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Predictive modeling (also known as machine learning or predictive analytics) is defined as the process of using data and probability statistics to forecast future outcomes.
Smart organizations are using predicative analytics and machine learning techniques to solve problems, increase sales and gain the much-coveted competitive advantage. It's a secret sauce, if there is such a thing in business.
And although the concept is not new, its popularity and growth due to digital marketing and the ease of accessing all sort of data is undeniable. Below is a trending chart showing the interest in machine learning over the last 5 years.
Predictive modeling relies heavily on data to build a good model. And website data, most frequently obtained through an export of Google Analytics, is a source that must be clean. Not only is the quantity of data important, but the quality of data is critical. Imagine spending the time and resources to invest in predictive modeling, but 50% of your organic traffic is lost due to technical issues on your website.
Or the 50% of traffic you are receiving is not the right audience because your pages are not optimized to attract the correct visitors to your website. These quantitative and qualitative issues will directly impact the overall model?
Audit your website data before you jump knee deep in predictive modeling. And by audit, I mean allow a "real human" to review your website and inform you on technical, content and practical issues that may be directly impacting the quality and quantity of traffic.
This is especially important if you are building models to optimize website performance. And yes, we understand that other data sets, outside of your website may be considered for your models, but your website is your foundation, the home in which your brand and business is built.
Or at least it should be.
We're very selective about who we work with and find that we are able to do our best work with companies that fit these criteria: