PURCHASE DATA

I am working with a set of mobile wallet technology suppliers, and they have given me some insights on the rich data we can collect from purchases. There are several sources of this data. Credit card providers carry information about credit card transactions. Their data contains merchant, transaction location, and transaction amount data tracked by consumer. Mobile wallets carry similar data, and sometimes have additional money movement data if the wallets are being used for lending money, especially in the growth markets where currency and banks are getting replaced by mobile wallets.

With online transactions, we are beginning to see technologies that can help consumers and marketers organize the purchase data. I use

Slice (www.slice.com) to keep track of my online purchases. Slice scans my email for any online purchases and extracts relevant information, so I can track shipments, order numbers, purchase dates, and so on. I do a fair amount of business as well as leisure travel and often make advanced reservations for airlines and hotels. Slice provided me with a convenient app on my iPhone to track all of these reservations. With a couple of clicks on this app, I can get access to valuable data about my confirmation number, date of reservation, and the hotel address and phone number. Slice extracts the relevant ordering information and keeps it organized for me for easy access to this data.

Slice also lets me “slice and dice” the orders. That is, it analyzes my purchases against a set of categories to report the number of items and money spent in each category. Figure 3.2 shows Slice’s category analysis: Travel & Entertainment, Music, Electronics & Accessories, and so on. In doing so, Slice is doing rigorous unstructured analytics and user interaction to identify what is considered “Movies & TV” and how that is different from “Music.”

The classic product categories originated from the Yellow Pages. We remember the classic Yellow Pages books that we received yearly and that are nowadays being incorporated into online Yellow Pages and other shopping and ordering tools. However, categories are typically tree structured, where each node is a subclass of the node above and can be further subclassified into further specialized nodes. For example, a scooter is a subclass of a two-wheeler, while an electric scooter is a subclass of a scooter. A node can be a subclass of more than one entity. A subclass shares the attributes of its superclass. Therefore, both scooters and electric scooters should have two wheels. While the classic product catalogs were static and were managed by administrators without organized feedback, the unstructured analytics provide the ability to make a dynamic hierarchy, which can be adjusted based on usage and search criteria.

As companies like Intuit and Slice deal with their users, they provide a categorization of a transaction based on their collective understanding

Slice and dice of my purchase data of the product catalog

Figure 3.2 Slice and dice of my purchase data of the product catalog. However, they provide the consumer with the ability to change the classification. The analytics of reclassification allow these algorithms to constantly adjust their classification based on real data from their customers.

The data that Slice is collecting provides significant value to marketers. Here is a Washington Post analysis of iPhone sales from a recent launch of iPhone 5:

The sales figures outstrip analysts’ expectations for the opening weekend. Estimates for the phones’ sales ranged from 5 million—the number of iPhone 5 models Apple sold on that model’s opening weekend—to 8 million. Apple didn’t specify which iPhone colors were the big favorites with consumers, though more anecdotal reports suggest that the gold iPhone 5s was far more popular than the silver or “space gray” models. The gold version was the first to run out on Apple’s Web site, and several customers reported that they were having trouble finding the phone in stores if they weren’t at the very front of the line. As for the more colorful, plastic-backed iPhone 5c, at least one firm estimates that nearly half of its customers favored a blue or pink phone. Slice, an online firm that helps users track their online purchases, reported that 28 percent of pre-orders it tracked for the iPhone 5c were for blue phones, 20 percent for pink phones. What was the least popular color for the iPhone 5c, according to Slice’s data? Yellow, which accounted for 10 percent of the orders.18

Grocery stores have been equally busy developing their understanding of customers. Most of them offer frequent shopper cards that can be used by the grocers to track purchase habits as well as used by shoppers to redeem discounts and other useful campaigns. With identifying information collected from the customer, this shopper card can be correlated with a name and an address. Retailers toyed with the idea of providing shopping gadgets to shoppers and eventually realized that creating a smartphone app to run on an existing device would be easier than engineering a new device. Shoppers may activate a mobile app as soon as they enter a retail store. The app starts to collect GPS-level accurate location information about the shopper and lets him/her check in grocery items on the smartphone. At the checkout counter, the shopper connects the smartphone to the point-of-sale (PoS) device, and the grocery bill is automatically paid by the credit card associated with the app. As the person walks through the grocery store and checks in grocery items using a smartphone, a campaign management system starts downloading mobile coupons based on customer profile, past grocery purchases, and currently active promotions.

 
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