool is going to do. The deployment we have [with machine learning], the algorithms run on the execution system and do the work that the user used to do. They learn from their experience and get smarter over time.”

The basis of this approach—deploying AI on execution systems to learn from experience and optimize based on timely changes to data—is applicable to operations for any industry or line of business. As such, it may very well be advanced machine learning’s most redeeming quality for the enterprise.

Deep Learning Pattern Recognition

The central component in the network optimization capabilities of advanced machine learning is the profound pattern recognition of its deep learning algorithms. Those algorithms are peerless in their ability to identify patterns in enormous amounts of big data related to specific business applications or overall network optimization. Moreover, they can identify these patterns quicker than alternative methods can, using those results to inform network operation and optimize its functionality. Regarding AI’s utility in this regard, “Everything is predictive,” Brady said. “It’s always looking forward in time. It’s trying to understand what the problem’s going to be before it exists. With enough speed, it fixes the problem before it ever occurs.” Leveraging this capability of deep learning for networking concerns requires sizable amounts of data for its algorithms to analyze. It also involves predictions about what networking results should be, then uses other advanced machine learning techniques to measure the results of data in near real-time to see how they compare to the predictions.

Autonomous Agents

Composite approaches of deep learning in tandem with other aspects of machine learning can optimize networks with a striking degree of accuracy. By deploying autonomous agents, which Brady described as “like Jarvis on Iron Man but instead they actually do the work”, within networks to compare the results of deep learning pattern recognition to incoming data about network efficiency, organizations can determine how their actual networking compares to optimal levels. “What these agents do is every hour look at if the pattern is being met as we assumed,” Brady revealed. “If not, it adjusts its forecast.” Oftentimes, those adjustments are made while seeking to identify reasons why patterns are not met. A restaurant chain looking to optimize its supply chain ordering process while accounting for real-time factors such as inventory consumption and store traffic implemented autonomous agents with a deep learning pattern recognition engine. In this example, the store used historical data “for a pattern recognition engine to determine what the traffic buying patterns are,” Brady said. “You get a level of detail in the data that’s unbelievable.” The pattern recognition capabilities then devise daily forecasts for each store. Millions of autonomous agents are deployed to “read the point of sales data all day long; every hour we give them more data to chew on. The agents will interpret what it’s learned from the data consumption.” Agents not only adjust the forecasts for ordering supplies, but also “automatically generate orders on their own,” Brady added, implementing a pivotal layer of automation for time-sensitive event data. The result was order accuracy at approximately 80 percent, which Brady remarked was “completely unheard of” for the organization.

Crucial Caveats

Although there may not necessarily be limits to the automation capabilities of advanced machine learning for networking concerns, there certainly are to the prudent implementation of them. According to Brady, it’s necessary for users to understand the impact of the decision-making process of machine learning for networking because some situations may “cost you more than what it’s worth to fix”, especially when considering downstream ramifications of decisions for distributed locations. It’s equally valuable for advanced machine learning engines to “see all the data from all the parties” Brady cautioned, primarily for the same reason—to avoid situations in which there are repeated “fast local decisions that just cause all kind of problems in the network.” By taking measures to prevent these situations from occurring, users can ensure that they optimize their networks for maximum productivity, regardless of the specific system or type of network it may be.

Source by jelaniharper

Using Big Data to Kick-start Your Career

Gordon Square Communications and WAAT offers tips about how to make the most of online resources to land a dream job – all without spending a penny.

Left to right: Vamory Traore, Sylvia Arthur and Grzegorz Gonciarz

You are probably familiar with or, huge jobs websites where you can upload your CV together with other 150 million people every month.

The bad news is that it is unlikely that your CV will ever get seen on one of these websites, discovered attendees of London Technology Week event Using Tech to Find a Job at Home or Abroad.

“There are too many people looking for a small number of jobs,” says Sylvia Arthur, Communicator Consultant at Gordon Square Communications and author of the book Get Hired! out on 30th June.

“The problem is that only 20% of jobs are advertised, while 25% of people are seeking a new job. If you divide twenty by twenty-five, the result of the equation is that you lose,” explains Ms Arthur.

So, how can we use technology to effectively find a job?

The first step is to analyse the “Big Data” – all the information that tells us about trends or associations, especially relating to human behaviour.

For example, if we were looking for a job in IT, we could read in the news that a new IT company has opened in Shoreditch, and from there understand that there are new IT jobs available in East London.

Big Data also tells us about salaries and cost of living in different areas, or what skills are required.

“Read job boards not as much to find a job as to understand what are the growing sectors and the jobs of the future,” is Ms Arthur’s advice.

Once you know where to go with the skills you have, you need to bear in mind that most recruiters receive thousands of CVs for a single job and they would rather ask a colleague for a referral than scan through all of them.

So if you are not lucky enough to have connections, you need to be proactive and make yourself known in the industry. “Comment, publish, be active in your area, showcase your knowledge,” says Ms Arthur.

“And when you read about an interesting opportunity, be proactive and contact the CEO, tell them what you know and what you can do for them. LinkedIn Premium free trial is a great tool to get in touch with these people.”

Another good advice is to follow the key people in your sector on social media. Of all the jobs posted on social media, 51% are on Twitter, compared to only 23% on LinkedIn.

And for those looking for jobs in the EEA, it is worth checking out EURES, a free online platform where job seekers across Europe are connected with validated recruiters.

“In Europe there are some countries with shortage of skilled workforce and others with high unemployment,” explains Grzegorz Gonciarz and Vamory Traore from WAAT.

“The aim of EURES is to tackle this problem.”

Advisers with local knowledge also help jobseekers to find more information about working and living in another European country before they move.

As for recent graduates looking for experience, a new EURES program called Drop’pin will start next week.

The program aims to fill the skills gap that separates young people from recruitment through free training sessions both online and on location.

To read the original article on London Technology Week, click here.

Source: Using Big Data to Kick-start Your Career

How Sports Data Analytics Is Upsetting The Game All Over Again

One or two games in MLB is often the difference between advancing to the post-season or staying home, and an entire season can be determined by a couple of good or bad pitches. There is a huge competitive advantage to knowing the opponent’s next step. That’s one reason sport analytics is a booming field. And it explains why data scientists, both fan and professional, are figuring out how to do more accurate modeling than ever before.

One notable example is Ray Hensberger, baseball-loving technologist in the Strategic Innovation Group at Booz Allen Hamilton.

At a workshop during the GigaOm Structure conference, Hensberger shared his next-level data crunching and the academic paper his team prepared for the MIT Sloan Sports Analytics Conference. His team modeled MLB data to show with 74.5% accuracy what a pitcher is going to throw—and when.

Hensberger’s calculations are more accurate than anything else published to date. But as Hensberger knows, getting the numbers right isn’t easy. The problem: How to build machine-learning build models that understand baseball decision-making? And how to make them solid enough to actually work with new data in real-time game situations?

“We started with 900 pitchers,” says Hensberg. “By excluding players having thrown less than 1,000 pitches total over the three seasons considered, we drew an experimental sample of about 400,” he says. “We looked at things like the number of people on base, a right-handed batter versus a left-handed batter.”

They also looked at the current at-bat (pitch type and zone history, ball-strike count); the game situation (inning, number of outs, and number and location of men on base); and pitcher/batter handedness; as well as other features from observations on pitchers that vary across ball games, such as curveball release point, fastball velocity, general pitch selection, and slider movement.

The final result? A set of pitcher-customized models and a report about what those pitchers would throw in a real game situation.

“We took the data, looked at the most common pitches they threw, then built a model that said ‘In this situation, this pitcher will throw this type of pitch—be that a slider, curveball, split-finger. We took the top four top favorite pitches of that pitcher, and we built models for each one of those pitches for each one of those pitchers,” Hensberger said.

They are methods he and his team outline in a book published by his team called The Field Guide To Data Science. “Most of [the data],” he says, “was PITCHf/x data from MLB. There’s a ton of data out there.”

Modern Baseball Analytics.Booz Allen Hamilton

Cross Validation Is Key

“Each pitcher-specific model was trained and tested by five-fold cross-validation testing,” Hensberger says. Cross-validation is an important part of training and testing machine learningmodels. Its purpose, in English: to ensure that the models aren’t biased by the data they’re triangulated by.

“The cross-validation piece, the goal of it, you’re defining a data set you can test the model with,” says Hensberger. “You’ve got to have a way of testing the model out when you’re training it, and to provide insight on how the model will generalize to an unknown data set. In this case, that would be real-time pitches.”

“You don’t want to just base your model on purely 100% on what was done historically. If we just put out this model without doing that cross-validation piece, people would probably say your model is overfit for the data that you have.”

Once the models were solid, Hensberger and his team used a machine-learning strategy known as “one-versus-rest” to run experiments to predict the type of the next pitch for each pitcher. It is based on an algorithm that allowed them to establish an “index of predictability” for a given pitcher. Then they looked at the data in three different ways:

  1. Predictability by pitch count, looking at pitcher predictability: When the batter is ahead (more balls than strikes), when the batter is behind (more strikes than balls), and when the pitch count is even.
  2. Predictability by “platooning” which looks at how well a right-handed batter will fare against a left-handed pitcher, and vice versa.
  3. Out-of-sample test, a test to verify the predictions by running trained models with new data to make sure they work. “We performed out-of-sample predictions by running trained classifier models using previously unseen examples from the 2013 World Series between the Boston Red Sox and the St. Louis Cardinals.”

“Overall our rate was about 74.5% predictability across all pitchers, which actually beats the previous published result at the MIT Sloan Sports Analytics conference. That that was 70%,” says Hensberger. The report published by his team was also able to predict exact pitch type better than before. “The other study only said if a fastball or not a fastball that’s going to come out of a pitcher’s hand,” says Hensberger. “The models we built were for the top four pitches, so [they show] what the actually pitches were going to be.”

Hensberger’s team also made some other interesting discoveries.

“Some pitchers, just given the situation, were more predictable than others,” he says. “There is no correlation between predictability and ERA. With less predictable pitchers, you would expect them to be more effective. But that’s not true. We also found that eight of the 15 most predictable pitchers came from two teams: the Cardinals and the Reds.”

This may be a result of the catchers calling the game, influencing the pitchers and their decisions. But it also may be attributed to pitching coaches telling pitchers what to do in certain situations. “Either way,” Hensberger says, “it’s interesting to consider.”

His findings around platoon advantage are worth thinking about as well. Statistically in baseball, platoon advantage means that the batter usually has the advantage: They have better stats when they face the opposite-handed pitcher.

“What we found [in that situation] is the predictability of pitchers was around 76%. If you look at the disadvantage, the overall predictability was about 73%,” Hensberger says. “So, pitchers are a little more predictable, we found, when the batter’s at the advantage. That could play into why the stats kind of favor them.”

This work was done over the corpus of data, but Hensberger says that you run the models real-time during a game, using the time interval between pitches to compute new stats and make predictions according to the current game situation.

According to Jessica Gelman, cofounder and co-chair of the MIT Sloan Sports Analytics Conference, that type of real-time, granular data crunching is where sports analytics is headed. The field is changing fast. And Gelman proves it. Below, her overview on how dramatically it has evolved from where it was just a couple of years ago.

How Sports Data Science Has Evolved

“If you’ve read Moneyball or watched the movie, at that point in time it was no different than what bankers do in looking for an undervalued asset. Now, finding those undervalued assets is much harder. There’s new stats that are being created all the time. It’s so much more advanced,” Gelman says.

Though it may surprise data geeks, Gelman says that formalized sport analytics still isn’t yet mainstream—not every sport or team uses data. The NHL is still lagging in analytics, with the most notable exception of the Boston Bruins. The NFL is slow to adopt as well, though more teams like the Buffalo Bills are investing in the space.

However, most other leagues are with the program. And that is accelerating. In a big way. In Major League Soccer, formal analytics are now happening. Data analysis is now standard in EnglishPremier League football, augmented by global football by fan sites. And almost every baseball and basketball team has an analytics team.

“Some sports have been quicker to accept it than others,” says Gelman. “But it’s widely accepted at this point in time that there’s significant value to having analytics to support decision making.”

So how are analytics used in sports? Gelman says there’s work happening on both the team side and on the business side.

“On the team side, some leagues do a lot with, for example, managing salaries and using analytics for that. Other leagues use it for evaluating the players on the field and making decisions about who’s going to play or who to trade. Some do both,” says Gelman.

On the business side, data science increasingly influences a number of front office decisions. “That’s ticketing, pricing, and inventory management. It’s also customer marketing, enhancing engagement and loyalty, fandom, and the game-day experience,” Gelman explains. A lot of data science work looks at how people react to what in the stadium and how you keep them coming to back—versus watching at home on TV. “And then,” Gelman says, “the most recent realm of analytics is wearable technology,” which means more data will soon be available to players and coaches.

Hensberger sees this as a good thing. Ultimately, he says, the biggest winners will be the fans.

“Data science is about modeling and predicting. When this gets in the hands of everyone across the leagues, the viewing experience will get better for everybody,” he says. “You want to see competition. You don’t want to see a blowout, you want to see close games. Excitement and heart-pounding experience. That’s what brings us back to the sport.”

Originally posted via “How Sports Data Analytics Is Upsetting The Game All Over Again”

Source: How Sports Data Analytics Is Upsetting The Game All Over Again by analyticsweekpick

One Word Can Speak Volumes About Your Company Culture

Employee surveys are used to help manage the employee relationship. The questions in the employee survey are used to elicit employee responses that will be used to better understand how to improve that relationship. I crafted a new employee survey question that combines the best of both structured and unstructured measurement approaches. This approach provides both qualitative and quantitative information.

Here is the new question: What one word best describes <Company Name> as an employer?

I have one client (startup B2B technology company) who conducts an annual employee survey. Last year, a total of 157 employees completed the survey (response rate of 61%). In addition to the one-answer, open-ended question above, the employee survey included other traditional survey questions, including employee loyalty questions (likelihood to stay, likelihood to recommend) and employee experience questions (26 different questions across variety of areas – supervisor, pay, benefits, work group, promotions, training).

The one word answer can be used in a few ways to provide employee insight. First, you can examine the content of the words to understand your company’s strengths and weaknesses. Second, the employee responses can help you determine the level of sentiment employees have toward the company. Finally, you can use your current employees’ words (e.g., branding purposes) throughout your employment and recruitment collateral to attract prospects.

1. Identify Company Strengths and Weaknesses

Figure 1. Examine the content of words that your employees use to describe you to understand your strengths and weaknesses.

The most frequently used words by the employees are presented in Figure 1. Some words used by many employees included general adjectives such as “Awesome,” “Exciting,” “Great” and “Good.” While these words tell you that employees are generally happy, they are less useful in pinpointing the reasons why they are happy. There were, however, a few words that reflected specific adjectives that provide some insight about the work environment (e.g., “Flexible / Flexibility,” “Teamwork,” “Innovative,” “Agile” and “Hectic”). Taken as a whole, these diverse adjectives paint a generally positive picture of a work environment that is innovative, flexible, hectic and one that supports teamwork.

2. Use Employee Sentiment as a KPI

Figure 2. ;lkj
Figure 2. The Employee Sentiment Index (ESI) is predictive of important organizational variables like employees’ intentions to stay with the employer and recommend the employer as a place to work.

Calculating a sentiment score is an exercise of mapping each word into a numeric value of sentiment. I used an existing sentiment lexicon that is based on prior research in the area of sentiment measurement (see here, here and here). Each word that the employees use is assigned a value (based on the lexicon) on a scale from 0 (negative sentiment) to 10 (positive sentiment). This value represents the Employee Sentiment Index (ESI).

The average ESI value across the entire set of employee responses was 7.2, reflecting that, on the average, the employees generally have a positive attitude about their employer.

To understand the usefulness of the ESI, I correlated it with the other employee loyalty measures. As you can see in Figure 2, the Employee Sentiment Index is positively related to employees’ intentions to stay with the employer and intentions to recommend the employer as a place to work. Employees reporting positive sentiment about the company are more likely to recommend the company to a friend as a place to work and more likely to stay with the company compared to employees reporting less positive sentiment about the company.

The ESI could be used as a key performance index for use in employee analytics efforts that identify the causes of employee sentiment. Furthermore, the ESI could be included in executive dashboards as a good overall metric of the health of the employee-employer relationship.

3. Improve Company Branding

Figure 3. Use employee responses for communication and branding purposes.

The list of words that employees use to describe you paints a general picture of your company. You can create a word cloud to help you communicate the survey results to the company. Additionally, you can use the word cloud as part of your recruitment efforts to attract new employees. My client used their word cloud as part of their employee on-boarding process (see an initial mock up of their word cloud in Figure 3).


I presented a measurement approach to help businesses manage the health of the employee relationship. The proposed method reflects an intentional measurement approach using unstructured data. The measurement approach offers a variety of benefits:

  1. Identify company strengths/weaknesses. The content of the words that the employees use can be examined to to understand common themes.
  2. Use employee sentiment as a KPI. The ESI measures the extent to which employees hold positive sentiment toward their employer. This metric can be used to track progress over time. The ESI is predictive of other important organizational measures including turnover intentions and likelihood to recommend the employer.
  3. Improve company branding. Depending on the results, you can use word clouds for employment collateral to support recruitment and employee orientation activities.

Results of the analyses show that the proposed measurement method provides useful information about your company culture and your employees. This measurement approach allows you to categorize the words that employees use (unstructured data) into different levels of employee sentiment (structured data). The results of this case study show that employers can obtain useful information from a single question to help them understand how to better manage employee relationships.

This article first appeared on

Source by bobehayes

The 7 Most Unusual Applications of Big Data You’ve Ever Seen!

It’s all well and good to talk about customer experience and managing inventory flow, but what has big data done for me lately?

I’ve rounded up seven of the most interesting — and unique — applications for big data I’ve seen recently and how they may be impacting your life.

Big Data Billboards

Outdoor marketing company Route is using big data to define and justify its pricing model for advertising space on billboards, benches and the sides of busses. Traditionally, outdoor media pricing was priced “per impression” based on an estimate of how many eyes would see the ad in a given day. No more! Now they’re using sophisticated GPS, eye-tracking software, and analysis of traffic patterns to have a much more realistic idea of which advertisements will be seen the most — and therefore be the most effective.

iPhone’s ResearchKit

Apple’s new health app, called ResearchKit, has effectively just turned your phone into a biomedical research device. Researchers can now create studies through which they collect data and input from users phones to compile data for health studies. Your phone might track how many steps you take in a day, or prompt you to answer questions about how you feel after your chemo, or how your Parkinson’s disease is progressing. It’s hoped that making the process easier and more automatic will dramatically increase the number of participants a study can attract as well as the fidelity of the data.

Big Data and Foraging

The website combined public information from the U.S. Department of Agriculture, municipal tree inventories, foraging maps and street tree databases to provide an interactive map to tell you where the apple and cherry trees in your neighborhood might be dropping fruit. The website’s stated goal is to remind urbanites that agriculture and natural foods do exist in the city — you might just have to access a website to find it.

Big Data on the Slopes

Ski resorts are even getting into the data game. RFID tags inserted into lift tickets can cut back on fraud and wait times at the lifts, as well as help ski resorts understand traffic patterns, which lifts and runs are most popular at which times of day, and even help track the movements of an individual skier if he were to become lost. They’ve also taken the data to the people, providing websites and apps that will display your day’s stats, from how many runs you slalomed to how many vertical feet you traversed, which you can then share on social media or use to compete with family and friends.

Big Data Weather Forecasting

Applications have long used data from phones to populate traffic maps, but an app called WeatherSignal taps into sensors already built into Android phones to crowdsource real time weather data as well. The phones contain a barometer, hygrometer (humidity), ambient thermometer and lightmeter, all of which can collect data relevant to weather forecasting and be fed into predictive models.

Yelp Hipster Watch

Whether you want to hang with the hipsters or avoid them, Yelp has you covered. With a nifty little search trick they call the Word Map, you can search major cities by words used in reviews — like hipster. The map then plots the locations for the reviews in red. The darker the red, the higher the concentration of that word used in reviews — and when it comes to hipsters, ironic tee shirts and handlebar mustaches.

Even Big Data Bras?

Website True&Co. is using big data to help women find better fitting bras. Statistics show that most women wear the wrong bra size, and so the website has stepped up to try to solve that problem. Customers fill out a fit questionnaire on the site, and based on the responses, an algorithm suggests a selection of bras to choose from. The company’s in-house brand is even developed and designed based on feedback from customers and data the company has collected.

The possibilities of using big data are endless and it might be time to find the big data applications in your business.

As always, thank you very much for reading my posts. You might also be interested in my new book: Big Data: Using Smart Big Data, Analytics and Metrics To Make Better Decisions and Improve Performance

You can read a free sample chapter here.


Originally Posted at: The 7 Most Unusual Applications of Big Data You’ve Ever Seen! by analyticsweekpick

Surge in real-time big data and IoT analytics is changing corporate thinking

Big data that can be immediately actionable in business decisions is transforming corporate thinking. One expert cautions that a mindset change is needed to get the most from these analytics.

Gartner reported in September 2014 that 73% of respondents in a third quarter 2014 survey had already invested or planned to invest in big data in the next 24 months. This was an increase from 64% in 2013.

The big data surge has fueled the adoption of Hadoop and other big data batch processing engines, but it is also moving beyond batch and into a real-time big data analytics approach.

Organizations want real-time big data and analytics capability because of an emerging need for big data that can be immediately actionable in business decisions. An example is the use of big data in online advertising, which immediately personalizes ads for viewers when they visit websites based on their customer profiles that big data analytics have captured.

“Customers now expect personalization when they visit websites,” said Jeff Kelley, a big data analytics analyst from Wikibon, a big data research and analytics company. “There are also other real-time big data needs in specific industry verticals that want real-time analytics capabilities.”

The financial services industry is a prime example. “Financial institutions want to cut down on fraud, and they also want to provide excellent service to their customers,” said Kelley. “Several years ago, if a customer tried to use his debit card in another country, he was often denied because of fears of fraud in the system processing the transaction. Now these systems better understand each customer’s habits and the places that he is likely to travel to, so they do a better job at preventing fraud, but also at enabling customers to use their debit cards without these cards being locked down for use when they travel abroad.”

Kelly believes that in the longer term this ability to apply real-time analytics to business problems will grow as the Internet of Things (IoT) becomes a bigger factor in daily life.

“The Internet of Things will enable sensor tacking of consumer type products in businesses and homes,” he said. “You will be collect and analyze data from various pieces of equipment and appliances and optimize performance.”

The process of harnessing IoT data is highly complex, and companies like GE are now investigating the possibilities. If this IoT data can be captured in real time and acted upon, preventive maintenance analytics can be developed to preempt performance problems on equipment and appliances, and it might also be possible for companies to deliver more rigorous sets of service level agreements (SLAs) to their customers.

Kelly is excited at the prospects, but he also cautions that companies have to change the way they view themselves and their data to get the most out of IoT advancement.

“There is a fundamental change of mindset,” he explained, “and it will require different ways of approaching application development and how you look at the business. For example, a company might have to redefine itself from thinking that it only makes ‘makes trains,’ to a company that also ‘services trains with data.'”

The service element, warranties, service contracts, how you interact with the customer, and what you learn from these customer interactions that could be forwarded into predictive selling are all areas that companies might need to rethink and realign in their business as more IoT analytics come online. The end result could be a reformation of customer relationship management (CRM) to a strictly customer-centric model that takes into account every aspect of the customer’s “life cycle” with the company — from initial product purchases, to servicing, to end of product life considerations and a new beginning of the sales cycle.

Originally posted via “Surge in real-time big data and IoT analytics is changing corporate thinking”


Underpinning Enterprise Data Governance with Machine Intelligence

Initially, data governance was a means of ensuring long term sustainability of data assets, then it became a prerequisite for regulatory compliance. Today, the holistic scope of enterprise data governance encompasses these two vital precepts in addition to a third, transcendent one which could supersede the value of its predecessors: it’s now a means of effecting competitive advantage.

Deploying the knowledge of the processes, people, and practices that provide consistently quality data assets throughout an organization in a manner that distinguishes it from its competitors is significantly supplemented by machine intelligence.

The synthesis of these two facets of the data landscape—enterprise data governance and machine intelligence—systematically produces situations in which, “You have a lucid view of the enterprise data,” explained Ralph Hodgson, Co-Founder/Executive VP and Director of TopBraid Technologies at TopQuadrant. “Once it arrives in a place where you can have machine learning techniques and deep learning techniques process it, you’re starting off with a much richer, semantically integral foundation.”

Visibility and Traceability
The lucidity referenced by Hodgson is one of the more demonstrable facets of governing data in a linked enterprise way which allows those data, and their potential uses and context, to be discoverable to organizations in a method superior to that of relational technologies. Utilizing the standards-based approach of semantic technologies allows users to discern “what data you have—being able to search and find it, being able to combine it in ways that are used on a daily basis in all your applications and systems and know how that’s happening and track that,” remarked TopQuadrant Co-Founder/CMO and VP of Professional Services Robert Coyne. The machine intelligence inherent in linked enterprise data is essential for determining contextualized inferences between data elements through the use of triples; the overall linked enterprise data environment readily facilitates the lineage of data which is pivotal to data governance. According to Hodgson, one of the ways organizations can effect competitive advantage from this transparent traceability is “not just for regulatory reports but impact analysis. If I change this table what effect is it going to have. When you’ve got thousands or even hundreds of object tables, you can imagine how it is to assess impact.”

According to Ben Szekely of Cambridge Semantics, enterprise data governance naturally extends the benefits of enterprise architecture to provide an enriched customer experience. “Our experience is that originally data architects were a little bit resistant to many technologies,” Szekely acknowledged. “Once they started to understand that it fits into their existing best practices and governance methodologies, they’re really starting to eat this stuff up. It’s allowing them to deliver value to their customers much more quickly.”

Machine Intelligence
One of the more valuable benefits of strengthening enterprise data governance with machine intelligence capabilities is an expeditious efficiency that is otherwise difficult to match. Semantic technologies allow for machine-readable data which can accelerate most processes involving those data, decreasing time spent on data modeling and other facets of data preparation. “The ability for data to be discoverable and linkable through an adoption of identifiers in a consistent way allows that data to move and to be reached more rapidly,” Hodgson said. “Whether you get the data into a machine learning environment is another matter. But at least you’re insured of its integrity, and that’s a big issue as well.” A particularly cogent use case for creating competitive advantage with machine intelligence capabilities and enterprise data governance pertains to the finance industry, and the need to sift through innumerable documents for valuable data assets and terms. According to Hodgson, “There is growing interest in being able to use machine learning and AI techniques on content management systems” such as SharePoint and others that are internal to organizations.

Enhanced Search
Nonetheless, it is quite possible that the more convincing use case of deriving competitive advantage from enterprise-wide linked data governance and artificial intelligence pertains to applications of data external to organizations. An artful combination of ontological modeling and taxonomical vocabulary management enabled TopQuadrant to assist eminent financial organizations—including The Federal Reserve Board and the European Central Bank—in effecting “a powerful-faceted search over, in this case, research papers of macroeconomics,” Hodgson noted. “So you could maybe ask a question like how will banks affect the GDP or the interest rate of countries in Europe, and you would find research reports that were relevant to that.” Central to this sort of enhanced semantic search is the concept of vocabulary management which, in a standards-based environment, is greatly facilitated by hierarchical taxonomies. These techniques are able to categorize terms and their definitions in ways in which they are represented by broader terms and relationships that exceed what Hodgson called the “flat” definitions of mere business glossaries, which simply map a term to a definition. Taxonomical vocabulary management is crucial to leveraging meaning from unstructured data in particular, expanding the possibilities for machine intelligence accordingly. “Having an environment where you at least have the metadata and have a handle on where all your data is and how it’s connected and can be connected, gives you a lead for that direction of using artificial intelligence and machine learning in different ways as well on the data,” Coyne mentioned.

Linked Metadata
The benefits of machine-readable linked enterprise data reverberate at the level of metadata as well. Vocabulary management is a key means of determining metadata for the enormous amounts of unstructured data found within the realm of big data, which is one of the reasons Hodgson referred to vocabulary management as a “bottoms up approach. The need to be able to discover metadata is also very much a driving force.” Metadata management is a necessary component of enterprise data governance, and helps to provide the foundation upon which higher manifestations of this practicality—such as data lineage, data quality, and their relevance to machine intelligence—depend. “In terms of the data space of enterprises, what they need is a map of the territory,” Coyne asserted. “That map is metadata. That metadata has to be interconnected. It has to be something that can grow and be maintained by people. Not just IT people but people involved in the daily use of data.”

Competitive Positioning
Supplementing enterprise data governance with machine intelligence, and in turn augmenting machine intelligence with enterprise data governance, is a key means of creating competitive advantage with these two staples of the data sphere. Implementing them in a linked data environment facilitates comprehensive governance throughout the enterprise with tangible results such as enhanced search, increased discoverability and context of data assets, improved visibility and data lineage, and more effective data integrity. Powered by intelligent inferencing and standardized data adherence, this combination can well propel data governance forward in the realm of overall enterprise strategy based on what Hodgson called, “The ability to make this data available in a machine-readable way, in a linkable way, inside an enterprise.”

Originally Posted at: Underpinning Enterprise Data Governance with Machine Intelligence by jelaniharper