Customer reports: The Forgotten Touchpoint

Customer loyalty. It’s no secret that it’s a make or break aspect of business. Loyalty converts a one-time sale into an ongoing stream of revenue from the same customer. But what keeps a customer loyal? Much of the current literature suggests loyalty stems from touchpoints – those contact points between a customer and a brand that shape perception. These varied events occur throughout the customer journey cycle.

But you’ll notice something missing from the list above. Customer reporting. It’s virtually never listed as a touchpoint and yet so often a crucial component in shaping a customer’s view of your value - which ultimately determines their loyalty to you.

When Reporting misses the mark

Reviewing the images in the last post reminded me of an incident from a prior job.  Our Marketing Department spent significant time and money with an internet company to revitalize online adwords and boost search engine optimization (SEO). It was a detailed endeavor and I know they logged a lot of hours with the internet company prior to the go-live date to make sure everything was just right. Several weeks after the launch, curious if all the stress was worth it, I asked Marketing about the campaigns’ performance.


The answer I received was surprising. Unfortunately, they weren’t really sure. While their partner company wrote good copy, created great landing pages for the ads and put in place solid practices for boosting SEO, they didn’t provide useful reporting to help their customers measure success. The reports they delivered each week contained so little information that Marketing couldn’t determine which ads were performing well or why, so it was impossible to make decisions to know where to adjust. Logging into the portal provided by the adword platform had the opposite effect. The information there was overwhelming, and they could spend hours trying to decipher the countless pages of charts, drop down choices, metrics, etc. and still not really understand what they were seeing.


Reporting, whether obtained via self-service, delivered by a services team, an automated notification or through a customer portal is an interaction that a customer has with your company. And as such, every presentation of data should be treated as the valuable touchpoint that it is and seen as an opportunity to build loyalty and positively influence customer perception. Truly, this interaction should be considered as valuable as any other touchpoint in the overall customer experience. In the story above, if Marketing had been happy with the reporting they were receiving, the internet company would have had been able to convert them to a loyal customer. Marketing would have become a customer who went from receiving a one-time service to one who not only received on-going reporting services but also a customer who had the potential to drive additional revenue for the internet company by creating ads for other areas of the business, using additional services such as A/B testing or buying more products such as drip campaign creation.

What are some ways customer reports can be a positive touchpoint and increase loyalty?

1.     Make sure the report answers more questions than it raises and allows viewers to quickly consume the information that’s most important to them. This ensures customers are excited to receive the reports because they know they’ll quickly be guided to the answers they seek.

2.     Ensure the data presentation initiates conversations with customers.  Design reports such that customers think about the data and and can envision how they intend to solve problems with it.

3.     Think of it as a product versus a report.  You're not just sharing data, but giving customers something you want them to actively use.   Thinking like a product gives you a construct of thinking about how and when it will be used.

4.     Keep stickiness in mind. Provide information in such a way that it will become ingrained into the customer’s routine and way of conducting business. If done correctly, reporting becomes a driver behind renewal discussions.

Creating these types of data presentations isn’t easy.  They require the right level of data readiness and the knowledge of how to maximize their value.  Join us for our upcoming webinar on Turning Data into Dollars where we’ll walk through a series of examples on how to deliver more valuable information to customers and impact loyalty through reporting.

Common Myths Tied to Data Monetization

Bigfoot, Loch Ness, Chupacabra. We’ve all heard them: these stories gain traction because of their folklore element. Someone, somewhere, saw something that they couldn’t explain, and in the course of investigating, a fantastical tale emerged that captured the minds of many.   The same can be said of the business world. All too often you hear of “industry standards” and “best practices”.  It’s hard to pin down where they started, and often even harder to figure out why they’re perpetuated. Most frightening is that many of these standards or practices have actually morphed into myths. What may have once originated from someone, somewhere trying to help a specific customer in a specific situation has now seen so many iterations that it simply no longer holds true. When it comes to what works best when monetizing data, we’re finding many a myth that needs busting.

As a product manager, you’re probably facing growing pressure to package or “productize” your data. Your organization may be in search of greater return on their Big Data investment or could be looking to add incremental value to an existing product. No matter the situation, let’s go through some of the most common myths concerning the creation of data products.

Myth: No One’s Asked for It

Fact: While members of your organization may not be asking you for a data product explicitly, they might be saying it indirectly.  Their requests could be hidden in questions posed to you, your sales or support teams. Questions such as:

  • How do I compare to others?

  • How do I compare to the industry average?

  • Can I get more frequent access to my data?

  • Can others in my organization get access?

  • Does a summary version of this data exist for my boss?

Don’t wait for a specific product request, but listen to what they are asking the data to do. Don’t be surprised if more than one request is identified.

Myth: You Can’t Monetize Data That’s Already Owned

Fact: The value isn’t in the ownership of the data itself, it’s in the value add of the industry-specific metrics, customer benchmarks, and recommendations. The data itself is not what’s being sold, but the insights, metrics, algorithms, display, etc. that’s baked into the analysis. Remember all those questions in the previous myth? Providing thoughtful, easy to navigate visualizations that guide others to those answers is the key to monetizing data. Don’t position a data product as easy access to raw data, but rather as a solution that solves a problem.

Myth: More is Better

Fact: Not really.  This is probably the most common myth encountered and can often be one of the hardest to overcome. Those asking for data products often think that more is better – more data fields, more ways to “slice and dice” results, more metrics, more dimensions, more chart views. In their minds, they’re asking for flexibility to manipulate the data. The reality is that these requests almost always stem from uncertainty. They’re unsure what exactly to do with the data, so they figure they might as well as ask for all of it.

Our experience suggests that most users want to be guided to their answers. They want the data presented to remove uncertainty -- not just raise more questions. Users, particularly non-analytical ones, don’t invest more than a few minutes using data trying to answer their questions. Sure, creating the uber - report with lots of filters, a date range selector and the ability to download the report is pretty easy to implement.  However, if the member of your organization can’t easily derive value, then they won’t use what you’ve given them and even minimal efforts on a simple report download interface are wasted.   

For example, compare the two reports below. In the first, the user is confronted with so many filters, columns and data points that making an informed decision from this information would be extremely time consuming. In the second, the user is immediately drawn to the key pieces on data that are needed to quickly understand the important details of what is happening and what’s driving the information.  Investing time on designing for the data consumer and providing them a clear path of guided exploration is the way to go.

Assembla filtered view
Slice campaign example

So tell us, what’s the most outrageous request you got for a “more is better” data product or visualization report? Send in your stories to with “more is better” in the subject line and we’ll pick our favorite. The winner will get to spend two hour with us and together we’ll turn that unwieldy request into something functional, informative and cool. Bonus points if there’s a snapshot of the current report attached to your story!  

Think you’ve run into a data product myth that is more mysterious or pervasive? Drop us a note at



A Fantasy Visualization for Fantasy Football

At the height of my fantasy football obsession, I probably checked the score on my match-ups more than 50 times a week. As NFL football fan, you have lots of time to do such things -- if you have fantasy players in all four game times (Thursday night, two Sunday games, and Monday night), you have around 13 hours of televised games a week.

This year I quit my fantasy football league. I'm not saying it is because the fantasy football site we used didn't present the data in an interesting way, but an awesome visualization might have made a difference for me. With such a dedicate audience, Yahoo!, ESPN and the rest would be wise to create an great way to track the performance of your team versus an opponent. Here is a blueprint:


This visualization would answer the important questions as I obsessively dissect the scoring:

  • How am I trending in my match-up? That is, am I on pace to win? Most fantasy football systems have built prediction engines to project out results, but these results aren't shown in a chart.
  • How are individual players contributing to the scores? The trend lines show when and how a player is scoring. Rolling over the points in the line would reveal the big plays that are helping or hurting your cause.
  • What confidence can I have in the projected outcome? The dark parts of the chart are actual points earned whereas the lighter blue is projections. As your column chart "hardens" into dark blue, you can have confidence in the final tally.

As I pointed out recently, Fantasy Football has done an amazing job of making more people data literate. Why not finish the job with a great interface for team owners to spend their weekends cursing over?

Fantasy Football is Teaching Data Fluency


Fantasy football season is here again (along with the actual NFL season). I thought it a good time to share a section from our upcoming book Data Fluency, scheduled to be published in October through Wiley and with Nathan Yau of FlowingData as editor. In this excerpt, we suggest that Fantasy Football has taught an enormous audience to understand the language of data:

It may not be a stretch to say more Americans have learned about data and statistics through fantasy football than every college statistics course in the country. Each week, some 19 million NFL football fans spend their Sundays meticulously setting team line-ups based on statistical projections, historical patterns, and analysis of week-to-week variance. The couch potatoes who once relished on-field hits and in-game strategies now spend an average of more than eight hours a week diving into the data of the sport.

For the uninitiated, fantasy sports let fans play the role of team owners and managers by picking players for their own fantasy team and making weekly roster decisions. As the action plays out each week on the field, fantasy owners collect points against other competitors within their fantasy leagues. To win, fantasy owners quickly realize that success often depends on studying player and team performance data closely.

Here are a few ways that NFL fantasy players incorporate data into their thinking:

Variation in Player Performance

The best fantasy owners understand the nature of week-to-week variance and its relationship to earning points. For example, touchdowns generally earn a fantasy owner six points; but touchdowns occur rarely and can fluctuate wildly. In contrast, the number of touches players receive may be a better indicator of how much the team is using them and their opportunity to provide the owner with points. Because consistent performance matters, successful owners often focus on players with more stable predictors of success (for example, touches) versus more sporadic events (for example, touchdowns).

Rankings Can Be Misleading

Fantasy football cheat-sheets offer rankings of players in every position. These ranking mask the differences and dispersion of expected performance. For instance, the top running back may be expected to perform 20 percent better than the second rated running back, who in turn is only expected to score 5 percent more points than the third through sixth rated running back. The data shows that players often cluster into tiers of performance. This statistical understanding was publicly explained by Boris Chen who stated that “players within a tier are largely equals. The amount of noise between the ranks within a tier and actual results is high enough that it is basically a dice roll in most situations.” This concept has been widely adopted by fantasy owners as a player drafting strategy. 

The Only Constant Is Change

The worst fantasy football owners are stuck in the past and pick players and teams that they have relied on in the past to generate points. That is, they fail to update their assumptions about the best teams, players, and trends. Following the data closely reveals when certain players have gone past their prime and when teams that once had high-scoring offenses can no longer put up big points. Clinging to past success may be a formula for disaster because the only constant in fantasy football is change.

Context Fills Out the Picture

Data viewed in isolation can be deceiving. Say, for example, that your top wide receiver scored only one-half the number of points that he scored on average in a season. Is this a new and troubling trend? Should you trade? A little research might reveal that he matched up against one of the league’s top cornerbacks, or his quarterback was knocked out of the game, or perhaps he tends to perform poorly in cold weather, away games. These environmental factors make a difference with respect to outcomes. Performance data cannot be understood in isolation—context matters.

So how did fantasy football create legions of fans who have developed a specialized dialect of data fluency? It has been a combination of education, effective data presentation, common data conventions, and incentives. Fantasy football owners have been taught how to use data to their advantage through the efforts of the NFL, ESPN, Yahoo!, and a cloud of other websites dedicated to football analyses. Organizations like Football Outsiders built new media businesses around data modeling and projections of player performance. 

Leading online fantasy football sites like ESPN and Yahoo! have been aggressive in pushing data and data visualizations to their users. These sites include trend charts for every player, drive charts, player comparison graphics, and predictive models for estimating game outcomes.

The educated fantasy football community is also highly engaged with the sport. The community loves football! The fantasy league has provided a whole new (and rewarding) dimension to its fandom. No longer is it tied down to rooting for a single team—instead, the whole league becomes fodder for its attention as it picks and chooses players from each of the 32 NFL teams. In addition, the fantasy football industry has coalesced around consistent formats for leagues, points, and key metrics. Terms like PPR, running back by committee, waiver wire, and flex are well understood, facilitating conversations among league owners. And with $1.18 billion bet in fantasy football leagues annually and a passionate fan base, fantasy owners have huge incentives to make informed decisions. When money or bragging rights are on the line, individuals invest time and energy into developing the skills and abilities to become data fluent.

In short, these factors have brought data fluency to the masses. Millions of fans have learned how to read charts, grasp basic data concepts, and allow deeply embedded data to inform how they make decisions—all critical skills associated with quadrant one in our framework. 

Visualization techniques we all knew at 4 years old

A few years ago my niece sat down at the table with me and drew a picture. Here it is:

An afternoon drawing by my niece. Marker and Paper.

Whether you play with $100k dashboarding tools or the latest and greatest open source reporting solution, they have no secret sauce in the visual thinking department that wasn’t already exhibited when you were 4 years old and drew something for your uncle. 

Let’s walk through 6 principles of visual comprehension I observed after she drew it. The 6 aren’t meant to be all encompassing nor the only way to interpret these visual principles, but they are fundamental aspects of what makes data visualization so special. I like to think of them as parts of the grammar for speaking visually.

Let's see what each principle would say...

Things that are enclosed by a shape will be seen as a group.
— Enclosure

First, she drew me. Yes, that’s me to the right. She started with my eyes, nose, and mouth. Then she grouped those items through the principle of enclosure to say, “Here’s James’ big head and all those facial features belong inside it.” 


Young children learn to read the face first, so this becomes much of your identity to them at an early age. In fact, the rest of my entire body is represented by two lines. Somehow my big head does need to move around after all. 

Young children learn to read the face first, so this becomes much of your identity to them at an early age. In fact, the rest of my entire body is represented by two lines. Somehow my big head does need to move around after all. 

Things that are connected are part of the same group.
— Connection

Thankfully, she also acknowledged my hair. It’s not floating in mid-air, but touches the enclosure of my head to say, “While not inside, these things in the group of things that make up James." 

Things that are near each other belong to a group.
— Proximity

Next she drew her face and body next to me — her way of saying, “We have a relationship. I like hanging out with him.” Perhaps, if I was that weird uncle in the family she would have been on a corner of the page, but instead this proximity indicated that we are in the group friends and family. 

Things that are aligned are perceived as a group.
— Continuity

Not only did she draw herself close to me but also on the same vertical plane. She's a rather grounded girl, so, instead of drawing her floating about, she emphasizes that we’re both in the group of things that obey the laws of gravity and stand on the floor. 

Some other objects around us have the freedom to fly about. They’re just principles after all; not laws.

Some other objects around us have the freedom to fly about. They’re just principles after all; not laws.

We strive to perceive shapes as complete.
— Closure

When drawing her ears (with earrings, of course), you can see how the circles were sure to be complete through the overlapping beginning and ending of those lines. This assurance of closure says, “My little ears can definitely support big girl earrings.”  

Things that share color, size, or shape belong to a group.
— Similarity

I imagine she drew the cactus floating above us because it has a visual similarity to her hair (which she drew after my hair). This is her saying, “What else could I draw that would belong on this page? I’d like to draw a cactus. That feels right.” 

Lest you doubt it is a cactus, I asked her at the time and, no doubt about it, it is obviously a cactus. The swirling thing to the right is also obviously a snail.

Lest you doubt it is a cactus, I asked her at the time and, no doubt about it, it is obviously a cactus. The swirling thing to the right is also obviously a snail.

One thing I’ve learned is that design or visual comprehension principles make more practical sense looking backwards. We all have various beliefs or observations of the world that have been internalized and are unique to us. We probably don’t even know what many of them are — just as I’m fairly confident my niece hadn’t studied the laws of gestalt grouping from the early 30’s when she sat down to do this drawing. When you want guiding principles to guide a new product, look back at what is most natural and pervasive. 

We all were born with this visual grammar, and they have been incorporated in all sorts of data visualizations and products in recent years. One hope of mine is that we’ll start seeing data products that allow us to not just see the data, but see through it to those “aha” moments, where people are seen and lives are truly impacted, where insights are revealed as effortlessly and confidently as drawing a picture on a blank page.

Customer Flashcards: Customer Analysis Using Pictures and Patterns

Some recent work in online training reminded me of this concept that we discussed almost eight years ago. It is an analysis-visualization approach that I still believe is underutilized.

The Value of Film Study

In business as in sports, behind-the-scenes analysis is the foundation for on-the-field success. That is the promise of business intelligence – and the reality of film study in the National Football League (NFL).

NFL coaches and players spend hours analyzing film to identify the strengths and weaknesses of their opponents. Coaches scour video for opponents’ tendencies and use this knowledge to build their game plan. For players, film study gives them understanding that lets instinct take over on the field. Consider some of the techniques involved:

  • Get granular: Examine raw data such as where players are positioned, who gets the ball from different formations, what plays are called at different field positions, and even the technique used by individual players. 
  • Use your eyes: Rely on your brain’s ability to recognize patterns; look for unusual actions and note when they occur.
  • Group common patterns: Record these patterns by player, by formation, by down and distance. These patterns are the building blocks of analysis, letting the coach ask questions like: Does a formation give clues about the play being called?
  • Build strategy from the bottom up: Finally, use this deep understanding of the opposing team’s tendencies as the foundation for the game plan.

This type of approach is different from most business analytics. Imagine if an NFL team depended solely on statistics and reporting tools to build their game plan. Football teams wouldn’t see much success if they only looked at average yards per carry and which players on the opposing team touched the ball most.

Slicing and dicing statistics doesn’t help much when deciding on a game plan. Business intelligence tools can explain the size of the problem (how good is the opponent?) and trends (what are their preferred offensive weapons?). These same tools do not, however, provide real perspective on customer behaviors or insights that give your organization data-driven direction.

Customer Flashcards: Making Pictures

How do we bring the value of film study to business intelligence? The solution we've developed is inspired in equal parts by Edward Tufte and Malcolm Gladwell.

Tufte is an expert at information presentation and design. One approach he has popularized is small multiples: placing sets of identically structured graphics on a single view to show different instances of the same data, as illustrated below.

This example of small multiples compares the annual deaths by assault per capita across countries. The size of this problem in the United States is evident.

This example of small multiples compares the annual deaths by assault per capita across countries. The size of this problem in the United States is evident.

Small multiples enhance comparison and reveal the scope of variation. By using the same dimensions and scale, small multiples also relieve the viewer from relearning the data graphic’s structure.

We extend this technique to understand customer behaviors by combining usage data, marketing touchpoints, service calls, and other interactions to create a simple graphic that shows many aspects of a single customer’s behavior. Here are a few examples from our work:

Visual representation of credit card accounts. The blue line is the account balance; yellows are purchases and cash advances; green is payments; the grey background is credit line; red bars show delinquency. Notice full vs. gradual pay-down of account, building credit lines, transaction inactivity.

Visual representation of credit card accounts. The blue line is the account balance; yellows are purchases and cash advances; green is payments; the grey background is credit line; red bars show delinquency. Notice full vs. gradual pay-down of account, building credit lines, transaction inactivity.

Four examples of individual students progressing through an online curriculum. Vertical axis is the lessons in order; horizontal axis is time. The grey line shows the “optimal” progression over time. Notice steady vs. erratic progress, breaks in progress, and out of order lessons.

Four examples of individual students progressing through an online curriculum. Vertical axis is the lessons in order; horizontal axis is time. The grey line shows the “optimal” progression over time. Notice steady vs. erratic progress, breaks in progress, and out of order lessons.

These pictures are intriguing, but are they useful? In his book Blink, Malcolm Gladwell introduces the idea of thin slicing: "the act of relegating the decision-making process to the adaptive unconscious by focusing on a small set of pertinent key variables, as opposed to consciously considering the situations as wholes over much longer periods of time." He explains how people become experts at quickly evaluating the relevant data and arrive at a rapid understanding of a situation.

We want to give businesspeople a sense of their customers in a blink of an eye. To do so, customer flashcards need to be intuitive and easy to learn. Success is the ability to show these pictures to anyone in the organization – from senior executives to front-line customer service reps – and have them grasp what they are seeing with just a few minutes of explanation.

Finding the Patterns

Customer flashcards, thousands of them, are raw material for analysis. They are the game tapes of business film study. Like NFL coaches, the next step is to put your visual pattern recognition abilities to work. As Stephen Few put it recently:

"When used effectively, visualization extends the reach of traditional business intelligence to new realms of understanding – not as one means among many, but often as the only effective means available. I believe that information visualization will enable the next significant leap in BI’s evolution."

Think of the game Memory you used to play as a child, turning over one game card after another looking for matching pairs. Now imagine flipping through hundreds of customers, opening your mind to the patterns that emerge. You could spot common behaviors, note irregularities, and build a close-up perspective of customers' actions. It is an exercise that every business executive should try: sit in a quiet, windowless room and look at visual representations of customer behaviors one at a time, deducing what their behavior implies about their needs. The results are eye-opening; customers are screaming out their needs through their behaviors. By seeing and appreciating these behaviors, a business has an opportunity to build a customer intimacy that too often gets lost.

Putting Customer Flashcards to Work

Finding new patterns can be interesting, but how do you quantify them? In our work, we develop pattern recognition algorithms to capture the behaviors that are first identified by eye. Behaviors are then tagged – each customer can be tagged with multiple behaviors. With this new quantification of customers, you are positioned to take action.

The value from customer flashcards can be both strategic and tactical. Here are a couple examples from our experience:

  • A car rental company was able to tailor its offerings based on behavioral segmentation of customers. We visualized individual customer car usage, including where, when, and how long customers were driving. The customer flashcards revealed different types of trips (e.g., errand running around town, long trips) and different customer relationships (e.g., loyal repeat customers, trialers). These dimensions provided a rich landscape for ideas to match specific customer needs with promotions, pricing, and targeted marketing. 
  • In the credit card business, understanding cardholder risk is a key to profitability. To whom do you extend more credit? Which cardholders bring in steady interest income without fear of bankruptcy? Traditionally, credit card companies have built complex scoring models to segment customers based on their credit history and a snapshot of credit risk. Customer flashcards added a new and nuanced tool to these operational decisions. Trending of purchases, balances, balance transfers and available credit revealed a number of interesting behaviors. For instance, some cardholders were making big purchases on credit, then gradually paying down this debt over a series of months. Just as they prepared to pay off their balance, these cardholders would treat themselves to another big ticket item. Visualizing behaviors made this “sawtooth” activity obvious and gave the bank an ability to treat these customers with proper appreciation.

For those responsible for embedding business intelligence into the fabric of the organization, customer flashcards provide an immediately accessible and visually appealing way to engage senior executives. In addition, visual representations can create a common language for describing customers. The customer images let employees in different functions consider problems in the context of real, data-derived understanding.

Finally, we have found that customer flashcards are effective at unearthing data irregularities or process failures. When something doesn't look right in a picture of behavior, there is often something wrong with data quality, or with an internal process.

Reestablishing Customer Intimacy

Chris and I grew up in Lincoln, Vermont, a town of 900 people tucked away in the Green Mountains. At the center of this no-stoplight village is a general store. Vaneesa, the proprietor for more than three decades, greets her friends and neighbors at the counter everyday. She has grown to know each of their habits and needs and can tailor her stock and service in response. Everyone in town appreciates it.

This type of customer intimacy has long been lost as companies scaled beyond personal relationships. In an attempt to rebuild this bond, companies pile customer data – a digital representation of customers – into customer relationship management and business intelligence databases. Storing this information does little to get your business closer to understanding customer needs. Traditional data analysis falls short by aggregating behaviors and depending on the business to ask the right question. Surveying, another approach to staying in touch with customers, is hampered by customers’ imperfect knowledge of their own needs and by their limited memory of their own actions.

On the football field, a shared understanding and a targeted game plan are keys to victory. It’s the same in business. Customer flashcards can give you a new perspective on your customer data and help you succeed by knowing more.

10 Ways to Reduce to Improve Your Data Visualizations

Less is often more. Here are ten lessons we've learned about how to better communicate with data by giving readers less.

1. Reduce chart junk. Edward Tufte taught us this fundamental lesson. Eliminate elements on a chart that aren't contributing to comprehension.

Even tables have chart junk.

Even tables have chart junk.

2. Reduce color. Instead of using a rainbow of colors, pick an emphasis color and use color with clear purpose and consistent meaning.

3. Reduce jargon. Consider how your audience speaks in their everyday work. Are there more natural ways of expressing your points?

4. Reduce pie charts. They are seldom the most effective way to show your data, and pie charts can often be misleading or difficult to interpret.

A pie chart showing total medals won at the Olympics (sort of), brought to you by

A pie chart showing total medals won at the Olympics (sort of), brought to you by

5. Reduce unnecessary precision. Provide enough precision in your number formats to tell the difference between values and shorten numbers to make the values more readable (e.g. MM, k).

A favorite example of a software piracy application that could target the source of the piracy down to the micron, the width of a very small bacteria.

A favorite example of a software piracy application that could target the source of the piracy down to the micron, the width of a very small bacteria.

6. Reduce metrics. "Data isn’t like your kids. You don’t have to pretend to love them equally." -- Amanda Cox, New York Times. Create constraints to show only the metrics that matter most.

Beautifully rendered, but dense dashboard via

Beautifully rendered, but dense dashboard via

7. Reduce density. It is a rare instance that all the relevant information needs to be crammed on the same page. Give your readers some whitespace to help them focus and comprehend the content.

8. Reduce dimensions. Individual visualizations quickly become difficult to interpret when many dimensions are shown simultaneously. If necessary, gradually "build" the visualization by adding complexity to ensure your audience can follow along.

Temperature, humidity, geography, flows of something, heights of something, ...I give up.

Temperature, humidity, geography, flows of something, heights of something, ...I give up.

9. Reduce quantity of messages. It can be tempting to try to tell numerous stories about data at the same time. Try to tell them one at a time to help each one land with the audience.

10. Reduce chart types. It can be tempting to use different types of charts as a way to add variety to a report or dashboard. In the process, you are putting a burden on your audience to learn how to interpret each new chart.

Different types of charts are used even though the information structure is very similar

Different types of charts are used even though the information structure is very similar

Three-and-a-half lessons learned from network diagrams

Once a month here in Atlanta, we invite a few folks from the data community together to discuss the "data value chain" and sharpen each other's thinking in the area of using data better. In a recent gathering we were discussing the merits and challenges of network diagrams. The stake I firmly planted in the middle of the table was this: for the vast majority of problems that folks have to deal with, network diagrams don’t help. Ever.

Ok, so maybe that was a little harsh. And as we discussed it, I had to soften my position. We concluded that there are most definitely situations where network diagrams can be successfully used. Here’s what we uncovered.

When most people think about network diagrams, this is what first pops into their heads:

Simple Network Diagram

It’s great for showing the hierarchy that would otherwise only be represented in some sort of over-bloated frankenstein of a table. And I think it works pretty well for a situation with a finite number of nodes that represent physical elements that can readily be counted such as “number of routers.” This is our first lesson:

Lesson Learned #1: If you can reasonably count the nodes, a network diagram can reasonably add clarity about relationships.

So, the concept of a network diagram feels like it makes tiered data more accessible. But let’s look at more complex relationships. Take your LinkedIn network. There are lots of layers of relationships that it seems a network diagram would seem to make sense of. In case you missed it, a couple of years ago, LinkedIn Labs made network maps available to LinkedIn members in their InMaps. Here’s mine:

LinkedIn map

It is beautiful. They’re using a Gephi-inspired in-house development to lay out the nodes, chose the colors and stuff (if you’re interested in more on this topic, check out the Quora post - oh yeah, that guy Sal Uryasev who worked on creating inMaps is a former Juicer. Nicely done, Sal!).

I love, love, love the groupings. In my opinion, this is the most useful part of the layout. At this number of nodes, it’s not the individuals that are meaningful, but rather how those nodes group together. The approach Sal et al. used nicely summarizes a good portion of my career in about 5 large chunks such as “7 years on the roller coaster” at a dot com, and “Todo: attend a reunion” for connections I made while at Georgia Tech (the labels are mine - wouldn’t that be cool if InMaps could do that?).

But, as far as network diagramming goes, you’ll see that they’re just plotting the first-generation relationships (of which I have 500-ish) and it’s still fairly dense. Imagine what would happen if second-generation+ relationships were added (there are supposedly 11 million “in my network”). Yuck. So here is our next lesson:

Lesson Learned #2: Network diagrams with many nodes are most useful when showing aggregated groupings and relationships.

And the corollary this quickly brings us to:

Lesson Learned #2.5: When many nodes are aggregated into a few relationships, network diagrams can be used as a presentation medium. Otherwise, stick to exploration.

Ok, we have time for one more lesson. Here’s another example offered by a small company you might have heard of:

If you think about it, this is nearly the perfect problem for a network diagram to solve: making it easy for a person to find images similar to the one they’re looking at. But, this offering, inspite of it’s well crafted-ness, went nowhere.

Why? Well, one reason might be because those of us who are visual pundits would love to see these complex relationships simplified by just the right visual representation. But the fact remains that for the vast majority of people out there, advanced visualizations are just not enticing enough -- and too complex feeling -- to incite broad use. There, I said it. So, finally:

Lesson Learned #3: Even for relationships that “normal people” can easily understand, network diagrams aren’t easily traversable by “normal people.”

So, there you have it. Three-and-a-half lessons we’ve learned with network diagrams. Apply them to your next network display challenge and see how they work for you. If you need some technology to help you, check out the wikipedia article on network diagramming tools. Let us know if you find any that reveal other lessons to you.

The Best Product Manager: Hustler, Designer, Hacker

Much of what makes a great product manager is empathy and a desire to serve others. Tulsi demonstrates these qualities better than most I’ve come across.  As you will see below, her passion for design as part of product management is only surpassed by that for her customers, products and causes.  Oh, and there is usually much laughter involved. Enjoy and feel free to reach out to her at

Even after years of product management experience at several companies, I still get frustrated when folks frequently say “So, you are a Project Manager”. I usually respond with a vehement “No!” and go on to describe what it is I actually do everyday.

With this in mind, let’s begin this discussion by describing what a Product Manager is.  A (good) Product Manager is the champion of the customer and the market: part product visionary and part liaison officer between external and internal needs, pressures, and limitations.

As Catherine Shyu, Product Manager at Send Grid puts so nicely:

“Much of a Product Manager’s responsibility is to juggle multiple streams of conversation and move them towards closure.”

Successful disruptive and innovative brands like Basecamp, Airbnb, Fab, and many others have proven that features alone don’t improve the sales. Instead the infusion of design and love into the products is what creates real customer engagement and advocates. That’s why many of the companies mentioned have consolidated Product Management and Design into single roles or departments. Now the Product Management role is evolving even further.

In the words of Gary Tan

“The ideal startup team consists of: a designer, a hustler, and a hacker.”

The most successful Product Managers I’ve worked with and learned from seem to embody the qualities of all of these three roles. Just consider what these roles bring to the table:


This role can seem as nebulous as the Product Manager’s, so it’s no wonder they’re coalescing. Whatever the type of designer, success is based on the ability to emphatize,  perceive deep customer needs, and anticipate customer behaviors.

That’s why Product Managers with design and usability skills are able to create experiences rather than the features, simplify the interactions, and sketch and wireframe ideas to tell stories that others can understand.


Contrary to any negative (and possibly cheeky) connotations, the hustler knows the market, knows how to sell, and knows how to work with what they have to turn a profit. In other words, she knows how to connect products with customer and market needs. The hustler’s skills can help a Product Manager think beyond product design to the critical marketing and sales activities that will make products and companies thrive.


Hackers can think creatively, come up with solutions quickly, and iterate through problems they encounter along the way. They are also curious about technology and how things work. Hacker instincts help Product Managers communicate well with engineering teams, and work lean to get the best possible outcomes with the least possible time and resources.

The bottom line: the days of the traditional product manager are gone. Lines are naturally blurring around the Product Management role and discipline, and that’s a good thing! The better you are at blending these three roles, the more equipped you will be at juggling the responsibilities that are on today’s product manager. So, hustle, design, and hack your product into shape. And then tell somebody what you do!

Many thanks to @Imusicmash and @apmcinnes for their comments and feedback.