Predictive Analytics and Predictive Modeling Made Simple

Predictive Analytics and Predictive Modeling Made Simple

Predictive analysis allows companies to make decisions based on data, large amounts of data, often known as big data. This data could be keywords in tweets, weather patterns or vast amounts of consumer data. .With customer data integration software a company can completely break down the demographics of their customer base. With real time customer data integration software, changing demographic trends can be analyzed in real time and companies can change their marketing strategies accordingly.

Today though, changing your marketing strategies is also an automated real time process. When a potential customer comes to web site, their consumer data profile is read and the advertising is reconfigured to match the consumers profile.The potential buyer's profile then  is matched with the ad database. Probability rankings are generated for each type of ad based on the potential buyer's profile. Then the ad is placed on the potential buyers screen.


It doesn't stop there though. Whether or not the ad resulted in a  purchase is made is then recorded. The probability rankings are then recalculated for the ad and statistical data stored in  the predictive model. In this way the predictive model is continually improved so that optimal sales revenue is achieved.

Developing a predictive model requires one to have a database of stimulus  often ads or words. Ideally, it also requires one to have subjects, the potential buyers, to view or read those ads and words. The more subjects that are exposed to the stimulus the more data the predictive model has to work with. So over time as the number of stimulus grows and the number of subjects increases, the better the predictive model gets.

The factors that go into building a consumer predictive model are many. These can include the consumer's buying history, geographic location, occupation, interests, and time of year, just to name a few.

However, the first step in building a predictive model doesn't necessarily require any advanced analytics,  technology or big data. . Simple surveys of the customer base can be conducted and the results used to generate  the predictive model seed.