Economic indicators snapshot, July 2014

Every now and again, something bugs me, a little voice in the back of my head that says, “Go take a look at some other data sources to see a bigger picture”. This stems from my years in financial services, where every chart held the potential to be the insight you were looking for to get an edge. That little voice comes and goes – sometimes, I’m so immersed in the world of marketing that it’s quieted. Other times, when I have some breathing room and thinking time (like on long holiday weekends), the voice reasserts itself.

I decided to listen to the voice and check out the landscape. Let’s see what the financial runes tell us.

On the one hand, initial jobless claims appear to be near a bottom. All other things being equal, this is generally a good thing:

Initial_Claims_-_FRED_-_St__Louis_Fed

So why doesn’t it feel so good when you head out into the real world, when you walk down the streets of your town?

Alternate_Unemployment_Charts

Part of the reason may be because there are a lot more people who are not fully utilized. The official unemployment rate is closing in on 5%, but the U-6 measure of everyone who isn’t being used to their fullest capacity (and thus not hitting their fullest earning potential) is still significantly higher, around 12%. If you look at pre-1984 long-term discouraged workers (people who are no longer counted anywhere), the number of people who aren’t doing as well as they could be is nearly 23%.

Then there’s the other side: the expenses. There are two semi-permanent invisible taxes on us right now (at least in America):

Europe_Brent_Spot_Price_FOB__Dollars_per_Barrel_

That’s oil. Brent Crude has been hovering over $100 a barrel for more than 3 years. Generally speaking, take Brent Crude and divide it by 4 and you get retail gas prices, which have indeed been in the $3.50 – $4.00 / gallon range for quite some time. That’s an invisible tax on everyone who drives or rides to work, and an invisible tax in the form of price boosts to everything that requires petroleum products to be made or transported.

Here’s the second invisible tax, a side consequence of the persistently high oil prices:

Rough_Rice_Monthly_Commodity_Futures_Price_Chart___CBOT

That’s the price of rice, rough rice by the Chicago Board of Trade, the CBOT. The price of one of the grains most eaten in the world has been consistently high for about the same period as Brent Crude. When the price of food goes up, it imposes another invisible tax on your wallet. It’s not just rice, either:

Commodity_Food_and_Beverage_Price_Index_-_Monthly_Price_-_Commodity_Prices_-_Price_Charts__Data__and_News_-_IndexMundi

That’s all food and beverage commodity prices. Between energy and food, life is more expensive.

These invisible taxes impact our ability to buy stuff, as shown here:

Real_disposable_personal_income__Per_capita_-_FRED_-_St__Louis_Fed

Real disposable income is leveling out, and has been for years. If you did a basic trend line from 1990 to 2005 and extended it to 2014, real disposable personal income should be about $3,000 more per person than it currently is. Those invisible taxes are taking their toll.

Don’t forget about real taxes, too:

Personal_current_taxes_-_FRED_-_St__Louis_Fed

Food and energy are eating the consumer from the top, while taxes are eating the consumer from the bottom. With this much chewing up the wallet of the average consumer, it’s no wonder other indicators are starting to look soft. People just don’t have the money to buy stuff like they used to. For example, houses:

Housing_Starts__Total__New_Privately_Owned_Housing_Units_Started_-_FRED_-_St__Louis_Fed

Housing starts are still recovering from multi-decade lows. The last time the housing market was this soft was in the early 1990s.

The other place the wallet’s weakness is showing up is in the Baltic Dry Index (BDI). For those who are new, BDI is the cost to ship stuff by sea on big container ships. It’s a good leading indicator because companies don’t buy up space on container ships unless there’s product to actually ship. What we see here is that BDI has been soft and remains soft. In fact, BDI is on the decline right now, which means the economy overall might be stalling.

BDIY_Chart_-_Baltic_Dry_Index_-_Bloomberg

The only saving grace in all of this is if you’re a B2B marketer. Corporate profits are at an all-time high, so your job as a B2B marketer is probably safer than most:

Corporate_Profits_After_Tax__without_IVA_and_CCAdj__-_FRED_-_St__Louis_Fed

What picture do all of these indicators paint? If you’re looking to the consumer for growth, it’s probably not going to happen for a long time. If you’re a B2C marketer, chances are things have been tougher than normal the past few years, and there’s no sign of pressure being released on the consumer. If you’re a B2B marketer, chances are you’re doing better than your colleagues on the B2C side of the house.

Keep an eye on BDI if you’re a B2C marketer especially! It’ll tell you about the upcoming holiday season and how weak or strong the consumer is likely to be. Right now, things are not looking great for a strong 2014 close for consumer B2C.


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Cherry picking your marketing data

Over the holiday weekend, I had a chance to bring a statistics aphorism to life, as I went cherry picking at a local farm. If you’re unfamiliar with the expression, cherry-picking one’s data means selecting only those case studies or data points that reinforce your point, while ignoring the rest. This expression never made a ton of sense to me until I actually went cherry picking.

IMG_9194Believe it or not, half of these cherries aren’t ready to eat.

Here’s why it now makes sense. Cherry trees have a wide, wide spectrum of fruit ripeness. At any given time, on any given tree that is in season, about 5% or so of the cherries will be picture-perfect, ready to pick and eat. About 20% are reasonably close to ripe, but might need a few more days to mature. 5% or so will be past ripeness and on the way to rotten. 10% will inevitably be partially eaten by pests. The remainder will be in various stages of ripening but nowhere near ready to eat.

From a statistical perspective, if you wanted a true understanding of a tree’s ripeness, you’d randomly pick cherries from it and get a wide selection of cherries at various stages of ripeness. If, however, you wanted a more practical, more useful harvest, you’d only pick the ones that were ripe or near ripe, even though your harvest would be statistically non-representative of the tree as a whole.

Cherry picking one’s data isn’t universally bad, however. It’s bad if what you’re after is statistically representative data. It’s good if you only want to look at certain pieces of data. For example, while understanding where your entire marketing database is in terms of readiness to purchase is important, cherry-picking only those prospects who are close to buying or ready to buy makes logical sense from a resource management perspective. You want your sales and marketing efforts to focus first on those opportunities that are most ripe before they cross into overripe (and likely buy from someone else).

Understanding what your end goal is – statistically valid representation or the best of the best – will help you to understand whether cherry-picking your data is a bad or good choice.


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8 easy steps to finding returning visitors in Google Analytics

One of the most important metrics in web analytics is the number of returning visitors to your site. This simple number tells you just how well your site is really doing; after all, it’s easy to get someone to visit your site once. You can run ads, engage on social media, run a great PR campaign etc. It’s harder to get them to come back – for that to happen, you have to be providing some reason for them to want to return. Your content has to be compelling, your site has to be navigable, your value must be strong enough to make a visitor choose you over something else they could be reading.

Yet in most web analytics packages, this simple number is hidden away. Here’s how to find it in Google Analytics, the most popular web analytics package.

First, go to your Audience menu [1]. Then choose New Segment from the Segment Navigator [2]:

Audience_Overview_-_Google_Analytics

Next, choose System segments [3] and uncheck All Sessions [4]:

Audience_Overview_-_Google_Analytics

Scroll down until you find Returning Users [5]. Click it to turn it on, then click the blue Apply button [6]:

Audience_Overview_-_Google_Analytics

Now for clarity’s sake, adjust the date to be the last 3 months or so [7] and change the view to weekly so that it smoothes out the graph enough to see a trend [8]:

Audience_Overview_-_Google_Analytics

With these 8 steps, you should now see whether your site is working better or worse for you:

Audience_Overview_-_Google_Analytics

If the line isn’t going up and to the right, you have a retention problem. You have a stickiness problem. You may have a navigation or content quality problem. Once you know this, once you know whether your site is getting people to come back or not, you can begin testing and deeper analysis to determine why your site isn’t working.

If the line is going up and to the right, then you can dig deeper into your analytics to find out why. You can look at things like bounce rates, time on page, which pages are most popular, etc. and play to their strengths.

Try this out and see how sticky your site is!


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