Reading Atlanta Analytics

All of this business about paid tools vs free tools, and dare I say the whole concept of #measure, all boils down to the fact that today, we are a tool-centric industry, often to the detriment of being an expert-centric industry. — Stop giving web analytics tools the credit YOU deserve

Atlanta Analytics is a quite interesting blog – however, there aren’t so many posts. The author, Evan LaPointe, does have some nice visions and an interesting perspective, because he comes from a finance background.
I think he makes some important points, these are:

  • It isn’t about page views or uniques – it’s about money
  • Drive actions not data
  • Be a business person not a technologist
  • Demand your share – if you increase your company’s profit by $500,000 per year, you should demand a share of it

What is web analytics?

  • Quantify today’s success and uncover usability, design, architecture, copy, product, advertising, pricing and marketing optimization that will breed even more success tomorrow
  • Web analytics isn’t:
    • WA is not the measurement of something
    • WA is not defining success but translating it
    • WA is not Omniture, Google Analytics or Clicktracks
  • Web analytics answers the following questions:
    1. Who is coming to my web site?
    2. What are they trying to do?
    3. What is the gap between what they are doing and the ideal?
    4. What are some concrete ways we can close the gaps?
    5. How can we get more of these people?
  • These answers should be answered in context of growth and profitability
  • Analyst shouldn’t become married to one discipline otherwise they are losing the big picture
  • They are central and recommendations are driven by company impact and not by personal impact
  • Even if you cannot solve a problem by yourself, you have uncovered an important problem

Three enormous wastes of your web analytics time

  1. Analytics isn’t implemented in the dev process but afterwards
  2. You care about the correct unique visitors count
  3. You are trying to match two numbers from different tools: Trends not accounting

3.5 things that keep you from finding good web analytics people

  • 1: Good WA can be in your company
  • 2: A lot of experienced WAs are actually reporting writers
  • 3: Your interview process prevents you from hiring good people: if you fear change / that your flaws will be revealed and the application is able, then you probably won’t hire them
  • 3.5: Your salary is too low: increasing your conversion rate by 0.3% can mean hundreds of thousand of dollars additional revenue per month

Web analytics sucks, and it’s nobody’s fault

This is a handmade description for yet another propellerhead analyst who will sit around and run reports for people, get in arguments with other people (or those same people), “agree to disagree” with other departments, and will eventually call everyone else an idiot and will recede into their cave before ultimately quitting for a director-level position at a different, big, resume-enhancing company where the process will repeat itself.

It’s not their fault because a good position for a web analytics person does not exist in the companies that can use these people most. The bigger the company, the more important a small difference becomes. For a site with 10,000 visits a month, an analytics person would have to improve conversion by double-digit percentages to scarcely pay for themselves. For Wal Mart, moving the conversion needle a tenth of a percent probably pays their lifetime salary in a week

The effective web analytics person knows usability, they know some design, they know information architecture, they know HTML, they are good communicators and can thusly write good web copy, and ultimately they are businesspeople who realize the purpose behind all of these crafts is cash flow […] Rather than being careful, politically aware employees, effective analytics people are data-driven, quickdraw decision makers because they have two key assets:

1. Cold, hard facts in the form of data (and I don’t mean just Omniture data)
2. The ability to not have to decide: they can TEST

Big companies are ruled by coalitions of opinions, meetings, conference calls, and semi-educated executives. Data is actually a threat. Data is what gets people fired in big companies, not what gets them bonuses. Data is scary.

What are the REAL web analytics tools?

  • Question: How can you improve the long-term cash flow?
  • Where you need a decent degree of competency:
    • Usability
    • Information Architecture
    • SEO
    • Web marketing (PPC, display, email)
    • Social Media
    • Design
    • Copywriting
    • Website technology (HTML, CSS, SQL, JS, PHP/Ruby/Python/whatever)
    • Communication skills
  • Learn business goals -> department goals -> campaign goals -> personal goals

Have you lost faith in web analytics?

  • Make decisions as often as possible – aka fail faster
  • It isn’t about the newest technology – it’s about money
  • Don’t live in a vacuum – interact with different people and viewpoints

The purpose of web (or any) analytics

  • “We talk about being data-driven businesses. But these aren’t businesses built around a culture of measurement. They’re built around a culture of accountability.”
  • “The purpose of web analytics, or any analytics, is to give your organization the confidence needed to accelerate the pace of decisions.”
  • “We’re talking about being accountable to outcomes, not to some Tyrannosaurus on a power trip. That’s a big deal.”
  • “It’s about making big decisions often.” – Iterate, iterate, iterate

#11/25: Principles of Marketing Engineering

Marketing Engineering: A systematic approach to harness data and knowledge to drive effective marketing decision making and implementation through a technology-enabled and model-supported decision process.

Market response models

  • Inputs: actions that the marketer can control + environment
  • Response model
  • Objectives: monitor/measure, etc.

Value = Benefits – Price

  • Benefits
    • function (does the job)
    • psychological (status)
    • economic (saves money)
  • Price
    • Monetary
    • Perceived risk
    • Inconvenience

Customer Need

  • subject and importance of need
  • temporal aspects of need (urgency, frequency and duration)

Measuring Customer Value

  • Objective Customer Value
    • Internal engineering assessment (internal estimate)
    • Indirect survey questions (pay for better quality, etc.)
    • Field value-in-use assessment (economic benefit): Not only marginal costs but initial investment, etc.
  • Perceptual Customer Value
    • Focus groups
    • Direct survey
    • Importance ratings
    • Conjoint analysis
    • Benchmarking
  • Behavioral Customer Value
    • Choice Models
    • Statistical models / Data mining

Segmentation -> Targeting -> Positioning

  • Creating
    • how does the segmentation fit into the strategy
    • which variables can be used
    • exclusive segments?
    • how many segments?
  • Traditional segmentation
    • reduce data (PCA)
    • develop measure of association
    • identify and remove outliers
    • form segments (cluster analysis): are they clear and robust?
    • profile segments & interpret

Positioning

  • Attribute-based perceptual maps
    • identify other products & attributes
    • get data from questionnaires
    • reduce
    • plot
  • Preference Maps
    • weights for attributes
  • Joint-Space Maps

Translating Preference to Choice

  • first choice rule: infrequent, expensive
  • Share of preference rule: often, cheap, etc.

For [target segment], the [offering] is [positioning claim], because [single most important support].

Forecasting

  • Judgmental Methods
    • Sales force composite estimates: let the sales force forecast
    • Jury of executive opinion: different stakeholders
    • Delphi method: anonymous and iterative
    • Chain ratio method: split up in its factors

Conjoint study

  • select attributes of product
  • develop bundles
  • do survey
  • segment customers based o part-worth function
  • design market simulation
  • select choice rule
  • adj market shares

Top 10 Lessons

  • Marking Engineering is Marketing
  • ME is a means to an end
  • frames the opportunity costs for alt. actions
  • requires judgment
  • whole greater than the sum of its parts
  • data & info do not automatically result in value
  • rapid prototyping
  • every model has its downside
  • ME requires lifelong learning
  • be a coach rather than a teacher

Insights for better Implementation

  • Be opportunistic
  • Start Simple; Keep it Simple
  • Work backward
  • score inexpensive victories
  • develop a program, not just a project

I really liked Principles of Marketing Engineering. The book gives a great overview over the topic and different approaches. It’s written like a textbook, so it can be a bit lengthy in parts but otherwise, a nice book.

#10/25: Mean genes


I picked Mean genes up because a book about behavioral economics recommended it. Let’s see what we can learn from it.

Money / Food

The !Kung San’s behavior provides the clue to resolving the paradox between Americans’ chronic undersaving and the strong evolutionary pressure to prepare for lean times. In a world without refrigerators or banks, preparing for hard times means eating enough food to store some fat on your body.

Many of us would survive, albeit unpleasantly, for more than two months without a single morsel of food.

In this context our genes work quite well. We consume as much as we can and are prepared for hard times. Luckily, however, is that they aren’t so common anymore in the developed world.

Can people really change such firmly entrenched behaviors? Absolutely. The truth is, they’re not even that firmly entrenched.

One method for limiting overspending is to pay in cash rather than with debit. However, the cure to limit overeating is not not eating enough.

The authors describe an experiment in a Biodome where people weren’t allowed to eat enough. This are the results:

On their sparse diets, the Biodomers also argued constantly, go into ugly food spats, and frequently squabbled over dinner portions. After leaving what they dubbed “the hunger dome”, one of the eight said, “If we ever all start talking to each other, that would be a major accomplishment.”

You probably experienced something like that if you are hungry. You are less happy, tense and short-tempered. How about our ancestors?

Of these types, who has the biggest surplus of energy stored in their thighs and buttocks when food is scarce? Who weathers the famine with calories left over for reproducing? Who is most likely to be your ancestor? Fatties, fatties, and fatties again.

What can you do to lose weight?

As expected, some of the new drugs work and others fail, but here’s the strangest finding: people in the placebo groups always lose weight. […] But here’s the trick: while those who take the placebo aren’t using drugs, they are keeping track of their weight and are more aware of what they eat than usual.

Just like in business: You can’t control what you don’t measure. And even just measuring something helps making it more prevalent. You can see the same behavior if people start looking at their energy consumption.

Laziness is good for most animals. To understand this, we have to leave our couches and think like wild primates. Energy in the form of food is hard to obtain, and once gotten, not to be squandered.

Let’s rejoin our mice who hate running. While it’s hard to get them to jog frivolously, they do love a good run under the right conditions. For example, if they are hungry, they spend a good part of the day running. Why? Well, among other things they are looking for food.

Laziness is good because you don’t have to burn energy but using energy is good if this gets you more food.

The subjects were also asked to keep diaries of all their eating on the days around the cookie-fest. Those in the Nutrasweet group [fake sweetener] ate more than those who ate the sugar cookies. So much more, in fact, that the total caloric intake of the two groups was identical. Moreover, those in the Nutrasweet group preferentially ate more sugar.

This is an interesting observation. It seems that, at least for sugar, we eat at much as we need.

Drugs

Isabella’s messed-up enzyme is called aldehyde dehydrogenase, and fully half of Asian people have the same genetic mistake. But hold on. Perhaps we ought to call this mistake a molecular godsend. In a study of thirteen hundred alcoholics in Japan, guess how many were fast-flushers? Not one.

Is addiction in our genes?

While genes have thus been shown to play a role in smoking, drinking, and the use of other drugs, we have clear evidence that genetic factors are not the whole story. Identical twins show similar — but not identical — propensities for drug use. IF a person has a problem with alcohol, and identical twin is 25% – 40% more likely than a fraternal twin to exhibit the same behavior.

Not completely, but the best strategy is not to try drugs. Especially, if you have a family history of addiction, you should avoid them as best as you can.

There’s a drug named Anatabuse which helps fight alcoholism. Does it work?

Anatabuse seems perfectly designed to foil alcoholism. Most studies conclude, however, that it is of minimal help in treating alcoholism. How can that be? Take a look in the user’s bedroom or garbage or toilet. Stories abound of alcoholics who flush their daily pill down the toilet or “”cheek” it, only to deposit it later.

Again. Even with medicine helping fight addiction, the addiction is hard to fight.

Gambling

Imagine two types of humans, those who cowered in their caves and those who explored new areas. While many of the risk-takers died, those who gambled and won populated the entire globe.

Not only were our ancestors risk-takers, it also has an other effect:

Risky behavior stimulates the dopamine reward systems. Some people are born with systems that muffle the buzz they get from taking risks. […]

Our overconfidence even allows people to believe they might win the lottery. By creating such unrealistic beliefs, our genes goad us into taking greater risks than we might otherwise choose.

A common use of this information is the following:

Publisher’s Clearinghouse struggled for many years until it decided that instead of rewarding many entrants with medium-sized prizes, it would offer great prizes with tiny odds. As one executive recounts, “People don’t care about the odds, only the prizes.”

Happiness

When it comes to happiness, the most common story is that happy children grow up to be happy adolescents who then become happy adults. The best way to ensure that we’ll be surrounded by upbeat people in the future is to be friends with happy people now.

As you enter the crowded scene, you must choose one of two lines. The first choice is an hour’s wait in a short line that movies slowly. [..] The second choice is an hour’s wait in a long line that moves quickly. […]
Which line would you prefer? For most people, the second line is much better.

Today, most super markets and other places with waiting lines implement this strategy. People like making progress.

We can start by recognizing the quirky ways our brain creates happiness and then capitalize on that knowledge. There are three important features of our genetic system. First, absolute levels have little effect on happiness. Second, we love making progress. Third, expectations play a central role.

Expectations are indeed important. For example, wine tastes are influenced by expectations. The same works for Cola vs. Pepsi. The best you can do is under-promise and over-deliver. Technically, you should read the lowest ratings of a movie only, then you won’t be disappointed or maybe even astonished how good the movie was.

Mean genes was okay. It’s written as a popular science book and lacks deeper insights. Furthermore, there are no notes in the book, only on their website. It’s a nice read but that’s it.

#9/25: Introduction to Applied Bayesian Statistics and Estimation for Social Scientists


This will be a super short review, mainly because most of the content of this book are formulas. The book starts off with a repetition of basic statistical concepts like random variables, ML estimates etc. Afterwards, Lynch presents the basics of Bayesian statistics. He softly introduces Gibbs sampling and shows why Metropolis-Hasting is useful. Afterwards, Lynch shows different methods to check the model fit, like trace plots. In the last sections he shows how to construct and estimate linear Bayesian models and GLMs. In the last section he shows the power of Bayesian models in hierarchical modeling and introduces you to WinBUGS.

Lynch explains everything in detail and even without advanced statistics knowledge, you can work through this book. It’s really focused on social scientists but approachable for other people. There are several examples and one which impressed me the most was the following:
He took polling data from the Bush vs. Kerry presidential campaign. The polls said that Kerry was leading with 50% against Bush’s 46%. The CI, however, was +/- 3%p, so not statistical significant.
With Bayesian methods, you model the probability distribution that Kerry wins. You will still come to the same result but you can also check other things, like P(Kerry wins < 0.5) which is just about 32%. Later he shows different methodologies to model this distribution, like incorporating past data.Introduction to Applied Bayesian Statistics and Estimation for Social Scientists helped, at least me, to understand Bayesian statistics in more depth and why things like Gibbs sampling and MH algorithm are necessary. If you are interested in Bayesian statistics and like application, than this book is for you!