Posts Tagged → data strategy

Does Amazon Know What’s In Your Bank Account?

I watched a marketing webinar last night.  

It was free.  Typical of that genre, the point was to give away some information but sell you on a product or service that will give the rest of the picture.  That seems a good trade off considering their time and effort put into the creation of the webinar.  In return, learning from their experience made my time and effort watching it worthwhile.  That’s a fair market return.

Amazon knows what’s in your bank account 🙂

The presentation wasn’t interactive other than the pop up order form at the end.  So it was basically a recording, once produced it can be repeated with only the additional broadcast cost.  From what I can tell, it is played twice a day, most likely for a “limited time.”  The product being sold is more of the same information.  More extensive knowledge and examples are digitally boxed for download. Some level of customer service is implied.

For me, it potentially could be a good product.
The topic – Marketing – isn’t my strong suit, so I make an easy, albeit skeptical, target.  I might have bought it … but the price was a bit more than what I would spend.  I have a technique for shopping where if I like something enough to want to purchase it, I mentally calculate what it is worth – to me.  The process includes thinking about my bank balance, the relative “need” for the product or service, how long it will last, will it pay me in return, alternative resources that may or may not do the same things for more/less time and/or money.  The final consideration is Quality of Life  – will I be happier/faster/slower/better for having purchased it.  Whether a house or a pair of shoes, I scramble all that data to run it through the gonkulator that is my brain and contrive a cost I am willing to pay.

Then I look at the price tag.

Marketing & Economics 101

Ah price point – where supply and demand meet the wealth of nations. Wrigley made a fortune from penny sticks of gum.  Today’s $.99 ebook downloaded a million times is close to a million dollars (depending more on its one time set up cost.)  On the other end of the spectrum, the multi-million dollar yacht salesman needs 3 to hit the annual sales target.  One puts him out of business and five will hold over for another year.

Adam Smith touted that without the tethers of government interference, private monopolies, lobbyists or “privileged” entities, the free market wields the perfect price where supply and demand meet.  When banana crops fail, the price of those that make it to market goes up enough to meet whomever is willing to pay for more expensive bananas.

I’m sure he would have been spellbound by the much more elaborate threading of airline flight tickets.  An incredibly (I don’t use that word lightly) sophisticated algorithm cultures those seat prices like a mother hen, sub-minute by sub-minute searching databases and optimizing just how much the seat should be priced. Would Mr Smith’s contemplations and calculations hold under the intense smoothing of the pricing integral?  His theories have been smeared all of the earth with both creamy taste and reputed disgust.

He would’ve been further floored with the near-zero production costs of electronic products and services, although I’m sure he’d have much to say on it.  These are the elements of economic evolution that test free market capability in a global economy.

Big Data Price Point

Of course, you probably wouldn’t be here reading this unless you’re more interested in Big Data than my spending habits.  This is hardly business continuing education credit either.

But these “small” data examples are all market driven.  The future – the Big Data Price Point – is the price will shift according to your ability as much as willingness to pay.  That means the webinar product that I didn’t want to pay $497 would have been further discounted to the $247 that would have made me uncomfortable but ready to use my credit card.

How is this possible?  Supply and Demand.  For one, it is a product with almost zero production cost.  Unlike the widget  which requires materials, labor, manufacturing, storage, and distribution.  The digital warehouse doesn’t need a water supply or a janitor.  The one stored copy (and backup) replicates instantly.  There is a set-up cost; the knowledge must be conveyed creatively.  People are needed to develop the product which must be marketed and maintained.  The elegance of the digital effort minimizes the cost as the theoretical sales count grows infinitely.  This is not a scenario Mr Smith would have anticipated.

 As for demand, Big Data Price Point (PP) kicks in by knowing YOU along the same lines I mentioned that I use to make a purchase.  Big Data PP would figure out if I have enough cash or credit for the sale.  Big Data PP knows if I spend money on these types of products already.  Big Data PP knows whether this fit my spending lifestyle or if it is a reach.  Big Data PP determines if I can or cannot deduct it as a business expense.   Even more so, Big Data PP calculates whether I need deductions at this point, against how many deductions I have already accrued in my fiscal year.

Big Data PP will charge me a different amount than the next sale to a different customer.  An entity with a bigger bankroll may get charged more, or they may be offered a morphed package of sales for services I cannot afford:  X downloads, unlimited downloads, additional webinars or custom services.  

That’s not fair!

The same product is sold for different prices – because of how much I make?  Well … yah.  Want to write your Congressman or call your attorney?  Think again.

Buy now!!

Google (of course) is already doing it

And there are others.  As early as 2000, Amazon was “price testing” multiple prices for the same DVD.  They took some heat when consumers found out, so they dropped the practice (as well as the price.)  In 2005, dynamic pricing came into play again when a University of Pennsylvania study noticed prices differed according browsing history – someone who had shopped competitors would get the more competitive price.  In 2012, the Wall Street Journal reported how Orbitz ‘s offerings priced up to 30% higher for Mac users – Mac users have a higher income average.

Although the level of complexity may be surprising, we have become accustomed to cookies and their impact on our experience.  Most don’t appreciate the impact though on the bottom line in the shopping cart..  The Atlantic explains:

the immense data trail you leave behind whenever you place something in your online shopping cart or swipe your rewards card at a store register, top economists and data scientists capable of turning this information into useful price strategies, and what one tech economist calls “the ability to experiment on a scale that’s unparalleled in the history of economics.” –

The price of the headphones Google recommends may depend on how budget-conscious your web history shows you to be, one study found. For shoppers, that means price—not the one offered to you right now, but the one offered to you 20 minutes from now, or the one offered to me, or to your neighbor—may become an increasingly unknowable thing.

https://www.theatlantic.com/magazine/archive/2017/05/how-online-shopping-makes-suckers-of-us-all/521448/

I’m going to need to see your zip code, ma’am

Physical or virtual, cost of living has always been tied to location.  You can expect prices to be higher in New York City vice Greenbow, Alabama.  I’ve known friends who won’t shop the grocery stores near their house because they are reputed to have higher prices.  Then there’s the contra-pricing.  I live in a resort area and to help out those of us who “suffer” through the throngs of vacationers to live the other nine months in peace, there is “locals only” pricing.  That includes parking, some attractions and often secret pricing whispered over the phone ordering pizza or with the cashier for lattes.

Around the World

All the way back in the physical world, in Kenya most shopping is done in the very personal, one on one basis.  Outside the upscale Village Market shopping mall in Nairobi, the Masai Market meets weekly in an open lot across the street.  Advertised as an artisan fair of sorts, the goods are nevertheless likely to be found in a variety of souvenir shops anywhere in the country.  It’s the experience though, another country’s version of selling what makes them unique.

I was excited.  I was looking to find perhaps practical items to bring back home to share this lovely country’s culture.  I was accompanied by a local.  Although well-practiced at haggling from many countries around the world, I would need his native language and color to get a decent price.  Otherwise, I would get the “mzungu” price, their understandable upcharge for an American, which I deserve.  I didn’t expect to pay the same price as locals but I couldn’t afford the inflated price.  The average monthly wage in Kenya is $76.  Pricing is relative – to my location, my income, my nationality, my experience (traveler, savvy, job, education, common sense).  I should pay more but not more than what the gonkulator tells me.

In the cloud

Where you live is already calculated in your costs and the virtual world does not provide escape any more than it does anonymity.  My daughter realized a typical price difference phenomena when she went to college.  The prices she knew from shopping online at home increased noticeably on her laptop while in Washington DC.  She text a friend back home to shop simultaneously and compare.  She was shocked to find the results.  When she tried to change the zip code for delivery purposes – hoping to trick the system – the price refused to budge.  Amazon knew where she was.

Final Morph

So pricing in the virtual world has not gone into our personal pocket books yet (that we know.)  The online market does use digital information such as browsing history and location to triangulate your willingness to pay a certain price.  This is still within the Small Data genre of capability, utilizing mean and median sources.

Big Data Price Point though – and I believe it will – knows YOUR personal bottom line.  This is not a random variable calculated through the local and not so location population supply and demand.  Big Data Price Point knows exactly what price to set for you from all your transaction history in stores and online, your taxes, your job, your household status, and much more.

Is that scary?  Perhaps.  But it is already very close to possible.  

What would have happened with the Housing crash of the early 2000s? Big Data Price Point would have offered houses at rates that individuals could afford.  Would it have curbed the domino effect or accelerated it that much more?  Perhaps Big Data Price Point would have sensed the cumulative errors the isolated banks were unknowingly committing and eventually unwilling to admit.  It’s a twist again for Mr Smith’s legacy to wrap around.

The world out there is waiting to sell you the next Best Thing and Big Data or not, marketing will continue to morph to find the magic price you are willing to pay.  Big Data Price Point though will be oh-so intimately familiar with you and your money.  In the end though, Big Data Price Point can only posture the question:  will you buy?  

The answer is still up to you.

Big Data, Bird Flocks and Figuring Out World Hunger

Do you notice the flocks of birds that pass overhead?

I love watching the graceful flow of the flying inhabitants of the beach: pelicans, sandpipers, seagulls, cranes. Some are ‘regulars’ – seen day after day. Some come and go. Last week I watched an array of over 20 stork-like creatures I’ve not seen before fly by. Another favorite is the transitory flights of geese that mark the passing of time through the change of seasons. I am a far cry from being a bird watcher though. I just enjoy observing.

Rewind a couple thousand years to the pre-republic days of Rome. Bird watching was more than a hobby. The augur or auspex was a religious official who observed natural signs, especially the behavior of birds, interpreting these as an indication of divine approval or disapproval of a proposed action. He (always men) derived the gods’ intent from how the birds flew. In this highly esteemed position, the Augur watched for bird movements in the skies at specific times for signs to regard holidays or elections. They also watched in general to portend evil activity or warn of possible enemy movement. This bird observation was reading the auspices. People would consult augurs for guidance on personal matters too – from business dealings to wedding dates. Government officials consulted the auspex for holidays. Roman military campaigns would utilize augers before battle.

Murmuration from Islands & Rivers on Vimeo.

Big decisions … based on how the crow flies (figuratively)

Seems silly or crude? What do the birds know about politics, or war plans or whether this year’s crops will be fertile?

Bird traffic does provide information though.

Romans didn’t have computers or cell phones. Romans didn’t have weather forecasters; they didn’t have any way to know what weather was coming. The best they could do was look out the window or maybe across a field. How many times has that worked out for you when trying just to predict the commute home?

Bird activity does say something about current conditions in the air, water and earth. A single bird can go further and see farther than any human many times over day after day. Their action as a group signifies a coalition of instinct and knowledge. They also fly upon air current, which is driven by barometric pressure, which is result of uneven heating of the earth’s surface, which is … weather. If today we were stripped of so many data sources taken for granted, perhaps we might learn to study the signs of nature very, very, very carefully. We would want to be able to predict bad conditions, or worse – disasters.

Not Ancient History

“First news from Galveston just received by train which could get no closer to the bay shore than 6 sq mi (16 km2) where the prairie was strewn with debris and dead bodies. About 200 corpses counted from the train. Large steamship stranded 2 sq mi (5.2 km2) inland. Nothing could be seen of Galveston. Loss of life and property undoubtedly most appalling. Weather clear and bright here with gentle southeast wind.”
— G.L. Vaughan
Manager, Western Union, Houston,
in a telegram to the Chief of the U.S. Weather Bureau on the day after the hurricane, September 9, 1900

It was the early days of fall in 1900. The deadliest hurricane in US history struck Galveston Texas with little portend. The day’s weather forecasting methods did not predict the 15 foot storm surge that covered the entire island that lay at a mere 7 feet. Entire buildings pulled off their foundations and 145 mph winds ripped at whatever held above the tide. The deaths were only able to be estimated and reached 6,000-8,000.

By comparison, Hurricane Andrew struck Miami in 1992 with all the full warning of the National Hurricane Center as the mighty Category V storm hit with winds of 165 mph. Miami’s population alone was hundreds of thousands more than turn of the century Galveston, and over 1.2 million people were evacuated from Miami and surrounding counties. The result was a still unfortunate loss of life, but minimized to 65 persons.

Even by 1935, the Weather Bureau was able to send widespread warnings and Coast Guard aircraft even transited the shoreline dropping message blocks concerning an approaching storm. The effect was apparent when the most intense storm to ever hit the US travailed upon the west coast of Florida with over 185 mph winds and 18 foot storm surge. Deaths were curtailed to an amazing 465.

Obvious, and less obvious

Weather affects everyone, every day. What to wear? Need an umbrella? How about needing disaster response? That’s the direct, tangible effect. Weather also has indirect reach: how well crops grow, the cost of those crops, the economy that depends on people affording and eating those crops, the politics that influence all of those reaches.

So without telecommunications or computers or the mechanics of electricity or the knowledge of weather, perhaps studying the birds was actually pretty damn smart. The Romans had a lot of good ideas, tangibles such as roads, bridges and aqueducts that are still in use today. Their influence too is in our government, architecture, language, law, and military tactics and equipment.

Data use has been likened to searching a dark room with a penlight. The room is stacked to the ceiling with information, but we can only find what we need within the narrow confines of a very small beam. This is a great comparison to the Romans using birds. They were right, but context and content were still in the works. They did a helluva lot with what they had.

So How Does Bird Watching relate to Big Data?

Big Data gets a lot of attention. It’s not quite the reverence given the Roman augurs, but it does tend to attract believers and non-believers.

Like the augurs, Big Data is not wholly left brain activity. It is not a Newtonian equation that takes variables and outputs a product. But as Einstein first got us bending time with thought experiments about quantum capability, the Laws of Nature aren’t as solid as we think.

If we stay within the Left Brain and Newton’s confines, we will eventually be trapped there. That’s why cancer, hunger, and terrorism are still very much a part of our world. These are Big Problems that require human interaction with data in ways we haven’t figured out yet. These challenges are dynamic and non-linear. Cause and effect thinking fails.

“Chaos theory becomes critical in understanding the way things work.  We must look for flow patterns rather than linear cause-effect explanations. ”  – Jean Houston, Forward for Chaos, Creativity and Cosmic Consciousness

Our world is chaotic, not in the conversational context of pure disorder but in the scientific posture of “behavior so unpredictable as to appear random.” Chaotic study has proven things are not random as they appear; it is only our ability to perceive the patterns that emerges. That is where Big Data begins.

Unlike the augurs

Big Data is nascent capability. The tools and techniques to master its volume, velocity and variety are as yet quite experimental. The pen light has perhaps grown to searchlight proportions, but now the room has expanded into coliseum size. The beam too is not quite a surgeon’s hand but more so likened to an elephant meandering through the jungle. Strong, powerful, with significant intelligence and excellent latent memory but … not so delicate.

So there is knowledge in bird flight patterns. So there is more knowledge in the 3 Vs of Big Data. It won’t be Newton’s apple clunk on the head; it will be in the whispers and wails of the wind and our ability to interpret the direction.