The adventures of an analog engineer and digital storyteller who studies emerging networks and their impact on the great game of business.

For the past year, I’ve been focusing my research on the power of networked resources. I’ve written blog posts that have demonstrated how both companies and individuals have used digital network technologies to innovate, solve problems, or even spread marketing messages. In each case, I’ve tried to quantify the monetary value created through such access.

Last week, I experienced an “Ah-hah!” moment after reading a MarketingProfs article that demonstrated the monetary value a story can add  to cheap knickknacks. The goal of the Significant Objects experiment wasn’t to mislead. According to its rules, misleading would have voided the test. Instead, the experiment involved writing knickknack-inspired stories that future owners could appreciate and therefore pay a premium.

According to the website, the experiment “…sold $128.74 worth of thrift-store junk for $3,612.51,” simply by attaching a story to each item. Doing the math, storytelling increased the average monetary value of each object by 2,806%.

The X 28 Factor

Twenty Thirty years ago, my uncle gave me a three-inch stack of early 1900s post cards. Here’s an example, circa 1915, called “The Great White Way,” Broadway, New York City:

I’ve always known that these cards carried some monetary value. A quick glance at eBay reveals that their average price tag is about $5 per card, bringing the stack’s estimated value to about $500. However, after reading the results of the Significant Objects experiment, I considered the upside of adding a story to the collection. Would it be possible to increase the stack’s value twenty-eight fold too? Could adding a story to these old postcards increase their total value to $14,000?

Meet Elizabeth

What if I told you that the majority of these postcards were addressed to a woman named Elizabeth who lived in the greater Boston area over one hundred years ago? What if I told you that the cards contained clues to her life? For example, we know that she was married but we don’t know much about her husband. We know that she had a son named Eddie, she worked at a Boston-based Cigar Factory, and she vacationed on Peaks Island in Maine? Do any these tidbits make the postcards sound a little more interesting?

For twenty thirty years, I’d always viewed this stack of postcards as antiques to be sold-off piece-wise. However, after transcribing ninety-nine postcard messages and entering them into a spreadsheet last weekend, I saw them as something very different: a century-old mystery.

I wondered. Who was Elizabeth? What was her life like? Did she have to work in a Cigar Factory, or was it her choice? Who were Nellie, Sadie, Annie, Frank, Ella, Tom, Joe, Ruthie, Margaret, Debby, Mamie, and Kathryn? Why did so many of the messages end with “give my love to the girls?” Who were they? Her daughters? Her sisters? Something more…hmm…interesting?

Could a mystery centered around a woman who lived a century ago multiply the monetary value of these cards? Could the story become a book? A movie? Or, what if the story was less about Elizabeth and more about a quest to eventually return these cards to Elizabeth’s family? Would you watch a documentary of such a quest unfolding?

An Open Source Mystery

If we published all of these postcards online, could we harness the power of the network to help solve this mystery? Would participants from around the globe be willing to use the resources available to them to collectively piece together Elizabeth’s story, with the ultimate goal of delivering it with her postcards to her great-great (great?) grandchildren?

I’d love to hear your thoughts.

A few months ago, Southern California Edison installed a new smart meter onto my house. Those who read this blog regularly know that I love data, so I admit that my heart raced a little when I learned that my new meter could not only take up to four readings per hour, but that I could also access this information online.

It took a few months for the data-logging to begin, but I’m now in data-heaven. Here’s what one day of data looks like.

Figure 1: 24 readings daily

At first glance, this chart may not seem overly special. But, I contend that it represents a large step toward a very different world.

Consider the fact that before my smart meter, the power company could only measure my annual power consumption in twelve, monthly chunks. The new smart meter subdivided those chunks into 8,760 hourly readings, thus increasing the resolution of my power consumption picture by a factor of 730.

With the cost of sensors, processing power, wireless communications and cloud storage plummeting, we’re on the cusp of being able to measure things with unprecedented granularity.  Just as telescopes and microscopes opened access to worlds that were once invisible to the naked eye, devices like smart meters are too opening up new worlds. The finer we chop the data, the higher the resolution. The higher the resolution, the more visibility that we have.

For example, Figure 1 illustrates that my house burns 0.45 Kilowatt-hours (KWh) for each hour between 12:00 and 4:00 a.m.–a time when the Ploof household was sleeping. The only devices running at that time were a refrigerator, 60-watt porch light, clock radios, cable modem, wireless router, and other items that are in “sleep mode” (computers, DVR, etc…)

So, how much does it cost to run my house at its lowest electrical consumption level? At $0.17 per KWh, it costs 7.75 cents for every hour. Think that number is small? Try multiplying it by the 8,760 hours in a year. It costs me $657 per year just to run the house at its lowest level without anything else running!

Let’s play with this number a little more. According to the US Energy Information Administration, the average California household consumes 562KWh/month (6,744 KWh/yr.) Since my household consumed 8,168 KWh last year, let’s say that my usage is 21% higher than the average residence in my little town. Assuming that the average baseline power consumption of its 18,000 residents is 21% lower than mine, at a minimum, they consume:

  • 6,399 KWh/hour
  • 153,576 KWh per day
  • 56,055,240 KWh/year

which costs the residents $9.529 million annually. Start multiplying that number by the residents of the state or the residents of the country, and we’re talking huge numbers. By increasing the resolution of our data, we get to see the big picture in a totally different way.

And we still haven’t even come close to hitting the highest resolutions as power readings can be subdivided even more to offer insight into how much power is being consumed by individual appliances or fixtures. Such granularity will allow us to actually measure phantom load, the estimated (we don’t yet have the granularity to measure it) amount of electrical energy that is consumed by an appliance while plugged in yet switched off. In other words, most phantom load is just wasted energy.

According to Wikipedia, phantom load is estimated to be 10% of our power consumption. Using the baseline electrical consumptions calculated earlier, phantom load (with everything off) for my little town is estimated at 5.6 million KWh/year at an annual cost of $950,000.

$950,000 paid for wasted energy. Think about what the city could do with this amount of money.

Today’s technology allows us to cost-effectively measure the total power consumption of our homes on an hourly basis. It’s only a matter of time for when new technologies enable us to cost-effectively do the same thing for everything that we plug in, thus increasing the granularity to actually measure phantom load. With the ability to measure it, we’ll be able to do something about it–like automatically disconnecting appliances from the grid when they are not needed.

Throughout history, resolution enhancing devices like telescopes, microscopes and now smart meters have lead to new discoveries that shake our fundamental assumptions and ultimately lead to innovation. They allow us to measure things as opposed to estimating them. Since most businesses are built on measurements, this increased resolution offers fertile ground for the development of new products, services and ultimately businesses.

Are you ready for this new granular age?

Oct 10, 2011

Only two Major League Baseball teams won 103 regular-season games in 2002. Each couldn’t have been more different. The New York Yankees built their team the way that it had been done for over a century, by combining individual player statistics (batting averages, stolen bases, RBIs, etc.) with the instincts of talent scouts. Since the rest of the league used the same evaluation system, the resulting player-economy favored large-market teams who could afford to fill their rosters with the highest rated ballplayers.

As General Manager of the smaller-market Oakland Athletics, Billy Beane didn’t have the financial resources to pick from the top of that list. So, rather than following the herd, he created his own. Rather than agonizing over RBIs and batting averages, he looked at a player’s ability to get on base. He theorized that a team stacked with such players would statistically score more runs and as a result, they’d likely win more games than their competition. And the best part of Beane’s ranking system? Since the players that he desired were ranked so low by the herd, he could sign them at bargain basement prices.

When Beane fielded his team of “misfits” for $40 million, the herd laughed at him. But at the end of the regular season, he had the last laugh. You see, the New York Yankees had spent $125 million to win their 103 games. The Oakland Athletics spent less than one-third as much to do the very same thing.

While watching the movie, Moneyball, this past weekend, I was struck by the similarities between the baseball herd and the social media herd. When it comes to evaluating the value of social media investments, companies rely on the same herd-like tendencies.

  • Those from the advertising side of the herd see the value of social media as measured in impressions and click-through rates.
  • Those from the marketing side of the herd see the value in terms of “brand messaging/awareness.”
  • Those from the public relations side of the herd see the value in terms of influence.

A slew of analytics companies have stepped up to feed the herd. Their pretty little dashboards serve tasty, herd-favorite morsels such as impressions, click-through rates, fans, and page views. They’ve even invented new measurements such as “klout” and “engagement.”

But here’s the problem. Generic measurements mean nothing without considering the reason for them in the first place. The goal of a MLB General Manager is to field a team that wins more games than the competition. The role of corporate communications is to support a customer’s entire journey to, through, and beyond their purchasing decisions. If the metrics that your company measures don’t support that goal, then why measure them at all?

It’s time to bust away from the social media herd. If your company has been investing in social media this past year, you have a responsibility to analyze it. All of it! Look underneath the pretty dashboard data. Start at January 1st and look at every tweet, every Facebook update, and every blog post. Study every retweet, comment, and “like” that a specific piece of digital content sparked. Look for patterns. What content resonated most with customers? What content did they ignore? What was the single most valuable piece of content that helped the most customers in their moment of truth? The answers to such questions will reveal two things: the value of your efforts and a digital program road map for next year.

But let’s face it. Most companies won’t do this. Analysis is hard work, which is why so few do it. Add the fact that running with the herd is less risky, and most communicators will continue throwing more money at meaningless things.

Billy Beane could have accepted his fate as a small market GM. Instead, he changed the rules. Are you willing to change the rules? Are you willing to dig deep into YOUR data and find out what YOUR CUSTOMERS need to support their journeys to, through, and beyond their purchasing decisions? Or will you acquiesce and continue to run with the herd?

The MLB herd scoffed at Billy Beane…well…that’s right up until he won all those games.

Photo Credit: freefotouk