Where Everybody Knows More Than Just Your Name

The theme song from the 1980s television series Cheers included the lyrics:

Sometimes you want to go
Where everybody knows your name,
And they're always glad you came

Today these lyrics apply to more than just the local neighborhood bar which knows both your name and your usual beverage of choice.

How Retailers Use Facial Recognition

For Further Reading:

Intelligent Video: The Key to Analyzing Big Video Data

Intelligent Authentication Using Data Analytics

What's Next for Your Customers?

Many retail establishments are using facial recognition software to identify customers. The goal is to close a sale and/or increase revenue by cross-selling (selling add-on or complementary products) and up-selling (convincing the buyer to choose a more costly version with additional functionality).

Although a vast amount of data exists today about nearly every person's demographics, purchase history, buying patterns, and preferences, this data is not usually available to retail salespeople who have never seen you before. To circumvent this, many stores aggressively attempt to sign up consumers for loyalty programs with enticements that include discounts and special promotions.

Consumers must present the loyalty card or an alternate identifier such as a phone number to obtain these discounts, and this benefits the merchants by allowing them to identify their customers and thus track and link their purchases. This enables the merchants to augment the customer data in their data warehouses and thus better target consumers with customized promotions.

However, this is not usually effective during an unidentified customer's current visit because the loyalty card is not presented (and thus the customer's purchase history is not known) until checkout, when customers are paying for their purchases and leaving the store.

This is in contrast to visiting a merchant's website where a variety of methods such as a login, IP address, or a previously stored cookie could be used to identify the visitor. In this situation, the customer's current and historical data would be available in near real time and there would be ample opportunity to up-sell or cross-sell to the customer while he or she browses the website.

Consequently, if a retail store identified a customer using facial recognition software, it could access the customer's profile and purchase history and alert a salesperson to potential sales opportunities. A very simple but common example would be to target a customer with a history of buying extended warranties and sell that person an additional one for a product he or she was about to purchase.

In addition, some state-of-the-art facial recognition software can analyze customer emotions and thus enable a salesperson to determine if his sales pitch was working or, if the prospect reacted negatively, needed to be modified.

Other Potential Uses

Facial recognition has also been successfully used in a wide variety of security and fraud applications such as identifying known shoplifters as they walk into a store, spotting known terrorists in public places, or identifying scam artists and known cheaters in a casino.

Unfortunately, it can be misused by casinos to identify and ban known card-counters who are merely skilled enough to win at blackjack. On a more positive note, it can also be used to identify accident victims or emergency room visitors and retrieve associated medical records and patient histories.

In addition to identification purposes, facial recognition software can also capture and generate data for further analysis by BI practitioners. An example of this might be not just identifying known shoplifters but also tracking when they enter stores; this data could then be analyzed to determine when additional security personnel should be deployed to watch for new, as yet unknown shoplifters.

We should realize that as the deployment of facial recognition software grows, it can provide both an enabling technology for accessing our data warehouses and a new data source for analytics.

About the Author

Michael A. Schiff is founder and principal analyst of MAS Strategies, which specializes in formulating effective data warehousing strategies. With more than four decades of industry experience as a developer, user, consultant, vendor, and industry analyst, Mike is an expert in developing, marketing, and implementing solutions that transform operational data into useful decision-enabling information.

His prior experience as an IT director and systems and programming manager provide him with a thorough understanding of the technical, business, and political issues that must be addressed for any successful implementation. With Bachelor and Master of Science degrees from MIT's Sloan School of Management and as a certified financial planner, Mike can address both the technical and financial aspects of data warehousing and business intelligence.


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