Business Intelligence Blog from arcplan
19Sep/130

Build vs. Buy: Business Intelligence for Big Data Analytics

by

build-business-intelligence-arcplanWhy It’s a Bad Idea to Build a Business Intelligence Platform From Scratch

A friend of mine is a Python developer for a billion-dollar corporation. His team is building a custom call center reporting app that connects to the company’s cloud data storage via APIs. I’ve seen some of the application and while it’s impressive for a custom system, it’s mostly tables of numbers with the occasional pie chart. This is after 8 months of work, and the only people accessing the system are a select few big data scientists.

Believe it or not, a number of companies are doing this kind of in-house development of analytical platforms. All the hype surrounding big data has them convinced that they’re missing out on the action. Consequently, companies large and small are devoting huge amounts of time, money and human resources to developing custom business intelligence systems for big data (Google BigQuery, Hadoop, etc.) reporting rather than simply choosing a platform that already exists and is proven to work in a similar environment.

At the heart of this trend is a desire for big data to have a greater impact in the organization. Since it’s usually small teams of data scientists who are dealing with big data, their impact and effectiveness is equivalent to a small drop in a much larger body of water – their ripple effect throughout the organization is often minimal and short-lived. To extend the reach of big data in the company and get important insights out to a greater number of decision makers, a BI platform is a necessary next step – one that leverages big data insights in easily-digestible executive reports and dashboards.

Some companies are going down the road of custom BI platform development, but their efforts are no match for solutions like arcplan that are already available. Below is a list of what you’d need to do to build a BI platform from scratch. You’ll quickly see why the effort and expense aren’t worthwhile.

Continue reading this post >>

12Sep/130

Customer Analytics: See the Truth with Data Visualization – Part III

by

I’m wrapping up my series on customer analytics and their associated visualizations with a discussion of 3 more metrics you should track on your customer dashboards – customer lifetime value, accounts receivable, and bookings & backlog – and why these simple visualizations are effective because they extend the reach of data to more people in the organization.

Track Customer Lifetime Value with a Stacked Bar Chart

One of the most underutilized metrics is customer lifetime value (CLTV), which sits at the intersection of sales, marketing and finance. CLTV calculates the historic value of a customer over time and helps establish how much customer relationships are worth – and therefore how much money can be spent to acquire them.

It’s conventional wisdom that it costs more to acquire new customers than it does to maintain existing business. For many companies, customers become more profitable over time. It costs less to pick up the phone and sell additional licenses to an existing software customer than it does to fly a sales person to meet with a prospect for the first time. It costs less to run loyalty programs to keep customers happy than it does to sponsor a trade show to meet new prospects. But the costs of these programs, all of which are necessary for running a business, have to be taken into account when using the CLTV metric. Customer lifetime value shouldn’t be something that only finance teams look at. Sales and marketing professionals must use it to ensure that their acquisition program costs are in line with CLTV.

There are many ways to calculate customer lifetime value, but the simplest is…

Continue reading this post >>

6Sep/130

Customer Analytics: See the Truth with Data Visualization – Part II

by

In Part I of this series, I covered some of the key metrics and visualizations that contribute to a 360 degree view of your customers. Today let’s explore two more metrics that reveal insights about your customers’ relationship with your company, and the graphs that best explain the data.

Track Net Promoter Score with a Gauge or Bar Chart

At B2B companies especially, you’ll often need to call upon your customers to serve as references in order to close new business. Some CRM systems like Salesforce.com enable you to add checkboxes to indicate whether a particular customer will serve as a reference or not. It’s simple to run a report on that information, summarizing the number of checkboxes over the total number of customers, but visualizing a more robust metric on a dashboard keeps customer relationships top-of-mind with your marketing, sales, and customer support teams.

Many arcplan customers are going back to an old standby metric, the Net Promoter Score (NPS), which segments the percentage of customers who would recommend your products and services (“Promoters”), versus those who are satisfied but unenthusiastic (“Passives”) and those who aren’t happy and might go out of their way to voice negative opinions (“Detractors”). On a scale of 1-10 for the question “How likely would you be to recommend <<company>> to a colleague?”, Detractors are the people who choose 1-6, Passives choose 7-8, and Promoters choose 9 or 10:

nps

Continue reading this post >>

30Aug/130

Customer Analytics: See the Truth with Data Visualization – Part I

by

The idea of having a 360 degree view of your customers is not a new one, but actually getting it is a real challenge in our world of silo’d data. Customer analytics give you a better understanding of your customers, and the ability to spot trends, identify opportunities to cross-sell, up-sell or simply target them more effectively, ultimately optimizing your customer relationships. But to get these insights in one place, like a dashboard, you need to integrate separate data from CRM, accounting, and customer support systems. Without a BI platform in place – one that integrates data from many sources like arcplan – it falls on you, the decision maker, to waste time assembling the pieces necessary to come up with a view of your customers from these different systems.

But if you are looking to utilize your business intelligence software for customer analytics, this series of articles will help you define the metrics you should be tracking as well as the visualizations that most effectively portray the data.

Graph Customer Growth with Bridge Charts

The most common way to track customer growth over time is by using a bar graph to show year over year comparison. A simple bar graph might show you that 2 years ago, you had 100 customers, last year you had 107 customers and this year you have 120 customers. Using arcplan’s linear regression formula, you expect to have 125 customers next year.

The numbers and the forecast look great, but the real story may be a little more shocking once you properly visualize it.

Continue reading this post >>

23Aug/131

Dynamic Data Visualizations

by

There’s a lot of discussion happening in the BI world right now over data visualization. On the one hand, you have analysts pushing the idea that data visualization = visual data discovery = self-service BI = advanced BI. I’ve seen Gartner and Aberdeen both touting the idea that data visualization and data discovery are the same and that they’re the key to unlocking analytics for more users in your enterprise.

On the other hand, you have organizations who think data visualization = dashboards. They want to present their data graphically, have some interactive capabilities like drill-down and drill across, and use advanced features like animated graphs and motion charts.

At arcplan, we offer our customers all types of data visualization, from sophisticated desktop and mobile dashboards to visual ad-hoc reporting. Today let’s examine some of the dynamic, interactive visualizations you can employ in your BI dashboards to enhance data visibility and tell stories that are more expressive than static charts.

MotionCharts_220Motion Charts for Trend Analysis
A motion chart is a dynamic chart that shows the flow of data across a dimension – for example, time. It’s a great way to look at large amounts of data at once to discover patterns.

For example, a sales manager may want to conduct a trend analysis for the company’s product line over the course of a year to analyze profits and losses for a set of product categories. A motion chart provides a more dynamic option than a table of numbers. By simply sliding the time bar along the x-axis, the sales manager obtains a visual of the fluctuations in the product categories over time. It’s the difference between reading a book and watching a movie on the same topic: though the information is the same, a visual aid allows some users to better absorb it.

Zoom Line Chart for Dynamic Drill-Down
Don’t be fooled by this ordinary looking line chart…

Continue reading this post >>