One Step Forward and Two Steps Back

In a recent article by The Financial Brand, publisher Jim Marous discusses some surprising statistics regarding the financial industry. The persona of most FI’s has begun to change as they, like everyone else, fully immerse themselves in the 21^st Century – part of which is understanding a communicating better with customers.

Along the lines of communication, it’s no surprise that in the 2017 Financial Marketing Trends survey, when asked “Which of the following will be a priority for your organization over the next 12-18 months?” personalization, mobile marketing, cross-channel/cross-device marketing were all selected as “very important” by at least 50% of respondents with content marketing chosen by 49%, all landing the top spots in the list.

What makes this notable, are not what was selected, but the fact that data analytics/capabilities, marketing automation, and predictive modeling/mapping analysis all fell into the bottom half of the list.

FI’s have noted the shifts in marketing from traditional mediums to digital and have rightfully adjusted their strategic goals and objectives. What many of these FI’s have overlooked is the important role data analytics will play in helping them achieve these goals.

Personalization, mobile marketing, cross-channel/cross-device marketing and content marketing are nothing more than “buzz-words” if there are no analytics to back them up. FI’s that can first concentrate on a procedure to gather, analyze and interpret data (if they’re savvy they’ll work with some type of automated platform) are those who will see the greatest impact to not only their strategic goals and objectives, but also their bottom line.

Based on this survey, it seems FI’s have their eyes on the “right” prize… but they may find themselves working backwards if they don’t use data along their journey.

CDs, Not the Only Item of Low Interest in the Financial Industry

In this digital age we live in, of ever-increasing data and analytics it’s no surprise that financial institutions (FI’s) and the financial industry overall are tracking analytics and using data to make informed decisions. However, what you may not expect is, the use of marketing automation platforms to assist in interpreting and making use of this data is extremely low.

According to The Financial Brand’s article, Achieving Advanced Financial Marketing Maturity, by Jim Marous, less than a quarter of FI’s surveyed in the 2017 Financial Marketing Trends survey said they use some type of marketing automation platform. More shockingly, more than half of respondents simply answered “No”, insinuating they had no plans of using any type of platform.

So the question is “why” – why do FI’s have such little interest in data analytics and general marketing automation platforms? Many FI’s, to their credit, have come a long way in “opening up” to the 21^st Century, from a time when they were typically thought of as a large, impenetrable brick building full of “suits”, to today where many customers now stop by to meet with their local advisor to have a conversation rather than for a business transaction.

FI’s are making great strides in becoming human and personable again; perhaps this is a reason why they lag so far behind when it comes to the use of data and automation? Whatever the answer may be, times are changing. While cash is still king, that king is getting younger – if your FI isn’t using some type of platform to interpret data, they will continue to fall behind.

Looking at the Financial Industry

In my next series of blog posts, I will be focusing on financial institutions (FI’s) and the financial industry overall. As someone working within the industry from the data and marketing perspective I find it interesting to see how FI’s are handling this new digital age and data analytics overall.

Unfortunately, it seems that most FI’s are not using the data available to them to the best of their abilities. I’m proud to say at my FI, we are using data to make informed decisions not only about our own customer base but our footprint as well. I find it interested and troublesome how the financial industry is approaching data analytics. I’m hoping to use this newly found research to continue to improve my efforts as well as inspire other FI’s to begin using this wealth of knowledge available in data analytics.

Choosing the Right Graph

In my last post I highlighted Tableau and it’s abilities, as a data visualization tool. As mentioned, much of data these days is how you interpret that data and present it, or using the data to tell a story. According to Tableau, here are 13 graphs you should know of and how to use:

1. Bar chart. Bar charts are one of the most common ways to visualize data. Why? It’s quick to compare information, revealing highs and lows at a glance. Bar charts are especially effective when you have numerical data that splits nicely into different categories so you can quickly see trends within your data.
2. Line chart. Line charts are right up there with bars and pies as one of the most frequently used chart types. Line charts connect individual numeric data points. The result is a simple, straightforward way to visualize a sequence of values. Their primary use is to display trends over a period of time.
3. Pie chart. Pie charts should be used to show relative proportions – or percentages – of information. That’s it. Despite this narrow recommendation for when to use pies, they are made with abandon. As a result, they are the most commonly mis-used chart type. If you are trying to compare data, leave it to bars or stacked bars. Don’t ask your viewer to translate pie wedges into relevant data or compare one pie to another. Key points from your data will be missed and the viewer has to work too hard.
4. Map. When you have any kind of location data – whether it’s postal codes, state abbreviations, country names, or your own custom geocoding – you’ve got to see your data on a map. You wouldn’t leave home to find a new restaurant without a map (or a GPS anyway), would you? So demand the same informative view from your data.
5. Scatter plot. Looking to dig a little deeper into some data, but not quite sure how – or if – different pieces of information relate? Scatter plots are an effective way to give you a sense of trends, concentrations and outliers that will direct you to where you want to focus your investigation efforts further.
6. Gantt chart. Gantt charts excel at illustrating the start and finish dates elements of a project. Hitting deadlines is paramount to a project’s success. Seeing what needs to be accomplished – and by when – is essential to make this happen. This is where a Gantt chart comes in.

To read the other seven charts Tableau is capable of using to display data be sure to read the rest of the article by clicking here.

Tableau Around the Classroom

Now that we know how important it is to visualize data, the way we present it becomes even more crucial in interpreting it. But before I get into different ways to present data, let’s take a look at what other students are saying:

• Hear how Tableau is changing the music industry through data in Naomi Santiago's post, Taking a Look at Tableau's Music Viz Contest.

• Read from Amanda Kunrath's post about the importance of data visualization and the story it tells in, Visual Appealing Data Visualization: It's All About a Good Storytelling.

• Don't forget supply chain management - Tableau is much more than a data visualization tool in Norah's post, Data Analytics Tools - Tableau Software.

Perspective is something that data tools, like Tableau enable us to understand. Perspective is what I've also gained from reading these blog posts and many more. It's intriguing to see how one platform is being used in a variety of ways, from use in different industries to different uses within one organization.

Visualizing Tableau

Tableau seems to have taken as a frontrunner by those who have commented on my previous blog post. With this in mind, I will expand more on the functionality of Tableau in my final two posts as why it is one of the better options when searching for a data mining platform – in comparison to Excel, of course.

Where Tableau gains a large lead on Excel is when it comes to the visualization aspect. Excel and Tableau are both leaders in their own regard for holding mass amounts of data and being able to successfully sort. Tableau has raised the bar by offering, “visualizations [that] are interactive, easy to share, and help everyone in your business get results. Make confident data-driven decisions with a platform that supports your cycle of analytics.”

The more I research about data, the more important its interpretation becomes. More often than not, interpretation of said data relies not on what is spoken by a presenter but rather how it is presented by that presenter. This new age of data visualization is where Tableau leaps lightyears ahead of where Excel is. For this reason, Tableau takes a clear lead when it comes to trying to decide which data mining platform is better suited for insights.

Read more about Tableau at https://www.tableau.com/solutions/customer/tableau-vs-excel#sT6QSgxSpg7182Z2.99

Excelling Headaches

O.K. Grammar folks, before jumping all over my headline, take a second to read the post – spoiler alert (!) it’s a pun on the article.

For someone who has recently gotten into analytics and tracking metrics, Excel is a great platform for keeping track and illustrating this data. However, as many will quickly notice, Excel often accelerates headaches (excelling headaches – get it now?). While Excel holds its own, it has become quite pedestrian compared to its counterparts specifically developed to filter, track and help illustrate data.

Here are four great data analytics tools, defined by Boost Labs, self-proclaimed as “data analysts, innovative coders, and clever designers.”

1. MicroStrategy Analytics Desktop. “MicroStrategy Analytics Desktop is a fast and user-friendly software for visual data analytics. Quick to download and install, this visualization software allows you to get to work quickly. Included sample data and pre-built interactive dashboards further serve to lower the learning curve.”
2. Domo. “Domo offers an online business intelligence tool that has a sleek UI and is specifically designed to allow users to build sophisticated dashboards with no IT involvement. Because the software is accessible online, Domo and the dashboards it creates, are available to your entire organization.”
3. Tableau. “Tableau offers a suite of tools that include an online, desktop and server version. All of these versions provide an easy-to-use drag and drop interface that can help you quickly turn your data into business insights. The online and server versions allow your entire team to build and work with the visualization tool.”
4. QlikView. “The QlikView business discovery platform is one of a few visual analytics tools offered by Qlik. QlikView can’t create the same elegant visualizations that the other tools offer, but the software’s dynamic model means that you can quickly analyze your data in multiple dimensions. In addition, QlikView is able to work off of data in memory instead of off your disk, allowing for real-time operational BI environments (like monitoring financial transactions).”

These four analytics tools are certain to take your spreadsheets to the next level and eliminate much of the challenges presented by working on and with Excel.

Stay tuned for what I define as the best analytics tool in my upcoming post! To read the full article by Boost Labs, click here.