Traditionally, the bulk of a marketers work is to either be creative, or apply statistical data and conceptually rationalize the feasibility and success of their creativity. Online marketing however is a whole other ball game when it comes down to statistics inference and application of campaigns to target markets. Luckily Tanzibad Marketing is here to give you the run-down on web analytics and how they can both be used and applied to what you do as a marketer.
Before the internet existed marketing efforts were based on data gathered incrementally from buyer behaviour as well as strenuous analyzation of surveys and consumer research. Lots of people became rich and a lot of success was found with an ability to direct and influence purchasing decisions around the world. As the internet has grown to be the economically lucrative asset that it is today, so has the information and buyer behaviour data which is gathered every time someone flicks on a computer or cellphone screen.
Such developments in this new consumer information is valuable to the extent that companies and marketing agencies pay big money for such information. Doug Alexander (2015) defines this collecting of consumer information as data mining, which focusses on collecting specifically the information which is most valuable about customers in regards to their online purchasing and web usage. He further discusses that companies collecting web data can be data rich, though they can be information poor. This is highly relevant because although we may be able to access seemingly infinite amounts of data about consumers, companies around the world have taken on the burden of developing software and extracting meaningful information and statistics from that data.
An example of a data mining company is Mozenda
To go deeper, the true value isn’t just being able to target who buys where and how they do it. Web analytics also help us understand and identify customer profitability, how to predict certain types of consumerism online as well as a focal point in web analytics, RFM (recency, frequency and monetary) analysis.
Margaret Rouse discusses RFM analysis as a marketing approach used to identify customers quantitatively based on the recency of their last purchase, how often they buy, and how much they spend (2005). Such information is simnifically important to modern marketers in that in not only helps to identify which customers are profitable, but in turn where and how to apply their marketing efforts online. This allows us to hone in and target our preferred type of consumer based on information their activity online provides, without the need to spending absorbidant amounts of money trying to gather the needed information ourselves on top of the expense of implementing a campaign. Instead, web analytics provides us with a resource to identify and target our ideal customers and focus our budget and our human resources on developing ideas as well as implementing successful and highly potential and current buyer focused marketing communications.