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How FleetOptic’s data analytics smooth the last mile of a parcel’s journey
SEPTEMBER 27, 2019 | The Globe and Mail| by JOANNA PACHNER, SPECIAL TO THE GLOBE AND MAIL.

FleetOptics specializes in so-called last-mile delivery, from a retailer's distribution centre to the customer's door—the hardest and most expensive portion, estimated to account for a least 30% of total transportation cost. It's also the most vital as, in the e-commerce era, receiving the package is often the only contact consumers have with a human during the transaction. FleetOptics' software makes the parcel's progress transparent for both business and consumer. Customers can track the driver on-screen as they might an approaching Uber car, avoiding that infuriating experience of the deliveryman arriving just after they jump in the shower. Retailers, meanwhile, can check packages' status in real time through FleetOptics' online portal. As co-founder Vince Buckley pithily sums it up, “Tesla is a battery company that also makes cars. We're a technology company that also makes deliveries.”
analytics  data  data_driven  delivery  delivery_networks  delivery_services  distribution  distribution_centres  e-commerce  FleetOptics  fulfillment  last_mile  logistics  package_delivery  retailers  same-day  start_ups  shipping  third-party  traceability  tracking  trucking  warehouses 
11 weeks ago by jerryking
Ad Giant Wins Over Disney With Big Data Pitch
Oct. 15, 2019 | The New York Times | By Tiffany Hsu.

Advertising pitches have come a long way since the 1960s, when creative teams tried to impress potential clients with snappy slogans, catchy jingles and arresting visuals while pledging to attract the housewife segment or the businessman demographic.

These days, big companies look to ad companies for their data smarts as much as their marketing expertise. The agencies with the most persuasive pitches are those that have increasingly personalized data on the patterns and preferences of a broad range of consumers.

Disney already has plenty of data on its customers. But the prospect of precisely targeting potential moviegoers, theme-park visitors, hotel guests and subscribers for its coming Disney Plus streaming service appealed to the company, according to two people familiar with the pitch process.

While the Disney-Publicis deal may benefit both companies, some worry that it may put consumer privacy at risk.

“This is in essence creating a data broker division to Disney, expanding what Disney already knows, which is a lot,” said Jeffrey Chester, the executive director of the Center for Digital Democracy, a nonprofit consumer advocacy group. “You’re telling your entire life history to Mickey Mouse.”

On Nov. 12, the Disney will start its streaming challenger to Netflix, Disney Plus.
In North America, Publicis will take charge of media strategy for the Disney Plus streaming service as well as Disney resorts and amusement parks. Epsilon was a major draw because of the extremely detailed data it has compiled. The company may very well know if you are lactose intolerant or are in the market for a pickup truck with 60,000 miles on it. If you are into astrology or have taken out a home-equity loan, it may know that too. Epsilon could, for example, beam a Disney Plus ad to parents who have bought a Lion King costume for their toddler.....“They have the capacity to really understand who is a likely prospect for the streaming service and where that person resides online, and they can send messages in the appropriate media to that individual,” .....most of the advertising industry is struggling to compete against Facebook and Google, analysts said. The platforms dominate the business of buying and selling digital ads, leaving the agencies little room to negotiate. Facebook and Google have also started working directly with many advertising clients, luring them away from traditional ad companies.

In leaning on data to improve its fortunes, Publicis is part of a larger industry trend. Dentsu bought a majority stake in the data marketing firm Merkle Group in 2016, and Interpublic Group bought the data marketing firm Acxiom in 2018.....a “huge consolidation” within advertising that has allowed huge holding companies to gobble up agencies and data companies that are increasingly looking for ways to advertise using personal data.

He said that viewership data from the ad-free Disney Plus, including details involving children, could be passed on to Epsilon, which could use the information to target consumers with marketing for other Disney offerings.

“It’s Madison Avenue bringing you Silicon Valley,”
advertising  advertising_agencies  analytics  big_bets  data  Disney  Epsilon  Madison_Avenue  marketing  Omnicom  personal_data  pitches  privacy  Publicis  Silicon_Valley  streaming  target_marketing  theme_parks 
october 2019 by jerryking
The Mystery of the Miserable Employees: How to Win in the Winner-Take-All Economy -
June 15, 2019 | The New York Times | By Neil Irwin.
Neil Irwin is a senior economics correspondent for The Upshot. He is the author of “How to Win in a Winner-Take-All-World,” a guide to navigating a career in the modern economy.......
What Mr. Ostrum and the analytics team did wasn’t a one-time dive into the numbers. It was part of a continuing process, a way of thinking that enabled them to change and adapt along with the business environment. The key is to listen to what data has to say — and develop the openness and interpretive skills to understand what it is telling us.......Neil Irwin was at Microsoft’s headquarters researching a book that aims to answer one simple question: How can a person design a thriving career today? The old advice (show up early, work hard) is no longer enough....In nearly every sector of the economy, people who seek well-paying, professional-track success face the same set of challenges: the rise of a handful of dominant “superstar” firms; a digital reinvention of business models; and a rapidly changing understanding about loyalty in the employer-employee relationship. It’s true in manufacturing and retail, in banking and law, in health care and education — and certainly in tech......superstar companies — and the smaller firms seeking to upend them — are where pragmatic capitalists can best develop their abilities and be well compensated for them over a long and durable career.....the obvious disadvantages of bureaucracy have been outweighed by some not-so-obvious advantages of scale......the ability to collect and analyze vast amounts of data about how people work, and what makes a manager effective (jk: organizing data) .... is essential for even those who aren’t managers of huge organizations, but are just trying to make themselves more valuable players on their own corporate team.......inside Microsoft’s human resources division, a former actuary named Dawn Klinghoffer ....was trying to figure out if the company could use data about its employees — which ones thrived, which ones quit, and the differences between those groups — to operate better......Klinghoffer was frustrated that ....insights came mostly from looking through survey results. She was convinced she could take the analytical approach further. After all, Microsoft was one of the biggest makers of email and calendar software — programs that produce a “digital exhaust” of metadata about how employees use their time. In September 2015, she advised Microsoft on the acquisition of a Seattle start-up, VoloMetrix, that could help it identify and act on the patterns in that vapor......One of VoloMetrix's foundational data sets, for example, was private emails sent by top Enron executives before the company’s 2001 collapse — a rich look at how an organization’s elite behave when they don’t think anyone is watching.
analytics  books  data  datasets  data_driven  exhaust_data  Fitbit  gut_feelings  human_resources  interpretative  Managing_Your_Career  massive_data_sets  meetings  metadata  Microsoft  Moneyball  organizational_analytics  organizing_data  people_analytics  quantitative  quantified_self  superstars  unhappiness  VoloMetrix  winner-take-all  work_life_balance 
june 2019 by jerryking
Opinion | The Surprising Benefits of Relentlessly Auditing Your Life
May 25, 2019 | The New York Times | By Amy Westervelt, a journalist and podcaster.

"The unexamined life is not worth living" is a famous dictum apparently uttered by Socrates at his trial for impiety and corrupting youth, for which he was subsequently sentenced to death, as described in Plato's Apology (38a5–6).
analytics  data  evidence_based  happiness  housework  marriage  note_taking  patterns  quality_of_life  quantitative  quantified_self  record-keeping  relationships  relentlessness  self-assessment  self-examination  self-improvement  spreadsheets 
may 2019 by jerryking
‘Math men’ not mad men rule advertising’s data age, says Lévy
May 5, 2019 | Financial Times | by Anna Nicolaou.

Maurice Levy: 'The future [of advertising] is based on data. It is not based on any mass media.' We know that mass media is [declining] every day,” “And if an advertising agency wants to have a future, data is absolutely indispensable.”

the advertising industry was undergoing a “metamorphosis” that required big bets.......As consumers shift attention away from pricey television commercials and towards the internet, where Facebook and Google dominate, the industry is more “math men” than mad men......In light of digital disruption Publicis, the world’s third-largest advertising agency by revenues, has made a big bet on data. In April the company made its largest acquisition with the purchase of Epsilon, a digital marketing company owned by Alliance Data Systems......Like its rivals WPP and Omnicom, Publicis is under pressure as Facebook and Google have disintermediated the traditional agency model. The two tech groups account for two-thirds of digital advertising sales in the US.....The industry has been consolidating as traditional agencies look to position themselves as data analytics gurus who can help brands target shoppers online. Last year Interpublic bought data business Acxiom for $2bn, while just last month buzzy agency Droga5 sold itself to Accenture......Despite lingering fears that an economic slowdown is looming, “the situation is much better now,”.... making the Epsilon decision easier. “The fastest-growing segment in our industry is data, technology, internet. Period. All the rest is suffering.”
advertising  advertising_agencies  analytics  big_bets  data  decline  disruption  disintermediation  Epsilon  Facebook  Google  Interpublic  Mad_Men  marketing  mass_media  mathematics  Maurice_Lévy  Omnicom  Publicis  WPP 
may 2019 by jerryking
Spy tactics can spot consumer trends
MARCH 22, 2016 | Financial Times | John Reed.
Israel’s military spies are skilled at sifting through large amounts of information — emails, phone calls, location data — to find the proverbial needle in a haystack: a suspicious event or anomalous pattern that could be the warning of a security threat.....So it is no surprise that many companies ask Israeli start-ups for help in data analysis. The start-ups, often founded by former military intelligence officers, are using the methods of crunching data deployed in spycraft to help commercial clients. These might range from businesses tracking customer behaviour to financial institutions trying to root out online fraud......Mamram is the Israel Defense Forces’ elite computing unit.
analytics  consumer_behavior  cyber_security  data  e-mail  haystacks  hedge_funds  IDF  insights  intelligence_analysts  Israel  Israeli  Mamram  maritime  massive_data_sets  security_&_intelligence  shipping  spycraft  start_ups  tracking  traffic_analysis  trends  trend_spotting 
april 2019 by jerryking
Supercharging retail sales through geospatial analytics
March 2019 | | McKinsey | By Rob Hearne, Alana Podreciks, Nathan Uhlenbrock, and Kelly Ungerman.

A retailer can now use geospatial analytics to understand the interactions between its online and offline channels. With these insights, it can create a higher-performing retail network.
====================================
Is our outlet store in San Francisco hurting foot traffic and sales at our full-price store two miles away? Or is it doing the opposite—attracting new customers and making them more likely to visit both stores? How are our five Manhattan stores affecting our e-commerce revenue? Are they making consumers more likely to shop on our website or to search for our products on Amazon? If we open a new mall store in the Dallas metro area, what impact will it have on sales at our existing stores, at our department-store partners, and online?

The answers to these kinds of questions are increasingly crucial to a retailer’s success, as more and more consumers become omnichannel shoppers......most retailers don’t give adequate thought to the cross-channel impact of their stores. They rely on gut feel or on high-level analysis of aggregated sales data to gauge how their offline and online channels interact.....there’s a way for retailers (and other omnichannel businesses) to quantify cross-channel effects, thus taking the guesswork out of network optimization. Through advanced geospatial analytics and machine learning, a retailer can now generate a detailed quantitative picture of how each of its customer touchpoints—including owned stores and websites, wholesale doors, and partner e-commerce sites—affects sales at all its other touchpoints within a micromarket......US retail sales are on an upward trajectory.....despite the growth of e-commerce, the vast majority of these purchases still happened in brick-and-mortar stores. .....So why have US retailers closed thousands of stores in the past year, with thousands more closures to come?....Because the consumer journey is changing!!......Consumers are transacting in different channels....engaging across multiple channels, often simultaneously rather than sequentially. It’s critical for omnichannel retailers to have a detailed understanding of the interplay between online and offline touchpoints, and between owned and partner networks.

Quantifying cross-channel effects

the starting point is data......from a wide range of internal and external sources. Inputs into a geospatial model would ideally include not just transaction and customer data but also store-specific details such as store size and product mix; site-specific information such as foot traffic and retail intensity; environmental data, including local-area demographics; and anonymized mobile-phone location data.......A simulation model can then quantify the sales effect of each of the retailer’s customer touchpoints on its other channels within a local market. The model must be sophisticated enough to simulate the upward or downward revenue impact of adding or removing a particular touchpoint.

Geospatial analysis reveals that the consistency and magnitude of cross-channel effects vary significantly across channel types and markets.
analytics  bricks-and-mortar  cross-channel  customer_journey  customer_touchpoints  data  e-commerce  foot_traffic  geospatial  gut_feelings  location_based_services  McKinsey  moments_of_truth  omnichannel  privacy  retailers  store_closings  security_consciousness  site_selection 
march 2019 by jerryking
Marijuana data firm Headset to partner with global analytics company Nielsen
March 6, 2019

Global measurement and data analytics company Nielsen has formed an alliance with Seattle-based cannabis analytics firm Headset to provide insights about the U.S. marijuana market to consumer packaged goods (CPG) businesses.

According to a news release, the two firms are joining forces as New York-based Nielsen develops “a full suite” of cannabis measurement capabilities to help inform CPG companies about the marijuana industry. Nielsen’s consumer research capabilities will be intertwined with Headset’s point-of-sale data for cannabis products, collected from “key” states with legal recreational marijuana markets.
alliances  analytics  cannabis  CPG  Headset  insights  Nielsen 
march 2019 by jerryking
Big data: legal firms play ‘Moneyball’
February 6, 2019 | Financial Times | Barney Thompson.

Is the hunt for data-driven justice a gimmick or a powerful tool to give lawyers an advantage and predict court outcomes?

In Philip K Dick’s short story The Minority Report, a trio of “precogs” plugged into a machine are used to foretell all crimes so potential felons could be arrested before they were able to strike. In real life, a growing number of legal experts and computer scientists are developing tools they believe will give lawyers an edge in lawsuits and trials. 

Having made an impact in patent cases these legal analytics companies are now expanding into a broad range of areas of commercial law. This is not about replacing judges,” says Daniel Lewis, co-founder of Ravel Law, a San Francisco lawtech company that built the database of judicial behaviour. “It is about showing how they make decisions, what they find persuasive and the patterns of how they rule.” 
analytics  data_driven  judges  law  law_firms  lawtech  lawyers  Lex_Machina  massive_data_sets  Moneyball  predictive_modeling  quantitative  tools 
february 2019 by jerryking
Music’s ‘Moneyball’ moment: why data is the new talent scout | Financial Times
JULY 5, 2018 | FT | Michael Hann.

The music industry loves to self-mythologise. It especially loves to mythologise about taking young scrappers from the streets and turning them into stars. It celebrates the men and women — but usually the men — with “golden ears” almost as much as the people making the music....A&R, or “artists and repertoire”, are the people who look for new talent, convince that talent to sign to the record label and then nurture it: advising on songs, on producers, on how to go about the job of being a pop star. It’s the R&D arm of the music industry......What the music business doesn’t like to shout about is how inefficient its R&D process is. The annual global spend on A&R is $2.8bn....and all that buys is the probability of failure: “Some labels estimate the ratio of commercial success to failure as 1 in 4; others consider the chances to be much lower — less than 1 in 10,” observes its 2017 report. Or as Mixmag magazine’s columnist The Secret DJ put it: “Major labels call themselves a business but are insanely unprofitable, utterly uncertain, totally rudderless and completely ignorant.”......The rise of digital music brought with it a huge amount of data which, industry executives realized, could be turned to their advantage. ....“All our business units must now leverage data and analytics in innovative ways to dig deeper than ever for new talent. The modern day talent-spotter must have both an artistic ear and analytical eyes.”

Earlier this year, in the same week as Warner announced its acquisition of Sodatone, a company that has developed a tool for talent-spotting via data, another data company, Instrumental, secured $4.2m of funding. The industry appeared to have reached a tipping point — what the website Music Ally called “A&R’s data moment”. Which is why, wherever the music industry’s great and good gather, the word “moneyball” has become increasingly prevalent.
........YouTube, Spotify, Instagram were born and changed the way talent begins its journey. All the barriers came down. Suddenly you’ve got tens of thousands of pieces of music content being uploaded.......Home computing’s democratization of recording removed the barriers to making high-quality music. No longer did you need access to a studio and an experienced producer, plus the money to pay for them. But the music industry had no way to keep abreast of these new creators. “....The way A&R people have discovered talent has barely changed since the music industry began, and it’s fundamentally the same for indie labels, who put artistry above sales, as it is for major labels who have to answer to shareholders. It’s always been about information.....“We find them by listening to new music constantly, by people giving us tips, by going out and seeing things that sound interesting,”.....“The most useful people to talk to are concert promoters and booking agents. They are least inclined to bullshit; they’ll tell you how many people an act is drawing,”...like labels, publishers also have an A&R function, signing up songwriters, many of whom will also be in bands)....“Journalists and radio producers are [also] very useful people to give you information. If you know you’ve got particular DJs or particular writers who are going to pick up something, that’s really good.”
.......Instrumental’s selling point is a dashboard called Talent AI, which scrapes data from Spotify playlists with more than 10,000 followers.....“We took a view that to build momentum on Spotify, you need to be on playlists,”....“If no one knows who you are, no one’s going to suddenly start streaming a track you’ve just put up. It happens when you start getting included on playlists.”......To make it workable, the Talent AI dashboard enables users to apply a series of filters to either tracks or artists: to sort by nationality, by genre, by number of playlists they appear on, by the number of playlist subscribers, by their industry standing — are they signed to a major? To an independent label? Are they unsigned?
.......What A&R people are looking for, though, is not totals, it’s evidence of momentum. No one wants to sign the artist who has reached maximum popularity. They want the artist on the way up....“It’s the direction. Is it going in the right direction?”....when it comes to assessing what an artist can offer, the data isn’t even always about the numbers. “The one I look at the most is Instagram, because that’s the easiest way for an artist to express themselves in a way other than the music — how they look, what they’re into,” she says. “That gives a real snapshot into [them] and whether they really have formulated a world for themselves or not.”......not everyone is delighted with the drive to data. “[the advent of] Spotify...became the driving force for signings...“A&Rs were using their eyes rather than their ears — watching numbers change rather than listening to music, and then jumping on acts....they saw something happening and got it out quickly without having to invest in the traditional A&R process.”... online heat tends to be generated by transient teenage audiences who are likely to move on rather than stick around for a decade: online presence is a big thing in electronic dance music, or some branches of urban music, in which an artist might only be good for a single song. In short, data does not measure quality; it does not tell you whether an artist has 20 good songs that can be turned into their first two albums; it does not tell you whether they can command a crowd in live performance..........The music industry, of course, has always had an issue with short-termism/short-sightedness: [tension] between the people who sign the cheques and those who go to bat for the artists is built into the way it works..........The problem is that without career artists, the music industry just becomes even more of a lottery. It is being made harder, not just by short-termism, but by the fact that music has become less culturally central. “It’s so much harder to connect with an audience or grow an audience, because there’s so much noise,”
.......Today the A&R...agree that the new data has its uses, but insist it still takes second place to the evidence of their own eyes and ears.......As for Withey, he is not about to tell the old-school scouts their days are done....Instrumental can tell A&R people which artists are hot, but not which are good. Also, there will be amazing acts who simply don’t get the traction on the internet to register on the Talent AI dashboard.....All of which will come as a relief to the people running those A&R departments. .....when asked if data will become the single most important factor in scouting talent: “I hope not. Otherwise we may as well have robots.” For now, at least, the golden ears are safe.
A&R  algorithms  analytics  data  dashboards  tips  discoveries  filters  hits  Instagram  inefficiencies  momentum  music  music_industry  music_labels  music_publishing  Moneyball  myths  playlists  self-mythologize  songwriters  Spotify  SXSW  success_rates  talent  talent_spotting  tipping_points  tracking  YouTube  talent_scouting  high-quality  the_single_most_important 
july 2018 by jerryking
Location Analytics: Bringing Geography Back
October 31, 2012 Reading Time: 11 min 
Simon Thompson (Esri), interviewed by Renee Boucher Ferguson
analytics  location_based_services  geography  Esri  MIT 
april 2018 by jerryking
Novartis’s new chief sets sights on ‘productivity revolution’
SEPTEMBER 25, 2017 | Financial Times | Sarah Neville and Ralph Atkins.

The incoming chief executive of Novartis, Vas Narasimhan, has vowed to slash drug development costs, eyeing savings of up to 25 per cent on multibillion-dollar clinical trials as part of a “productivity revolution” at the Swiss drugmaker.

The time and cost of taking a medicine from discovery to market has long been seen as the biggest drag on the pharmaceutical industry’s performance, with the process typically taking up to 14 years and costing at least $2.5bn.

In his first interview as CEO-designate, Dr Narasimhan says analysts have estimated between 10 and 25 per cent could be cut from the cost of trials if digital technology were used to carry them out more efficiently. The company has 200 drug development projects under way and is running 500 trials, so “that will have a big effect if we can do it at scale”.......Dr Narasimhan plans to partner with, or acquire, artificial intelligence and data analytics companies, to supplement Novartis’s strong but “scattered” data science capability.....“I really think of our future as a medicines and data science company, centred on innovation and access.”

He must now decide where Novartis has the capability “to really create unique value . . . and where is the adjacency too far?”.....Does he need the cash pile that would be generated by selling off these parts of the business to realise his big data vision? He says: “Right now, on data science, I feel like it’s much more about building a culture and a talent base . . . ...Novartis has “a huge database of prior clinical trials and we know exactly where we have been successful in terms of centres around the world recruiting certain types of patients, and we’re able to now use advanced analytics to help us better predict where to go . . . to find specific types of patients.

“We’re finding that we’re able to significantly reduce the amount of time that it takes to execute a clinical trial and that’s huge . . . You could take huge cost out.”...Dr Narasimhan cites one inspiration as a visit to Disney World with his young children where he saw how efficiently people were moved around the park, constantly monitored by “an army of [Massachusetts Institute of Technology-]trained data scientists”.
He has now harnessed similar technology to overhaul the way Novartis conducts its global drug trials. His clinical operations teams no longer rely on Excel spreadsheets and PowerPoint slides, but instead “bring up a screen that has a predictive algorithm that in real time is recalculating what is the likelihood our trials enrol, what is the quality of our clinical trials”.

“For our industry I think this is pretty far ahead,” he adds.

More broadly, he is realistic about the likely attrition rate. “We will fail at many of these experiments, but if we hit on a couple of big ones that are transformative, I think you can see a step change in productivity.”
adjacencies  algorithms  analytics  artificial_intelligence  attrition_rates  CEOs  data_driven  data_scientists  drug_development  failure  Indian-Americans  kill_rates  massive_data_sets  multiple_targets  Novartis  pharmaceutical_industry  predictive_analytics  productivity  productivity_payoffs  product_development  real-time  scaling  spreadsheets  Vas_Narasimhan 
november 2017 by jerryking
Art market ripe for disruption by algorithms
MAY 26, 2017 | Financial Times | by John Dizard.

Art consultants and dealers are convinced that theirs is a high-touch, rather than a high-tech business, and they have arcane skills that are difficult, if not impossible, to replicate..... better-informed collectors [are musing about] how to compress those transaction costs and get that price discovery done more efficiently.....The art world already has transaction databases and competing price indices. The databases tend to be incomplete, since a high proportion of fine art objects are sold privately rather than at public auctions. The price indices also have their issues, given the (arguably) unique nature of the objects being traded. Sotheby’s Mei Moses index attempts to get around that by compiling repeat-sales data, which, given the slow turnover of particular works of art, is challenging.....Other indices, or value estimations, are based on hedonic regression, which is less amusing than it sounds. It is a form of linear regression used, in this case, to determine the weight of different components in the pricing of a work of art, such as the artist’s name, the work’s size, the year of creation and so on. Those weights in turn are used to create time-series data to describe “the art market”. It is better than nothing, but not quite enough to replace the auctioneers and dealers.....the algos are already on the hunt....people are watching the auctions and art fairs and doing empirics....gathering data at a very micro level, looking for patterns, just to gather information on the process.....the art world and its auction markets are increasingly intriguing to applied mathematicians and computer scientists. Recognising, let alone analysing, a work of art is a conceptually and computationally challenging problem. But computing power is very cheap now, which makes it easier to try new methods.....Computer scientists have been scanning, or “crawling”, published art catalogues and art reviews to create semantic data for art works based on natural-language descriptions. As one 2015 Polish paper says, “well-structured data may pave the way towards usage of methods from graph theory, topic labelling, or even employment of machine learning”.

Machine-learning techniques, such as software programs for deep recurrent neural networks, have already been used to analyse and predict other auction processes.
algorithms  disruption  art  art_finance  auctions  collectors  linear_regression  data_scientists  machine_learning  Sotheby’s  high-touch  pricing  quantitative  analytics  arcane_knowledge  art_market 
june 2017 by jerryking
The Data Behind Dining
FEB 7, 2017 | The Atlantic | BOURREE LAM.

Damian Mogavero, a dining-industry consultant, has analyzed the data behind thousands of restaurants—which dishes get ordered, which servers bring in the highest bills, and even what the weather’s like—and found that these metrics can help inform the decisions and practices of restaurateurs.....Mogavero recently wrote a book about analytics called The Underground Culinary Tour—which is also the name of an annual insider retreat he runs, in which he leads restaurateurs from around the nation to what he considers the most innovative restaurants in New York City, with 15 stops in 24 hours.....they really understood the business problem that I understood, as a frustrated restaurateur. There was not accessible information to make really important business decisions.

Lam: Why is it that the restaurant business tends to be more instinct-driven than data-driven?

Mogavero: It is so creative, and it really attracts innovative and creative people who really enjoy the art and the design of the guest experience. When I was a frustrated restaurateur, I would ask my chefs and managers simple questions, such as: Who are your top and bottom servers? Why did your food costs go up? Why did your labor costs go up? And they would give me blank stares, wrong answers, or make up stuff. The thing that really killed me is why so much time gets spent in administrative B.S.

They were frustrated artists in their own way, because all those questions I was posing were buried in a bunch of Excel spreadsheets. What I like to say is, nothing good ever happens at the back office. You can't make customers happy and you can’t cook great food there. That was the business problem that I saw. I assembled a chef, a sommelier, a restaurant manager, and three techies as the founding team of the company. The message was: We’re going to create software, so you can get back to what you love to do with a more profitable operation.......Mogavero: Because information is flowing so quickly, you’re likely to see trends from a big city go to a secondary city more often. But you’ll see regional trends come to the big city as well. It’s all part of this information flow that’s more transparent and faster. The secondary-market awakening is coupled with the fact that it’s really expensive for chefs to live in big cities, and we’re seeing many chefs leaving the big cities.
bullshitake  dining  data  books  restaurants  data_driven  New_York_City  innovation  restauranteurs  analytics  back-office  information_flows  secondary_markets 
may 2017 by jerryking
G.E., the 124-Year-Old Software Start-Up - The New York Times
By STEVE LOHRAUG. 27, 2016
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software  predictive_maintenance  paranoia  Jeffrey_Immelt  GE  analytics  Industrial_Internet 
august 2016 by jerryking
Ryerson University and Rogers Update Next Big Idea in Sport Competition - Sportscaster Magazine
launch of a new national competition to help realize tech opportunities in sports applications

A number of such techno-sporting possibilities were on display during the event as Ryerson, in partnership with Rogers Communications, launched the first ever Next Big Idea in Sport Competition.

The competition will now provide up to 10 selected start-ups with four months of mentoring and support and the chance to win cash prizes totaling $100,000.

That’s to encourage start-up companies to explore innovation opportunities in the sports business, including anything from analytics, athletic performance technologies, analysis of business management, fan engagement, consumer experiences and media innovation.....Rogers is keen to inspire students and start-ups to develop new and creative solutions for athletes, coaches, teams, sport media and even professional sports leagues,
Ryerson  Rogers  sports  LBMA  analytics  start_ups 
march 2016 by jerryking
Toronto aims to use data for traffic insight - The Globe and Mail
OLIVER MOORE - URBAN TRANSPORTATION REPORTER
The Globe and Mail
Published Friday, Oct. 02, 2015
Toronto  data  transportation  hackathons  analytics  traffic_congestion  John_Tory  GPS  location_based_services  LBMA 
october 2015 by jerryking
When Big Data Isn’t an Option
May 19, 2014 / Summer 2014 / Strategy + Business | by David Meer
When Big Data Isn’t an Option
Companies that only have access to “little data” can still use that information to improve their business.

Many companies—probably most—work in relatively sparse data environments, without access to the abundant information needed for advanced analytics and data mining. For instance, point-of-sale register data is not standard in emerging markets. In most B2B industries, companies have access to their own sales and shipment data but have little visibility into overall market volumes or what their competitors are selling. Highly specialized or concentrated markets, such as parts suppliers to automakers, have only a handful of potential customers. These companies have to be content with what might be called little data—readily available information that companies can use to generate insights, even if it is sparse or of uneven quality....the beverage manufacturer developed an algorithm based on observable characteristics, then asked its sales professionals to classify all the bars and restaurants in their territories based on the algorithm. (This is a classic little data technique: filling in the data gaps internally.)

. Little data techniques, therefore, can include just about any method that gives a company more insight into its customers without breaking the bank. As the examples above illustrate, mining little data doesn’t mean investing in expensive data acquisition, hardware, software, or technology infrastructure. Rather, companies need three things:

• The commitment to become more fact-based in their decision making.

• The willingness to learn by doing.

• A bit of creativity. ...

The bottom line: Companies have to put in the extra effort required to capture and interpret data that is already being generated.
small_data  data  analytics  data_driven  market_segmentation  observations  call_centres  insights  data_quality  data_capture  interpretation  point-of-sale  mindsets  creativity 
september 2015 by jerryking
Hoxton Analytics: Counting footfall for retailers - FT.com
September 8, 2015 12:02 pm
Hoxton Analytics: Counting footfall for retailers
Richard Newton

The retail research company was founded with the aim of reconciling the desire of retailers to monitor footfall and shopper demographics with their customers’ dislike of in-store video surveillance.

Owen McCormack, CEO and one of the two UCL data scientists who set up the business, says pointing the camera at shoes preserves customer privacy....The information is used to manage staffing, merchandising and the measurement of customer conversion rates.
retailers  analytics  foot_traffic  privacy  in-store 
september 2015 by jerryking
The Analytics Mandate
May 12, 2014

by: DAVID KIRON, PAMELA KIRK PRENTICE AND RENEE BOUCHER FERGUSON
analytics  MIT  decision_making  data_driven  LBMA  location_based_services 
september 2015 by jerryking
Google Correlate: Your data, Google's computing power
Google Correlate is awesome. As I noted in Search Notes last week, Google Correlate is a new tool in Google Labs that lets you upload state- or time-based data to see what search trends most correlate with that information.

Correlation doesn't necessarily imply causation, and as you use Google Correlate, you'll find that the relationship (if any) between terms varies widely based on the topic, time, and space.

For instance, there's a strong state-based correlation between searches for me and searches for Vulcan Capital. But the two searches have nothing to do with each other. As you see below, the correlation is that the two searches have similar state-based interest.

For both searches, the most volume is in Washington state (where we're both located). And both show high activity in New York.

State-based data

For a recent talk I gave in Germany, I downloaded state-by-state income data from the U.S. Census Bureau and ran it through Google Correlate. I found that high income was highly correlated with searches for [lohan breasts] and low income was highly correlated with searches for [police shootouts]. I leave the interpretation up to you.

By default, the closest correlations are with the highest numbers, so to get correlations with low income, I multiplied all of the numbers by negative one.

Clay Johnson looked at correlations based on state obesity rates from the CDC. By looking at negative correlations (in other words, what search queries are most closely correlated with states with the lowest obesity rates), we see that the most closely related search is [yoga mat bags]. (Another highly correlated term is [nutrition school].)

Maybe there's something to that "working out helps you lose weight" idea I've heard people mention. Then again, another highly correlated term is [itunes movie rentals], so maybe I should try the "sitting on my couch, watching movies work out plan" just to explore all of my options.

To look at this data more seriously, we can see with search data alone that the wealthy seem to be healthier (at least based on obesity data) than the poor. In states with low obesity rates, searches are for optional material goods, such as Bose headphones, digital cameras, and red wine and for travel to places like Africa, Jordan, and China. In states with high obesity rates, searches are for jobs and free items.

With this hypothesis, we can look at other data (access to nutritious food, time and space to exercise, health education) to determine further links.

Time-based data

Time-based data works in a similar way. Google Correlate looks for matching patterns in trends over time. Again, that the trends are similar doesn't mean they're related. But this data can be an interesting starting point for additional investigation.

One of the economic indicators from the U.S. Census Bureau is housing inventory. I looked at the number of months' supply of homes at the current sales rate between 2003 and today. I have no idea how to interpret data like this (the general idea is that you, as an expert in some field, would upload data that you understand). But my non-expert conclusion here is that as housing inventory increases (which implies no one's buying), we are looking to spiff up our existing homes with cheap stuff, so we turn to Craigslist.

Of course, it could also be the case that the height of popularity of Craiglist just happened to coincide with the months when the most homes were on the market, and both are coincidentally declining at the same rate.

Search-based data

You can also simply enter a search term, and Google will analyze the state or time-based patterns of that term and chart other queries that most closely match those patterns. Google describes this as a kind of Google Trends in reverse.

Google Insights for Search already shows you state distribution and volume trends for terms, and Correlate takes this one step further by listing all of the other terms with a similar regional distribution or volume trend.

For instance, regional distribution for [vegan restaurants] searches is strongly correlated to the regional distribution for searches for [mac store locations].

What does the time-trend of search volume for [vegan restaurants] correlate with? Flights from LAX.

Time-based data related to a search term can be a fascinating look at how trends spark interest in particular topics. For instance, as the Atkins Diet lost popularity, so too did interest in the carbohydrate content of food.

Interest in maple syrup seems to follow interest in the cleanse diet (of which maple syrup is a key component).

Drawing-based data

Don't have any interesting data to upload? Aren't sure what topic you're most interested in? Then just draw a graph!

Maybe you want to know what had no search volume at all in 2004, spiked in 2005, and then disappeared again. Easy. Just draw it on a graph.

Apparently the popular movies of the time were "Phantom of the Opera," "Darkness," and "Meet the Fockers." And we all were worried about our Celebrex prescriptions.

(Note: the accuracy of this data likely is dependent on the quality of your drawing skills.)

OSCON Data 2011, being held July 25-27 in Portland, Ore., is a gathering for developers who are hands-on, doing the systems work and evolving architectures and tools to manage data. (This event is co-located with OSCON.)

Save 20% on registration with the code OS11RAD

Related:

Data science democratized
Dashboards evolve to meet social and business needs
A new focus on user-friendly data analysis
Social data is an oracle waiting for a question
causality  Data  Future_of_Search  analytics  datatool  googlecorrelate  via:moon  house  LBMA  OPMA  correlations  time-based  geographic_sorting  tools  digital_cameras 
july 2015 by jerryking
Why growth hacking is a foreign concept to many business owners - The Globe and Mail
MIA PEARSON
Special to The Globe and Mail
Published Thursday, May. 21 2015,

Quite simply, growth hacking is about focusing your energy in the right areas, being creative and using a combination of analytical thinking, social metrics and long-term thinking to power low-cost innovation....“The most successful businesses are always trying to find scalable and repeatable methods for growth, and their marketing strategies and tactics are rooted in data and technology,”...Use data analytics Markus Frind, CEO of PlentyOfFish and a speaker at Traction Conf, describes growth hacking for him as “applying data to marketing to achieve growth, via virality.”

Mr. Frind started his company in 2003 and grew it into one of the largest online dating sites in the world. With more than 100 million users and $100-million in revenue, he knows what he’s talking about. And luckily, Google Analytics is available to everyone.

For Mr. Frind, growth hacking boils down to a combination of “SEO, split-testing and understanding the virality of the users.” He believes understanding that made it “easy to see what was working and what wasn’t.”

By understanding where traffic is coming from and why people are seeking you out, you have a stronger understanding of your consumer – and you’re incredibly short-sighted if you don’t think your consumer defines your brand. This is a significant piece of the puzzle for growing a business.
analytics  customer_insights  effectiveness  growth_hacking  innovation  long-term  marketing  repeatability  SEO  short-sightedness  small_business  virality 
june 2015 by jerryking
How Not to Drown in Numbers - NYTimes.com
MAY 2, 2015| NYT |By ALEX PEYSAKHOVICH and SETH STEPHENS-DAVIDOWITZ.

If you’re trying to build a self-driving car or detect whether a picture has a cat in it, big data is amazing. But here’s a secret: If you’re trying to make important decisions about your health, wealth or happiness, big data is not enough.

The problem is this: The things we can measure are never exactly what we care about. Just trying to get a single, easy-to-measure number higher and higher (or lower and lower) doesn’t actually help us make the right choice. For this reason, the key question isn’t “What did I measure?” but “What did I miss?”...So what can big data do to help us make big decisions? One of us, Alex, is a data scientist at Facebook. The other, Seth, is a former data scientist at Google. There is a special sauce necessary to making big data work: surveys and the judgment of humans — two seemingly old-fashioned approaches that we will call small data....For one thing, many teams ended up going overboard on data. It was easy to measure offense and pitching, so some organizations ended up underestimating the importance of defense, which is harder to measure. In fact, in his book “The Signal and the Noise,” Nate Silver of fivethirtyeight.com estimates that the Oakland A’s were giving up 8 to 10 wins per year in the mid-1990s because of their lousy defense.

And data-driven teams found out the hard way that scouts were actually important...We are optimists about the potential of data to improve human lives. But the world is incredibly complicated. No one data set, no matter how big, is going to tell us exactly what we need. The new mountains of blunt data sets make human creativity, judgment, intuition and expertise more valuable, not less.

==============================================
From Market Research: Safety Not Always in Numbers | Qualtrics ☑
Author: Qualtrics|July 28, 2010

Albert Einstein once said, “Not everything that can be counted counts, and not everything that counts can be counted.” [Warning of the danger of overquantification) Although many market research experts would say that quantitative research is the safest bet when one has limited resources, it can be dangerous to assume that it is always the best option.
human_ingenuity  data  analytics  small_data  massive_data_sets  data_driven  information_overload  dark_data  measurements  creativity  judgment  intuition  Nate_Silver  expertise  datasets  information_gaps  unknowns  underestimation  infoliteracy  overlooked_opportunities  sense-making  easy-to-measure  Albert_Einstein  special_sauce  metrics  overlooked  defensive_tactics  emotional_intelligence  EQ  soft_skills  overquantification  false_confidence 
may 2015 by jerryking
The lost art of political persuasion - The Globe and Mail
KONRAD YAKABUSKI
The Globe and Mail
Published Saturday, Apr. 25 2015

Talking points are hardly a 21st century political innovation. But they have so crowded out every other form of discourse that politics is now utterly devoid of honesty, unless it’s the result of human error. The candidates are still human, we think, though the techies now running campaigns are no doubt working on ways to remove that bug from their programs.

Intuition, ideas and passion used to matter in politics. Now, data analytics aims to turn all politicians into robots, programmed to deliver a script that has been scientifically tested...The data analysts have algorithms that tell them just what words resonate with just what voters and will coax them to donate, volunteer and vote.

Politics is no longer about the art of persuasion or about having an honest debate about what’s best for your country, province or city. It’s about microtargeting individuals who’ve already demonstrated by their Facebook posts or responses to telephone surveys that they are suggestible. Voters are data points to be manipulated, not citizens to be cultivated....Campaign strategists euphemistically refer to this data collection and microtargeting as “grassroots engagement” or “having one-on-one conversations” with voters....The data analysts on the 2012 Obama campaign came up with “scores” for each voter in its database, or what author Sasha Issenberg called “a new political currency that predicted the behaviour of individual humans.
Konrad_Yakabuski  persuasion  middle_class  politicians  massive_data_sets  political_campaigns  data_scientists  data_driven  data_mining  microtargeting  behavioural_targeting  politics  data  analytics  Campaign_2012 
april 2015 by jerryking
Toronto to use big data to help reduce traffic congestion - The Globe and Mail
Apr. 07 2015 | The Globe and Mail | OLIVER MOORE - URBAN TRANSPORTATION REPORTER

Toronto is creating a “big data” traffic team as the city tries to manage congestion better by learning what is actually happening on its streets.....The push is a start toward filling that vacuum of information. The city has released a job posting for someone to lead the data unit and will spend the rest of the year deciding what they want to learn. A “hackathon” in September will let people come in, look at the available data and see what they can do with it.

Big data has become a buzz phrase in traffic circles as smartphones and GPS units make it easier to track people’s movements. But in most places, the promise looms larger than the reality. Many cities are still trying to figure out how to turn the flood of data into useful information.
massive_data_sets  traffic_congestion  Toronto  John_Tory  transportation  analytics  data  information_vacuum 
april 2015 by jerryking
IBM to Invest $3 Billion in Sensor-Data Unit - WSJ
March 31, 2015 | WSJ | By DON CLARK. Can CBC get good at communicating the final product on behalf of clients of Pelmorex. So CBC considers supplying the communications platform?

IBM plans to invest $3 billion over four years on a new business helping customers gather and analyze the flood of data from sensor-equipped devices and smartphones.... IBM announced that it is forming an alliance with the Weather Company, which owns the Weather Channel and other information providers. The two companies plan jointly to exploit data about weather conditions to help businesses make better decisions....the centerpiece of IBM's new business unit is a collection of online software called IoT Foundation that runs on IBM’s existing cloud services and allows customers and partners to create new applications and enhance existing ones with real-time data and analysis....IBM is betting that correlating dissimilar kinds of data will yield the highest value. “It’s essential to federate information from multiple sources,” said Bob Picciano, IBM’s senior VP of analytics.... the Weather Channel serves up 700,000 weather forecasts a second. It already sells data to a range of customers in agriculture, transportation and other industries that rely on weather.

But the opportunities have expanded, Mr. Kenny said, as weather sensors installed in many more places have contributed to more timely, localized forecasts. The added detail helps farmers predict more precisely, for example, where hail could impact their fields, Mr. Kenny said.

The Weather Company is turning to IBM, he said, because of its software expertise and relationships with customers in many industries.
sensors  IBM  weather  massive_data_sets  data  data_driven  analytics  Industrial_Internet  smartphones  cloud_computing 
march 2015 by jerryking
Mirtle: Sloan conference leading Big Data revolution in sports - The Globe and Mail
JAMES MIRTLE
BOSTON — Globe and Mail Update (includes correction)
Published Thursday, Feb. 26 2015
sports  MIT  massive_data_sets  analytics  Moneyball  Octothorpe_Software 
february 2015 by jerryking
Meet the SEC’s Brainy New Crime Fighters - WSJ
By SCOTT PATTERSON
Updated Dec. 14, 2014

The SEC is mustering its mathematical firepower in its Center for Risk and Quantitative Analytics, which was created last year soon after Mary Jo White took charge of the agency to help it get better at catching Wall Street misconduct. The enforcement unit, led by 14-year SEC veteran Lori Walsh, is housed deep within the warrens of the SEC’s Washington headquarters, and staffed by about 10 employees trained in fields such as mathematical finance, economics, accounting and computer programming.

Ms. Walsh says access to new sources of data and new ways of processing the data have been key to finding evidence of wrongdoing. “When you look at data in different ways, you see new things,” she said in an interview
alternative_data  analysis  analytics  arms_race  data  data_driven  enforcement  fresh_eyes  hiring  information_sources  mathematics  misconduct  models  modelling  patterns  perspectives  quantitative  quants  SEC  stockmarkets  Wall_Street 
december 2014 by jerryking
The man who made data play ball - FT.com
November 14, 2014 11:01 am
The man who made data play ball
Simon Kuper
analytics  Moneyball  baseball  sports 
november 2014 by jerryking
How Consumers Are Using Big Data - WSJ
By LORA KOLODNY CONNECT
March 23, 2014

An app called Neighborland, created by social entrepreneurs Candy Chang and Dan Parham, aims to help community groups and government offices work well together. The app combines photos, data and APIs from sources including Twitter, Google Maps and Instagram, agencies that report on real-estate parcels, transit systems, and "311" complaints about nuisances like noise, broken lights and garbage.

In 2012, the New Orleans Food Truck Coalition used Neighborland to collect community ideas, map "food deserts," which are areas lacking easy access to groceries and healthy food, and show what the economic and health impact could be if coalition members were permitted to work in more areas.
311  massive_data_sets  APIs  data  analytics  Amazon  Pandora  Netflix  Nike  Jawbone  fitness  CDC  infertility  travel  Skyscanner  Routehappy  open_data  mobile_applications  consumers  hyperlocal  neighbourhoods 
november 2014 by jerryking
Forget the CV, data decide careers - FT.com
July 9, 2014 | FT |By Tim Smedley.

The human touch of job interviews is under threat from technology, writes Tim Smedley, but can new techniques be applied to top-level recruitment?

I no longer look at somebody's CV to determine if we will interview them or not," declares Teri Morse, who oversees the recruitment of 30,000 people each year at Xerox Services. Instead, her team analyses personal data to determine the fate of job candidates.

She is not alone. "Big data" and complex algorithms are increasingly taking decisions out of the hands of individual interviewers - a trend that has far-reaching consequences for job seekers and recruiters alike.

The company whose name has become a synonym for photocopy has turned into one that helps others outsource everyday business processes, from accounting to human resources. It recently teamed up with Evolv, which uses data sets of past behaviour to predict everything from salesmanship to loyalty.

For Xerox this means putting prospective candidates for the company's 55,000 call-centre positions through a screening test that covers a wide range of questions. Evolv then lays separate data it has mined on what causes employees to leave their call-centre jobs over the candidates' responses to predict which of them will stick around and which will further exacerbate the already high churn rate call centres tend to suffer.

The results are surprising. Some are quirky: employees who are members of one or two social networks were found to stay in their job for longer than those who belonged to four or more social networks (Xerox recruitment drives at gaming conventions were subsequently cancelled). Some findings, however, were much more fundamental: prior work experience in a similar role was not found to be a predictor of success.

"It actually opens up doors for people who would never have gotten to interview based on their CV," says Ms Morse. Some managers initially questioned why new recruits were appearing without any prior relevant experience. As time went on, attrition rates in some call centres fell by 20 per cent and managers no longer quibbled. "I don't know why this works," admits Ms Morse, "I just know it works."

Organisations have long held large amounts of data. From financial accounts to staff time sheets, the movement from paper to computer made it easier to understand and analyse. As computing power increased exponentially, so did data storage. The floppy disk of the 1990s could store barely more than one megabyte of data; today a 16 gigabyte USB flash drive costs less than a fiver ($8).

It is simple, then, to see how recruiters arrive at a point where crunching data could replace the human touch of job interviews. Research by NewVantage Partners, the technology consultants, found that 85 per cent of Fortune 1000 executives in 2013 had a big data initiative planned or in progress, with almost half using big data operationally.

HR services provider Ceridian is one of many companies hoping to tap into the potential of big data for employers. "From an HR and recruitment perspective, big data enables you to analyse volumes of data that in the past were hard to access and understand," explains David Woodward, chief product and innovation officer at Ceridian UK.

This includes "applying the data you hold about your employees and how they've performed, to see the causal links between the characteristics of the hire that you took in versus those that stayed with you and became successful employees. Drawing those links can better inform your decisions in the hiring process."

Data sets need not rely on internal data, however. The greatest source of big data is the internet, which is easy for both FTSE 100 and smaller companies to access.

"Social media data now gives us the ability to 'listen' to the business," says Zahir Ladhani, vice-president at IBM Smarter Workforce. "You can look at what customers are saying about your business, what employees are saying, and what you yourself are saying - cull all that data together and you can understand the impact.

"Most recruitment organisations now use social media and job-site data," says Mr Ladhani. "We looked at an organisation which had very specialised, very hard to find skill sets. When we analysed the data of the top performers in that job family, we found out that they all hung out at a very unique, niche social media site. Once we tapped into that database, boom!"

Ceridian, too, has worked with companies to "effectively scan the internet to see what jobs are being posted through the various job boards, in what parts of the country," says Mr Woodward. "If you're looking to open a particular facility in a part of the country, for example, you'll be able to see whether there's already a high demand for particular types of skills."

Experts appear split on whether the specialisation required for executive recruitment lends itself to big data.

"I hire 30,000 call-centre people on an annual basis - we don't hire that many executives," says Ms Morse, adding "there's not enough volume". However Mr Ladhani disagrees, believing that over time the data set an organisation holds on senior management hires would become statistically valid.

As more companies start to analyse their employee data to make hiring decisions, could recruitment finally become more of a science than an art?

"The potential is clearly much greater now than ever before to crunch very large volumes of data and draw conclusions from that which can make better decisions," says Mr Woodward. "The methods and computing power being used in weather forecasting 10 years ago are now available to us all . . . who knows where this may go."

It is a trend worth considering - to get your next job, perfecting your CV could well be less important than having carefully considered the footprint you leave in cyberspace.

Case study Demographic drilling-down helps LV=recast recruitment ads

Kevin Hough, head of recruiting at insurance firm LV=, was a pioneer of big data before he had heard the term.

A year ago, the question of where best to target the firm's recruitment advertising provided an innovative answer. LV= looked up the postcodes at which its current staff lived and organised the findings by the employee's level of seniority, explains Mr Hough. "Using software called Geo-Maps, which works similarly to Google Maps, we could zoom in and out of clusters of our people to see where they are willing to travel from to get to work."

Next, the insurer looked at the locations from which candidates were applying and compared those with the postcodes of current staff. It also looked at the locations and interests of its followers on social media sites, such as Facebook and LinkedIn. The analysis included their interests, stated sexual orientation, ethnicity and gender.

This allowed the firm to create a profile of its typical, successful candidate, also taking into consideration their age and location.

"What was really interesting was the reach some of our advertising was having and, more importantly, some of the gaps," Mr Hough says.

The analysis, which took little investment or expertise, has allowed LV= to redesign its recruitment advertising.

"Sometimes, with all the clever systems that people have in organisations, you can be blinded to the simple, raw data that is there," says Mr Hough.

Next, LV= will add performance review data, taking the analysis to a higher level. He explains that this piece of work will ask who of the group recruited a year before is still there.

"It will help shape not only how we attract people, but will even start to shape some of the roles themselves," he says.

Tim Smedley

By Tim Smedley
analytics  call_centres  Ceridian  data  data_driven  data_storage  Evolv  executive_management  FTSE_100  hard_to_find  hiring  internal_data  job_boards  Managing_Your_Career  massive_data_sets  personal_data  predictive_analytics  recruiting  résumés  small_business  social_media  unstructured_data  Xerox 
july 2014 by jerryking
Aldo seizes ‘put up or shut up’ moment for shoes - The Globe and Mail
SUSAN KRASHINSKY - MARKETING REPORTER
TORONTO — The Globe and Mail
Published Thursday, Feb. 27 2014,

Aldo announced the biggest investment in development that the company has made in its 41-year history. Over the next five years, it will spend $363-million and hire roughly 400 people in an effort to better market itself to customers who have more options than ever.

“We’re being confronted with more competition from so many different angles at this point. It’s basically a ‘put up or shut up’ moment,”....Fundamentally, Mr. Bensadoun sees this as a marketing problem.

Clothing retailers have the luxury of showing you a shoe in its proper context – in other words, as part of an outfit. One of the things Aldo is planning for its store of the future is more screens in-store (e.g. digital signage) that will help to do that, in the absence of any apparel stock.

The store could choose a top 10 looks of the week, Mr. Bensadoun suggests, which could be browsed on the screens (and on a mobile-friendly version of the same service for people on smartphones.) Those looks would specify which shoes to wear with them so that customers could pick footwear based on an overall style they identify with. It would also go the other way: for those who pick up a shoe they like, it will be possible to see how to wear it, and with what....Data are another key part of this transformation project.

Part of Aldo’s multimillion-dollar investment will be devoted to building a better data analytics team as well as hiring research and behaviour experts. This is a priority for all marketers, who face a buying public that has never been more inundated with messages – on television, on their mobile phones, tablets, and computers.

“The consumer insights and analytics department at Aldo was very much in its infancy, up until very recently,”
Aldo  shoes  retailers  e-commerce  marketing  analytics  data  Susan_Krashinsky  SHoeMint  ShoeDazzle  Zappos  customer_insights  consumer_research  contextual  seminal_moments  consumer_behavior  in-store  footwear 
july 2014 by jerryking
A 25-Question Twitter Quiz to Predict Retweets - NYTimes.com
JULY 1, 2014 | NYT | Sendhil Mullainathan.

how “smart” algorithms are created from big data: Large data sets with known correct answers serve as a training bed and then new data serves as a test bed — not too differently from how we might learn what our co-workers find funny....one of the miracles of big data: Algorithms find information in unexpected places, uncovering “signal” in places we thought contained only “noise.”... the Achilles’ heel of prediction algorithms--being good at prediction often does not mean being better at creation. (1) One barrier is the oldest of statistical problems: Correlation is not causation.(2) an inherent paradox lies in predicting what is interesting. Rarity and novelty often contribute to interestingness — or at the least to drawing attention. But once an algorithm finds those things that draw attention and starts exploiting them, their value erodes. (3) Finally, and perhaps most perversely, some of the most predictive variables are circular....The new big-data tools, amazing as they are, are not magic. Like every great invention before them — whether antibiotics, electricity or even the computer itself — they have boundaries in which they excel and beyond which they can do little.
predictive_analytics  massive_data_sets  limitations  algorithms  Twitter  analytics  data  data_driven  Albert_Gore  Achilles’_heel  boundary_conditions  noise  signals  paradoxes  correlations  causality  counterintuitive  training_beds  test_beds  rarity  novelty  interestingness  hard_to_find 
july 2014 by jerryking
Tame big data and you'll reap the rewards - The Globe and Mail
HARVEY SCHACHTER
Special to The Globe and Mail
Published Tuesday, Apr. 15 2014

“It’s a catchall term for data that doesn’t fit the usual containers. Big data refers to data that is too big to fit on a single server, too unstructured to fit into a row-and-column database, or too continuously flowing to fit into a static data warehouse. While its size receives all the attention, the most difficult aspect of big data really involves its lack of structure,”....He cites some industries that have big data but aren’t making proper use of it. Banks have massive amounts of information about their customers but have been underachievers in helping them make sense of it all and presenting targeted marketing offers. Retailers have purchase behaviour information from their point-of-sales systems but, with the exception of Wal-Mart and Britain’s Tesco, haven’t done a lot until recently.
Harvey_Schachter  Thomas_Davenport  banks  retailers  massive_data_sets  behavioural_data  books  book_reviews  unstructured_data  analytics  competingonanalytics  sense-making  point-of-sale  Wal-Mart  Tesco 
june 2014 by jerryking
Big data: What’s your plan?
March 2013 | | McKinsey & Company | byStefan Biesdorf, David Court, and Paul Willmott
analytics  data  McKinsey  massive_data_sets  howto 
june 2014 by jerryking
Getting Started in ‘Big Data’ - The CFO Report - WSJ
February 4, 2014 | WSJ |by JAMES WILLHITE.

executives and recruiters, who compete for talent in the nascent specialty, point to hiring strategies that can get a big-data operation off the ground. They say they look for specific industry experience, poach from data-rich rivals, rely on interview questions that screen out weaker candidates and recommend starting with small projects.

David Ginsberg, chief data scientist at business-software maker SAP AG , said communication skills are critically important in the field, and that a key player on his big-data team is a “guy who can translate Ph.D. to English. Those are the hardest people to find.”

Along with the ability to explain their findings, data scientists need to have a proven record of being able to pluck useful information from data that often lack an obvious structure and may even come from a dubious source. This expertise doesn’t always cut across industry lines. A scientist with a keen knowledge of the entertainment industry, for example, won’t necessarily be able to transfer his skills to the fast-food market.

Some candidates can make the leap. Wolters Kluwer NV, a Netherlands-based information-services provider, has had some success in filling big-data jobs by recruiting from other, data-rich industries, such as financial services. “We have found tremendous success with going to alternative sources and looking at different businesses and saying, ‘What can you bring into our business?’ ” said Kevin Entricken, the company’s chief financial officer.
massive_data_sets  analytics  data_scientists  cross-industry  recruiting  howto  poaching  plain_English  connecting_the_dots  storytelling  SAP  Wolters_Kluwer  expertise  Communicating_&_Connecting  unstructured_data  war_for_talent  talent  PhDs  executive_search  artificial_intelligence  nontraditional 
june 2014 by jerryking
Tachyus, a Data Start-Up for Oil Industry, Raises $6 Million From Founders Fund - NYTimes.com
By MICHAEL J. DE LA MERCED APRIL 10, 2014

Tachyus has been developing hardware and software to gather information wirelessly about various aspects of oil production, tracking production using iPads or a web app. The company’s products are in the process of being tested by prospective customers.

Mr. Sloss declined to comment on other financial details of the financing round, including the company’s valuation. But he said that Tachyus planned to use the money to enlarge its team to 30 to 40 people over the next two and a half years.

“Despite exciting advancements in renewable energy, fossil fuels will continue to drive the world’s energy supply for decades, and doing more with these limited resources is incredibly important,” Scott Nolan, a partner at Founders Fund, said in a statement. “Tachyus’s work in bringing a new level of operational intelligence to the oil and gas industry represents a huge opportunity on multiple levels.”
oil_industry  massive_data_sets  sensors  data  analytics  start_ups  Tachyus  Stanford  alumni  mobile_applications 
june 2014 by jerryking
Fighting fires with data: How killing the long-form census hurt community planning - The Globe and Mail
JOE FRIESEN - DEMOGRAPHICS REPORTER
The Globe and Mail
Published Wednesday, May. 14 2014

Most people use the company’s data in conjunction with a mapping tool and segmentation analysis, which sorts the population into lifestyle categories such as “Middleburg Managers” and “Young Digerati,” to better understand their habits and tastes. A library, for example, found that despite having a large population of senior citizens, programs advertised to “seniors” were a bust. Having looked more closely at their income and lifestyle data, they targeted the same group as “mature adults” and had much more success.

“Often, the real power is in the melding of the data. They know things about their users, but not their neighbourhood, then they marry them,” said Doug Norris, chief demographer at Environics Analytics.

Robert Dalgleish, an executive director at the United Church of Canada, is eagerly awaiting new data sorted down to the DA level. He said more than 500 local congregations in the church use this kind of data to better understand the areas they inhabit. One puzz-ling finding was that for every identified member of the United Church in a congregation, there are nine others living within a few kilometres who never attend a service.

“The data doesn’t give us answers, but it gives us really good questions,” Mr. Dalgleish said. “It really allows congregations to drill down into their communities.”
Joe_Friesen  demographic_changes  data  mapping  local  data_melding  neighbourhoods  market_segmentation  analytics  churches  Statistics_Canada  firefighting  Environs  customer_segmentation 
june 2014 by jerryking
Small Area Data
Environics Analytics 416.969.2733

"Due to changes in methodology, Statistics Canada is not officially releasing dissemination area (DA) data from the 2011 NHS. But we are"
neighbourhoods  data  analytics  Statistics_Canada  small_data  Environics 
may 2014 by jerryking
Parental Opposition Fells inBloom Education-Software Firm - WSJ.com
By ELIZABETH DWOSKIN and LISA FLEISHER CONNECT
Updated April 21, 2014
education  software  analytics  students  data  schools 
april 2014 by jerryking
Smile, you're on WiFi
January 31, 2014
That cellphone in your pocket is emitting a constant stream of information - and retailers are starting to listen in

Ivor Tossell

Mexia, a Winnipeg-based "location analytics" company that's one of a new crop of firms that are supplying retailers with technology that listens in to smartphone signals. Mexia installs Bluetooth and WiFi receivers in specific zones around a store. By measuring the occurrence and relative strength of your phone's passive, unwittingly sent signals, it can tell whether customers are lingering longer in the housewares department, the kitchen aisle or near the checkout. The company says it has deployed sensors in between 80 and 100 stores so far; it also does malls and airports. "We report on a multitude of things, from the traditional traffic count to the time spent in the store," says Glenn Tinley, Mexia's founder and president. "It gets pretty interesting, to say the least."
wi-fi  Bluetooth  mobile_phones  location  location_based_services  tracking  Mexia  Turnstyle  customer_loyalty  shopping_experience  privacy  analytics  confidentiality 
february 2014 by jerryking
Reince Priebus: 'The Party of Growth and Opportunity' | National Review Online
By NRO Staff
March 18, 2013
Throughout this process both the co-chairs and I have heard a great deal about the quality of our data–and how that affects our ability to target and persuade voters.

Numerous voices emphasized how we must move to integrate new sources of data and expand access to that data beyond the RNC.

Overhauling our data infrastructure won’t happen overnight. But we will move to invest more resources into data collection and management, and we will integrate data into everything we do.

We will lead by example because we want every campaign, group, and committee to make data a priority.

Therefore, as recommended, we’re hiring a new Chief Digital and Technology Officer who will build out and oversee three important and distinct teams: data, digital, and technology. Those teams will work together to integrate their respective areas throughout the RNC and provide a data-driven focus for the rest of the organization. And they will be the new center of gravity within the organization.
GOP  data  data_driven  political_campaigns  massive_data_sets  analytics  RNC 
january 2014 by jerryking
Silicon Valley Big Data Startup Bought for $930M by ... Monsanto? -
October 2, 2013 Liz Gannes - News - AllThingsD Liz Gannes - News - AllThingsD.

Climate Corporation had built a network of insurance sellers for both crop insurance and weather insurance, and offered Web and mobile tools for farmers so they could make decisions about how to do their work. It has thousands of customers with many millions of acres in the U.S.
Monsanto  Climate_Corporation  Silicon_Valley  analytics  farming  agriculture  crop_insurance  weather  insurance 
january 2014 by jerryking
Q: What is the difference between analytics and microtargeting and can I afford either in a city council race?
[ILLUSTRATION OMITTED]

A: According to Tom Bonier of Clarity Campaign Labs, the nomenclature of analytics vs. microtargeting is not settled, reflecting the relative newness of the field. "Analy...
analytics  political_campaigns  microtargeting  cities  local  municipalities  elections 
january 2014 by jerryking
In Search of the Next Big Thing
May 2013 | HBR | Adi Ignatius interviews Marc Andreessen.

Tries to find CEOs who are product innovators, have bandwidth and discipline to become CEO. It is hard to pair those skills if they do not reside in one person. It is easier to train an innovator to become CEO than to train a CEO to become an innovator. Andreessen is counter-intuitive: he went into venture capital precisely because the prior decade to his launch had been the worst decade in the industry's history. He believes in cycles and so thought that 2009 was a good time to launch Andreessen Horowitz... Take/Understand a long view....Build "fortresses"--a company so big, so powerful , so well defended that it can withstand the pressures of going public. Focus on the substance of what your company is all about. Be about the substance....companies that are built to be independent are the most attractive...generally companies need to have at least two years' worth of cash on the balance sheet in case your revenue goes to zero....takes sales and marketing seriously--lots of products are being sold and you need a way to get the word about your company into the public space...companies are worth investing in (it's value)only if its going to be an innovation factory for years to come....We are in the early phases of Andreessen's "Software is Eating the World" thesis....best of companies AH is looking at today are unbelievably good at analytics. Good at the feedback loop created by analyzing data and feeding those number sback into the process in real time, running a continuous improvement loop....The best founders are artists in their domain. They operate instinctively in their industry because they are in touch with every relevant data point. They‘re able to synthesize in their gut a tremendous amount of data—pulling together technology trends, their companies’ capabilities, their competitor's’ activities, market psychology, every conceivable aspect of how you run a company.
Marc_Andreessen  Andreessen_Horowitz  venture_capital  start_ups  vc  HBR  hedge_funds  SOX  IPOs  lean  analytics  lessons_learned  fingerspitzengefühl  contextual_intelligence  counterintuitive  specificity  long-term  software  virtuous_cycles  software_is_eating_the_world  pairs  skills  founders  product-orientated 
december 2013 by jerryking
Finding gems of insight from customer data
Dec. 16 2013 | The Globe and Mail | by AMANDA HAY.

"Albert Einstein – “If you can’t explain it to a six-year-old, you don’t understand it yourself.” It’s not enough to be able do the calculations, build the models, run the analyses, you have to be able to articulate and justify your methods simply. Clients want insights they can use to generate revenue. Understanding your client’s business is key so that you can communicate with them in meaningful terms."
customer_loyalty  customer_insights  massive_data_sets  Aimia  loyalty_management  data  analytics  Albert_Einstein  insights  Communicating_&_Connecting  storytelling 
december 2013 by jerryking
Can I build a company on open data?
September 27, 2013 | MaRS Data Catalyst | By Joe Greenwood.
MaRS  open_data  start_ups  analytics  entrepreneurship  presentations 
december 2013 by jerryking
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