Could Big Data Help Insurance Agencies Actually Be A Good Neighbor?

Could Big Data Help Insurance Agencies Actually Be A Good Neighbor?

State Farm has a catchy little phrase that says, ” Like a good neighbor state farm is there”, but are insurance companies really good neighbors? Many may say no because insurance companies are based on making money off of the safe people they insure. The people who best practice safety are the ones who should be rewarded!

Now that insurance companies are using more technology to watch over their clients, they can better predict who their safest customers are and who may pose a risk of costing them money. This issue with this is that being “big brother” gives insurance companies the ability to raise people’s rates just because the insurance company sees that person as a liability.

What if insurance agencies used technology and more specifically, Big Data, for good? Imagine if insurance companies analyzed their data to determine which customers were more likely at risk of experiencing frozen pipes or a house fire and then alerted these individuals of their higher level of risk. Insurance agencies could easily use Big Data to help educate people on how to live more safely to prevent the many different disasters that no one thinks will ever happen to them.


To learn how else Big Data is making industries better, check out this awesome article!

Big Data Makes Big Profits For Casinos

Big Data Makes Big Profits For Casinos

I recently took a trip to the infamous Las Vegas and as I walked through the rows of flashing lights and sounds of slot machines I couldn’t help but think about the amount of data each casino must produce. In Vegas especially, casinos are packed full of slot machines seducing people to come test their luck and their money. With so many different machines to choose from, how do casinos know which slots to fill their floors with?

All of the slot machines are equipped with the bells and whistles of flashing lights and catchy tunes, so there must be some sort of sensory appeal that draws a person to one specific slot machine and not another. This is where Big Data comes in. Casinos can track which machines are more popular than others. The data can also determine if the slot machine remains popular even when people are losing money on it.

If people are losing money while playing on a slot machine and they continue to sit there, it must be a really great game. Well, what constitutes a great game? For some people it is the machines that have a fun theme, like a favorite movie or television show and for other people it is the quiet, straight forward keno games. Big Data can show casinos which machines are more appealing and even give them an idea of where to strategically place certain games to attract the most players. The more attractive a casino’s games are, the more money they will make, which is a part of why Big Data is heavily used in Las Vegas.

As for me I like the machines with interactive bonuses and games, but because of Big Data I’m sure the casinos already know this about me!

Big Data Improving Customer Service

Big Data Improving Customer Service

Trying to operate in today’s world without utilizing optimization tools such as Big Data is like trying to drive a car with a flat tire. Although Big Data is not the answer to all of life’s problems, it is an incredible resource used to enable companies to perform with the utmost efficiency. 

Zendesk, a software company that controls customer service calls and websites has learned how to utilize big data to perform where human intuition lacks. Customer service calls are usually unpleasant, for both the customer and the service specialist. Often times customers become very upset with the service specialist ( even though the service specialist usually has no control over the issue) leading the service specialist to drown out the yelling. The other  issue is that not all angry customers yell, some can be very call and then turn around and write a horrible review about the company’s service.

With Social Media’s strong influence, a few negative tweets or posts can be devastating to a company’s reputation.

Zendesk’s hope is that their Big Data software will be able to pick out trigger words that have often been correlated with “dangerous” customers. If service specialists are given this information about unusually angry customer, they can make that customer a priority and hopefully solve any issues.

Some may think that it is a bit excessive to use a computer to determine if someone is upset, but sometimes emotions can be lost in translation. Customer service is not something that a machine can do , but when a customer service specialists uses Big Data as a tool for catching an emotions they may have missed, there is a good chance we can make the customer service experience much more enjoyable.

What Makes Amazon Such A Good Friend?

What Makes Amazon Such A Good Friend?

Have you been noticing how the internet seems to know you? It is almost like you two are friends, knowing each others likes and dislikes. I guess it is kind of a one sided relationship, as the internet probably knows much more about you than you know about it. But that is a much more creepy analogy so let’s stick with the friends  one.

Friends help each other out and always suggest products or places to each other, sharing information that would benefit one another. This is sort of what happens with the internet. The internet keeps track of what you buy or look at so it knows what products or interests to advertise to you.

Personally I find this saves me a lot of time. I have recently been planning a wedding and Amazon became my best friend. We would get together everyday. Amazon would listen to what I wanted to buy and then recommend other products I was completely forgetting about that went perfectly with the theme of my wedding. Amazon made it very quick and easy recommending the best products and even remembering my information so I didn’t have to find my credit card number for every purchase!

The reason Amazon is such a good friend is because of Big Data, they collect my data like a dear friend listening to my needs and save it to better know me. I hate to say this but I’m not Amazon’s only friend, they have a lot of friends they do the exact same thing with! Thanks to Big Data, my relationship with Amazon has been an effortless joy.

New Procedures Reducing Installation Times and “No Shows” Result in Excess of $10 Million in Savings

New Procedures Reducing Installation Times and “No Shows” Result in Excess of $10 Million in Savings

Reliable Reports and Metrics Improve Customer Service

In the initial phases of the telephony over Hybrid Fiber Coax, one of the largest Multi-System Operators (MSO) in the broadband industry knew they had issues with both customer installations and customer satisfaction, but lacked the ability to identify the extent of the problems or the reasons behind them. The organization called upon Cliintel to develop a system to capture reliable, actionable data and to assist them with interpretation of that data.

The Business Issue:

The corporate telephony field operations group at the MSO needed a better understanding of the total installation process. This group was receiving conflicting information around the daily installs from disparate markets across the United States, each of which were performing thousands of installs per day. Reporting was not standardized, and the accuracy of information was very much in doubt.

To manage the situation, the MSO needed to:

  • Identify which data was important to capture
  • Design a reliable means to obtain the data
  • Determine how to interpret the data

Management wanted to know the reasons for procedural problems during an installation, how long the installs were taking, and set a reasonable baseline a true time per task. Additionally, they wanted to find out the causes of the number of “no shows” for service appointments

As new procedures were adopted, installation times and “no shows” were reduced resulting in revenue generation in excess of $10M.

The Solution:

Cliintel worked with the MSO to identify and source all of the critical data points. This included creating Reason Codes to describe why an appointment was missed, cancelled or rescheduled. Additionally Cliintel resources created business rules for allocating technician drive time. Once the critical data elements were identified, an automated reporting system was developed and deployed. This system greatly reduced the “human error” element that had confounded earlier attempts at manually gathering data. Meetings were facilitated between the corporate and individual market groups to define the standards against which the data would be measured, such as: How long each type of job should take? The meetings were continued as reports were made available to the organization on a daily, weekly, monthly and quarterly basis. Charts, tables and graphs were utilized to help the organization understand the data. The meetings with the corporate and market groups continued to aid the organization in understanding what it was seeing and to develop strategies to deal with the identified problems.

The Results:
Trends were uncovered highlighting problems with provisioning efficiencies, installation times and customer satisfaction levels. These trends were identified in the reports and provided decision support and agnostic intelligence, actionable at the various levels within the organization. The publication of the results enabled the global installation standards team to identify new procedures to address the trends. As new procedures were adopted, installation times and “no shows” were reduced resulting in revenue generation in excess of $10M. A concurrent rise in customer satisfaction levels confirmed that the team had identified the important variables. This was a two-year project that left the MSO with a reporting tool that worked to help the organization understand and improve the daily installation process for the telephony product line across the enterprise.

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