A simple yet highly effective strategy in machine learning, which can be used to enhance user experience, automate complex and intelligent tasks, reduce operational expenditure, and improve revenue.
Benefits of Classification
Automate Business Process
Certain aspects of business are done manually, even today. Although, some of these jobs require human intelligence, they can be automated using classification. Consider a scenario in a healthcare company which receives questions from patients. Based upon the received query a medical expert, who understands the query, reviews it and then routes it to the specialist, to answer. This process can be automated, such as to automatically route queries to experts, and thus, result in faster response time.
Enhance User Experience
Small usability concerns which may cause frustration and increase the time to get a particular task done, are a good place to begin with, for enhancing user experience. The story of how automated spam filtering enhanced webmail experience and became a defining and winning feature for webmail clients, to this date, remains to be a classic example for classification to enhance user experience.
Reduce Operational Expenditure
In any business context, increased automation essentially means reduced operational expenditure. There can be multiple such opportunities in an organisation whose operating procedure follows a strict process. Some of these processes can be automated, thus resulting in reduced expenditure. For example, if your organisation receives customer support queries and a good chunk of these emails are related to FAQs, an automated response would lead to reduced manpower required to reply to such emails.
The quality of data is directly proportional to the accuracy of any classifier. Substantial amounts of money are spent in cleansing, wrangling, curating and selecting right data set for a classifier. Datoin provides you help by means of experts, tools, corpus and off-the-shelf dataset. Further, with the help of the experts, an organisation can take advantage of the power hidden in huge organisational data.
There are a plenty of open source classification algorithms that make it easier to get a simple classifier working. Once the baseline is established, the experts spend most of their time in improving the accuracy (from baseline) of a classifier. This process is called as ‘Feature Engineering’, and is more of art than it is a science. Datoin’s classification training dashboard enables our expert to engineer the features efficiently.
The use cases of classification are many. Following are some of working examples of it.
Measuring emotion/s of a customer over conversation with your support executive, helps understand your team’s performance. Play around with our demo
For any news aggregation and analysis app, classifying crawled news item is an important use case. This example classifies any publicly available news item into its type.
Business Type Classification
For automatic lead generation from multiple websites, automatic classification of the generated lead into business type helps target your customer effectively.
If you are gathering data from multiple ecommerce sites for your competitive intelligence, you must have generalized product category hierarchy to conduct further analysis.
Understanding the sentiment of your marketing campaign helps to measure its effect on brand value. This example shows the sentiments of tweets.
Query routing to concerned subject matter expert is one of the common use cases in many business types. Play with the example of routing health queries to specialist doctors.
Some algorithms work better than the others for a given dataset. And, there is no universally accepted algorithm which works better on all datasets. Thus, it is about choosing the right algorithm for the problem. Datoin provides a range of algorithms to choose from and carry out your experiments with.
Our classification dashboard provides an easy to use interface for you to select a combination of pre-existing feature sets and experiment freely multiple times. One can also choose a different algorithm to experiment with. The dashboard helps to conduct experiments quickly and compare the result.
Text as well as Numerical Features
The classification frameworks allow you to experiment with both, texts as well as numerical features. Datoin has many off-the-shelf feature engineering modules that come in handy while assembling the vector generation pipeline.
While working with text data, it is expected that you have a large number of feature sets. The dimensionality reduction algorithms come in handy to achieve the maximum accuracy out of your classifier.
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