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Therefore, MIML prediction functions trained months ago cannot predict purchase intentions at present accurately.
The Theory of Planned Behaviour has been generally applied to predict Purchase Intentions, nevertheless we have used a modified version of this theory to design the conceptual model.
Types of reputation were expected to impact the extent to which initial brand attitude predict brand attitude after receiving negative reputation and to which initial purchase intention predict purchase intention after receiving negative reputation (H6).
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For example, computer scientists can design machine learning algorithms to predict purchasing styles and spending behaviors from communication and mobility patterns [19, 74, 75].
The study focuses on customer segments for predicting purchase rather than on individual buyers.
As emphasized by Sabbe et al. (2008, 2009), in the studies carried out on Belgian consumers, some tropical fruits are better known and more often bought than others, underlining that product familiarity is one of the most important determinants predicting purchase choice for tropical fruits.
In its 2005 Holiday Forecast, Jupiter Research predicts purchases will grow 18% from last year to about $26 billion.
Our MIML style approach, however, is an integrated solution which can directly predict the purchase intentions for unseen new players.
Facilitated by statistical and machine learning models the study seeks to predict the purchase decisions based on adaptive or dynamic pricing of a product.
Direct marketers use past buying behavior to predict future purchases, sending catalogs and promotional mail based on that data.
By analyzing our information streams, it can predict what purchases make us happy, and remind us to keep shopping.
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