Category Archives: Web Analytics

Build a Google Analytics campaign spreadsheet that also crafts the links!

Dorcas Alexander wrote on the Luna Metrics blog recently about an important and often-overlooked topic: Organizing the campaign information you can gather in Google Analytics. I’m following up here with a way to document your campaigns. This method also solves the problem of constructing the special URLs used to create those campaigns in the first place.

If that seems a little opaque to you, read on. I suggest you start with this excerpt of Dorcas’ post:

It’s so easy to tag your campaigns for Google Analytics that you can quickly fill your reports with a mishmash of labels and end up with campaign tag soup! But what’s the best way to get organized? Even if you know what medium and source mean, it’s not always obvious how you should fit campaign info into those slots. And what about the extra slots we get for campaign tags like campaign and content and term?

It goes on to list four simple steps to preventing confusion. The fourth discusses documenting your work. It recommends how — by setting up a Google Docs spreadsheet, which can be shared among all content or analytics team members. He goes on to say, “Another good thing about using a spreadsheet is that a formula can pull all your labels together into a campaign-tagged URL.”

That’s a great idea, but how exactly can this be done?

Here’s my how-to, an addendum to that Luna Metrics post.

Above is the Google Spreadsheet I created for a former client (I needed to stop working with them when I joined Accenture). I’ve replaced the live information they were using with some of my own, to protect confidentiality. I’ll assume you already know how to set up a free Google Docs account, which includes the use of their cloud-based Excel competitor, named Spreadsheet.

  1. Create five columns: Output URL, Target URL, Formula, Campaign, Source and Medium. But wait!, you say. Where is that third column? It’s the Formula column, and is hidden here. I hid it because, a.) It looks identical to Output URL when you have live data in there, so it was redundant, and b.) I prefer to keep it hidden because each cell of that column contains the same forumla — one that you definitely don’t want to accidentally change or delete. If I were setting up the system in Excel, I’d make those cells protected.
  2. Before “hiding” column C, place this formula in it: =((((((((B2&IF(ISERROR(FIND(CHAR(63),B2,1)),"?","&"))&"utm_campaign=")&D7)&"&utm_source=")&E2)&"&utm_medium=")&F2)) This formula confirms that the target URL (in cell B2) does not already contain a question mark in it. If it finds one already, none will be added. If it finds no question mark, it added one. After that it builds a trailing URL string that will be familiar to those who roll their own URLs, or use Google’s URL Builder. Once you’re done you’re safe to highlight the column and hide it.
  3. In the Output URL column, place a far smaller formula: =C2 Yes, that’s all. Just display the contents of the hidden cell C2 in the visible cell B2.
  4. Populate the Target URL cell in that row with the web address of the landing page you want to tag with campaign information.
  5. Finally, fill in the Campaign field, along with the Source and Medium fields. These are the unique names of the campaign you wish to credit that visit to, along with the web site or social app it was came from (e.g., Twitter, or Jason Falls’ Social Medial Explorer blog), and the general medium (e.g. social, or web).

That’s it! In the Output URL you’ll find the line. Copy it, and paste it wherever you are setting up a hyperlink on another site or digital channel. For example, that top line shows the URL I used when I was Tweeting about my recent blog post extolling the nw release of an Excellent Analytics upgrade.

In the rows to the right of those I’ve shown, you can make notes about when it was used, why, and how you promoted the link. All of this can be helpful when you pull the campaign, source and media statistics for analysis.

I hope this helps. Let me know what improvements you might have experienced in how to catalog your campaign information.

Using Google Analytics’ New Report Dashboard

My work with Accenture has meant this blog has been silent since I joined. I’m loving my work there, by the way. But as for the central focus of this blog, I’ve been continuing to have fun in my off hours with web marketing analytics, especially using Google Analytics. If you use this app, you know they’ve launched a major upgrade of their reporting. It includes a way to create custom dashboards. Below you’ll find one small way I’ve used these new custom dashboards to save time and gain valuable insights.

Until I joined Accenture I was one of the contributors to Jason Fall’s exceptional social media marketing blog, Social Media Explorer. I miss being in such terrific company (they haven’t kicked me out of their Facebook group, something I’m very pleased about). I also miss those posts and the greater audience they had afforded me for my ideas on measuring social media.

But all was not well. I had always wondered how often people viewed my posts, the way I can with this blog. Yes, I could see which posts were the most likely to go viral. I could get that like anyone, from this summary of all of my posts there.

Then Jason shared with his contributors full reporting access to his Google Analytics metrics. Heaven!

Now I had a different problem: I could see aggregate information, but there was no easy way to view just the information about my pages. If the structure of the site had been, say, “domain.com/jefflarche/blogname,” I could view only the pages starting with /jefflarche/. That’s not the case, though. So I walked away, vowing to someday find a way to create a report that would give me a breakdown of my posts, at least for the KPI of Page Views. I got busy today by creating a new Dashboard for the profile. I then populated it with Widgets. Here you can see what the set up looks like for each widget I added (one per post):

Below are the steps taken in this form:

  1. I chose the widget called “Metric.” This shows one number only (along with a couple of others, for context), instead of a chart, a timeline or a table
  2. I chose the metric of Pageviews. But I needed to add a filter. For that, you can see I chose to only show the count for pages that contain a unique string. For this example, I chose the unique string social-media-awareness-measurement/ portion for this post’s URL
  3. I gave the widget the title of that post and linked to it so reviewing content for hints of popularity (or lack thereof!) would be easier

Pretty easy, no? Once I had added a widget for each, this is what I got:

So what insights can I glean from this? First of all, it took a while to build an audience. I learned as I went along, from the first post (lower right corner) to the latest (upper left). I knew this from other measures, which made it particularly sad for me to walk away from the posts. I saw a growth for 693 percent, comparing the views my first post got versus my last.

Turning Information Into Insights

Here are other insights:

  1. People love “how to” content, and respond to headlines that contain those magical words. (I knew this from my direct response days, but it’s cool how thoroughly this has been carried to the online world.)
  2. People like to read reviews of relevant books. That’s what I did with the extremely popular post Lessons from the Twitter Love Guru
  3. Sparklines can give valuable hints to user habits

This last one isn’t readily apparent. I’m going to assume you know what a sparkline is and just say that each of them above shows a sharp rise and fall in readership. After the week it has been posted you can see the view plateau very near zero. It’s to be expected. But there was an outlier, which you could only see if you viewed the full report. It’s shown above right.

Not only did this post not immediately “click” with readers (look at the leading tail), but once it did, its tail at the end is thicker, showing more ongoing popularity. If you’ve been a reader from the start, you’ve already read here and elsewhere about The Long Tail. Here it is in action!

This odd sparkline caused me to dig deeper, and I saw this report for all sources of visits to that page since it post (to the right).

It shows a significant number of links from referring sites and search engines. The referrers obviously liked the content enough to send their readers to it. And search engines? This is the ultimate long tail. I even got four visits from Google for the phrase “measure if people share your content on social media.” Believe it or not, this is hotly contested (I no longer show up for this phrase — at least in the top three pages).

By the way, “feed” stands for Feedburner, which means the fourth (or third, depending on how you look at it) source of visits is people who read Jason’s blog using an RSS reader.

As I said, it pays to be in cool company. By the way, here’s a shout-out to Argyle Social. They’re right near the top as a source for clicks to this page. Their latest post, Is Post Automation Effective? particularly fitting. I would say certainly say yes!

A Link To All of My Social Media Explorer Posts

If the headlines of the above got you curious about my content, I encourage you to visit this summary page, with links to all of them. I’ll be watching this new dashboard to see just how many of you do!

When Virtual Pageviews trump Events

There was much excitement when Google Analytics unveiled its Events metric. This meant web analytics could store several levels of information on a specific action, and associate that information with a unique web visit and visitor. Before that, if you wanted to — let’s say — record a download, you’d need to create a Virtual Page View.

So why did I recently blog on Jason Falls’ site about creating Virtual Pageviews when recording interest actions, such as “Send to a Printer,” or sharing actions, such as “Email a Friend?” or “Share on Facebook?” Why don’t I just create Events?

Using AddThis To Talk To Google Analytics

The answer is simple: If you consider sharing to be a goal of your site, you may want to set it as a Google Analytics (GA) Goal. Events, for all of their power, can’t be set as Goals.

Another action that Events are commonly used for is downloading white papers. Events seem perfect for this because you can set and capture a number of variables, such as title. In other words, you can set the Event Label as the title of the paper. But if you want to measure this as a Goal in GA, you’re out of luck.

Events don’t event “talk” to Goals. Let’s say you want to generate a report showing how many people who downloaded a white paper remained on the site for three or more minutes. The time on site can be set as a GA Goal, but you can’t easily generate a report showing the percentage of those who downloaded that remained on the site for that time period.

You can do all of this with GA Virtual Pageviews.

My rule of thumb is this: If you need to identify more than one variable with an event (such as identifying various Actions and Labels), and you do not need to correlate these with GA Goals, used Events. For all else, stick with Virtual Pageviews.

How To Track Content Interest Index In GA Using AddThis

Here is that how-to post I was referring to:

How To Measure Interest Using Google Analytics and AddThis, posted on Social Media Explorer by Jason Falls.

Studying a Twitter ecosystem one user at a time

If you’ve been following my (roughly) monthly posts on Jason Falls’ blog you know that I’ve taken this tack: On his blog I cover the key concepts of a particular web analytics approach, then provide additional support for that idea here.

A recent example is from two months ago. I posted about the use of Brownie Charts as a way to report Content Interest Index. I posted a parallel piece here on another use of the technique (Using Brownie Charts to Measure Bounce Rates). You could say this blog has become my laboratory: Results of preliminary experiments are described here, while the “real” story is broken on Jason’s blog. Tomorrow will be a little different.

Tomorrow, on Jason’s blog, I’ll be posting on someone else’s innovation. It is a review of an extraordinary book: Hashtag Analytics. I’m a huge fan of its author, Kevin Hillstrom, and over the years I’ve spent way too many hours creating Excel-driven models in order to replicate and fully understand his findings.

I’ll be doing that again, this time in support of Kevin’s approach to monitoring Twitter communities. Check back at this tag (hashtag-analytics) to read updates on my “lab work.” Ill be reporting over the next several weeks.

When A Hashtag Community Member Is “Removed”

You may want to check Kevin’s blog as well — especially later this week, when Kevin reports on the future vitality of the hashtag community #measureHe posted about it last week. Now he plans to theoretically whack an active member. Here’s an excerpt from his post, where he invites readers to suggest whom to “remove”:

In every e-commerce company, somebody is responsible for forecasting sales for the next twelve months, by day. So it makes logical sense that any community manager would want to know what the future of his/her community is, right? This is something you don’t find in any of the popular Twitter-based analytics tools. This is my focus. This is what I love doing, it’s completely actionable, and it’s an area of analysis not being explored!

Next week [the week starting January 24 — that’s today!], we’ll do something neat — we’ll remove one important user from the community, and we’ll see if the absence of the individual harms or helps the future trajectory of the community. If you are an active participant in the #measure community, please send me a user_id that you’d like to see removed in the forecast … I’ll run an example for the individual who gets the most votes.

And in two weeks, we’ll compare the #measure community to the #analytics community … competing communities doing similar work … which community is forecast to have a stronger future?

It’s a fun stunt / modeling experiment that has real world implications. It should serve as a proof of sorts of the predictive power of his Hashtag Digital Profiles and the statistical work behind them. More relevant to online community managers, it should illustrate why showing your participants “love,” lest they never return, is of tremendous importance.

What To Expect Here

I will be applying my own Hashtag Analytics to a different online group — one that has the advantage of weekly meetings. It’s a fairly new group, so the rules may not fully apply (Does an acorn sprout follow the same natural laws of growth as a full-grown tree?). To ensure I don’t jinx my test or influence the community — in a far more direct way than Heisenberg was referring to — its identity will remain unknown until I’ve gathered and analyzed a critical mass of data.

Do stop back.

January 25, 2011 Update:

Here are two related links I didn’t have yesterday. The first is Kevin’s post where he removes that member to the #measure Hashtag community. The second is my review of his book today on Jason Falls’ blog:

  1. Hashtag Analytics: Removing a Member of the Community
  2. Lessons from the Twitter Lover Guru

How to resolve those infuriating analytics discrepancies

Yesterday I was conducting a “Web Analytics Forensics” session with a new client. They posed a common question: The monthly reports on clicks that they were getting from suppliers of their ad buys were off by 10 percent — sometimes even more — compared to their own Google Analytics metrics. The number differences were veering all over the road. Sometimes these vendor reports seemed to overstate traffic, other times the clicks seemed to be understated. When I responded, I was reminded of the reassurances that a friend of mine gives. He’s a pediatrician.

My friend Paul had told me once that the bane of pediatricians everywhere are the late-night calls from parents who are worried about their child’s fever. He doesn’t mind being awakened (well, not much), but he has trouble fully reassuring parents of this fact: Fevers are normal, even healthy. If a child doesn’t run a fever every so often, it’s then that he’d be alarmed!

Don’t take Paul’s word for it. Here is a post in the New York Times a couple of days about on this very subject.

Client concerns about analytics discrepancies are my own profession’s “fever fears.” They can be a distraction from deeper problems. (The doctor in the post mentioned a mom whose child had a fever and abdominal pains. He said his primary concern was the abdominal symptom, but Mom kept steering things back to the fever!)

So the answer to the promise I gave you in the headline is this. For media buy discrepancies, don’t bother trying to resolve them!

Unless you think you are being overcharged for the traffic you’re buying from vendors, in the form of ad clicks or affiliate links, rest assured. I’d only be worried if their numbers were identical to yours. No two systems measure web traffic precisely, or the same way. They are all uniquely flawed.

So instead, focus on what you’re doing with this traffic after it arrives. Are visitors finding what they came for? Are they returning in healthy numbers? And most importantly …

Are they converting?

Usually when I’m called in to consult, the answer to all of these questions appears to be no. Focus your attention, and your boss’s, on these issues. They are the only path to online marketing success.

Finding B2B leads from your web logs

Occasionally I share links to blog posts of others in my industry. Some things are too good to keep to myself. Here’s a perfect example, from Luna Metrics:

We have a customer who considers the SEO we do for her to be one of her “sales channels” and we get ranked along with her other channels. She sends us reports when a lead comes in and when a lead is closed. The other day, I saw that she closed one that was worth not quite half a million dollars. (!! that was my reaction, too.) So I wrote her and said, how awesome. To which she replied,

“Started with google analytics. Saw that they spent some time on the site… sicked Jane on a cold calling mission… after a bunch of calls she found the engineer at the company who was interested in the product. I flew out.. presented… sold and they put out a public bid. Our company is the low bidder and need to send a sample next week for review then release of contract.”

So in case you are wondering, she was talking about the [Google Analytics] Network Locations report, which she mines daily for sales leads.

It’s hard to believe all of this was accomplished by reviewing a typically-overlooked report in a totally free analytics package. Read the rest of their post, and check out this helpful post from the past on how to exploit the fact that many large organizations will self-identify in this report instead of resolving to their ISP’s name.

Visualizing bounce rates using brownie charts

Today on Jason Fall’s Social Media Explorer, I discuss my new favorite data visualization technique — one that I’m starting to move into production with web analytics reports I create for clients. Its official name is the Tree Map, but as I mention in that post, I prefer to call it The Brownie Chart.

That post has an example of how I use brownie charts to show a promising new web metric, the content interest index (CII). My example on the site uses a made-up business, Everything Brownies, with a web address of EB.com.

Note: Yes, I know. That web address resolves to a real encyclopedia site. The reason I didn’t just make up a domain name is you never know when one will go live with a site. I didn’t want to have someone inform me, two months from now, that my blog is now pointing to a porn or gambling site! Unless Encyclopedia Britannica takes a surprisingly sleazy turn, I think I’m safe.

Here is another example of how the tree map / brownie chart can make web analytics reporting easier to understand:

Charting Bounce Rates: “I came, I saw, I puked.”

I agree with Avinash Kaushik that bounce rates are a helpful way to measure how well you’re connecting with site visitors. Actually, he’s a little more enthusiastic than I am, with blog post titles such as this model of understatement: Bounce Rate: The Sexiest Metric Ever? Three years ago, on his own blog, Avinash described bounce rates this way:

So what is this mysterious metric? In a nutshell bounce rate measures the percentage of people who come to your website and leave “instantly.”

They’re the one-page visitors. Yes, they might be finding what they were looking for — but more often than not, these people just didn’t dig the neighborhood.

Avinash has refined his description over time. In his recent, truly outstanding book on measuring web traffic, Web Analytics 2.0, he characterizes bounce rates this way: “I came, I saw, I puked.”

Bounces can be reviewed for all traffic to a site, or only for certain important segments — traffic from search engines is a good example. Reporting of bounce rates can also be broken down by page.

The brownie chart becomes particularly handy for this per-page bounce rate reporting. It helps those responsible assess the severity of a site’s problem pages.

You see, you can’t easily be sure that a page with a high bounce rate really is a problem page. Think of it: If nearly everyone ups and leaves when they arrive at a particular page, but that page gets relatively little traffic, there’s no huge emergency. Content management resources are usually scarce, so it’s better to keep looking, for other pages that attract more page views that happen to have comparatively high bounce rates. It’s those more popular pages that require immediate first aid!

To illustrate, take a look at Everything Brownies’ bounce rates on this brownie chart. The graphic shows all major pages of this fictitious site, and shows the pages as more red if they have the highest bounce rates relative to the others. You should know that size represents the relative numbers of page views. The bigger the “brownie piece,” the more views that page gets.

What does this chart tell us? Quite a bit.

The Holiday Brownie Baking Kit, which I placed my mouse over in this screen capture, has an excellent (i.e., low) bounce rate. It also has a ton of page views.

That means this page is doing quite well in keeping visitors from leaving immediately. Well done! On the other hand, Deluxe Baking Pan is not nearly as successful. Its relative bounce rate is quite high, and because it has the most page views of the entire site, it’s clear this page is majorly dropping the ball!

There are plenty more insights, but you get the picture.

As I mentioned on Jason’s blog, what I like about this charting format is non-math types (such as myself!) can understand these statistics immediately, and know exactly what needs to be investigated further — and in what order of priority. As my friend Bob likes to say, “That’s good stuff!”

I hope you find the potential of this charting technique as exciting at I do.

A Round of Applause for BeGraphic and Sparklines for Excel

This example of a fake report for EB.com, as well as the one on Social Media Explorer, was produced using an “Add-on” for Excel called BeGraphic. The Add-on consists of a whole suite of graphic tools — all based on Excel data and rendered within that application. The particular functions I used were part of Sparklines for Excel within the BeGraphic suite. I urge you to support the folks behind these amazing visualization tools.

Join the Bum Rush for Charity Water!

I’ve been blogging about the book series Age of Conversation, and its affiliated Social Media Bum Rushes for charity, since the first one in 2008. I’m proud to have a chapter in their latest volume, Age Of Conversation III: Time To Get Busy. Within its pages I’m in extremely good company, with 170 other social media pros. My very brief chapter (really a micro-chapter, with a lower word count than most of my blog posts) is on social media analytics.

I am pleased to announce that we’re staging another Bum Rush today, on Blog Action Day.

Each year any blogger who wishes to participate in Blog Action Day writes on the same theme. This year’s theme is water. So let me tell you about Charity Water. It’s a nonprofit organization that brings safe and clean drinking water to developing nations.

Now, every sale of Age Of Conversation III: Time To Get Busy goes to support Charity Water and help developing nations get clean, safe drinking water.

How cool is that? Please join our bum rush. You’ll learn more about the power of social media, and become part of the solution to one of the world’s toughest health challenges.

How do you join the Bum Rush? Generate more sales of book at Amazon.com. Purchase it yourself and encourage others to as well. If you work for an organization that hands out Christmas gifts, get them to pop for multiple copies. They make great gifts!

One caveat: Please only purchase 1 copy at a time because Amazon.com counts bulk orders as one.

You can buy the Kindle version here (it’s the Charity Water affiliate link). You can get the paperback version here and the hardback version here (the other two Charity Water affiliate links). What else can you do? Here are seven ideas:

  1. Register for Blog Action Day.
  2. Blog about Blog Action Day and mention Age Of Conversation: Time To Get Busy. Use the same affiliate links that are in this post so that Charity Water can get its contribution.
  3. Join the conversation on Twitter. Use hashtags #aoc3 and #BAD10 for your comments about the book.
  4. Trackback or comment on today’s post about the Bum Rush at http://ageofconversation.com.
  5. Digg, Stumble and bookmark on Delicious.com all the posts you see about the event, including yours.
  6. Become a Facebook fan of Age of Conversation 3 (“AoC3“) and interact with us on Facebook.
  7. Send an e-mail to all your friends and get them involved too.

If we all band together and work for a common cause, we can make a difference. Join us in the bum rush on October 15, 2010 and help us raise money for Charity Water.

Which is better? Google Analytics’ $ Index or CII?

Today I posted my first entry as guest blogger on Jason Falls’ Social Media Explorer. Not surprisingly for those who know me, I kicked things off with a description of Content Index Index — a general description and a case for its use. Posting something in such esteemed company is truly humbling and frankly more thrilling than is probably healthy to admit. (I can hear friends and loved ones chiming in now about all of my work / life balance hoo-hah!)

Content Interest Index — CII for short — forgets for a moment whether a particular user has “converted” in that user session. It scores a page’s content on behavior that takes place on that page only (or offline, regarding that page’s content, in social media). That’s quite different from the scoring of, say, a page in a conversion funnel. Google Analytics (GA) has a Funnel Report that gives value to the pages leading directly to a conversion (Google calls these conversions “Goals”).

Another GA metric that tries to rank based on conversion is its “$ Index.” This can be compared to Google’s PageRank,  but it’s for estimating dollars earned by a page view, not search engine Google juice conferred by the quality of back links a page receives. It confers a portion of the dollar value of a conversion (Goal) onto pages that were viewed in the same session. Here’s an explanation and some examples (the graphic below is from that post):

Those GA scoring systems are all about the conversion, which I’m usually all for.

But as I mentioned in my post on Jason’s blog site, and yesterday, at a Translator Lab Hours discussion, people “snack” on content. They may come back to your site many times before they convert.

That means the session where they convert is likely to be brief, and the pages viewed (the ones given $ Index value) can be unfairly inflated in value.

Follow me here, and on Jason Falls’ Social Media Explorer, to learn more about how CII is calculated and how it can be used to improve the content on your web site that surrounds your conversion funnels.

Overcoming the treachery of analytics

What do these three quotes have in common?

  • “Worship no false idols” — The First Commandment in the Old Testament
  • “If you meet the Buddha on the road, kill him” — Zen adage
  • “Become a ruthless killing machine when it comes to metrics/data” — Avinash Kaushik in a recent blog post.

If you guessed that they all warn (with increasing violence!) against mistaking the symbol of something for the real thing, congratulations! You won a pipe. Surrealist painter René Magritte painted this particular pipe as a way to get us thinking about the paradox of symbols. Under it he painted a caption, “This is not a pipe.”

To drive his point home, Magritte named the painting, The Treachery of Images.

So how can we know when it’s time to wage war against our own treacherous web analytics? And once the body count has been tallied, what takes their place? What do we use to answer key questions and spur appropriate actions?

Results Simply Summarized

The answer: Show your audience only what they really want to know — not mere numbers and measurements, but the other RSS: results simply summarized. Here’s Avinash’s post for the full story.

And here it is in a nutshell:

He describes a favorite application he downloaded for his Nexus One phone. It’s a cardio trainer. The app starts out just like another popular body monitoring system. I’m thinking of the Nike Plus application for the iPhone. Both give the standard run-down of miles run and progress achieved, compared with past sessions.

The cardio trainer app then makes an elegant attempt at RSS. Avinash, for one, feels that it succeeds. I agree. It refocuses attention past the numbers to the actual workout goal.

At the end of each run, to reflect his level of exertion, Avinash is presented with an award of sorts. The screen shows two pieces of fruit — two pairs. They represent the number of calories burned. The pairs are his to enjoy guilt-free. (Or he can imagine a calorie-laden equivalent, in a mental swap of one food for another. Perhaps the next version of the app will allow users to actually do this; To replace the outline of two pears with a rendering of a candy bar, or a couple of bottles of beer!)

You might think these graphics are the same as Magritte’s pipe — mere symbols; not the real thing. You’d be overlooking a major distinction.

All Magritte was doing was showing the pipe. Avinash’s workout app was presenting the pears — awarding them. It was the summarization of the data behind it, proving to Avinash that this was his hard-won snack. It says, “Here you go. You earned it.”

Connecting On Two Levels

The representation of the pears became something he could connect with — both rationally and emotionally. Unlike Magritte’s pipe, the pears are results, supported by evidence. Consequently, for the recipient, they become so real that person could almost taste them!

Sadly, if most analytics pros were asked to cut out their own distracting and unpersuasive metrics, little would be left. Most metrics talk and talk but never get to the point.

This is precisely what senior management does not want. They want quick and truthful take-aways. Will they be dining on one delicious pear this month, or two? Or will there be none at all?

Of course business leaders wouldn’t want to see pears in their analytics. That would be absurd. So what do they care about? They want to see money of course. Or at least, clear proxies for money. Showing images of people is always good, since selling things to people is the surest path to making money. With that in mind, consider using generic silhouettes of them, shown judiciously, and with data that supports their numbers on the page.

Be bold. Show senior management that their site generated more sales leads this month — as represented by silhouettes of cookie-cutter executives (presumably eager to know more about the product). Count them. How many more are there this month compared to last? Line them up for comparison, month-to-month or year-to-date.

Or, as another example, show how the website is lowering operational costs. Illustrate the success of answering more consumer questions online versus having these people call your pricey phone center. In this case, the graphic could be a string of telephone headset icons. Compared to last month, are there fewer of them shown, or more?

Go on your own metrics killing spree, but first, know what you’re pursuing.

Kill any metric that produces more smoke than light. Allow the remaining metrics to build upon each other and add richness to your story. Then, as a satisfying grace note, find that single graphic which best sums up the current situation. Use symbolic language that is meaningful to your audience, to transcend facts and figures.

Do this, and far from being Magritte’s pipe, this graphic will be your own “Avinash’s pears.”