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SmartData Collective > Big Data > Data Mining > How Habitat UK *should* have used Twitter
Data Mining

How Habitat UK *should* have used Twitter

ThemosKalafatis
ThemosKalafatis
4 Min Read
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Following the great post from Gloria Tiphereth I wanted to take the opportunity to show an example of how Habitat UK should be using Twitter.

My suggestion would be that instead of the “initiative” they took they should identify the values, beliefs and needs of their customers by capturing and analyzing relevant tweets instead. And here is how they could do it :

First they should capture all relevant Tweets every -say- month :


The second step would be to identify what people want when they talk about furniture. If they used text mining, they would have found specific furniture products that customers want to buy and the values associated with these types. For example, look at the following table :


The table shows us (pay attention to dark red cells) that customers looking to buy baby furniture have Safety as their number one associated value. With this knowledge, perhaps Habitat UK would make sure that when they advertise Baby furniture they would use this word on their advertisements to capture the interest of their customers. Of course what was shown above is not some new information but is meant to be given as an example.

Some more things that Habitat UK could have done with text.. …


Following the great post from Tiphereth Gloria I wanted to take the opportunity to show an example of how Habitat UK should be using Twitter.

My suggestion would be that instead of the “initiative” they took they should identify the values, beliefs and needs of their customers by capturing and analyzing relevant tweets instead. And here is how they could do it :

First they should capture all relevant Tweets every -say- month :


The second step would be to identify what people want when they talk about furniture. If they used text mining, they would have found specific furniture products that customers want to buy and the values associated with these types. For example, look at the following table :


The table shows us (pay attention to dark red cells) that customers looking to buy baby furniture have Safety as their number one associated value. With this knowledge, perhaps Habitat UK would make sure that when they advertise Baby furniture they would use this word on their advertisements to capture the interest of their customers. Of course what was shown above is not some new information but is meant to be given as an example.

Some more things that Habitat UK could have done with text mining would be to see :

  • How important it is to suggest solutions to customers
  • Which rooms people want to re-furnish more often and -more importantly- why.
  • How problems (such as furniture received is damaged or difficult to assembly) affect their brand.
  • How people feel excited when they wait for their new furniture…and how bad they feel when furniture is not delivered on time.

There is much more that can be done. By running Cluster analysis many kinds of customer thoughts can be grouped together. One of them was how much “Feeling good” is closely related to new furniture and how it affects people’s psyche.

By using Social Media Analytics, Habitat UK -and most other companies- would understand their customers better, see what is important for them and with this knowledge they would be able to take informed decisions that would – most likely – make a real difference.

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