New ways of using existing personalization tools can make this job much more quick, easy and effective.
I won’t go into the details of an advanced e-commerce personalization engine here. Suffice to say that the e-commerce engine should be able to identify your shopper’s customer group and adjust things like product sort order, discounts and product selection on the fly.
I’ll use apparel in the following examples because it is the most common and intuitively understood form of the ensemble.
Using an advanced e-commerce personalization engine, it is fairly easy to present a selected group of pre-built apparel ensembles. If your data indicates the shopper is a young man who buys things in a smaller size, show him five or six appropriate ensembles. If he is an older man who buys big-and-tall sizes, show him ensembles of more conservative clothing that come in larger sizes.
A far more complex, but still workable, system involves building ensembles dynamically according to the individual. You need to overcome two challenges here:
• selecting pieces that look good together according to your chosen theme, and
• selecting items that your shopper will like.
Your first step is to go to your product database and use its categorization features to group products that coordinate according to color, style, theme or any other criteria you choose. Your second step is to set up your personalization parameters or business rules to identify what each shopper is most likely to buy according to demographics, previous buying patterns, answers to surveys or other data.
If the young man in the previous example likes to buy college-themed sweatshirts, then the system automatically should select a college-themed garment he has not bought and pair it with something calculated to go well with it: jeans, shorts, sweatpants, hat, shoes, watch and so forth. Throw in a couple of other ensembles of different styles to catch his interest such as casual dress, club wear, etc.