Analysing conversations help us to understand the context of product demand

The relationship between social media, social search and search engine marketing is becoming more important. This week we have been investigating how it’s evolving and why product conversation analysis can be valuable to search marketers.

I’m continually fascinated by the relationships between different data sets and their implicit and absolute meanings.

Keyword search volumes from Google, over time, can highlight seasonal trends and critical demand for products, people and just “things” in general. The accuracy of this data has allowed search marketers to develop highly effective marketing campaigns, which have later been re-classified as customer acquisition models in their own right.

My recent use of various social media monitoring tools, including our own, have provided another use for the same keywords. These tools deliver data and information according to your input, be it simplistic or complex Boolean geekery.

One could say that real-time product conversation volume can also be used as gauge of product demand and this should, possibly could, influence weekly, monthly SEM budgets; however, I believe this data is relevant in a very different, but applicable way.

Comparing Twitter conversation volumes surrounding popular crisp brands to their respective Google search volumes highlight very similar product demand trends:

However, qualitative analysis of product conversation can highlight the actual contexts of the conversation, their sentiment and meaning. These qualitative insights are very valuable for search marketers because they can help to define nature and sentiment of demand.

Snacks aside, if you look at the search volume for a competitive keyword we can see that it is consistently high:

Understanding local conversations surrounding the same keyword can potentially help to determine SEM ad-spend and search strategy. At any point in time a news story or discussion could increase search volume, but this increase, if it is trivial, could unnecessarily haemorrhage account budgets, reduce conversion rates and quality scores.

We plan to investigate this relationship further and analyse specific sectors at a more macro level. What would like to see in our study?

Comments are much appreciated. …