Big Data: How Target Knows You Are Pregnant

Big Data: How Target Knows You Are Pregnant

Consider the following scenario: in the past month Susan, a 20-something female shopper, purchased a large container of unscented lotion, an assortment of supplements such as zinc and calcium and a large purse from Target. As a result, Target can make the educated assumption that she is pregnant with an expected delivery date 5 months from today. Sound unbelievable? In reality, it’s not. Our hypothetical situation is exactly what’s happening.

As first written by the New York Times, retail giant, Target, has figured out how to successfully use shopper data to determine if an individual is having a baby and when. For Target, the importance of knowing this is that “the retailer has a chance to rope customers in around the birth of a child, when parents are so overwhelmed they are open to a one-stop shop.”

How Does Target Know?

Knowing someone is pregnant lies in the data gathering process. To start, Target assigns each customer a Guest ID Number. This ID Number, is then attached to their known credit cards, full name, and email address. By doing this, Target is then able to store and build out a historical timeline of purchases by customers.

By analyzing and reviewing the historical buying data of shoppers who were part of the Target Baby Registry, Target was then able to discover patterns in shopper behavior. For example, some of the discoveries included the following:

[One analyst noted] Women on the baby registry were buying larger quantities of unscented lotion around the beginning of their second trimester. Another analyst noted that sometime in the first 20 weeks, pregnant women loaded up on supplements like calcium, magnesium and zinc. Many shoppers purchase soap and cotton balls, but when someone suddenly starts buying lots of scent-free soap and extra-big bags of cotton balls, in addition to hand sanitizers and washcloths, it signals they could be getting close to their delivery date.

Similar to how economists study recession curves and historic patterns to predict possible economic climates in advance, Target uses data as a way to predict consumer behaviors so that they can market products most relevant to an individual shopper. As a result of Target cornering the expected mothers market, the NY Times suggests that “Target’s gangbusters revenue growth — $44 billion in 2002 to $67 billion in 2010” can be attributed to their better understanding of consumers.

For the restaurant and quick-service industry, Target’s application of data to predict purchase behavior so that they could target shoppers at the ‘perfect’ moment is an excellent reason as to why the industry should begin focusing on ways to capture data and execute upon it.

3 steps on how to use customer data for Restaurants and the Quick-service industry

Step 1: Collect Data

Just how you can’t start building a house without the proper materials, businesses need to first capture customer data. In order to data-mine, it’s important to understand the opportunity points at which you can gather data.

Points at which customer data can be captured include the following:

1. During Purchase: in most cases these means the point-of-sale. Via the POS, businesses will be able to gather line by line purchases as well as total order value. If you are on an older POS system and are unable to capture this data, consider using add-on technology products.

2. Post-purchase: thanks to technology, businesses can easily gather post-visit data on users. Some of these methods include receipt surveys (asking a user to go to a website with an access code to review their visit), satisfaction email marketing, and business reviews (example: Yelp.)

What’s important to note in the above is that a user must have an identity in order for a business to link behaviors to it. For example, Target was able to create user ID’s based on identities verified through avenues such as email opt-ins. For restaurants and QSR’s, this can prove to be more difficult as traditional methods of ‘identifying users” such as the fore mentioned email opt-ins have been proven ineffective as they don’t attract a large enough customer base.

Step 2: Analyze the Data

Depending on the size of the restaurant (mom and pops vs. multi-franchise corporation), the route in which the data gathered is analyzed can be different. Regardless, there are still basic principles that should go into data analysis:

1. Trends: Target was successful because they studied historical data and found trends. For a restaurant and QSR, this means looking at historical date (1 year or further) and looking for trends such as what items are often bought together or never, which items sell the best during particular seasons, what day of the week do certain products sell the best, etc. The key here is to look at the data from a micro-level.

If an item sells well during a certain day of the week, what’s the reason? Do your competitors not have promotions that day? Is it related to the ‘quality’ of it that day (ie: fresh fish)? These are the sorts of questions that should be asked.

2. Process: The process by which you dig into data to ‘slice and dice’ is just as important as the data itself. Without a game plan, the results that come out of data analysis can be inaccurate and may be biased. For example, if you only look at a certain group with no ‘control’ group to match up against, your results will be skewed in on direction since their is no comparison.

Consider working with a data analyst or a service that provides business intelligence. While not traditionally a part of the restaurant and QSR industry, every industry is now in need of a better understanding of data. As we’ve seen from examples like Target, data is no longer a ‘luxury,’ but may provide to be a growing necessity for long-term business success.

Step 3: Act on Data

Now that you’ve gathered the data, the last step is how to turn what you know about customers and their purchasing behaviors into action.

As a business, the core premise will likely be to increase overall revenue, but acting on the data should be focused on areas identified as having the most immediate impact on the overall goal (revenue).

In Target’s case, this meant focusing on pregnant mothers who had the tendency to do their all their shopping at the same place (higher average spending.) For a restaurant, the equivalent of pregnant mothers may be families: How do you increase the average order value of family?

Another important note in regards to data is the timeliness in which it’s acted upon. The key with data is that the more ‘recent’ it is, the more valuable it is. As a comparison, consider shopper data like sales leads. New leads = a higher likelihood of becoming a conversion because they have expressed ‘recent’ interest. If the lead is passed over for 30 days, lots of things can change: the person no longer needs your product, they went with another vendor, or they have lost interest completely. Remember that the data process from capture to action should never stay stagnant.

Lastly, remember that if done correctly, the data will tell you what you want to know. We re-visit one last time Target and what happened when a father was upset that Target had sent his daughter coupons for baby related items:

“My daughter got this in the mail!” he said. “She’s still in high school, and you’re sending her coupons for baby clothes and cribs? Are you trying to encourage her to get pregnant?”

The manager didn’t have any idea what the man was talking about. He looked at the mailer. Sure enough, it was addressed to the man’s daughter and contained advertisements for maternity clothing, nursery furniture and pictures of smiling infants. The manager apologized and then called a few days later to apologize again.

On the phone, though, the father was somewhat abashed. “I had a talk with my daughter,” he said. “It turns out there’s been some activities in my house I haven’t been completely aware of. She’s due in August. I owe you an apology.”

Big Data. How are you using it?

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