We live in a time, where the Web enables users to exchange information as easily and to an extent as never before: People write reviews, rate products, help other users in discussion forums and write blogs about their experience with products or services.
They exchange ideas on social networks like Facebook, mySpace, Twitter and on business webs such as Amazon. This phenomenon is not a passing “teenage hype” limited to people from the Millennial- or net-generation, it is going to last: According to Facebook, the fastest growing age group on the network is people of 35 years and older.
In this post I want to give some insight on how user contributions on the Web can influence companies’ information strategies and how this content can be turned into valuable information through BI and analytics tasks.
This new connectedness and information exchange provided by the Web 2.0 has two effects for companies:
- Companies lose authority in telling customers about strengths and weaknesses of their products: Users on the web begin to take over that function by providing public and permanently visible opinions.
- The user generated content is a massive amount of data, waiting to be turned into valuable information: If users of a product discuss the product on the Web and discover possible improvements or flaws, the manufacturing company will definitely be among those who want to notice first.
In the following, I want to give some examples to illustrate the points I made above.
Customers inform customers
The power of user contributions is not just theoretical: In May 2009, a viral thread of Amazon reviews about a t-shirt, many people would consider trashy, featuring wolves howling at the moon caused a rapid surge of sales going up to 100 shirts an hour, for a product that has been up for sale for several years (see here for the full story).
Conventional marketing would have a hard time duplicating this success. Clearly customer opinions influenced buying decisions of other users (the power of opinions is also stressed in a 2009 book by Tapscott, who reports that 32% of teenagers (the new customers) are buying things their friends have and nearly as many ask their friends for advice on a product). Another anecdotal evidence is the DellHell story, in which a blogger, unhappy with Dell’s customer service aroused a mass of other customers with a series of posts in which he described his experiences with Dell’s service.
Turning user generated data into information
Companies need to be aware of this new army of interconnected customers and develop an information strategy. The following two examples illustrate two measures, which can become part of such a strategy. As always with BI, the key challenge is to turn data from its useless and passive state into real and applicable information that can help businesses to make better decisions and to optimize processes.
By analyzing search query data, Google was able to create an application that is able to predict the spread of flu epidemics two weeks faster than traditional methods. (If you want to start your own social analytics with your personal keywords you can try using a related service here (try Christmas and Easter for a start)).
A second example for purposefully analysing user data is mining customer opinions towards a product: “For many businesses, online customer opinions have become a type of virtual currency that can make or break their products” (cf. here). Sentiment analysis mine collective opinions and can provide insight about whether a discussion on a given product is positive or negative and to what extent.
Already, commercial vendors like Attensity, Clarabridge, Lexalytics Limited, SPSS and Temis are developing sentiment analysis software. Once sentiment analysis has become mainstream, it can be integrated into standard business processes and provide companies with real time information that will improve customer relationship management and consequently profits.
The examples provided here, barely scratch the surface, the Web 2.0 and its user generated content provide a huge unstructured data mine waiting to be turned into valuable information through BI and analytics. More than ever, in the light of always informed users and the tougher times that some of Accenture’s clients are facing today.