In statistics and machine learning, lasso is a regression analysis method that performs both variable selection and regularization in order to enhance the prediction accuracy and interpretability of the statistical model it produces. It was introduced by Robert Tibshirani in 1996 based on Leo Breiman’s Nonnegative Garrote. Lasso was originally formulated for least squares models and this simple case reveals a substantial amount about the behavior of the estimator, including its relationship to ridge regression and best subset selection and the connections between lasso coefficient estimates and so-called soft thresholding. It also reveals that the coefficient estimates need not be unique if covariates are collinear.
Though originally defined for least squares, lasso regularization is easily extended to a wide variety of statistical models including generalized linear models, generalized estimating equations, proportional hazards models, and M-estimators, in a straightforward fashion. Lasso’s ability to perform subset selection relies on the form of the constraint and has a variety of interpretations including in terms of geometry, Bayesian statistics, and convex analysis.
NPS is a customer loyalty metric that measures customers’ willingness to not only return for another purchase or service but also make a recommendation to their family, friends or colleagues.
It is a powerful and effective technique, which can greatly increase a company's revenue if used properly.
The main advantages of NPS are close correlation with a company's growth and easy collection, interpretation and communication of the data.
Yes, it is.
Net Promoter Score is a number from -100 from 100.
Scores higher than 0 are typically considered to be good and scores above 50 are considered to be excellent.
The industry average for Technology / Software is 58.
The final Net Promoter Score of a company strongly depends on a context in which the satisfaction is measured.
Consider an example: If Lasso Excite sends out NPS surveys immediately after purchase, they are tracking their customers' initial excitement and the checkout experience.
On the other hand, if they survey their customers a few weeks after the purchase they are also tracking how satisfied their customers are with their products and services over time.
Therefore, comparing the NPS score of Lasso Excite with your own without any further context is not that useful.
What is extremely useful though, is using the NPS methodology to track the satisfaction of your customers over time. That's where Customer.guru comes in.
|Sunrun||Consumer Brands / Electronics||64|
|Liquid Web||Technology / Other||64|
|iiNet||Telecommunications / Internet Service Provider||63|
|MTR Express||Travel and Hospitality / Other||63|
|Apple iPhone||Consumer Brands / Electronics||63|
|Donlen||Logistics / Transportation||63|
|eWay||Financial Services / Other||63|
|Searchflow||Consumer Brands / Car Manufacturers||63|
|UHNS (University Hospital of North Staffordshire)||Healthcare / Hospitals and Care institutions||65|
|Avaya Inc.||Telecommunications / Other||65|
We have estimated the Net Promoter Score of Lasso Excite based on the publicly available information
including the sentiment of the company-related tweets, 3rd party reviews, and Alexa ratings.