TESCO会员卡发展
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所有超市雇员都知道的两个名字：the Likert scale（问卷5级答案） and Osgood’s Semantic
Differential Procedures（用多个形容词纬度的评级来描述某事物/概念）. Not though, generally speaking, two concepts
on the lips of every supermarket employee.
早期：Out of the 45,000 lines, 8,500 accounted for 90 per cent
of sales.
Working with that number would inevitably be quicker and
easier, and
common sense suggested that it could yield almost as
much insight as if
the other 36,500 lower-sales-contributing lines were
also included
To the team’s excitement, when they
examined the list of products in each cluster, they
seemed to make sense.
The team settled on 27 different clusters, which became
its first
customer segments. This was given the catchy title of
‘Tesco Lifestyles’.
ways; for
example, there was a ‘Snacking and Lunch Box’ Bucket.
‘Why not turn Lifestyles upside down?’ they reasoned. Take each
product, and attach to it a series of appropriate attributes, describing
what that product implicitly represented to Tesco customers. Then by
scoring those attributes for each customer based on their consistent
shopping behaviour, and building those scores into an aggregate measurement
per individual, a series of clusters should appear that would
create entirely new segments.
新方法：
They then set about imagining 50 things that our shopping baskets might say
about customers. What does it mean if we buy a lot of ready meals? Alot
of fresh produce? No meat? Did we like to try out new products, or
exotic ingredients? Are we motivated by price promotions?
Measuring customers on a number of these criteria could start to create
distinct profiles
the Likert scale and Osgood’s Semantic
Differential Procedures. Not though, generally speaking, two concepts
on the lips of every supermarket employee.
Out of the 45,000 lines, 8,500 accounted for 90 per cent
of sales.
Working with that number would inevitably be quicker and
easier, and
common sense suggested that it could yield almost as
much insight as if
the other 36,500 lower-sales-contributing lines were
also included
To the team’s excitement, when they
examined the list of products in each cluster, they
seemed to make sense.
The team settled on 27 different clusters, which became
its first
customer segments. This was given the catchy title of
‘Tesco Lifestyles’.
ways; for
example, there was a ‘Snacking and Lunch Box’ Bucket.
建立osgood profile，对45000种商品？By creating 20 scales on which to judge the attributes of every
product in the store, it could then create 20 numerical measures. Turning
numbers into insight was becoming a Clubcard speciality.
But what scales to choose? ‘Low fat’ against ‘high fat’, ‘big carton’
against ‘small carton’, ‘needs preparation’ against ‘ready to eat’, and
‘low price’ against ‘high price’ are just a few of the two-tailed Likert
scales that they ended up choosing. There were also single-tailed
measures, such as ‘Is it a promotion?’ and ‘Is it a Major Brand product?’
With 20 scales agreed as a way of grading every product on its shelves,
all that the team had to do was to produce the Osgood Profiles. That is,
45,000 Osgood profiles, one for every product from anchovies to
asparagus, whisky to washing powder. But judging 45,000 products on
20 different scales would mean agreement on 1.2 million individual
ratings before the segmentation could be used.
用滚雪球法，推导商品的osgood属性（先选出最“冒险”的商品，然后看各篮子里相似的商品，挑出一些“比较不可靠”的“冒险”商品，直到“新鲜”更适合分类
had ever tried to distinguish how ‘adventurous’
every product in a supermarket is. Tinned fish probably isn’t; extra
virgin olive oil is. Is Brie adventurous? How adventurous is it? More
than decaffeinated coffee? Less than a red pepper?
They set about devising a way to allocate attributes for every item.
The process created was known as the Rolling Ball. To create a Rolling
Ball categorization, Pavey and his team started with a small set of
products that definitely have the quality you seek: so if you want to find
out which products are adventurous, start with extra virgin olive oil and
ingredients for Malaysian curries, and see which customers bought
those products.
Then look at what else these customers have in their shopping basket.
Discard items that show up in everyone’s basket (bananas or milk, for
example), and keep looking, building bigger and bigger groups of
products. When can the process stop? This is where the rolling ball idea
came in.
The products that are picked up early will have a high ‘adventurous’
rating. As the ball gets bigger, those ratings are probably lower, and
certainly less reliable. So how to stop the ball? Well, the basic idea was
that each of the major attributes were large dips in a huge surface. When
the ball starts to roll into an adjacent hole, then the ball should stop. For
example, you might start off trying to predict adventurous products, but
after 400 or 500 products are coded, you start to find a lot of products
that are more ‘Fresh’ than ‘Adventurous’, and so the ball has started to
roll down an adjacent hole. The mathematics to solve this problem were
challenging, but the method created groups of products that intuitively
seem right.
Each time a cluster became apparent, fewer shoppers remained lost in
20-dimensional space. After six months, 13 well-defined and tested
groups had been identified. But the 14th made no sense.
为了分辨本cluster里的不同子类，增加了一个纬度：购物习惯
To make the segmentation work well, an extra
segmentation was born, Shopping Habits, which used not just what
people bought, but when people shopped.
VDI (Citrix infrastructure)
Spam bot welcomed (Graveyard, spammer's email will be listed and available for email harvesters)
所有超市雇员都知道的两个名字：the Likert scale（问卷5级答案） and Osgood’s Semantic
Differential Procedures（用多个形容词纬度的评级来描述某事物/概念）. Not though, generally speaking, two concepts
on the lips of every supermarket employee.
早期：Out of the 45,000 lines, 8,500 accounted for 90 per cent
of sales.
Working with that number would inevitably be quicker and
easier, and
common sense suggested that it could yield almost as
much insight as if
the other 36,500 lower-sales-contributing lines were
also included
To the team’s excitement, when they
examined the list of products in each cluster, they
seemed to make sense.
The team settled on 27 different clusters, which became
its first
customer segments. This was given the catchy title of
‘Tesco Lifestyles’.
ways; for
example, there was a ‘Snacking and Lunch Box’ Bucket.
‘Why not turn Lifestyles upside down?’ they reasoned. Take each
product, and attach to it a series of appropriate attributes, describing
what that product implicitly represented to Tesco customers. Then by
scoring those attributes for each customer based on their consistent
shopping behaviour, and building those scores into an aggregate measurement
per individual, a series of clusters should appear that would
create entirely new segments.
新方法：
They then set about imagining 50 things that our shopping baskets might say
about customers. What does it mean if we buy a lot of ready meals? Alot
of fresh produce? No meat? Did we like to try out new products, or
exotic ingredients? Are we motivated by price promotions?
Measuring customers on a number of these criteria could start to create
distinct profiles
the Likert scale and Osgood’s Semantic
Differential Procedures. Not though, generally speaking, two concepts
on the lips of every supermarket employee.
Out of the 45,000 lines, 8,500 accounted for 90 per cent
of sales.
Working with that number would inevitably be quicker and
easier, and
common sense suggested that it could yield almost as
much insight as if
the other 36,500 lower-sales-contributing lines were
also included
To the team’s excitement, when they
examined the list of products in each cluster, they
seemed to make sense.
The team settled on 27 different clusters, which became
its first
customer segments. This was given the catchy title of
‘Tesco Lifestyles’.
ways; for
example, there was a ‘Snacking and Lunch Box’ Bucket.
建立osgood profile，对45000种商品？By creating 20 scales on which to judge the attributes of every
product in the store, it could then create 20 numerical measures. Turning
numbers into insight was becoming a Clubcard speciality.
But what scales to choose? ‘Low fat’ against ‘high fat’, ‘big carton’
against ‘small carton’, ‘needs preparation’ against ‘ready to eat’, and
‘low price’ against ‘high price’ are just a few of the two-tailed Likert
scales that they ended up choosing. There were also single-tailed
measures, such as ‘Is it a promotion?’ and ‘Is it a Major Brand product?’
With 20 scales agreed as a way of grading every product on its shelves,
all that the team had to do was to produce the Osgood Profiles. That is,
45,000 Osgood profiles, one for every product from anchovies to
asparagus, whisky to washing powder. But judging 45,000 products on
20 different scales would mean agreement on 1.2 million individual
ratings before the segmentation could be used.
用滚雪球法，推导商品的osgood属性（先选出最“冒险”的商品，然后看各篮子里相似的商品，挑出一些“比较不可靠”的“冒险”商品，直到“新鲜”更适合分类
had ever tried to distinguish how ‘adventurous’
every product in a supermarket is. Tinned fish probably isn’t; extra
virgin olive oil is. Is Brie adventurous? How adventurous is it? More
than decaffeinated coffee? Less than a red pepper?
They set about devising a way to allocate attributes for every item.
The process created was known as the Rolling Ball. To create a Rolling
Ball categorization, Pavey and his team started with a small set of
products that definitely have the quality you seek: so if you want to find
out which products are adventurous, start with extra virgin olive oil and
ingredients for Malaysian curries, and see which customers bought
those products.
Then look at what else these customers have in their shopping basket.
Discard items that show up in everyone’s basket (bananas or milk, for
example), and keep looking, building bigger and bigger groups of
products. When can the process stop? This is where the rolling ball idea
came in.
The products that are picked up early will have a high ‘adventurous’
rating. As the ball gets bigger, those ratings are probably lower, and
certainly less reliable. So how to stop the ball? Well, the basic idea was
that each of the major attributes were large dips in a huge surface. When
the ball starts to roll into an adjacent hole, then the ball should stop. For
example, you might start off trying to predict adventurous products, but
after 400 or 500 products are coded, you start to find a lot of products
that are more ‘Fresh’ than ‘Adventurous’, and so the ball has started to
roll down an adjacent hole. The mathematics to solve this problem were
challenging, but the method created groups of products that intuitively
seem right.
Each time a cluster became apparent, fewer shoppers remained lost in
20-dimensional space. After six months, 13 well-defined and tested
groups had been identified. But the 14th made no sense.
为了分辨本cluster里的不同子类，增加了一个纬度：购物习惯
To make the segmentation work well, an extra
segmentation was born, Shopping Habits, which used not just what
people bought, but when people shopped.
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