From banking to sports – 4 use cases for natural language analytics

Natural Language Analytics

Natural Language Analytics is taking every industry by storm and revolutionizing the way we interact with data. Here are four examples of what is about to become the new norm.

Banking – Personal finance taken to another level

Most of us are still managing our personal finance by going through our bank transactions with a calculator in on hand and some creased receipts in the other. Some have converted to expenses management apps and we will always have Excel, but let’s face it, these aren’t very effective. We still have to import the data or go through the list of transactions, so it’s easy to skip something or simply fail to identify what a transaction was for.

The data we need is already available on the bank, the harsh part is to make sense out of it without much work and that is where Natural Language Analytics comes in.

So, how does it work? Users enter queries like “How much did I spend last month in restaurants” or ““What’s the average amount spent on fuel per month?” in a search box. Then it automatically retrieves an answer with the best visualisation for users to understand results.

Easy right? But there is more than meets the eye. For users to be able to do search their expenses by category, every transaction needs to be classified with a tag. It can be restaurants, groceries, fuel, mortgage and so on. Then the system use machine learning to automatically associate new expenses to existing tags, so you don’t need to lose time classifying it and can easily understand in which category are you spending the most.

Wizdee is currently developing this solution for European and US banks and maybe you are one of the lucky ones that will have this service embed in your online banking in the next months. Meanwhile check this video on how natural language analytics makes personal finance as easy as a web search.

 

Sales – Stop the wait for reports

Imagine you are a Sales Manager always on the go, you have your dashboards to monitor everything, but you need some extra information. Currently you have to ask your IT to add a metric to your dashboard or to create a report. This isn’t an automatic process so you are left waiting. The waiting time slows the sales cycle and decreases productivity.

With Natural Language Analytics users just pick their smartphone and speak or type queries like “Expected revenue for the next quarter” and a chart instantly appears in the screen.

In other words, anyone can find answers without overload Admins with reports requests and create their dashboards just by saving queries. There is always someone that will want things slightly different and now they don’t need to wait days for reports. It is all about empower sales teams with the right information anywhere, increasing productivity and saving time.

You can experience in first hand how natural language analytics takes sales data to the next level. Click here to try it for free either with your Salesforce or CRM sample data.

Natural Language Business Intelligence

 

Sports – An interactive way to explore statistics

Sports fans and betters are used to go over endless lists of statistics to find the information they need. Natural Language Analytics just made searching for sports data easier and addictive.

Imagine you could just speak or type queries like “best scorer” on a search box and get instant answers or even make dashboards to follow team’s progression. That what’s natural language brings to sports statistics.

You can actually explore it yourself. We connected Wizdee with Euro 2016 related data. The platform is absolutely free and it’s available for tablet and desktop. You can explore data using whether Portuguese or English. Try it on euro2016.wizdee.com

Wizdee Euro2016 Dashboards

 

Machine data – Log analysis as easy as a google search

Machine data is generated by IT infrastructures like applications, logs, social data, website clickstreams, sensors and more. As you can imagine it produces huge amounts of data.

Platforms that analyse this type of data are complex and only accessible for IT. An example of this is Splunk, a web search-style interface from which you can search and analyse machine-generated data. But querying on Splunk is actually hard for business users, as you don’t quite use plain language, you have to learn Splunk lingo and that means going through quite some tutorials and manuals.

With Natural Language, instead of learning “Splunkish” you just enter queries using everyday language like you do with Google.
You will be able to enter queries like “Average cost by project” or “Logs of Canada by week” and get instant answers.

Again, in sub-seconds it calculates and presents you the results in the form of a table. Check this video to watch it in action.

We could keep going with marketing, retail, healthcare and so on. The possibilities are endless.

The question is: how would use Natural Language Analytics in your everyday life?

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