Occam’s Razor by Avinash Kaushik

There is perhaps no challenge greater than tracking offline impact of your online presence (campaigns or other activity). It is perhaps one of the last few complex nuts left to crack.

Why? Because it is hard. Not impossible. Just hard. And for now it is equal parts quantitative, qualitative and faith.

This post bravely attempts to:

1] Highlight the importance of holistic multichannel analytics

2] Outline why online to offline tracking is a difficult exercise, atleast for now

3] Provide you with a bushel of specific multichannel measurement ideas to help quantify the offline impact of your online presence

It’s a tall order, but after two years of blogging why stop now. : )

Why should you care about measuring multichannel impact?

You have a delightful website, it is chugging along merrily at a 1.7% conversion rate (average as reported by shop.org) and you are dutifully reporting our revenue of $1 million as a result.

Happiness?

No.

While you might be doing great in terms of direct revenue impact of your website, pause and consider what in God’s name is happening to that other 98.3% “unconverted” traffic on your site?

It is quite likely that your website is delivering some value to that 98.3%, how about quantifying it? Ok ok some of them puked and bounced. But the rest probably got what they were there for and your website was helping them by doing all those “jobs” as well, along with pure ecommerce.

Your ecommerce website is helping people who will only do research online and then buy stuff in a store. Its job is also to provide information to people who want to learn and then call your phone center to buy.

Or just submit a lead and have you call them. Or there are people who will get their tech support question answered and as a result not call you on the phone (saving you $50 it costs you to answer the phone). Or help someone learn about your company and then apply for a job (saving you $2,500 in recruiting cost per person!). Or. . . .

There are many jobs your website is doing, it is your job to measure the holistic impact. In blue below is the typical direct conversion impact and in red it a very large area that we almost always ignore quantifying. . . .

It is quite likely that by the time you quantify the impact of your site on the 98.3% Visitors it turns out that the site delivered $4 million in value (which dwarfs the $1 million in direct value).

That’s why multichannel analytics is so important.

You struggle to get budget to hire Analysts or Marketers. Your head is sore from banging it to get funding for a new CMS. Your hair is thinning from repeatedly trying to get funding for online campaigns. Ever thought that you might be making a case from the wrong base ($1 million impact)?

The effort you invest in measuring offline impact will finally help your company understand how valuable the online channel is (or not!). Do it.

Framing the online to offline “data” problem:

Why is quantifying offline impact such a problem?

Two words: Primary Key.

In English: We simply don’t have a way of joining the online data to offline data.

What do I mean by that (to those of you who are not database geeks)?

Here’s your online data:

You are capturing the above information using your web log based or javascript tag based web analytics tool. Name, a unique persistent customer id (Geek ID), the keyword the person came on and their underwear size. Well just assume. :)

You also work at a large retailer that sells stuff cheap and when people purchase at your big box store you collect this information:

A unique persistent customer id (which the customer kindly forks over), the product purchased by the customer and the store at which they purchased it.

The two tables above have something in common, a primary key (Geek ID).

This means we can join the two tables with simple sql and understand which online shoppers ended up making a purchase offline (and what keyword they used to arrive on your site):

Boom! Nirvana!!

You know exactly how much online contributed to offline sales, you know how to optimize your online campaigns (buy Apple iPod terms to increase Microsoft Zune sales!!), and if you want to predict (after collecting enough data) if underwear size has a causal impact on what digital music player people purchase.

All this made possible by a simple thing, yes the Geek ID, the primary key.

The problem? [Cue sound of a balloon deflating...]

Usually no such thing exists in the real world.

For almost all the websites today the data that is collected is unique to online only, it is non-PII (personally identifiable information) and anonymous. When people visit our stores, call our phone centers etc, and ring up at our registers they give us their credit card and their names etc but not, as an example, their unique persistent cookie id.

There are small exceptions, like banks where your offline data can be tied to your online behavior using the primary key of bank_account_id.

But usually: No cookie_id = no primary key = no soup for you!

Still some web analytics vendors and consultants are fond of saying, “Yes we can track everything, online and offline and underwear sizes, and you won’t have to lift a finger!”.

Next time you hear that ask them in a sweet voice: “What is the primary key you use to join the two online and offline data?”.

Get your surprised look ready! : )

But. . . . hope :

The path to hard quantification between online to offline is littered with obstacles, for now, but it does not mean you can’t track anything at all (unlike say your TV campaigns!). The current obstacles simply mean that you’ll have to get a little creative, be a bit thoughtful and show a willingness to make a few leaps of faith.

If you are willing to create a small portfolio of initiatives then you can get a pretty decent understanding of the hidden impact (offline) of your web presence. Pick a few different correlating data points and you will be surprised as to how far you can get in this game!

In the longer term in corporations data won’t be quite as siloed as it is today, maybe consumers will be willing to share more of their PII data with companies (though I admit I am not budging with my privacy settings!), or perhaps magically we will have the primary key we need.

Either way it might be less of an issue in a few years, be hopeful.

But also be pragmatic, about how much, how accurate and when. Right now, yes today, try some (or all) of the things immediately below and make progress.

Tips for measuring offline impact (”conversions) of your online channel:

Some of these you might have heard of before, others might be new to you. New or not, with each my hope is to provide bonus tips and ideas that I think will be new to you. Let me know if you find that to be the case. Here we go. . . .

#1: Track your online store locator, directions, other direct offline dimensions.

I can’t believe how easy this one is and often people don’t value it. You have stores, you are smart enough not to hide your store locator on your site, you have integrated with Google Maps and hence now it is time to track usage of the store locator as a proxy for driving people to your stores.

First thing to do is track how many Unique Visitors (or Visits if you are so inclined, Matt!) are using the store locator in a give time period.

It is not that hard, check out the Omniture report for the above url and bam (!), a hint of offline value delivered by the site.

Bonus Tip :

Oh and if you want to get a smidgen more value, configure the store locater search parameter in your internal site search and bam bam (!!) you have a bit more data (what geographical areas have people that have a higher tendency to use our website). In the case of walmart.com (above) that would be the “sfsearch_zip =94043″ parameter in the url. You boss will be impressed with this additional set of insights.

But wait there is more. A little more. This guy….

If people use the maps and directions feature in your store locator feature then they are showing a higher intent to visit the store (else they would have bounced long ago!). Track it!

After I go through the pain of typing my address, doing next, next, and getting the directions I end up here and. . . .

. . . . and you have one more page in Omniture you can configure to compute customer offline intent (THDStoreFinderDirections baby!).

See how easy that was, a medium sized intent by measuring usage of maps and a strong sized intent by measuring customer offline intent.

Bonus Tip :

But don’t stop there. You have already identified these two buckets (location, directions) in Omniture (or in Google Analytics!), that took ten seconds. Now segment the Unique Visitors (or Visits) that display offline intent by referring urls or by email campaigns you are running or by search keywords or affiliates traffic or …. the list is nearly endless (a very good thing).

You now understand what online activity you are doing in terms of acquisition that is driving the kind of people to your site who are displaying a strong intent to visit your stores. Happy birthday!

#2: Use unique 800 (toll free) numbers on the website.

1-800 numbers are so cheap now that our local phone company sends us a free one when we sign up for a home phone line. Each person who calls us on that number is something like 7 cents. Really not a big deal.

So on your website use a unique phone number that is not available through any other channel.

Now track the calls to that 800 (or whatever) number via your phone (PBX) system and you have another signal for the calls driven by your website.

This is very effective for many kinds of websites, be they ecommerce or technical support or lead generation or whatever else is your quest in life. :)

On your website if your phone number is well hidden (like on most tech support websites, #$%@*&!), then using IndexTools or ClickTracks track the views of that page. Gives you something to correlate to your call center (PBX) data.

Bonus Tip :

Are you using unique phone numbers for your Paid Search landing pages? A quick Google search will yield a ton of companies that will put a unique phone number on all your landing pages and will only charge you when people call that number and will give you (in some cases) the data you need to track offline conversion information (by every single keyword / campaign!).

If you are spending “a lot” on AdWords and AdCenter, this can be a excellent add on to track conversions offline due to your Search Engine Marketing (SEM) campaigns. Obviously it would also work for all other kinds of online marketing you are doing and allow a very deep level of accountability.

Bonus Tip :

For a extra cute level of tracking why not let your phone system “ping” your web analytics tool so that you can track the phone calls generated by your website directly in WebTrends or CoreMetrics or Google Analytics?

A quick “hidden page”, a small chq to Mongoose Metrics, add campaigns tags, and now every time someone calls that number it pings your site, the phone call data shows up in your web analytics tool. Nice ain’t it? Check out: Offline Phone Call Tracking With Web Analytics Integration.

So you see there are atleast two simple things you can do that will greatly extend what you can do with good old phone number, needs a bit more elbow grease but nothing worth having in life is easy. Right?

#3: Use unique coupons, offers, promotions online.

Not that hard. Use unique prices, promotions, coupons etc in your online marketing and then track the redemption of these through your offline (phone or retail) channels.

[Yes I am using the Starbucks coupon as the example, yes it is ironic.]

Regardless of the redemption rates of coupons, many businesses find them to be very effective at tracking conversions offline. Not all online offers have to be fiascos (like Starbucks) and if they offer something partly of value they can be great at driving store traffic.

That last part above is important, something of value.

My peer Rene at OX2 showed a great case study with their client Panos, it shows measureability of coupons. Here is a picture from his excellent post :

[The emphasis with colors is mine.]

They measured “impressions”, “interactions”, “impact” and “income”. And they measured “fraud”! Not too shabby.

Of course you are also executing the oldest trick on the book: Send *unique* (single use) coupon codes / promotions to the relevant people on your email mailing lists and then track redemption of those coupons on your website, call centers and stores. For one of my clients the insights were astounding. The website conversion view was just a third of the picture! But what was most amazing, and it was amazing (!), was understanding the channel preferences of our customers. For example the young ‘uns went to the store and those born earlier preferred the website. What was up with that?

That example just shows how you can not only measure the complete conversion picture but also take a stab at understanding customer behaviour (remember for email lists you likely have their locations and demographic info and history and all that!). Try it, it is a lot of fun.

Bonus Tip :

Many companies are now targeting their offers and coupons to just Visitors with repeat visits or people who are in certain geographic location or for just certain products or at just certain hours (day parting!). All these methods provide excellent options when it comes to then measuring the value of online traffic (because rather than the generic catch all, the impact is siloed and makes it easier to assign attribution).

So you don’t want to give money off, that’s ok. Why not do a soft launch of a particular product, only “announce” it online, and only offer it for a limited time to people who visit your site from certain Geography (or campaigns). See if they then go buy the product in the stores. A great example of promotions vs coupons.

Or Tweet the promotion and see if people rush to the store. How hard was that?

#4: Marry / mine online and offline data.

It took this long to get to this “basic” idea, its because it is not that basic and is usually available only to a fraction of the companies out there.

This marriage can be tough to accomplish, but is totally worth it and amazing.

In our non-line world, example of could be Lucky or Safeway supermarkets. They both offer the ability to create an account online, they ask for your “club card” (the one you use to get discounts) when you set it up.

Now when you visit the website repeatedly (and of course get the actual physical stuff in the store, still no “virtual food” to keep you alive!) they collect data on you that they can, with a small effort, tie to your offline behavior.

Another great example of this what WalMart is doing. They offer 100% ecommerce but they allow the option of home delivery or pick up in the store. Sounds convenient.

But the sweet thing WalMart is able to do is not only track that you picked up the item in the store, but they are also able to track (even though it is a separate transaction!) all the other “stuff” (chewing gum, nachos, 39″ tv) that you picked up when you were in the store.

That is a excellent way to quantify the incremental lift that the online presence is driving in the stores.

Is your business measuring the chewing gum effect?

Bonus Tip :

Another thing that we did was to marry up the data for those Visitors who purchase online with their past historical data that we had in the company. In our business people would buy or upgrade each year (or every other year). Having their order data (name, address etc) from the online data store merged with the offline data store (which also had name, address etc) allowed a richer understanding of “channel shift” and other macro patterns.

#5: Leverage onexit online surveys (or Point Of Sale surveys)!

Enough of all this quant stuff, let’s truly use web analytics 2.0 !

Why not just ask the people (on your site and in your store) if your web presence had a net positive impact on them?

Just ask.

And they will tell you. :) No guesses.

You have a onexit survey on your website? No? Yes?

Well if you don’t then get one (4Q is free and a great start.) If you do then add a question to the survey, the question will measure “likelihood to buy offline” or “likelihood to visit a store” or . . . you get the point. . . .

All survey providers will give you this option in a heart beat. iPerceptions, crmmetrix, Foresee, WebIQ etc. In two heart beats (ok a couple weeks) you’ll have data like the picture above. Real multichannel impact of your website as rated by the people you are trying to impact: your customers .

Another great idea is what I see at CircuitCity stores. They use point of sale surveys, what a novel idea! When you purchase a something at a Circuit City store you will notice you get a long receipt. That’s because at the end of that receipt there is a request for you to fill out a online survey at www.bizrate.com.

The goal of the survey is to understand your shopping experience and various attributes that lead to that purchase, including how the website might have played a role.

The results are there for people to see on the website, but more importantly the detailed survey analysis (including open text VOC) that BizRate hopefully provides are the real gold mine of actionable insights. And yes it will help you quantify the value of not just the online channel in terms of driving traffic to the store but also if it was more qualified traffic or better educated or ways in which your site might be failing your store shoppers.

Bonus Tip :

Trend and segment. Really.

The true value of running surveys as a listening mechanism is that you don’t treat them as one night stands. There is some fun in that, but meaning is only created by longer term engagements. So have this as a continuous listening methodology, always on (and remember you can sample a small random number of website visitors and that is enough/ok).

This allows you to trend data over time and see how the website is getting better or worse for those (a majority!) who will not buy / transact online. You can also then isolate evolving needs of your customers and impacts of seasonality and other such factors.

And I’ll stress segmentation again. You are asking the Primary Purpose question already (”why are you here”), now segment likelihood to buy offline by those tasks - are you solving for one group of visitors better than others? Segment by products people might have looked at, segment by new or returning visitors, segment by campaign traffic and direct traffic and on and on.

Seems like some amount of work, but if you are asking the right questions in your onexit survey this is really easy to do, and the best of all your survey company will do all the work. So… do it, rather make them do it. : )

#6: Conduct controlled experiments.

This is a technique our peers in the offline world have been using for time immemorial, and we in the “advanced world” seem to be totally ignorant to it. Quite sad, because it can be awesomely cool.

What is it?

Do you notice why you can buy some products in your favorite store but when you are on vacation and visit five other stores in different places you notice that they all have a slightly different selection.

Or when I first came to California I noticed that in Mountain View they had a fast food outlet called Border Bell. It strangely looked and felt like Taco Bell but the selection was different, they served fresh salsa (three kinds!) and I loved it. But I could not find Border Bell any place else.

Both are examples of companies running controlled experiments to validate their ideas in the real world by running experiments (sadly Border Bell did not make the cut, it is now a Taco Bell).

Take that as your inspiration (not the failure of Border Bell part, the controlled experimentation part).

You want to see if your website can drive traffic to your stores? Do a email campaign to your customers in Florida, Iowa and Oregon, drive them to your site to learn more, and see if that causes a lift in store visits to Costco (your co-branded partner).

Or run PPC campaigns that are geo targeted to deliver a certain message / call to action on www.google.com to potential customers in California, Michigan and Georgia. Measure impact on your site, correlate it with any signal you pick up in your call center.

Or for a week don’t send newspaper inserts (yes those things that go directly into recycle bins) in Arizona, New York and Indiana and run banner ad campaigns on related sites and drive people to your website to look at relevant and unique campaigns in their zip codes. It’s an experiment to see if you can drive the right kind of traffic to your offline channels.

And on and on and on.

The core idea is to try something targeted so that you can correlate the data to your offline data sources (even if you can’t merge it) and detect a signal (impact).

By isolating it to different states (that are far from each other) you are isolating “pollutants” to your data (things beyond your control that might give you sub optimal results).

It is not a perfect methodology, and it takes time, and it needs you to be of decent size (or have enough impressions / customers) to quantify the impact. But few things will give your more confidence in the results you find.

Bonus Tip :

Before you say it, :), correlations don’t imply causality . So if in doubt, rinse and repeat.

#7: Primary research baby!

The second technique that I have learned from our well established offline brothers and sisters. Good old fashioned market research to isolate the impact of your online presence on your offline channels.

Field surveys, focus groups, interviews and more.

Let me give you a example.

Twice a year a tech company collects the names and info of all the folks who purchased something, online or offline or non-line. It then sends that information to their market research agency who, using a portfolio of methodologies, polls those customers to discern all the drivers that caused that purchase.

The data was a gold mine of information related to product attributes, the television ads, the website, impressions from visit to a store, percent of people who touched multiple channels before they purchased the product and so on and so forth.

The survey was done twice a year and, as stressed above, it was a continuous listening methodology and hence it provided a nice series of trends and segmentation data.

I remember the first time I got someone to pay some attention to the website in that company.

It was not my constant on the knees begging.

It was a slide (one slide!) in the analysis deck that showed two pieces of data (both as a pie chart :)), that 24% of the ultimate purchasers (through any channel) listed the company website as the most trusted source of product information and secondly that 40% of the purchasers used the website during the purchase consideration process.

That got me money for Analysts, and that got the poor starved web team two resources to improve the website. All from one slide.

But that’s the power of data.

You can think of many different ways in which you can use these kinds of approaches and how they uniquely apply in your case.

Bonus Tip :

I tend to think in terms of a portfolio model. No one method might be perfect or God’s gift to you. But a couple, or more, of the techniques above will help you get a robust understanding of this hard to solve problem. Combine that with things you are already measuring as a part of your web analytics 2.0 approach and you are sitting pretty.

Good luck!

Ok now your turn.

How are you solving the problem of tracking online to offline tracking? What has worked for you? What did not? Have you tried any of the above? Does anyone believe your analysis? If you could pick one thing to try, which one would it be?

Please share your stories and wounds, and for that we will love you just a bit more. :)

Thanks.

PS:
Couple other related posts you might find interesting:

• Eight Tips For Choosing An Online Survey Provider
• Excellent Analytics Tip #13: Measure Macro AND Micro Conversions
• Stop Obsessing About Conversion Rate
• Seven Steps to Creating a Data Driven Decision Making Culture
• Emetrics Summit Reflections: Peacocks, Woodpeckers, World Bank, Lifetime Value & Click Fraud

Know the difference between a Reporting Squirrel and a Analysis Ninja?

One is in the business of providing data.

One is in the business of providing, to use a old fashioned word, information.

This one of the core reasons why most dashboards are “crappy”, i.e. they are data pukes that provide little in terms of context and even less in terms of actionable value.

Here are some examples of sub optimal dashboards, sub optimal in my mind from a actionable perspective. . . .

Perhaps the most common type is above. Lots of data, even drill downs included, but you can’t look at it and go: “Wow we need to do . . . “. No sirrie bob you can’t.

I wanted to point the above out purely because of a common feature of 80% of Web Analytics Dashboards, in excel with a billion tabs to look through. This is not a dashboard, it is the result of a massive sum of money paid to a Consultant who is trying to impress you with his / her excel skills - without actually telling you anything.

You are walking down the street. You look at someone from behind and you think “hmmm she’s / he’s pretty”. So you speed up and overtake them and in the process you sneak a glance at them (yes you are married but looking is still ok :), and you are hugely disappointed. Not pretty. That’s the dashboard above. Very sexy and Web 2.0′fied and a ton of data there, but a lot less actionable than you might have hoped.

Why is this so? All the above efforts are well intentioned, took lots of honest work and probably took months to put together. So why?

Here are some hidden (corrosive) reasons why most dashboards tend to stink when it comes to helping the Executive make any decisions:

1. They leave the interpretation to the Executive (/ customer / requestor / other Squirrels). This is a fatal flaw because most dashboards are highly aggregated views of any KPI and are missing all the nuance and analysis (that only you as Ms. Ninja have, and you don’t go with dashboard).
2. Most Executives actually want insights / action recommendations but they don’t trust the Squirrels / Ninjas / VP’s / Data Providers. So they ask for numbers. We dutifully cram as many of them on to a A4 size paper in 3 size font and send it along with a magnifying glass.
3. Most Squirrels / Ninjas live in a silo. Going out to collect enough tribal knowledge to actually know what is going on to then make recommendations from the data is not something that we do, nor are we encouraged by our Executives or our organization structures. This incentivizes data pukeing.
4. Often dashboard creators tend to be “outsiders” (Consultants, Experts etc) and they often don’t have deep practitioner experience that would allow them to understand the human / “below the surface” issues like the above three. That leads non-Practitioners to make the common mistakes like creating the above three dashboards.

If you want your Executives / Customers to take action, you have to give them information and not data. It takes effort to get there, it will take all your charms (though no violation of any HR intimacy policies), and it will take some time.

Step one as always is to become aware of the above three problems.

Step two is to get a possible solution from the Occam’s Razor blog. :)

My attempt at solving this problem was to try and attack it from a human psychology perspective: How can I create a “dashboard” that will incent the right behavior from the Squirrels / Ninjas while giving Executives the information they need to make decisions (rather than engaging in a bitchfest which is the typical outcome).

Recommendation #1 was to move to a Critical Few philosophy for Executive reporting: Only report the three or five (at most!) metrics that define success for the whole business. Kill all the ancillary metrics that were nice to know (and my kill I mean let lower levels worry about it).

Recommendation #2 was my humble, admittedly ugly, attempt at a “Action Dashboard”:

4Q. (Sorry Jonathan! :)

Each quadrant representing a solution to a human problem that lead to crappy dashboards.
(Apologies for having to redact some of the data above, to protect the innocent.)

Let me walk you through it.

First very up top a clear identification of what the Critical Few metric was, who was responsible for that metric from a business perspective (translate into “head on the line”) and who was responsible for the analysis.

Also note the little red dot. That here indicated trouble. It can have two other colors, yellow for don’t fire anyone yet but get ready and green for send someone a big hug and a box of chocolates. Next. . . .

The first quadrant (the graphic) shows the trend for the metric. Ideally segmented (as is the case here, cart abandonment is illustrated for four key customer segments).

This quadrant is to satiate Executive curiosity that you know what you are doing, it will be glossed over (and that’s good!).

The second quadrant (Key Trends & Insights) is to add value by interpreting the trends and adding context. It says there that some things are up or down (in english :), and it also warns which data might be bad etc. You are starting to do your job here.

This quadrant is the one that Executives will read a lot initially, over time they will gain confidence in you, they will love that you share context (hello Ninja!), over time they will gloss over it (a good thing).

The third quadrant, clockwise, (Actions / Steps To Take) is force the shy Web Analyst to get out and talk to Marketers, Website Owners, VP’s, Whomever it takes to get all the tribal knowledge, identify root cause for the trends in the metric and recommend solid action to take. The Analyst will rarely be able to do this by themselves. It will require human contact with others, it will require conversations, it will mean identifying solutions collaboratively. It is a fantastic opportunity to become smart about the business.

This quadrant is key to driving action. No longer do you leave things to interpretation or let’s blame people etc. You are recommending what actually needs to get done. Your Executives will kiss you and over time this is the only quadrant they’ll read. It will also mean that monthly meetings will move from bitch fests to deciding who does what. Amen!

The fourth quadrant, (Impact on Company/Customer) exists in case it is not clear to the Executives why they need to take action (listen to poor old you the lowly Analyst). I feel it is the key thing missing from any dashboard, they are normally missing the kick in the rear end and this quadrant delivers it. It is the answer to this question: “As a result of this trend (up or down) what was the impact on the company and its customers”. It also forces you, Marketer / Analyst, to do hard work to estimate the impact and put it on paper.

This is the killer quadrant, if nothing else drives action this will, knowing exactly how much money was lost, how many customers were pissed, how much opportunity was wasted. Now when they ignore you they do that at their own peril and with their butt on the line. Trust me action you recommend will be taken.

See how simple it is?

You fix the human problems, you address the flaws in the system today and you actually become much smarter about the whole business (thanks to q3 and q4).

Win - win - win.

Over time you’ll gain a lot more trust from your Executives and all the crappy dashboards can die and be replaced with one that looks like this one. . . .

Now you are asking your Executives to simply layer their own judgment on the recommendations and help the company take action. Who needs to see the numbers? They pay you and I to deliver actionable insights.

I stress that it won’t happen overnight, but shoot for that nirvana state.

May the force be with you.

Ok now your turn. Care to share your own learnings and battle scars? Your success stories? Perhaps critique my “Action Dashboard” (sorry could not think of a better name, do you have suggestions?). Your perspectives are most welcome and would be greatly appreciated. Thank you.

PS:
Couple other related posts you might find interesting:

• Five Rules for High Impact Web Analytics Dashboards
• Web Analysis: In-house or Out-sourced or Something Else?
• How Should Web Analysts Spend Their Day?
• Seven Steps to Creating a Data Driven Decision Making Culture.
• Six Data Visualizations That Rock!