Para reflexão!

 

 "Thought Provoking Consulting. There is, he says, "serious oversupply" in the retail market — too many businesses vying for our custom — and, as recent retail collapses have demonstrated, not enough consumer spending to sustain them all. Yet separate ICS data shows that over 30 per cent of people would pay more for a product or service if they received exceptional customer care.

...

The best service I have received on the high street is from Hobbs. It's one of the few shops where sales staff have the confidence to say, "That style doesn't suit you as well as this one." How ironic that telling me not to buy something is the reason I keep going back.

...

Based on data it has collected between 2017 and 2023, firms with customer-satisfaction scores at least one point higher than their sector average achieved average compound revenue growth of 7.4 per cent. This compared to flat revenue growth for firms with average satisfaction scores at least one point below average. The impact was even greater on EBITDA (earnings before interest, taxes, depreciation and amortisation — a measure of profits), which averaged just over 20 per cent at the leading firms — twice the rate of those with below-average scores.

...

As I think back to the origins of service culture, to the close relationship between the customer spending money and the independent shopkeeper who has hit on a formula that keeps people coming back to spend more, I wonder, "How hard can it be?"

Bosses cannot and should not rely on technology or statistics to do the job of telling them about the customer experience. [Moi ici: Eheheheh! O tema do Big Data aqui no blogue] They should regularly be "on the shop floor" (or in the contact centre or with the tech team) listening to staff and customers, asking questions and understanding precisely where they're able to delight us, and frustrate us. In an online world, the need to make these human connections becomes more vital.

With years of high inflation forcing consumers to buy less stuff, getting value for money has never been more important. But this does not mean a race to the bottom with pricing. A third of customers say they will pay more for better service."

Recordar: Cuidado ao tirar os humanos da equação 

Trechos retirados de "How did customer service get so bad?" 

"A store manager is the CEO of their store."

Em tempos trabalhei com uma empresa em que a gerência não queria que os comerciais soubessem o que se vendia e quanto se vendia. Juro!

Entretanto, ontem no WSJ, "Why a Zara Bet Big on a Maxi Dress":

"The Zara store in London's Chelsea neighborhood received a new range of maxi dresses in various styles. After just one week, store manager Ana Oliveira analyzed the sales data and saw that the dresses with prints were selling much better in her shop than those in solid colors. So she moved the print designs to the tall rails by the entrance. They would be the first thing shoppers would see as they came into the store, she said.

Zara is going local, giving store managers control over their shops' inventory, displays and designs.

The strategy relies heavily on its proprietary data system and a willingness to break the standard fashion-chain practice of making centralized decisions on stores' behalf.

...

"It's my kingdom, ," said Oliveira. She credits the new tech with giving her a level of control that wasn't previously possible.

Especially useful, Oliveira said, is her ability to crosscheck what's happening regionally, using data compiled from a basket of comparable U.K. stores. If a top-selling item at other British stores isn't doing so well in Chelsea, that is her cue to change up her displays, move unpopular items to storage or request new designs from Zara's headquarters.

...

After a warm weekend in June, her store's data showed a rise in demand for linen menswear, Oliveira said. Sensing the beginning of a trend, she quickly rearranged the layout of the men's section to put linen shirts and trousers in the most visible display areas.

It doesn't make sense to keep commercial data locked away at headquarters when store employees can use it to maximize sales, said Inditex Chief Executive scar García Maceiras. He said the mirror system was developed in response to managers requesting access to more data to help them oversee their stores, crediting managers as the key ingredient to Zara's success: "A store manager is the CEO of their store."

When Oliveira saw a drop in demand for blazers and a corresponding bump in T-shirt purchases relative to the week before, she interpreted it as a sign of a seasonal shift as summer arrived. As a result, she changed the displays to give more space to warmweather clothes."

Risco versus incerteza e Big Data

Há muitos anos que nos rimos de quem acredita piamente no Big Data:

•  "a human touch becomes more important than ever" (Março de 2016)
• Cuidado com o Big Data (Junho de 2017)
• Outra religião, a do big data (Setembro de 2019)
• Dedicado aos fans do Big Data (Junho de 2022)

"‘Google Flu Trends completely flopped for the simple reason that uncertainty exists — the future is not like the past,’ Gigerenzer says, stroking his walrus moustache. ‘When using big data, you are fine-tuning the past and you’re hopelessly lost if the future is different. In this case, the uncertainty comes from the behaviour of viruses: they are not really predictable, they mutate. And the behaviour of humans is unpredictable.’ In other words, AI can’t predict ‘Black Swan’ events — major surprises that aren’t anticipated in modelling and plans.

Gigerenzer worries that important decisions are being handed over to AI, despite its clear limitations."

Trecho retirado de "The algorithm myth: why the bots won’t take over"

"This is the age of big data. We are constantly in quest of more numbers and more complex algorithms to crunch them. We seem to believe that this will solve most of the world’s problems - in the economy, society and even our personal lives. As a corollary, rules of thumb and gut instincts are getting short shrift. We think they often violate the principles of logic and lead us into making bad decisions. We might have had to depend on heuristics and our gut feelings in agricultural and manufacturing era. But this is the digital age. We can optimise everything.

Can we?

...

In the real world, rules of thumb not only work well, they also perform better than complex models, he says. We shouldn’t turn our noses up on heuristics, we should embrace them.

...

In short, Gigerenzer's arguments go like this. There is a big difference between risk and uncertainty. You are dealing with risk when you know all the alternatives, outcomes and their probabilities. You are dealing with uncertainty when you don’t know all the alternatives, outcomes or their probabilities.

When you are dealing with risk, complex mathematical models and fine-tuning them for optimisation work. However, when you are dealing with uncertainty, they don’t work well, because the world is dynamic.

What you then need is a set of simple rules of thumb that are robust and gut instincts sharpened by years of experience. 

...

I asked Gigerenzer if his work - spanning books, lectures, research papers - had one big message. He said, “We need to dare to think for ourselves, instead of anxiously adapting to our environment. We have in western world fewer and fewer people who are willing to take responsibility, to make decisions on their own and the tendency of the management to delegate to consulting firms which is often a waste of time and money.”

“My advise would be to trust more in expert knowledge, in long years of experience. Don’t buy statistical algorithms you don’t understand. Many managers buy big data algorithms which come in black boxes because they are not sure, they don’t really understand what all these are about. But they think, ‘if I don’t buy that, and if things go wrong, I am responsible, and have to take the blame. If I buy that, it costs the company something, but I am safe’. There is a lot of defensive decision in society and unwillingness to take responsibility, and the fear of one’s own common sense.”"

Trechos retirados de "Gigerenzer’s simple rules"

Dedicado aos fans do Big Data

Dedicado aos fans do Big Data: 

"Creating great choices requires imagination more than data.

Underlying the practice and study of business is the belief that business decisions must be driven by rigorous analysis of data. The explosion of big data has reinforced this idea. In a recent EY survey, 81 percent of executives said they believed that "data should be at the heart of all decision-making," leading EY to enthusiastically proclaim that "big data can eliminate reliance on 'gut feel' decision-making.

Managers find this notion appealing.

...

Can management decisions really be reduced to an exercise in data analysis? I do not believe that they can, and this brings me to an important truth about data: creating great choices requires imagination more than data.

...

It's important to realize that the presence of data is not sufficient proof that outcomes cannot be different. Data is not logic. In fact, many of the most lucrative business moves come from bucking the evidence.

...

Moreover, the absence of data does not preclude possibility. If you are talking about new outcomes and behaviors, then naturally there is no prior evidence. A truly rigorous thinker, therefore, considers not only what the data suggests but also what within the bounds of possibility could happen. And that requires the exercise of imagination-a very different process from analysis.

Also, the division between can and cannot is more fluid than most people think. Innovators will push that boundary more than most, challenging the cannot.

Breaking the Frame

The imagination of new possibilities first requires an act of unframing. The status quo often appears to be the only way things can be, a perception that's hard to shake."

Trechos retirados de "A New Way to Think" de Roger L. Martin. 

Big data e experiência dos clientes

A propósito de "Customer Experience in the Age of AI":

"brands can win by tapping a deep store of customer information to transform and personalize user experiences. From the pre-internet dawn of segment-of-one marketing to the customer journey of the digital era, personalized customer experiences have unequivocally become the basis for competitive advantage. Personalization now goes far beyond getting customers’ names right in advertising pitches, having complete data at the ready when someone calls customer service, or tailoring a web landing page with customer-relevant offers. It is the design target for every physical and virtual touch-point, and it is increasingly powered by AI.

...

We are now at the point where competitive advantage will derive from the ability to capture, analyze, and utilize personalized customer data at scale and from the use of AI to understand, shape, customize, and optimize the customer journey."

Recordei o que li em "The Data Detective" de Tim Harford:

"The algorithms that analyze big data are trained using found data that can be subtly biased.

...

One thing is certain. If algorithms are shown a skewed sample of the world, they will reach a skewed conclusion.

...

There are some overtly racist and sexist people out there—look around—but in general what we count and what we fail to count is often the result of an unexamined choice, of subtle biases and hidden assumptions that we haven’t realized are leading us astray.

...

Big found datasets can seem comprehensive, and may be enormously useful, but “N = All” is often a seductive illusion: it’s easy to make unwarranted assumptions that we have everything that matters. We must always ask who and what is missing. And this is only one reason to approach big data with caution. Big data represents a huge and underscrutinized change in the way statistics are being collected, and that is where our journey to make the world add up will take us next."

"That's our edge!"

Interessante, "AI 'outperforms' doctors diagnosing breast cancer", com base neste artigo "International evaluation of an AI system for breast cancer screening".

Acredito que em muitas áreas a Inteligência Artificial poderá substituir com vantagem os humanos. No entanto, nunca esqueço as palavras de Kasparov acerca do trabalho conjunto entre humanos e inteligência artificial.

"relatively weak computers working alongside human players will wipe the floor with the most advanced supercomputers. Collaborative rather than oppositional thinking has yielded radical advances in chess theory, and opened up whole new areas of play." (fonte)

"humans and machines will work together, and we, as customers, will be allowed, once more, to lazily beg for help" (fonte) 

Nunca esqueço:

- That's our edge! (fonte) 

Outra religião, a do big data

Ando mesmo interessado nos textos de Felin & Zenger

"No doubt bias and error are important concerns in strategic decision-making. Yet it seems quite a stretch to suggest that the original strategies developed by people like Apple’s Steve Jobs, Starbucks’ Howard Schultz, or even Walmart’s Sam Walton had much to do with error-free calculations based on big data. Their strategies, like most breakthrough strategies, emerged in settings with remarkably little data to process and little basis for calculation — situations in which the paths to value creation were highly uncertain and evidence was sparse. We are highly skeptical that debiasing decision making, eradicating errors, or ceding strategy to AI will improve strategizing, let alone lead to breakthrough strategies. [Moi ici: Pensamento bacteriologicamente puro, sem erros, totalmente justificável e matematizável é o da triade, dos encalhados. E perder o pé? E o optimismo não documentado? Valor não se calcula numa folha de cálculo, é um sentimento]
...
Composing valuable strategies requires seeing the world in new and unique ways. It requires asking novel questions that prompt fresh insight. Even the most sophisticated, deep-learning-enhanced computers or algorithms simply cannot generate such an outlook. But where does the uniqueness and novelty so essential to innovative strategic thinking come from? It comes from contrarian, perhaps even “distorted,” perceptions and beliefs about reality and the “facts” that surround us.
...
If everyone believes the same thing — or if everyone uses the same variables, information, and computational tools — the logical result is computational consistency, shared conclusions, and me-too strategies.
...
In setting strategy, deviation in judgment is a feature, not a bug.
...
It is tempting to believe that the right evidence and the right analysis will yield the right strategy. But just as customer surveys seldom lead to breakthrough products that capture the imagination of customers and markets, substantive strategy-making requires that we see well beyond the available data.
...
We view the strategist’s task as akin to an inkblot test, where participants are presented with highly ambiguous evidence and signals that afford many possible realities, but offer no single correct answer. With such tests, the very same evidence — an ambiguous picture or set of marks — can be interpreted correctly in many different ways.
...
Valuable strategizing demands this novel perception — an ability to see in ambiguous cues and data what others can’t see. Strategic thinking is fueled by the novelty of our observation, not its consistency. [Moi ici: Lembram-se do Serginho Centeno ou do André e as suas previsões do calçado, assentes em big data?] The object of strategic thinking is not to ensure that we all observe the same information and derive the same conclusion. It is precisely the opposite: If your desire is to be a value creator, you must aspire to see what others cannot."

Trechos retirados de "What Sets Breakthrough Strategies Apart".

"niche retailers with strong identities"

Tão interessante, tão em sintonia com a visão de Mongo como um mundo de tribos de interesses assimétricos, tão de acordo com "tu não és do meu sangue", tão de acordo com a ambivalência face a Bieber. Ninguém quer ser tratado como plancton. Trechos retirados de "The global village needs walls":

"Facebook has been a giant experiment in understanding humanity. It has proven that we actually don’t want to be part of a global community — we are instead a species of small groups and tribes. If you’re part of everything, you’re not invested in anything, and that feels bad. “Everyone, no exception, must have a tribe, an alliance with which to jockey for power and territory, to demonize the enemy, to organize rallies, and raise flags,”
...
So what does this mean for business? For one, it creates an opening for a smorgasbord of social networks and social businesses.
.
“There should be social networks around every conceivable interest,” [Moi ici: Como não recordar o que escrevi, ao arrepio do mainstream, sobre as plataformas universais, não é winner-take-all]
...
“The really exciting model is not one company to rule them all, but distributed companies and distributed wealth and revenue, and a social experience built around these supernodes,” said Bianchini, whose company is making a big bet that we’re headed in this direction. Some recent trends seem to bear it out. The number of niche social networks is exploding,
...
Such fracturing of the online world is a headwind even Amazon will eventually have to battle. The bigger Amazon gets, the less it feels like it connects with your personal identity — even with algorithms that figure out what you’re most likely to buy.[Moi ici: Recordar o que escrevo sobre o big data e a miudagem]
.
Just as microbrews stole customers from giant, least-common-denominator brands such as Budweiser, niche retailers with strong identities are likely to be more of a challenge to Amazon than some other centralized behemoth.[Moi ici: O que escrevo neste blogue desde sempre... cuidado com os gurus da Junqueira]"

"Gaining understanding is not always the same as predicting"

Algo interessante sobre a ilusão do Big Data, de que falo aqui há algum tempo e que McChrystal descreve de forma mais eloquente:

"The industrial world, where almost everything could be measured and mechanized—where individual variables could be isolated, tested, and optimized—lent itself to this model. As complicated as it was, it almost all lay under the manager’s capacity for calculation, prediction, and control. Planned efficiency became the lifeblood of “good management.” Everything else, from physical design to organizational structure to leadership behavior, was a natural extension of this goal.
.
As we have crept toward the “many to many” environment of complexity, we have engineered increasingly complicated solutions: gifted managers have developed intricate protocols and organizational hierarchies to cover all likelihoods. The baseline belief that any problem can be known in its entirety has never faded. Anyone who has worked in business or government for a few decades can testify to the seemingly endless increases in rules and paperwork.
...
We hear a great deal about the wonders of “Big Data,” which certainly has advanced our understanding of the world in dramatic ways. Retailers can track who bought what, and where they bought it. Sociologists can sift through vast amounts of political, economic, and societal information searching for patterns. There is tremendous potential for this technology, but, as with Blue Force Tracker and the other tools at our disposal in Iraq, it is unlikely that it will enable effective long- term prediction of the type that we crave. Data-rich records can be wonderful for explaining how complex phenomena happened and how they might happen, but they can’t tell us when and where they will happen. For instance, data on the spread of a virus can provide an insight into how contagion patterns look in our networked world, but that is very different from knowing exactly where the next outbreak will occur, who precisely will end up getting sick, and where they will go next. Gaining understanding is not always the same as predicting.
...
Big Data will not save us because the same technological advances that brought us these mountains of information and the digital resources for analyzing them have at the same time created volatile communication webs and media platforms, taking aspects of society that once resembled comets and turning them into cold fronts. We have moved from data-poor but fairly predictable settings to data-rich, uncertain ones."

Trechos retirados de "Team of Teams: The Power of Small Groups in a Fragmented World" de Stanley McChrystal e Chris Fussell.

Como criar valor?

Há pouco tempo eliminei do meu arquivo um artigo sobre o que se pode fazer com o Big Data relativamente à publicidade. Essa era a minha grande crítica ao artigo, quando se escreve sobre Big Data, escreve-se quase sempre como refere este título "Use Big Data to Create Value for Customers, Not Just Target Them".

Há tempos numa empresa dei comigo a ouvir alguém a sugerir como usar o Big Data para criar valor para um cliente, para o ajudar a ganhar dinheiro, para o ajudar na sua relação com os seus clientes:

"in the rush to uncover and target the next transaction, many industries are quickly coming up against a disquieting reality: Winning the next transaction eventually yields only short term tactical advantage, and it overlooks one big and inevitable outcome. When every competitor becomes equally good at predicting each customer’s next purchase, marketers will inevitably compete away their profits from that marginal transaction. This unwinnable short-term arms race ultimately leads to an equalization of competitors in the medium to long term. There is no sustainable competitive advantage in chasing the next buy.
...
To build lasting advantage, marketing programs that leverage big data need to turn to more strategic questions about longer term customer stickiness, loyalty, and relationships. The questions that need to be asked of big data are not just what will trigger the next purchase, but what will get this customer to remain loyal; not just what price the customer is willing pay for the next transaction, but what will be the customer’s life-time value; and not just what will get customers to switch in from a competitor, but what will prevent them from switching out when a competitor offers a better price.
.
The answers to these more strategic questions reside in using big data differently. Rather than only asking how we can use data to better target customers, we need to ask how big data creates value for customers. That is, we need to shift from asking what big data can do for us, to what it can do for customers.
...
Every company should ask three questions to examine how its big data can create customer value:
.
What types of information will help my customers reduce their costs or risks?
...
What type of information is currently widely dispersed, but would yield new insight if aggregated?
...
Is there diversity and variance among my customers such that they will benefit from aggregating others’ data with theirs?"

Um desafio que muitas mais empresas deviam assumir de forma deliberada: como criar valor?

Cuidado com o Big Data

Mongo é o mundo da ARTE, o mundo da criatividade.

Mongo é o mundo da proximidade, do interacção, da co-criação.

Sou um apóstolo de Mongo e tenho receio da crença inabalável no Big Data.

Por isso: "If You Want to Be Creative, Don’t Be Data Driven".

Como não relacionar com:

Advice from Jeff Bezos: If you use data from surveys, make sure you know what you’re getting into… https://t.co/pmAZ37srur pic.twitter.com/6iUCNWnAwj

— Alan Klement (@alanklement) May 29, 2017

E já agora, relacionar com "There is no right answer"

Um exemplo que muita gente podia copiar.

Um exemplo que muita gente podia copiar. Ainda na Terça-feira ao almoço conversei com um apaixonado por carros antigos. Podia ser um bird-watcher, um motoqueiro, um maker, o meu amigo dos aquários, ...

"After I made my BMX Pinterest board, I sent a tweet out saying how excited I was about my new project to build the ultimate retro BMX powered by the connections the internet makes possible. I already had a few posts of bikes for sale on eBay and links to old-school BMX forums. The tweet had a link pointing to the Pinterest board. After I got back from lunch I checked my Twitter feed to find someone had sent me a reply tweet about the BMX project. It was from a local BMX store. They informed me they had an old-school BMX section in their store for big kids like me. The way they did it was really cool. The tweet said, ‘Cool project Steve. Here’s a link to the best forum for Old School BMX … If you wanna reminisce, pop in some day’.[Moi ici: Reparem, não vendem, não empurram, convidam, partilham]
.
Needless to say, I went in the very next day to get some advice on the project, on where to get parts (it’s a bit like car restoration) and on how to get the genuine stuff. They earned my business in 140 characters.
This was such a clever play on so many levels. There are a lot of subtle marketing lessons to be learned from this tweet. I’ll spell them out clearly.

• Make it personal. They addressed me as Steve. You’d be surprised how few people do that when they find you online, even though your name is a mere click away.

• Offer resources first. They provided me with something of value to help me: the link to the forums. They didn’t try selling to me on the first connection.

• Focus on an ecosystem. They didn’t stress about where I went to solve my problem. They chose instead to embrace the fact that I was entering their market space. In some ways they recommended a competitor (the online forum that happens to sell old BMX parts). 

• Use real language and culture. They spoke the natural language of the group. It wasn’t corporate brochure-ware PR speak. It was human and real.

• Find tools of connection. I asked the owner how he found me. I mean, unless I was in his stream how would he know about my project? He said he does a social-media search every day with only two simple data parameters: the hashtag for #BMX and the geography of Melbourne. Very clever stuff. 

• Focus on one customer at a time. They focused on direct connection, one new fan at a time. They didn’t try to build an audience. They helped a person, which is a very different approach. It seems old-school BMXers are a little bit smarter than old-school marketers. What a great way to build a community; one that I’m now a part of.

While everyone gets enamoured with ‘big data’, there’s probably a lot more we can do with ‘little data’."

Trecho retirado de "The Great Fragmentation"

"the relationship is the holy grail"

"Big data may be all the rage. But often, “little data” about a customer’s prior interactions is more useful in determining how to optimize the experience to make the next engagement or, increasingly, the next transaction more likely. The best way to get a meaningful understanding of your customer is to go directly to the source. A senior marketer at a beverage company said, “If [you] can form a direct relationship with the consumer, even if you can’t transact directly, the relationship is the holy grail.” To that end, blue-chip marketers like Gillette and Luxottica are emulating digital natives like Harry’s or Warby Parker, which sell directly to consumers in the same categories. Knowledge of site usage and consumption can lead companies to develop more granular segmentation and more effective targeting of messages."

Em linha com o que escrevemos aqui há anos sobre o big data. Sorrio só de pensar no algoritmo parvo do Booking.com

Trecho retirado de "The Marketer’s Dilemma"

"Collaborative rather than oppositional thinking"

Na sequência de:

• Acerca da automatização das profissões;
• "humans and machines will work together";
• O truque é a interacção, a co-criação. Os robots não têm hipótese!

"In 1997, after many advances and setbacks, IBM’s Deep Blue finally bested the world’s greatest player, Garry Kasparov.
...
But something strange happened to chess after 1997. Instead of capitulating, its masters rethought their own thinking. The following year, Kasparov opened the first tournament of what has come to be known as Advanced Chess. In Advanced Chess, players are allowed to use a computer to assist them – and the results have been revelatory. Thanks to hardware and software improvements in the years since Deep Blue’s victory, even relatively weak computers now routinely beat Grandmasters at tournament level. But as Advanced Chess has shown, relatively weak computers working alongside human players will wipe the floor with the most advanced supercomputers. Collaborative rather than oppositional thinking has yielded radical advances in chess theory, and opened up whole new areas of play."

Trechos retirados de "What's wrong with big data?"

Big Data, pois!

Basta pesquisar o marcador "big data" neste blogue para perceber facilmente o quanto desconfio do tema.
Por isso, foi com um enorme sorriso nos lábios que li "What's wrong with big data?" e descobri que a crença está ainda mais entranhada e é mais ingénua do que eu pensava. O artigo tem matéria para várias linhas de reflexão. Por exemplo:

"This belief in the power of data, of technology untrammelled by petty human worldviews, is the practical cousin of more metaphysical assertions. A belief in the unquestionability of data leads directly to a belief in the truth of data-derived assertions. And if data contains truth, then it will, without moral intervention, produce better outcomes. [Moi ici: Como não recordar este texto de Esko Kilpi que li recentemente "Your facts are not my facts"]
...
But you don’t need to look to such extreme examples to see how a belief in technological determinism underlies much of our thinking and reasoning about the world.
.
“Big data” is not merely a business buzzword, but a way of seeing the world. Driven by technology, markets and politics, it has come to determine much of our thinking, but it is flawed and dangerous. It runs counter to our actual findings when we employ such technologies honestly and with the full understanding of their workings and capabilities. This over-reliance on data, which I call “quantified thinking”, has come to undermine our ability to reason meaningfully about the world, and its effects can be seen across multiple domains.
...
Implicit in Moore’s Law is not only the continued efficacy of technological solutions, but their predictability, and hence the growth of solutionism: the belief that all of mankind’s problems can be solved with the application of enough hardware, software and technical thinking. Solutionism rests on the belief that all complex activities are reducible to measurable variables. [Moi ici: Tudo se resume a mais 900 mil imigrantes à la amador a jogar bilhar] Yet this is slowly being revealed as a fallacy, as our technologies become capable of processing ever larger volumes of data. In short, our engineering is starting to catch up with our philosophy.
...
The view that more information uncritically produces better decisions is visibly at odds with our contemporary situation.
...
 The world is not something we study neutrally, that we gather neutral knowledge about, on which we can act neutrally. Rather, we make the world by understanding it, and the way we understand it changes it.
...
“More information” does not produce “more truth”, it endangers it. We cannot stand as dispassionate observers of supposedly truth-making processes while they continue to fail to produce dispassionate ends."

Kilpi:

"Reality is no longer viewed as a singular fact of nature but as multiple and socially constructed. In a relational model, identity is constructed from being in relationships, being connected, as contrasted with the mainstream view of identity through separation. Knowledge of self and the other thus becomes viewed as co-constructed, the same way as our future is co-constructed."

Recordar Cavaco, os adultos e os factos.

"The customer will be transformed from being an audience to an actor"

Leio "What Businesses Need To Know About The Fourth Industrial Revolution" e fico a pensar que há aqui qualquer coisa em falta. Questiono-me se será por causa da audiência-alvo do artigo e do seu âmbito de actuação.
.
Entretanto, encontro o que sinto falta lá naquele texto em "A pattern language of post-industrial work":

"The customer is now seen as being directly and actively involved in the key moments of value creation as opposed to passively consuming value. [Moi ici: No 1º artigo o cliente é visto como um alvo imóvel]
.
There are profound implications that result from this change of thinking. Products and services are not reproducible as such any more. Solutions are by default contextual, but they can be starting points for someone else to create value. Creative, connected learning is at the core of the post-industrial business.
...
To succeed you need relationships and interaction. When customers are identified as individuals in different use contexts, the sales process is really a joint process of solving problems. You and your customer necessarily then become cooperators. You are together trying to solve the customer’s problem in a way that both satisfies the customer and ensures a profit for you.
...
The focus should now be on cooperation and emergent interaction based on transparency, interdependence and responsiveness.
.
The really big objective of the digital transformation is to reconfigure agency in a way that brings relationships into the center.
...
The key understanding is that it is now the customers or members of the network who create value, not the network owner. The customer will be transformed from being an audience to an actor."[Moi ici: Frase assassina para os crentes no modo gringo de fazer negócio à la século XX]

É a continuação de ""a human touch becomes more important than ever""

Big Data e Kardashians

Eheheh

.
Estas pequenas vitórias para um anónimo da província.
.
O que li ontem de Seth Godin "Actually, more data might not be what you're hoping for":

"They got us hooked on data. Advertisers want more data. Direct marketers want more data. Who saw it? Who clicked? What percentage? What's trending? What's yielding?
.
But there's one group that doesn't need more data...
.
Anyone who's making a long-term commitment. Anyone who seeks to make art, to make a difference, to challenge the status quo.
.
Because when you're chasing that sort of change, data is the cudgel your enemies will use to push you to conform.
Data paves the road to the bottom. It is the lazy way to figure out what to do next. It's obsessed with the short-term.
.
Data gets us the Kardashians."

Como não recordar:

• "a human touch becomes more important than ever" (Março de 2016) 
• Big Data (Junho de 2013)

PME e código, já pensou nisso? (parte IV)

Parte I, parte II e parte III.
. 
Meter código nisso não precisa de começar pelo produto, pode muito bem começar por um destes vectores:

Imagem retirada de "Perspectives on Digital Business"

"a human touch becomes more important than ever"

Porque acredito em Mongo, a minha posição tem sempre sido de alguma cautela acerca do Big Data, há muitos anos que apelo ao olhar olhos nos olhos e a ter cuidado com os fantasmas estatísticos:

• Curiosidade do dia (Maio de 2013)
• Big Data (Junho de 2013)
• Big Data, pois (Outubro de 2013)
• Big data e PMEs (Dezembro de 2013)
• Cuidado com o Big Data (Abril de 2014)

Muita da conversa de "Achieving Hyper-Segmentation To Reach Personalization At Scale" é demasiado técnica para mim. No entanto:

"With the rising adoption of machine learning and automation, a human touch becomes more important than ever. Companies that have figured out how to utilize data to create deeper personalization are not only improving the overall customer experience by talking in a language customers appreciate; they’re also winning more deals and generating more revenue dollars.
...
[Moi ici: Depois, o artigo começa a dar uma receita para optimizar o Big Data] Prioritize Segments And Personalize Outreach
Both prioritization and personalization are key when it comes to your hyper-segmentation strategy. Rather than creating a few rigid personas or a large list based on broad firmographic characteristics, hyper-segmentation allows you to use all of your customer data to pinpoint specific marketing problems you can solve for smaller customer groups, aka a “segment of one.” [Moi ici: Oh, wait! Com segmentos de um para que preciso de Big Data? Esta é a vantagem das PME!!!]
...
With a highly segmented profile, you can speak to each prospect as an individual [Moi ici: Deve haver algo aqui que me escapa. Ignorância minha certamente, como é que gigantes conseguem lidar com segmentos de um?]"

Um outro artigo, ainda mais interessante e sobre o mesmo tema "Here’s How An Old Pair of Sneakers Saved Lego":

"As accurate as big data can be while connecting millions of data points to generate correlations, it is often compromised whenever humans act like, well, humans. As big data continues helping us cut corners and automate our lives, humans in turn will evolve simultaneously to address and pivot around the changes technology creates. Big data and small data are partners in a dance, a shared quest for balance - and information.
.
In our small data lies the greatest evidence of who we are and what we desire, even if, as Lego executives found out more than a decade ago, it’s a pair of old Adidas sneakers with worn-down heels."

Uma hipótese para o BIg Data em Mongo

Outra referência a Mongo:

"The successful user experience is about meeting a consumer’s need on an individual level – a “segment of one” not “one-size-fits” all, many experts say."

Material para reflexão:

• "Expand your definition of context beyond location and time. Situation and Emotion matter.

• Deliver immediate benefits to users before asking for more of their data. There is a fine line between useful and creepy.

• Segment your users based on digital behaviors, preferences, motivations and context to drive the most relevant interactions.

• Set up a big data and analytics environment capable of capturing and acting on behavioral analytics data in real time.

• Use analytics and machine learning to adapt the target interactions for each user-segment over time, based on user responses.

• Recruit a new breed of user experience designers—those with analytics skills to support the design of adaptive user experiences.

• Start with desired outcomes, then pilot and adjust quickly.

It’s no longer good enough to know your customers. It’s what you do with that knowledge that really matters. Your customers are willing to engage and share their data if they perceive a real benefit for them. Are you ready to live up to your end of the bargain?"

Trechos retirados de "The User Experience: Why Data – Not Just Design – Hits the Sweet Spot"