It’s Another Manic Monday… Meeting
It’s time for the weekly performance meeting again.
As one of the key individuals in your company, you are duty-bound to attend yet another presentation about key metrics and target numbers.
Pie charts, bar graphs, scatter points—nothing you haven’t seen before.
At the end of it all, you come away unimpressed, all the talk about important figures and performance levels fading into the background.
Sounds familiar? It should be. According to Rich Henson of ResourcefulManager, executives and high-level managers say that about half of the meetings they attend accomplish nothing.
In the sales industry, this revelation is a bad statistic.
Sales businesses are all about numbers when looking at performance. Decision makers need to make judgments that matter, and they have to have a good grasp of the data to be able to do exactly that.
However, presenting using ineffective or traditional visualizations simply won’t cut it. What’s needed is data presented in a form that captures the scope and urgency that the management must see.
Monkey See, Monkey Get: Why Seeing is Believing in Sales
In recent years, CRM tools have allowed managers and agents alike to view information and apply solutions in real-time.
It has helped enterprise-level operations organize their general process flow and—especially in sales—streamline the collection of important customer data.
Additionally, CRM tools these days allow their users to produce data visualizations that are updated almost as soon as more data comes in.
In essence, this removes the need for managers to hold meetings just to have an idea of where they stand and how their teams are performing.
However, an obstacle remains: how do they see exactly what their performance is in a simple, digestible way? How do they use the data present to identify their needs in the most effective way?
The answer is to start considering using newer and more applicable data visualizations—shortened as viz—in reports.
Heck, even better, ditch the reports and use these viz in real-time dashboards.
Imagine a mission-critical scenario wherein you need to identify not in minutes but in seconds. Say, you need to identify which United States region is having the worst performance in the last month. Additionally, let’s assume that all you have at your disposal is your dashboard and maybe access to the database.
Traditionally, you have to pull some data, aggregate the states by region, calculate some values, then come up with a report.
This won’t fly, however, in the given time frame. Time is tight and you can’t really fumble with a cumbersome process.
Hopefully, you have bar graphs or pie charts at the ready in your dashboard—but you still have to identify the data points you need. It’s not cleaned up. You need to do something here and there to present something that makes sense.
Still, we have a problem: that is still not possible in seconds, unless you had the data pre-prepared for you and ready to be plugged into your dashboard.
But what if you were told that you can view and understand this data—even play with it a bit or see historical changes—in seconds and in a most familiar way?
Isn’t that such a welcome difference?
Inside Sales Techniques: Five Viz Types Helpful for the Sales Biz
Without further ado, here are the data/dashboard visualizations you absolutely need to have integrated into your CRM to improve your performance, grab that sales advantage, and totally own that bottomline.
Technically known as choropleth maps, these are fast becoming the go-to viz for showing statistics by geo-location.
For the unfamiliar, heatmaps are basically maps of countries or cities that are then highlighted or annotated to show numbers. Data is usually shown as a gradient of colors or as scatter points of numbers.
The ease-of-use of preparing and understanding heatmaps is unbelievably high: sometimes, all you need are the names of cities and/or states and some corresponding performance numbers. With just that, the heatmap can practically be up and running.
With a simple assignment of color gradients—maybe green for high/good values, yellow for average, and red for low/bad levels—you can see at a glance which places are either assets or liabilities.
Let’s extend the scenario further.
Also, let’s add a heatmap of the United States using the greed-yellow-red legend to your dashboard. In one look, you see that the Mountain area is showing a bit of orange, with the Colorado area—one of your chief markets in this region—particularly more reddish than other places. Assuming you’re using even more advanced data viz tools, you click on Colorado to drill down.
Suddenly, you see that Denver is showing the reddest color among all Colorado cities. Then, it hits you: you’re selling camping gear, last month is September, and so it’s possibly hitting 60 degrees Fahrenheit outside and people just don’t want to camp out in the cold in the next three or four months.
With a quick look and one click, you identified the region and the city that just might be the worst target market you have this season.
You don’t even have to look at numbers or sort states and all that: you just have to have a basic idea of the location and a keen eye for the color you want to identify.
Sophisticated, high-level heatmaps can even be integrated with web analytics and market intelligence tools to produce real-time feeds of data to the dashboard.
Some huge enterprises even employ integration with Facebook and Twitter analytics, allowing businesses to identify regions in real time wherein important terms and opinions relevant to the seller are being mentioned and/or discussed in social media.
You can either have in-house social media tracking developed or just integrate with existing social media tools that support robust data capture.
Bubble Charts / Word Clouds
Bubble charts are not really new—MS Excel experts have been using them since the scatter charts were introduced. Really, that’s what bubble charts basically are.
However, taking a hint from the heatmap’s playbook, bubble charts are just as good as a visualization. It does its job.
Instead of using a map as a basis, it uses simple circular shapes—hence, the usage of “bubble”—whose size represents a value. If the data being used is two-dimensional, the bubble’s color can be used to represent that as well.
A spinoff of the bubble charts are word clouds, or using words themselves as a representation of data. This is most effective when counting the number of instances an important word appears or is mentioned.
Connecting this with a social media source helps managers and agents track in real-time which buzzwords are trending and what topics are going viral. You can target your ideal customer profiles and see what topics they’ve been interested in, posting about and reacting to.
Unlike the first two charts, waterfall charts are more similar to traditional charting styles.
Still, its intuitive design makes it a great addition to any dashboard.
Except for the first and last bars, instead of showing the actual value in a period of time, the waterfall bars only show the change in value between two data points, also known as the delta.
The first bar is the starting value, while the last bar stands for the ending value. The bars in between are visualized such that it shows exactly how much the bar shortens (lowers in value) or lengthens (increases in value).
A common concern that is raised regarding the usage of the waterfall chart is its advantage over traditional bar charts.
In a waterfall chart’s case, you are tracking a single variable across time, but you want to know exactly how the changes add up together to show the end result. Unlike bar charts that show static values by default, waterfall charts emphasize the movement of the value or values. In a way, it is actually telling the story of how a metric performed before arriving at its final value.
In comparison to the line graph which has a similar purpose, the waterfall has the advantage of showing in an intuitively visual way the rise and fall of a tracked value. Additionally, unless a line graph is annotated with the deltas, it’s much harder to intuitively extract the actual amount of movement in the data points involved.
Have you ever tried visualizing multiple performance variables that are just sub-components of another variable?
If yes, there is a big chance that you used a pie chart to show the breakdown quite easily.
But what if the sub-components are not actually parts of a whole?
What if they involve different ranges of values? Or do not even share any common unit of measurement?
When sub-variables can be quantified but share little commonality with each other besides being under a variable of a larger scope, consider using a radar chart.
Also widely known as spider charts, they can show multivariate data while being agnostic about the unit of measurement, or the range of values for each variable. An example is a customer satisfaction report: a customer might have been asked to rate their satisfaction with a service by scoring it in five domains. Let’s say these domains are as follows: price, user-friendliness of the service, accessibility, customer or after-sales support quality, and ease of sales process.
Already, you are faced with a difficult scenario: price is most definitely a monetary value, while the others might have been scored within a range (i.e. 1 to 5, or 1 to 10), or even a binary value like “Yes” or “No”.
Bar charts can be used for the first two situations but it cannot quantify the third one. Additionally, the difference of values between price and the other scores can be quite huge and will physically mess up the design of the chart.
Line charts can solve this issue by “normalizing” the data, or adjusting the data proportionally to show relative values. However, since there is no timeframe involved, line charts become a poor choice.
Enter the radar chart. By inputting the data into a pentagon-shaped chart (remember, we have 5 domains in this example), with each corner corresponding to a metric, we can easily see the relative values of each item by shading the bounded area formed by connecting the respective dots where our values lie. Even better, the surface area or size of the bounded area gives us a rough estimate on how well we’re doing with regards to customer satisfaction.
In some instances, really well-done radar charts can compare and contrast two different datasets immediately. A simple radar chart overlap of the performance of two sales agents, for example, can show right away their apparent strengths and weaknesses and how they compare with each other.
Dashboards are inspired by the object that shares the name: car dashboards.
And one thing that are always on dashboards of any modern car is the gauge.
Actually, even in data visualization, when report dashboards are mentioned, gauge charts automatically come to mind.
The idea of the gauge chart is very simple: show how close you are in achieving a target number.
For example, assume that you have a target of 100 leads to qualify, a minimum quota of 80, and you are currently at 60. A gauge chart simply shows just that: a gauge with the maximum value at 100 and the pointer at 60, with perhaps a colored line separating the range from 80 onwards to show that the quota is met once past this point.
For lead generationl dashboards, this is a necessity.
This is similar to the RPM meter in a car’s dashboard. However, with this one, the redder the region, the better the performance. A variation of the gauge chart is the bullet chart. The same concept applies, only that it dismisses the novelty of using a gauge and uses a simple bar representation.
From time to time, data analysts and reporting specialists develop even more revolutionary data visualizations.
While nothing can really replace the simplicity and straightforwardness of traditional charting tools, the viz designs discussed above are a great addition to any CRM dashboard: they are simple and intuitive yet very powerful and informative.
They lessen the complexity of understanding, reporting, and relating data, which is always a most welcome thing for managers and agents alike.