A major retailer’s customer base can potentially be as diverse as America itself. Some customers are from young families, others are seniors. Some have high incomes, some have low incomes. Some like to spend a little money often, others like to spend lots, but only a couple times a year.
Basically, all customers can be divided into different “types of segments” based on factors such as the demographic they represent and their spending habits. Customer segments are clearly defined attribute-based descriptions of a group of customers. Companies that gather data that can be broken down into segments have a great opportunity to understand their customers and predict their behavior. From a retail perspective there are four types of customer segmentation. They are:
Lifestyle: This represents where the customer is in life. Is the person just out of college? In a young family? An empty nester? A senior citizen? This is the most underutilized piece of information. It tells the retailer how it should communicate with the customers – what message is appropriate, what graphics should be used, what offer will work best and what products they might want.
Value-based: This represents the customer’s typical shopping behavior and how valuable she is to the retailer. Is the customer one who shops regularly at the store? Is it someone who spends large amounts one or two times a year? Or someone who spends small amounts of money several times a year? Does she only shop a single category? Often retailers will place customers into segments based only on spending levels, but the customers’ spending behaviors could be totally different (for example, some shoppers prefer to only buy items off the clearance rack, while others prefer to buy products advertised in flyers).
Attributes: This represents very specific data that can be attributed to a customer. Does the customer have a credit card? Is the customer part of a rewards program? The attribute could also be where the customer lives or what sport they prefer (for example, baseball versus basketball).
Time: This represents the customers’ past and predicted future behavior. Does the customer have a history of shopping at the store, and is the customer expected to return? This can also represent where the customer fits with the lifecycle of the store: Is it a customer who started shopping at the store six months ago? Two years ago? Five years ago?
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When a company is armed with data based on these types of customer segments, it can work with a data analysis company to begin developing well-informed marketing strategies. If the data shows there are a good number of customers who have a history of buying men’s pants, the data probably shows they will respond to sales on men’s shirts as well. Some behaviors are not as logical. In one case, shoppers likely to buy men’s suits were also likely to buy women’s dresses. It was most likely a significant other buying for the man.
But, at the same time, retailers have to learn to interpret their data properly, taking into account the volume of various product sales. For instance, a retailer could gather data that indicates one type of customer is four times more likely to buy a certain product than another. An advertising campaign targeting this customer would make sense if the data refers to a common product like shoes. But if the product is an obscure one such as, say, purple airbrush painting tools, the marketing campaign won’t be that helpful. Sales of that product are so low that even if they tripled it wouldn’t make a substantial difference.
The purpose of understanding customer segments is to better understand your customers – what makes them tick and what impacts their shopping behaviors. The data provided by customer segments also allows retailers to get the greatest return on investment when it comes to marketing, as communications become targeted only at groups that are expected to respond favorably.