Purchasing: Carol is interested in purchasing a new computer and she visits Tech

Question

Purchasing:

Carol is interested in purchasing a new computer and she visits TechStation.com, an electronics retailer. Carol is a first-time visitor to this site. After entering a few keywords to search the site and after browsing through several of the pages she selects the model she is interested in. Carol adds a printer to her virtual shopping cart and continues browsing. The observational personalization system used by the electronics store compares her point of entry to the site, the keywords she used in her initial search, her clickstream within the corporate site, and the contents of her shopping cart to the navigational patterns of existing customers already in [the] firm’s database. Through this comparison, the system fits Carol into the “young mother” profile that it developed by mining the Web navigation logs generated by previous visitors and existing customers. Accordingly, the recommendation engine offers Carol a discounted educational software package before she checks out. Carol was, in fact, not a young mother, but a middle-aged divorcée. She purchased the computer and printer she was interested in, but did not find the time management software she actually wanted to buy. A bit frustrated, Carol leaves the site in search of the software she needs. At about the same time, Steve entered the site and selected the same computer and printer. Although he chose the same products as Carol, Steve did not receive the same offer for discounted educational software. He entered the site from a different portal than that used by Carol; he had a different clickstream pattern from hers, and he used different terms in his keyword search. Steve’s navigational pattern resulted in his being assigned to a different profile. Steve fit best into the “college student” profile and, as a result, he was offered a discount on a statistical software package. In fact, Steve is an English major. Like Carol, Steve’s projected needs did not accurately match his real needs.

Is TechStation.com doing anything wrong?

What, if any, information would help you decide whether the company is doing anything wrong?

Explanation / Answer

Most of what tech station is doing is right. Based on the given information it is understood that the techstation predicted the gender of both the customers accurately and they were close to predicting the ages. The things techstation is doing to predict the needs of the customers is being done by most companies and all of them rely on big data to do it. Based on the given information I have only two instances where techstation failed.

Techstation obviously has a software to predict the needs of customers which anaylyse the entry point and click throughs and products to predict. This prediction may not be very good because of which the techstation is not able to predict accurately. Another thing is, after prediction the product offered may not be correct based on the logic that the profile likes to buy so and so stuff. Hence, a problem with the prediction software and products offered is possible. But, the overall idea or the basic things done by Techstation is not wrong based on the given information.

To decide if techstation is doing anything wrong, I would need much more information on techstations prediction software and information on more customers who have done their shopping with techstation. If many more customers are facing problem and techstation is unable offer right products then we can say techstation is doing something wrong. It is quite possible that these two are only a few isolated incidents of failure other customers are all happy with techstation.

Related Questions

Navigate

• Browse (All)
• Browse P
• Subjects

Integrity-first tutoring: explanations and feedback only — we do not complete graded work. Learn more.