Looking for loyal ltr

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Data scientists at Airbnb collect and use data to optimize products, identify problem areas, and inform business decisions.

Looking for loyal ltr

These are the moments that make or break the Airbnb experience, no matter how Looking for loyal ltr we make our website. Currently, the best information we can gather about the offline experience is from the review that guests complete on Airbnb. The review, which is optional, asks for textual feedback and rating scores from 1—5 for the overall experience as well as sub: Accuracy, Cleanliness, Checkin, Communication, Location, and Value. Starting at the end ofwe added one more question to our review form, the NPS question.

By measuring customer loyalty as opposed to satisfaction with a single stay, NPS surveys aim to be a more effective methodology to determine the likelihood that the customer will return to book again, spread the word to their friends, and resist market pressure to defect to a competitor. In this blog post, we look to our data to find out if this is actually the case. We find that higher NPS does in general correspond to more referrals and rebookings. But we find that controlling for other factors, it does not ificantly improve our ability to predict if a guest will book on Airbnb again in the next year.

Therefore, the business impact of increasing NPS scores may be less than what we would estimate from a naive analysis. In this study, we look at all guests with trips that ended between January 15, and April 1, If a guest took more than one trip within that time frame, only the first trip is considered. We then try to predict if the guest will make another booking with Airbnb, up to one year after the end of the first trip.

One thing to note is that leaving a review after a trip is optional, as are the various components of the review itself. A small fraction of guests do not leave a review or leave a review but choose not to respond to the NPS question. While NPS is typically calculated only from responders, in this analysis we include non-responders by factoring in both guests who do not a leave a review as well as those who leave a review but choose not to answer the NPS question. To assess the predictive power of LTR, we control for other parameters that are correlated with rebooking.

These include:. We acknowledge that our approach may have the following shortcomings:. Despite these shortcomings, we hope that this study will provide a data informed way to think about the value NPS brings to our understanding of the offline experience. Our data covers more thanguests. Our data shows that out of guests who submitted a review, two-thirds of guests were NPS promoters. More than half gave an LTR of While the overall review score for a trip is aimed at assessing the quality of the trip, the NPS question serves to gauge customer loyalty.

We look at how correlated these two variables are by looking at the distributions of LTR scores broken down by overall review score. Although the LTR and overall review rating are correlated, they do provide some differences in information. Keeping in mind that a very small fraction of our travelers are NPS detractors and that LTR is heavily correlated to the overall review score, we investigate how LTR correlates to rebooking rates and referral rates.

We count a guest as a referrer if they referred at least one friend via our referral system in the 12 months after trip end. We see that out of guests who responded to the NPS question, higher LTR corresponds to a higher rebook rate and a higher referral rate.

Interestingly, we note that the increase in rebooking rates for responders is nearly linear with LTR we did not have enough data to differentiate between people who gave responses between 0—6. We also note that guests who did not leave a review behave the same as detractors. In fact, they are slightly less likely to rebook and submit a referral than guests with LTR of 0—6. These indicate that when measuring NPS, it is important to keep track of response rate as well.

Next, we look at how other factors might Looking for loyal ltr rebooking rates. For instance, we find just from our 10 weeks of data that rebooking rates are seasonal. This is likely because more off season travelers tend to be loyal customers and frequent travelers.

Looking for loyal ltr

We see that guests who had shorter trips are more likely to rebook. We also see that the rebooking rate has kind of a parabolic relationship to the price per night of the listing. Guests who stayed in very expensive listings are less likely to rebook, but guests who stayed in very cheap listings are also unlikely to rebook. In addition to the Overall star rating and the LTR score, guests can choose to respond to the following sub in their review, all of which are on a 1—5 scale:.

In this section we will investigate the power of review ratings to predict whether or not a guest will take another trip on Airbnb in the 12 months after trip end. We will also study which sub are most predictive of rebooking. To do this, we compare a series of nested logistic regression models. We start off with a base model, whose dependent variables include only the non-review characteristics of the trip that we mentioned in the above section:.

Looking for loyal ltr

Then, we build a series of models adding one of the review to this base model:. AIC trades off between the goodness of the fit of the model and the of parameters, thus discouraging overfitting. If we were just to include one review category, LTR and overall score are pretty much tied for first place. Adding any one of the sub also improves the model, but not as much as we were to include overall score or LTR. Next, we adjust our base model to include LTR and repeat the process to see what is the second review category we could add.

Looking for loyal ltr

Given LTR, the next subcategory that will improve our model the most is the overall review score. Adding a second review category to the model only marginally improves the fit of the model note the difference is scale of the two graphs. We repeat this process, incrementally adding review to the model until the models are not statistically ificant anymore.

We are left with the following set of review :. These findings show that because the review are strongly correlated with one another, once we have the LTR and the overall score, we only need three of the six sub to optimize our model.

Looking for loyal ltr

Adding more sub will add more degrees of freedom without ificantly improving the predictive accuracy of the model. Finally we tested the predictive accuracies of our models:. Accuracy. LTR Only Trip Info Only Given just basic information we know about the guest, host and trip, we improve this predictive accuracy to Adding review not including LTRwe add an additional 0.

Given all this, adding LTR to the model only improves the predictive accuracy by another 0. Post trip reviews including LTR only marginally improves our ability to predict whether or not a guest rebooks 12 months after checkout. Out of all the review sub, LTR is the most useful in predicting rebooking, but it only adds 0.

Looking for loyal ltr

This is because LTR and review scores are highly correlated. Reviews serve purposes other than to predict rebooking. They enable trust in the platform, help hosts build their reputation, and can also be used for host quality enforcement. We found that guests with higher LTR are more likely to refer someone through our referral program. They could also be more likely to refer through word of mouth. Detractors could actually detract potential people from ing the platform. These additional ways in which NPS could be connected to business performance are not explored here.

But given the extremely low of detractors and passives and the marginal power post trip LTR has in predicting rebooking, we should be cautious putting excessive weight on guest NPS. Originally published at nerds. Creative engineers and data scientists building a world…. Creative engineers and data scientists building a world where you can belong anywhere. How well does NPS predict rebooking? AirbnbEng Follow. Check out all of our open source projects over at airbnb.

The Airbnb Tech Blog Creative engineers and data scientists building a world…. Data Science Net Promoter Score. The Airbnb Tech Blog. Written by AirbnbEng Follow.

Looking for loyal ltr

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