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L’avenir de la Business Intelligence passera par des Small Apps

L’avenir de la Business Intelligence passera par des Small Apps | Big Data, Data Scientist | Scoop.it
Le Big Data est indéniablement le mot à la mode dès que l’on parle de Business Intelligence. Et la croissance exponentielle des données récoltées par les entreprises participe à cet engouement. Ces dernières investissent massivement dans des technologies de pointe mais peinent à valoriser l...
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Big Data : premières leçons après 3 ans d’applications sur le terrain | Blog de Soft Computing

Big Data : premières leçons après 3 ans d’applications sur le terrain | Blog de Soft Computing | Big Data, Data Scientist | Scoop.it
La révolution numérique qui se déroule sous nos yeux a pour corollaire une explosion des données, encore accentuée par le développement des objets connectés...
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The Battle for the Internet of Things, and the Winner is…

The Battle for the Internet of Things, and the Winner is… | Big Data, Data Scientist | Scoop.it
There are a lot of amazing technologies and devices being brought to market these days. We’ve all seen them — Bluetooth capable health monitors, the Nest thermostat, fitness devices — and the number
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How Big Data Helped Germany Break Brazil's Hearts in the World Cup

How Big Data Helped Germany Break Brazil's Hearts in the World Cup | Big Data, Data Scientist | Scoop.it
According to the assistant coach Hansi Flick of Germany, a team of about 50 students at Cologne's sport university developed a huge database over the last two years mashing-up data from different
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Big data and open data: what's what and why does it matter?

Big data and open data: what's what and why does it matter? | Big Data, Data Scientist | Scoop.it
Both types of data can transform the world, but when government turns big data into open data it's especially powerful
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Parkeon, le Big Data réinvente le stationnement

Parkeon, le Big Data réinvente le stationnement | Big Data, Data Scientist | Scoop.it
Spécialiste français des horodateurs, Parkeon mise sur les technologies Big Data pour proposer des nouveaux services liés au stationnement. Ses clients ? Les plus grandes villes de la planète. Les volumétries en jeu ont poussé le Français à privilégier des solutions innovantes.
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Drive for Work : Google dévoile son arme pour contrer Office 365

Drive for Work : Google dévoile son arme pour contrer Office 365 | Big Data, Data Scientist | Scoop.it
A l'occasion de son événement Google I/O, le géant américain livre un service de stockage cloud avec une capacité illimité. Il prend donc l'avantage sur Microsoft.
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Gestion de l'information : quels risques pour les PME européennes ?

Gestion de l'information : quels risques pour les PME européennes ? | Big Data, Data Scientist | Scoop.it
PwC note un décalage entre compréhension des risques liés à la gestion des informations et adoption de mesures concrètes dans les PME européennes.
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Use Data to Tell the Future: Understanding Machine Learning | Innovation Insights | WIRED

Use Data to Tell the Future: Understanding Machine Learning | Innovation Insights | WIRED | Big Data, Data Scientist | Scoop.it
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Introduction to Big Data/Machine Learning

A short (137 slides) overview of the fields of Big Data and machine learning, diving into a couple of algorithms in detail.
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Comparison of MPP Data Warehouse Platforms

Comparison of MPP Data Warehouse Platforms, including relative market supply and demand
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The Data Science Toolkit - taking your first steps towards becoming a Data Scientist

The Data Science Toolkit - taking your first steps towards becoming a Data Scientist | Big Data, Data Scientist | Scoop.it
When I stumbled upon the phrase Data Scientist 3 years ago, I immediately recognized it as my best prospect for a productive career. How to start? What are t…

Via Celestino Güemes
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An Introduction to Data-Driven Decisions for Managers Who Don’t Like Math

An Introduction to Data-Driven Decisions for Managers Who Don’t Like Math | Big Data, Data Scientist | Scoop.it
A reading list.
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Using big data to make better pricing decisions

Using big data to make better pricing decisions | Big Data, Data Scientist | Scoop.it
Harnessing the flood of data available from customer interactions allows
companies to price appropriately—and reap the rewards.

It’s hard to overstate the importance of getting pricing right. On average,
a 1 percent price increase translates into an 8.7 percent increase in
operating profits (assuming no loss of volume, of course). Yet we estimate
that up to 30 percent of the thousands of pricing decisions companies make
every year fail to deliver the best price. That’s a lot of lost revenue.
And it’s particularly troubling considering that the flood of data now
available provides companies with an opportunity to make significantly
better pricing decisions. For those able to bring order to big data’s
complexity, the value is substantial.

We’re not suggesting it’s easy: the number of customer touchpoints keeps
exploding as digitization fuels growing multichannel complexity. Yet price
points need to keep pace. Without uncovering and acting on the
opportunities big data presents, many companies are leaving millions of
dollars of profit on the table. The secret to increasing profit margins is
to harness big data to find the best price at the product—not
category—level, rather than drown in the numbers flood.

Too big to succeed

For every product, companies should be able to find the optimal price that
a customer is willing to pay. Ideally, they’d factor in highly specific
insights that would influence the price—the cost of the next-best
competitive product versus the value of the product to the customer, for
example—and then arrive at the best price. Indeed, for a company with a
handful of products, this kind of pricing approach is straightforward.

It’s more problematic when product numbers balloon. About 75 percent of a
typical company’s revenue comes from its standard products, which often
number in the thousands. Time-consuming, manual practices for setting
prices make it virtually impossible to see the pricing patterns that can
unlock value. It’s simply too overwhelming for large companies to get
granular and manage the complexity of these pricing variables, which change
constantly, for thousands of products. At its core, this is a big data
issue (exhibit).

Exhibit


Patterns in the analysis highlight opportunities for differentiated pricing
at a customer-product level, based on willingness to pay.

* Enlarge

Many marketers end up simply burying their heads in the sand. They develop
prices based on simplistic factors such as the cost to produce the product,
standard margins, prices for similar products, volume discounts, and so on.
They fall back on old practices to manage the products as they always have
or cite “market prices” as an excuse for not attacking the issues. Perhaps
worst of all, they rely on “tried and tested” historical methods, such as a
universal 10 percent price hike on everything.

“What happened in practice then was that every year we had price increases
based on scale and volume, but not based on science,” says Roger Britschgi,
head of sales operations at Linde Gases. “Our people just didn’t think it
was possible to do it any other way. And, quite frankly, our people were
not well prepared to convince our customers of the need to increase
prices.”

Four steps to turn data into profits

The key to better pricing is understanding fully the data now at a
company’s disposal. It requires not zooming out but zooming in. As Tom
O’Brien, group vice president and general manager for marketing and sales
at Sasol, said of this approach, “The [sales] teams knew their pricing,
they may have known their volumes, but this was something more: extremely
granular data, literally from each and every invoice, by product, by
customer, by packaging.”

In fact, some of the most exciting examples of using big data in a B2B
context actually transcend pricing and touch on other aspects of a
company’s commercial engine. For example, “dynamic deal scoring” provides
price guidance at the level of individual deals, decision-escalation
points, incentives, performance scoring, and more, based on a set of
similar win/loss deals. Using smaller, relevant deal samples is essential,
as the factors tied to any one deal will vary, rendering an overarching set
of deals useless as a benchmark. We’ve seen this applied in the technology
sector with great success—yielding increases of four to eight percentage
points in return on sales (versus same-company control groups).

To get sufficiently granular, companies need to do four things.

Listen to the data. Setting the best prices is not a data challenge
(companies generally already sit on a treasure trove of data); it’s an
analysis challenge. The best B2C companies know how to interpret and act on
the wealth of data they have, but B2B companies tend to manage data rather
than use it to drive decisions. Good analytics can help companies identify
how factors that are often overlooked—such as the broader economic
situation, product preferences, and sales-representative
negotiations—reveal what drives prices for each customer segment and
product.

Automate. It’s too expensive and time-consuming to analyze thousands of
products manually. Automated systems can identify narrow segments,
determine what drives value for each one, and match that with historical
transactional data. This allows companies to set prices for clusters of
products and segments based on data. Automation also makes it much easier
to replicate and tweak analyses so it’s not necessary to start from scratch
every time.

Build skills and confidence. Implementing new prices is as much a
communications challenge as an operational one. Successful companies
overinvest in thoughtful change programs to help their sales forces
understand and embrace new pricing approaches. Companies need to work
closely with sales reps to explain the reasons for the price
recommendations and how the system works so that they trust the prices
enough to sell them to their customers. Equally important is developing a
clear set of communications to provide a rationale for the prices in order
to highlight value, and then tailoring those arguments to the customer.
Intensive negotiation training is also critical for giving sales reps the
confidence and tools to make convincing arguments when speaking with
clients. The best leaders accompany sales reps to the most difficult
clients and focus on getting quick wins so that sales reps develop the
confidence to adopt the new pricing approach. “It was critical to show that
leadership was behind this new approach,” says Robert Krieger, managing
director of PanGas AG. “And we did this by joining visits to difficult
customers. We were able to not only help our sales reps but also show how
the argumentation worked.”

Actively manage performance. To improve performance management, companies
need to support the sales force with useful targets. The greatest impact
comes from ensuring that the front line has a transparent view of
profitability by customer and that the sales and marketing organization has
the right analytical skills to recognize and take advantage of the
opportunity. The sales force also needs to be empowered to adjust prices
itself rather than relying on a centralized team. This requires a degree of
creativity in devising a customer-specific price strategy, as well as an
entrepreneurial mind-set. Incentives may also need to be changed alongside
pricing policies and performance measurements.

We’ve seen companies in industries as diverse as software, chemicals,
construction materials, and telecommunications achieve impressive results
by using big data to inform better pricing decisions. All had enormous
numbers of SKUs and transactions, as well as a fragmented portfolio of
customers; all saw a profit-margin lift of between 3 and 8 percent from
setting prices at much more granular product levels. In one case, a
European building-materials company set prices that increased margins by up
to 20 percent for selected products. To get the price right, companies
should take advantage of big data and invest enough resources in supporting
their sales reps—or they may find themselves paying the high price of lost
profits.

About the authors

Walter Baker is a principal in McKinsey’s Atlanta office
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Big data : gros business pour petites boites

Big data :  gros business pour petites boites | Big Data, Data Scientist | Scoop.it
De nouveaux opérateurs mettent leurs équipes de mathématiciens et d'analystes de données au service du marketing. De quoi optimiser les ventes et les promotions, notamment sur Internet, tout en minorant les risques de saturation des prospects.
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Applications of Big Data in Customer Segmentation

Applications of Big Data in Customer Segmentation | Big Data, Data Scientist | Scoop.it
By now, companies all over the world are coming to the realization that big data is crucial for future business growth and greater efficiency. There are many w…
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Little privacy in the age of big data

Little privacy in the age of big data | Big Data, Data Scientist | Scoop.it
With massive amounts of our personal data now being routinely collected and stored, privacy breaches are almost inevitable
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Paul Brook: big data can seem scary but it's about being informed – video

Paul Brook: big data can seem scary but it's about being informed – video | Big Data, Data Scientist | Scoop.it
Paul Brook says that although big data can seem scary, it has tremendous benefits
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Infographie : le Big Data, un défi incroyable

Infographie : le Big Data, un défi incroyable | Big Data, Data Scientist | Scoop.it
Les entreprises du monde entier investissent massivement dans la collecte et l'analyse du Big Data, mais elles sont nombreuses à ne pas savoir en tirer un avantage concurrentiel. Les sources d'informations sont pléthoriques et les opportunités d'aller de l'avant innombrables.
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Interview With Mike Baker Of DataXu

Interview With Mike Baker Of DataXu | Big Data, Data Scientist | Scoop.it
Mike Baker is the Founder and CEO of Boston-based ad-tech start-up DataXu. I talked with him recently about the space and his company's focus in particular.
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Thinking Big-Picture With Big Data

Thinking Big-Picture With Big Data | Big Data, Data Scientist | Scoop.it
Sanjay Dhar of the University of Chicago Booth School of Business is trying to help both academic marketing faculty and commercial marketing practitioners figure out what a dollar of marketing investment is really worth.
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Big data is cheap and easy

Big data is cheap and easy | Big Data, Data Scientist | Scoop.it
Big data is not expensive. You can process 10 terabytes of data per year on collocated servers using open source tools (Python - I do it in Perl), using your o…
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Confusion Reigns in Big Data – The basic differences between Hadoop, NoSql, Analytic Data Stores & RDBMS

Confusion Reigns in Big Data – The basic differences between Hadoop, NoSql, Analytic Data Stores & RDBMS | Big Data, Data Scientist | Scoop.it
Organizations are now creating more data than ever before, and as such a new set of tools and technologies are becoming popular to facilitate the storage & retrieval of this information in a timely and cost-effective manner. There are many technologies that are attempting to address these challenges, and as such there are different (and often incompatible) approaches, each with positives and negatives depending on the use-case.
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Six categories of Data Scientists

Six categories of Data Scientists | Big Data, Data Scientist | Scoop.it
We are now at 9 categories after a few updates. Just like there are a few categories of statisticians (biostatisticians, statisticians, econometricians, operat…
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