There was a time when all of us used to look for taste in our food, then came the time of health conscious, upward, mobile Indian, who used to look for vitamins and carbs in the food items. But now is the time of data science, data analytics, when from our desks and laptops in our air conditioned offices, we look for data in our food. What people are eating, what are they liking, and how can we use these insights to improve the quality of our food, health of customers and ROI of our organizations.
There are 2 cases where some brilliant analytics was done, but both created a different experience for customers. This further re-iterates that just analytics by itself won't solve anything, we need to ensure that we stay true to our marketing theories:
1) Target: Right customer, wrong way?
Being one of the biggest retailers of US, they are heavily invested in data analytics especially in marketing analytics. Not too long ago, Target though sold everything, from cotton balls to beer, but was known more for cleaning supplies. So, people would go to grocery stores for groceries, toy stores for toys, and Target for toilet papers. Extremely weird when you think that Target does sell everything else as well. This was credited to human habits, it's just like you driving on road, sometimes you probably don't even realize how you reached a certain place, because so used to of driving the same route, same car, in the same traffic, that it becomes a part of your habit.
Target needed to change this buying habit of people, and they selected the best possible segment for retailers, kind of holy grail, soon to be parents. People tend to change their habit only when there is a huge change in their lives, like when people get married, that's when they start going to different restaurants, when they get a job, they start purchasing different kinds of clothes, form different brands, similarly, when they are expecting a baby in their family, they again change their habits a lot. And if you are able to get this segment in a habit of buying in your store, then you would probably end up selling them everything, from baby diapers, beers, groceries, vitamins, clothes, everything, exactly what Target offers.
What they realized was that by third trimester, when females especially open up about baby showers etc, it's probably too late to reach out to these customers, they already get into a habit of shopping in a specific stores, and in second trimester it's too early to know if a family is expecting a baby. This was an un-enviable problem given to marketing team of Target, headed by one of the most famous mathematicians, Andrew Pole. Pole ended up creating a mathematical equation which had a list of 20+ items (like vitamins, calcium, lotions etc), when analyzed together, can predict probability of a female being pregnant.
When campaigns were launched on the basis of the results of Pole's algorithm, a man walked into one of the stores outside Minneapolis, demanding to see the manager, The reason was that his daughter, who was still in high school suddenly started getting mailers and coupons for baby diapers and cribs. He was extremely upset as it seemed like target is encouraging his daughter to get pregnant. It was only a few days later that this man realized that his daughter was actually pregnant.
2) Costco: You gave me bacteria, I still love you
When you are selling food, you need to be extremely careful of what's inside, If you are not more than cautious, there are chances you will put something in someone's body which is going to make him/her sick, and thereby making your organization directly responsible for that. But then at times it's extremely difficult to be 100% error free, especially when we talk about stores as big as Costco, where there are 100,000s of members and even more SKUs.
It was in 2010 when Center of disease control and prevention had a daunting task of identifying the root cause and origin of Salmonella case which sickened around 272 people in whole of US, and 5 were from Colorado region only.
There were no leads and no solution in sight, when CDC decided to look into the behavior of those who got sick. When analyzed the data, it emerged that many of them shop at Costco, a huge grocery store in US, with more than 70 million members.
When CDC approached Costco to find out further, they realized that all those people who got sick had one thing in common, that was black and red pepper in Salami that they consumed. This came from a whole seller based out of Rhode Island based manufacturer, Danielle International.
That's when data team of Costco took and and churned out a list of all those people who bought the food item in past few days, and begun a massive campaign to recall the product through mail, phone, email etc. This was possible because as any large retailers of it's size, Costco also keeps all information about buying habits, purchases, address and contact details of it's customers in it's massive servers. In their Big Data platform, they keep track of each transaction of a customer, along with customer's contact details, and to think about it, they have around 70 million members all over US. Since they had a record of every purchase of their customers, they were having an edge over most others because they knew to whom exactly they sold.
If this is impressive, think about this data, Costco returns around 10,000 keys in an year, because that's how many people lose their keys in Costco every year, and with their membership card attached to their key chains, Costco is able to reach out to them and return the keys.
And as far as customer reaction is concerned, after someone sent bacteria in their food:
2 different case studies, both utilizing data to the hilt, but extremely different feedback from customers. While being data analyst it's imperative to all of us to not to forget that we are at the end of the day, marketeers, last thing that we want is to get at the wrong side of our customers.
There are 2 cases where some brilliant analytics was done, but both created a different experience for customers. This further re-iterates that just analytics by itself won't solve anything, we need to ensure that we stay true to our marketing theories:
1) Target: Right customer, wrong way?
Being one of the biggest retailers of US, they are heavily invested in data analytics especially in marketing analytics. Not too long ago, Target though sold everything, from cotton balls to beer, but was known more for cleaning supplies. So, people would go to grocery stores for groceries, toy stores for toys, and Target for toilet papers. Extremely weird when you think that Target does sell everything else as well. This was credited to human habits, it's just like you driving on road, sometimes you probably don't even realize how you reached a certain place, because so used to of driving the same route, same car, in the same traffic, that it becomes a part of your habit.
Target needed to change this buying habit of people, and they selected the best possible segment for retailers, kind of holy grail, soon to be parents. People tend to change their habit only when there is a huge change in their lives, like when people get married, that's when they start going to different restaurants, when they get a job, they start purchasing different kinds of clothes, form different brands, similarly, when they are expecting a baby in their family, they again change their habits a lot. And if you are able to get this segment in a habit of buying in your store, then you would probably end up selling them everything, from baby diapers, beers, groceries, vitamins, clothes, everything, exactly what Target offers.
What they realized was that by third trimester, when females especially open up about baby showers etc, it's probably too late to reach out to these customers, they already get into a habit of shopping in a specific stores, and in second trimester it's too early to know if a family is expecting a baby. This was an un-enviable problem given to marketing team of Target, headed by one of the most famous mathematicians, Andrew Pole. Pole ended up creating a mathematical equation which had a list of 20+ items (like vitamins, calcium, lotions etc), when analyzed together, can predict probability of a female being pregnant.
When campaigns were launched on the basis of the results of Pole's algorithm, a man walked into one of the stores outside Minneapolis, demanding to see the manager, The reason was that his daughter, who was still in high school suddenly started getting mailers and coupons for baby diapers and cribs. He was extremely upset as it seemed like target is encouraging his daughter to get pregnant. It was only a few days later that this man realized that his daughter was actually pregnant.
2) Costco: You gave me bacteria, I still love you
When you are selling food, you need to be extremely careful of what's inside, If you are not more than cautious, there are chances you will put something in someone's body which is going to make him/her sick, and thereby making your organization directly responsible for that. But then at times it's extremely difficult to be 100% error free, especially when we talk about stores as big as Costco, where there are 100,000s of members and even more SKUs.
It was in 2010 when Center of disease control and prevention had a daunting task of identifying the root cause and origin of Salmonella case which sickened around 272 people in whole of US, and 5 were from Colorado region only.
There were no leads and no solution in sight, when CDC decided to look into the behavior of those who got sick. When analyzed the data, it emerged that many of them shop at Costco, a huge grocery store in US, with more than 70 million members.
When CDC approached Costco to find out further, they realized that all those people who got sick had one thing in common, that was black and red pepper in Salami that they consumed. This came from a whole seller based out of Rhode Island based manufacturer, Danielle International.
That's when data team of Costco took and and churned out a list of all those people who bought the food item in past few days, and begun a massive campaign to recall the product through mail, phone, email etc. This was possible because as any large retailers of it's size, Costco also keeps all information about buying habits, purchases, address and contact details of it's customers in it's massive servers. In their Big Data platform, they keep track of each transaction of a customer, along with customer's contact details, and to think about it, they have around 70 million members all over US. Since they had a record of every purchase of their customers, they were having an edge over most others because they knew to whom exactly they sold.
If this is impressive, think about this data, Costco returns around 10,000 keys in an year, because that's how many people lose their keys in Costco every year, and with their membership card attached to their key chains, Costco is able to reach out to them and return the keys.
And as far as customer reaction is concerned, after someone sent bacteria in their food:
2 different case studies, both utilizing data to the hilt, but extremely different feedback from customers. While being data analyst it's imperative to all of us to not to forget that we are at the end of the day, marketeers, last thing that we want is to get at the wrong side of our customers.
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