Quelle stratégie face à la diversité des mesures d’efficacité ?

La diversité des mesures capables de rendre compte de l’efficacité de l’entreprise ne cesse de s’élargir. Pas un jour sans qu’un nouvel indicateur émerge, toujours jugé complémentaire voire plus pertinent vis à vis des autres.Contacts clients, réseaux sociaux, e-commerce, réputation, objets connectés … autant de nouvelles formes d’interaction avec le client qui rendent nécessaire une vision holistique du parcours client avec la marque. Alors quels indicateurs pour juger de la performance des actions menées ?

Les indicateurs, de par leur diversité, doivent être considérés comme un ensemble cohérent, une vision globale de la vie de l’entreprise, son histoire relationnelle et business en quelques sortes. Il doivent parfaitement coller, si possible en temps réel, aux exigences de la lisibilité et de la simplicité. Mais c’est avant tout leur choix qui est primordial, ainsi que les assemblages de certains liés par les mêmes enjeux. Ces derniers, agrégats décisionnels, sont des super KPIs qui portent en eux la puissance du changement des entreprises. Ceux sont eux qui évaluent la pertinence des actions directement liées aux nouveaux modes organisationnels. Ils sont par définition évolutifs et doivent impérativement être maîtrisés dans leur analyse donc leur représentation.

Ainsi, au delà du choix des nouveaux indicateurs et de leur assemblage, leur représentation dans des dashboards est en soi un enjeu. Pour être fidèles à leur puissance et à leur diversité, les dashboards doivent être des outils dynamiques, capables de s’adapter à tout changement, évolution ou retournement de situation, c’est la règle aujourd’hui. Cette simplicité et cette agilité du dashboard sont la clé de l’efficacité décisionnelle, elles accompagnent l’organisation dans son développement rapide et tiennent compte d’un environnement mouvant en le rendant lisible aux yeux de chacun.

Les bons indicateurs sont ceux qui permettent de se poser les bonnes questions, ils sont souvent simples à comprendre pour l’ensemble de l’organisation et surtout permettent de réaliser ce qui est nécessaire pour impacter les résultats positivement.

Par exemple le NPS (Net Promoter Score) est souvent utilisé pour mesurer l'expérience client. Même si il est vrai que c’est un KPI de management simple, il peut aussi aider un agent qui est en contact avec le client à changer son comportement pour améliorer l’expérience donc l’indicateur.

Un autre exemple serait de consolider plusieurs KPI’s pour obtenir un agrégat qui servira de baseline, une sorte de super KPI, comme l’ensemble du temps passé sur la marque au global en additionnant les temps passés sur les assets digitaux comme les sites, les vidéos et les medias sociaux de la marque, ou l’advocacy en prenant la totalité des contenus partagés positivement sur les plateformes medias sociaux.

Un bon KPI est un KPI qui est aligné avec la performance et les objectifs individuels comme collectifs. C’est en l’atteignant que les individus comme les équipes seront valorisées et récompensées.

L’approche Captain Dash est basée sur cette simplicité et cette agilité. Faire du changement des organisations un levier de performance, tel est un de nos apports. Contactez-nous !

En savoir plus sur Captain Dash, suivez-nous sur Twitter ou abonnez-vous à notre blog.

De : Bruno Walther, Directeur Général et Co-fondateur de Captain Dash

The Explorer is Here!

Attention data universe! We have an announcement: After months of working in secret huddled over our desks late into the night, we have finally perfected the Explorer and are ready to unveil it! So, you may wonder, what is the Explorer?

The Explorer is a radical tool that allows you to view all data from your online platforms - that means Twitter, Facebook, Youtube, Foursquare, and Google Analytics. Hand-pick the lens through which you wish to view your online presence by choosing metrics and dimensions at will, and then experiment until your heart's content. The architecture behind it may be complex, but let us assure you that your experience will be a breeze.

Why would we dedicate time and resources to an entirely free product?

That's simple: because we believe in free data and the power it has to offer. We had a team of brilliant data engineers, the vast data universe at our disposal, and the inspiration to give you the tools to become empowered by your data…. so we decided to act.

Data doesn't have to be spreadsheets that loom over your head or drown your desk, and it certainly doesn't have to be complicated. We want to show you that it can be something sexy, fluid, and simple. And so for that reason, we've taken the job of collecting and organizing so that you can go straight to analyzing, strategizing, and benefiting from becoming more aware, well-informed, and empowered.

So what do you say…is it time for an adventure?

Explore here: http://explorer.captaindash.com


Napping Day: 5 Weird Facts About Naps

napping-day Here in Paris we don’t enjoy daylight savings time until March 30th, but we know that on Sunday night the rest of the world lost an hour of sleep. In honor of the hour lost, this week celebrates the mid-day ritual that is an integral part of some cultures, frowned upon in others, yet scientifically proven to be good for you: the glorious nap. Read on to learn 5 weird facts about naps that you likely weren’t aware of…


Polyphasic Sleep Schedule:

Although we’re told from a young age that we should ideally get 6-9 hours of sleep each night, it’s actually possible to function with far less per night.

A lot of Latin cultures follow a biphasic sleep schedule, with 5-6 hours of sleep per night and a 30-60 minute nap in the afternoon. That’s 5.5 hours minimum and 7 hours maximum- far less than we’re taught is healthy, yet somehow they're still functioning as a society.

Screen Shot 2014-03-11 at 10.53.19 AMSome experimental sleepers have adopted polyphasic sleep schedules that have allowed them to cut down on sleep to as little as 3 hours per day. Especially with the advent of the internet to learn about and dabble in alternative sleep patterns, many people have publicized their success with polyphasic sleep patterns. The Everyman sleep schedule involves a steady sleep block from 1 am – 4 am and then three 20-minute naps throughout the day and has actually cited success.  Successful adopters have said that they don’t feel tired, just sometimes bored with the large amount of time they’ve freed up throughout the day and night. Science can also back the potential benefits- in its sleep deprived state, the body will sometimes immediately transfer into the REM sleep state during each sleeping period, which could make it just as effective as much longer blocks of sleep.

However, lifetime effects of polyphasic sleeping are unstudied, unknown, and in my opinion not recommended.

Drink Coffee Before a Nap:

You wouldn’t believe it but caffeine takes 20-30 minutes to kick in after consumption. If you feel immediately awake after drinking a cup of coffee, you’re likely enjoying a fun little placebo effect. In reality drinking a cup of coffee, napping, then waking up right as it takes effect is an efficient way to time your caffeine boost to hit you the minute you wake up.

Sleep Inertia

If you sometimes feel groggy and disoriented after a long nap it’s because of a physiological effect called sleep inertia. A short nap of 20-30 minutes will keep you alert for several hours, but a nap of 60 minutes will keep you feeling awake for a solid 10 hours. The only downside is that sleep inertia can slow you down and leave you tired and unproductive for up to 4 hours.

This undesirable state results from waking up during the REM sleep cycle, so allowing yourself to wake up naturally from a nap will help you avoid it.

Alcohol Will Have a Stronger Effect

After just 5 nights of not getting enough sleep, 3 alcoholic drinks will affect you the same way that 6 would if you were perfectly rested.

Napping Can Save Your Life:

Studies have shown that napping raises stamina by 11%, increases your ability to sleep through the night by 12% and lowers the time required for you to fall asleep by 14%.  The Ancient Greeks even found that napping at least 3 times a week lowered the death rate due to heart problems by 37%.


Sleep Tight,

The Captain

Next-Gen Data Scientist

[Photo: KDNuggets]

From our last post about the role of the CMO, it is obvious that the Big Data Revolution has created a lot more new roles. One role in particular is that of the Data Scientist. It is easily the most hyped occupation of this decade, described by Hal Varian as "the sexiest job in the next 10 years." But is there any science actually involved in the role? Or is it simply a more 'sexier' way to describe a Data Analyst?

In essence, a scientist can be described as someone who attains knowledge through the application of specific methods. However, if you were to learn analytical tools the process would be much faster than simply attaining knowledge. Moreover, the term "data scientist" may appear new, but in actual fact it first appeared in the work of John Tukey in the 1960's. IBM gave a more recent definition of the role as someone who "represents an evolution from the business or data analyst role. The formal training is similar, with a solid foundation of typically in computer science and applications, modeling, statistics, analytics and math." The key characteristics of a Data Scientist are: business knowledge, artistry, problem solver, taking multiple perspectives, and being influential. There could easily be more ways that describe the role, and that goes to show how well defined it has come to be over the years.

In reality Data Scientists are scarce. Not every company wants to engage in Big Data therefore they feel there might be no particular position in their company for them. Only recently has there been any form of third level education to ever formally become a 'Data Scientist.' It is not surprising that the course is being offered by UC Berkeley, who in the past linked up with IBM and others to develop a center for Open Innovation. Given that they will be taught the basics of being a Data Scientist, will this be the going trend for other well respected Universities? Will there be as many as 20 or 50 'Data Science' Masters available in 2014?

The role seems quite difficult to uphold and maintain, as true Data Scientists have a lot of requirements. These include: advising executives and product managers on the implications of data; telling stories with data from having good skills at writing code; being able to structure large amounts of formless data and make sense of it all; and requiring their own time to work on projects, with the intention of building close relationships with executives. If Data Scientists can cut a lot of these requirements down into key, performance-measuring responsibilities then more companies will look into taking them on.

Faithfully yours,

The Captain.

Captain Dash is the most promising Company in Europe for IBM

Gilles Babinet and Bruno Walther, founders of Captain Dash, receiving the IBM Smartcamp French Prize in September.

Gilles Babinet and Bruno Walther, founders of Captain Dash, receiving the IBM Smartcamp French Prize in September.

We are very proud to announce that Captain Dash won the IBM SmartCamp on Wednesday 14th of November. We were competing against ten other finalist Start-ups that have been selected among 600 submissions all across Europe.

This victory occurs just three weeks after the release of Captain Dash and confirms our status as best business App on the Windows8 Store.

For those who haven't met with the Captain yet, let's sum up this long process:

On early September, Captain Dash won the IBM Smartcamp in France. This marks the beginning of an increasing cooperation with IBM. From then, we have been working on the integration of IBM most powerful BI tools, such as SPSS and eventually, CCI.


As explained in a previous article this partnership with IBM stems from a common vision:

  • CMO is the next leader in Innovation Strategy
  • Data changes the world and makes the world a smartest planet
  • 'Consumerisation of IT' is a far-reaching phenomenon
  • Hadoop is the key element for Big data

The next step for Captain Dash will be the final of the IBM Smartcamp in New York in January which will award the most strategic Start-up worldwide.

Let's rock!

You can watch Captain Dash presentation on stage here and the interview of Gilles Babinet after the victory here.

Comparing A and B data : the real challenge

Internet has really changed so many aspects of our daily life that it is sometimes hard to remember how we made things some years ago. Searching, buying, writing, creating are some of the few things that we can do better, faster and smarter today on the web.

Surprisingly, there is a a space where there are little changes in our life with the internet. That is about dealing with data and especialy with comparing it.

Some would say that the Internet is actually very good at comparing data... Price Comparators are great when it is about buying a fly-ticket, or to rent a car, and we would not argue with this. The point is about comparing -matching actually- data that are different. Take location and data for instance ;  that is a good example. Location and any type of data: A real estate price versus a location, the groundwater level of pollution at a country scale, or the price of gas in the 15 miles range around where you live. There are limitless example of that.


Indeed, you may find a website that make ONE of these things, but you would not find a generic place -such as a search engine- that would do ALL of these things as a one stop shopping place.

An utopia?  Technicaly unfeasible? Surprisingly, there are limited barriers to such innovation. Data, which is often seen as the key bootleneck are largely accessible as it was pointed out by a piece in The Economist. It seems that there is more of a mental or conceptual barriers at stake. "Dealing with data is boring and making data simple is therefore impossible" seems to be the common thought.