Slides da palestra "Como utilizar os dados que você tem nas mãos para conhecer melhor os seus clientes" feita pelo Rodolfo Ohl, Country Manager Brasil da SurveyMonkey.
Como utilizar os dados que você tem nas mãos para conhecer melhor os seus clientes.
1. Big Data:
Como utilizar dados
para tomar decisões
mais inteligentes
Rodolfo Ohl
Country Manager
SurveyMonkey
2. 2
Ajudar as pessoas a tomarem melhores decisões
3. Nós ajudamos as pessoas a obterem insights
clientes criam
pesquisas
distribuem para
outros
as pessoas
respondem
a pequisa
clientes
analisam
e obtém
insights
4. Avaliação de
Desempenho
Pesquisa
Acadêmica
TCC, Mestrado etc.
Pesquisa
de Clima
Planejamento
de Eventos
Testes
e
Quizzes
Avaliação de
Treinamento
Pesquisa
de Clima
Questionários
para ONGs
Pesquis
a sobre
Produto
Pesquisa
com pais
Pesquisa
de
Satisfação
de Clientes
Nossos clientes são criativos
5. Pessoal 1%
ONGs
27%
Educação
21%
Gov.
9%
Empresas
42%
99%
Das empresas da Fortune 500
Nossos clientes
Mais de 20 milhões de usuários
6. 80%
dos clientes optam
pelos planos anuais
$24
Opção de plano
mensal também
disponível
Modelo de Negócios Freemium
Clientes fazem upgrade para planos premium para adicionar funcionalidades
7. A nossa trajetória
Um caminho não convencional para o Vale do Silício
Mudou-se para Palo Alto
e contratou equipe para
crescer
Lucrativa
desde o 1º dia
1999 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Criada neste
apartamento
15. Quão confiante você está em relação a ter
todas as qualificações ou capacitação
necessária para competir em uma economia
cada vez mais orientada a dados?
SurveyMonkey, Brazilians & Big Data, Março 2014
16. Em geral, quão preparado você pensa
que a sua empresa está para lidar com
SurveyMonkey, Brazilians & Big Data, March 2014
“Big Data”?
17. 80% acreditam que suas empresas
Aumentaram investimentos nestas 3 áreas
Contratação/desenvolvimento ferramentas
relacionadas a base de dados
Contratação de funcionários
adcionais
Coleta/acesso a novos tipos de
dados
SurveyMonkey, Brazilians & Big Data, Março 2014
18.
19. O modelo preditivo de gravidez
How Companies Learn Your Secrets, The New York Times Magazine, February
2012
28. Vendas de SUV estavam
estáveis em 2007
“Vendas da maioria do carros
utilitários grandes de luxo estão
em alta em 2007, como Land
Rover, Range Rover, além de
outras (grandes e caras) SUVs
de marcas não luxuosas como
Chevrolet Suburban”
Fonte: AutoData
2007
29. O que você está procurando
num novo carro?
Market Research Driving Product Development at Ford Motor Company
30. Job exit by women
The Happiness Machine, Slate, January 2013
31. Qual foi a principal razão
por ter saído da empresa?
The Happiness Machine, Slate, January 2013
35. Alguns exemplos de pequisas
para e-commerce
- Pesquisa para validação de hipóteses
- Pesquisa de satisfação de clientes
- Pesquisa de usabilidade
- Pesquisa de intenção de compras
- Pesquisa com ex-clientes
- Pesquisa com potenciais clientes
- Pesquisa com assinantes do newletter
- Pesquisa de mercado
- Pesquisa para definição de nome, logo, campanha,
identidade visual etc
- As possibilidades são ilimitadas....
36. 36
Geração, verificação e balanceamento de insights
Dados implícitos verificados por dados explícitos = extremamente valiosos
Pesquisa
Dados Explícitos
Ação
Dados Implícitos
Estamos em 190 paises
Temos mais de 20 milhoes de usuarios
Nossa plataforma esta em mais 15 idiomas (portugues, espanhol, chines, russo entre outros)
Big Data. It’s the buzz word of the moment. Everyone is talking about it
Big Data went from an emergent idea to a Holy-Grail solution in minutes - record time - promising everything from better use of medical records to the smarter planet we’ve been hearing about in countless TV ads
However, it seems that while the term is quite popular [CLICK]
1/3 of Brazilians don’t know what it means
This data is from a study we commissioned to examine the use and understanding of Big Data by Brazilian professionals. We surveyed over 1,000 business people 18-64 about 2 months ago and found out some interesting things
Nearly four in 10 (36) Brazilian worker are “not too” or “not at all” familiar with the term. And some of those who say they know the term actually misidentify it as a “big day."
GOOG receives over 2M search queries a minute
Consumers spend over a quarter of a million dollars online EVERY MIN
Twitter users send over 100K tweets
At SurveyMonkey, we receive 50MB of new data from customers
The Internet has become a place where massive amounts of information and data are being generated every day. This is big data
Big data isn’t just some abstract concept created by the IT crowd, but a continually growing stream of digital activity pulsating through cables and airwaves across the world
Every minute giant amounts of it are being generated from every phone, website and application across the Internet
In the last two years, humans have created 90% of all information ever created by our species. Overwhelming.
There is too much data of too many different types. One doesn’t know where to start. This is big data
So, where to do we start? Let’s break it down
Big Data. It’s the buzz word of the moment. Everyone is talking about it
Big Data went from an emergent idea to a Holy-Grail solution in minutes - record time - promising everything from better use of medical records to the smarter planet we’ve been hearing about in countless TV ads
However, it seems that while the term is quite popular [CLICK]
Implicit data is gleaned or implied
That’s why Big Data carries a darker connotation, as it’s linguistic cousin - Big Brother, Big Oil and Big Government.
Some people find big data kinda creepy. For example…
Another sign of divorce – according to another study? A switch in beer brands
So watch what your partner is drinking!
Every single piece of data available is being crunched
Predictive analytics is the hot, new job
Almost every major retailer, from grocery chains to investment banks to the U.S. Postal Service, has a “predictive analytics” department devoted to understanding not just consumers’ shopping habits, but also their personal habits, so as to more efficiently market to them
And, Brazilians say they’re ready
-Most BR workers (64) express confidence that they have the education and skills needed to compete in an increasingly data-driven economy — with 22 pct saying they are “extremely confident.”
That’s good news. Brazilian companies are going to need the help, because [CLICK]
NOTE: this is among those who are familiar with the term big data
Nearly one in four – almost a quarter – of employees say their companies are ill-prepared to handle the trend.
But, they are planning to invest
About eight in 10 (80) expect their companies make increased investments in three key areas:
purchase or build software or tools (53 US);
to hire additional employees, (79, vs. 55 US); and
collect new kinds of customer data (US 60)
This is a larger investment than what’s planned in the US where large companies are already using data to drive all different types of decisions
Large companies like Target, for example, use big data analytics to determine when its customers are at a time in their lives when they may be inclined to alter their shopping habits and preferences
And among life events, none are more important than the arrival of a baby
At that moment, new parents’ habits are more flexible than at almost any other time in their adult lives. If companies can identify pregnant shoppers, they can earn millions
Target crawled their database and identified 25 products that, when analyzed together, allowed them to assign each shopper a “pregnancy prediction” score
AND it could estimate her due date! So Target could send coupons timed to very specific stages of her pregnancy
Lots of people buy lotion, but Target noticed that women on the baby registry were buying larger quantities of unscented lotion around the beginning of their second trimester [CLICK]
VISUAL: PREGNANCY PREDICTION SCORE + VITAMINS (10)
Another analyst noted that sometime in the first 20 weeks, pregnant women loaded up on supplements like calcium, magnesium and zinc [CLICK]
VISUAL: PREGNANCY PREDICTION SCORE + VITAMINS + SOAP AND COTTON BALLS (10)
Many shoppers purchase soap and cotton balls, but when someone suddenly starts buying lots of scent-free soap and extra-big bags of cotton balls, in addition to hand sanitizers and washcloths, it signals they could be getting close to their delivery date
This is all well and good, till it goes horribly wrong
About a year into the pregnancy prediction program, a man walked into a Target outside Minneapolis and demanded to see the manager. He was clutching coupons that had been sent to his daughter, and he was angry, according to an employee who participated in the conversation
“My daughter got this in the mail!” he said. “She’s still in high school, and you’re sending her coupons for baby clothes and cribs? Are you trying to encourage her to get pregnant?”
The manager didn’t have any idea what the man was talking about. He looked at the mailer. Sure enough, it was addressed to the man’s daughter and contained advertisements for maternity clothing, nursery furniture and pictures of smiling infants. The manager apologized and then called a few days later to apologize again
On the phone, though, the father was somewhat abashed. “I had a talk with my daughter,” he said. “It turns out there’s been some activities in my house I haven’t been completely aware of. She’s due in August. I owe you an apology.”
That’s the power of implicit data. However, there is an inherent creepiness involved in this type of data collection and analysis.
Should Target know a girl is pregnant before her father?
And, with implicit data, you never know if your predictions are correct
Implicit data is derived from observing and analyzing your and others’ behavior
It’s largely based on a passive collection of people’s behaviors or habits. But it’s inherently flawed
Amazon can’t tell the difference between when Anne, a grandmother, was shopping for a birthday gift for her grandson and her regular online purchases
Amazon doesn’t know when you’re buying a book for work, or a gift for a friend. So what happens is your recommendations get polluted, and never truly reflect what you are interested in purchasing
Prof Eagle is currently working with mobile carriers, governments, developmental organizations, and public health and welfare agencies around the world to mine cell phone records for meaningful data that can be used to solve some of the world’s biggest social problems
His team looks at how people move around – thinking of people as particles
Like particle, people tend to move around within predictable boundaries/patters. When a person's radius changes, it means little. BUT, if a whole village or region changes their movement, something big has happened
In Rwanda, Eagle tried to correlate the sudden changes of movements with cholera outbreaks. In one village, Eagle and his researchers suddenly noticed a sudden shrinking of the movement ranges of its residents
They thought they had predicted a cholera outbreak, BUT what they had actually detected was a flood caused by a broken dam, which had washed out local roads, greatly constraining the local population's movement.
Should've just called one of those mobile phones and asked what was going on!
Just ask. What a novel concept! Which leads us to explicit data
A great book called Exit, Voice and Loyalty by Albert Hirshman. It gives us a framework in which to think about implicit and explicit data and the benefits of soliciting opinions from your most loyal customers. Hirshman examines decline, rather than growth, and finds that there are two basic reactions to decline: [CLICK]
VISUAL: BOOK COVER + (bubble) EXIT/VOICE (28)
Exit explain
Voice explain
And, the difference between how people choose between exit and voice is LOYALTY
Loyalty is seen in the function of retarding exit and of permitting voice to play its proper role.
The book points out that typically, people and business rely on exit – we’d call it implicit – data to identify an issue, but can rarely, on its own, counteract decline
Declining sales #s, for example
But by that time, it might be too late. Exit is a lagging indicator
Voice is your canary in a coal mine. Explicit data gives Big Data a voice
Explicit Data is just that – explicit. It’s fully revealed or expressed without vagueness or ambiguity
Leaving no question as to meaning or intent
Explicit data are insights taken directly from the individual. It is inherently more reliable
And, it removes the creepiness factor from the equation
So, why wouldn’t we use explicit data all the time?
Well, historically, explicit data is slow and expensive. And, not available at scale. It’s easy to view explicit data as a dinosaur
That’s why people – traditionally – only think of Big Data as implicit data
However, technology has changed that
The internet allows people to gather explicit data at scale. The ability to provide explicit data through a variety of platforms is empowering for consumers and enlighting for businesses
Whenever you fill out a survey, rate a business, like a brand, review a service or school, you’re contributing to explicit data
Just to give you an idea of the type of scale we’re talking about, at SurveyMonkey, in a day we receive:
70GB of new data
2M survey responses
20M questions answered
Daily! That’s big data. Explicit data at scale
And, when you can get quality, explicit data at scale, it becomes incredibly valuable. And, can tell us things that implicit data, on its own, can’t
Pre-financial crisis sales data pointed to continued success of large SUV sales
However, Ford had the foresight to make a bet on investing in smaller, more fuel efficient cars. How?
Ford directly gathered consumer feedback and used this data set IN ADDITION to sales data to develop future product design
This move was credited with saving them from having to take a government bailout money
Both implicit and explicit data are needed and work well together. They work as a check and balance against each other
If you’re looking at data in silos, or at just one of these data sets, you may identify a trend, but you will never solve a problem
Google had a problem
Google has “a sophisticated employee-data tracking program where they gain empirical certainty about every aspect of Google’s workers’ lives.”
The team was analyzing the data generated from Google’s 15K employees and found that they were loosing one group of employees faster than any other. Women
The attrition rate for women was much higher than the rest of the employees. The team then surveyed female employees and realized, they didn’t have a “woman” problem. They had a “new mom” problem
Survey results showed that new mothers found the company’s standard 12-week maternity unsatisfactory
And, Google’s 5-month maternity leave program was born. Google was able to better retain its female workers, and improve their happiness at the workplace
At SurveyMonkey, we use both implicit and explicit data to run and optimize our business.
The implicit data we analyze includes:
# of questions asked
# of daily surveys
SEM terms
Free to pd conversion
Pricing packages
Churn
And, we couple this data with surveys:
Customer satisfaction
Cancellation
Product feedback
For example, looking at our churn numbers only gets us so far in understanding why our customers leave
The majority of our customers leave because they don’t need a survey right now, but tell us that they plan to come back when they do. This helps us better predict our business
Gut feel and intuition are being replaced by data every day, but just using one type of data is dangerous
Check implicit with explicit. The feedback loop between the insights of each is most critical
Analyze your data but make sure to ask why
Implicit is the what
Explicit is the why. And, I would argue, the more important data set
It’s dangerous to not ask why. Look at RIM
Now, the last chapter of this story hasn’t been written, so we’ll see
But, sales have declined so much that it’s virtually impossible for them to recover
RIM consistently ignored Voice. People LOVE our email and will stay with us
A few customers asked for apps; They don’t need apps
RIM executives needed convincing a color screen was necessary! Who needs to read their email in color, they stated?!
Explicit data is the voice of your most loyal customers. And, more valuable than implicit data. We need to not only listen to it, but encourage it
Encourage your customers AND employees to give you feedback
Make it easier for them to tell you what they think
Issues like privacy and creepiness disappear when they’re engaged in the conversation
People feel empowered and loyal when they are recognized and have a voice.
In turn, your Big Data will find it’s voice