The rate of impact on the social status of a brand

A few days ago we faced a challenge that we like. We had to find an indicator to help us measure the r etorno social media without having to estimate their impact on sales, because in reality, often we can only do that, we estimate how much it impacts.

With this assumption and based on the Net Promoter Score (NPS), which helps to measure how a brand is recommended by its customers create this new indicator, which helps us to know how they impact the content that users create in those around them . This model was created as a reflection in a finished article and reflected in the book called The Ultimate Question

The idea is simple, as we said before, we rely on the NPS, so we start with this type of scheme:

Traditional scheme of NPS

It assumes that people, to rate a brand, give 6 points or less are not really brand recommenders as not being "wet" enough. Those who give 7 or 8 points are passive, not just linked to it and those who are 9 and 10 are really recommend, those who bet on it.

In the social world we encountered a problem, already advanced by the author of the NPS in his last book , is that users do not always talk about brands in terms of recommendation, so you modify something on the model, we actually only Based on the concept of promoters and detractors. In our model, we have called impact factor in social consideration, we made ​​positive comments and negative mark, but we can not rule out neutral.

What impact does a picture of the views of a hotel? It can be good or bad for the brand, but without a clear sense of the commentary that accompanies the image is hard to tell. Therefore we believe that we can not dismiss all neutral comments, so we propose a scheme that allows weight them. Sticking with the example of the hotel, we are talking about this:

Each type of impact will be reflected in the index

In this way receive a weight neutral comments by type, ie according to whether they are photographs, checkins, comments on your location, reserve comments, questions or other category you can think of.

Thus, the index would be as follows:

(Positive Neutral +%) / Total comments

We can see an example:

For example for a hotel business

The steps for construction are:

1. Search and collection of comments that occur in a trademark
2. Classification between positive, neutral and negative
3. Classification according to the types of reviews we want to analyze
4. Weighting neutral comments
5. Construction of the index

NPS image via: FocusArrow