For years, Target has been using analytics to increase value from its shoppers. Using predictive analytics, Target and many of their competitors have been able to classify shoppers based on their projected purchasing patterns and impact those customers with campaigns or coupons. In business, you realize that customer shopping habits become deep-seated and are remarkably tough to change, so the old-school style of targeted advertisements meant to change the shopping habits of customers usually finds a small conversion success. However, Target has found that, when customers go through major life events such as childbirth, they now know that this the prime time to garner new brand loyalty from customers. “In 2002, sales have increased from $44 billion to $67 billion and the former CEO Gregg Steinhafel has attributed some of this growth to the “heightened focus on items and categories that appeal to specific guest segments such as mom and baby (Duhigg, 2012).”
KEYWORDS: Predictive, analytics, customers
HOW TARGET USED PREDICTIVE ANALYTICS TO INCREASE SALES
Retail is a term that covers a massive range of business types and models. “There are six business types in regard to retailing, which are convenience stores, specialty stores, supermarkets, discount stores, department stores, and online stores (Managementstudyguide.com, 2018).”
Convenience stores are considered a small to medium grocery and convenience shop usually operating near residential areas. This was the standard during the beginning stages of the retailing industry. This allowed for essentially no competition from other stores that were located a further distance from a residential area. The convenience store was also just what the name suggested, a store of convenience. Their focus was not on low price because they understood that you were here because you were not willing to drive far distances for the items that they sold. Then we have specialty stores, which is a retailer that focusses on smaller net product areas, such as electronics, video entertainment, toys. Examples of these stores are Fry’s Electronics, Toys “R” Us, formally Blockbusters. Next, there are supermarkets, which are a medium to large grocery and household store whose goal lay in low-margin / high-volume sales. Discount stores are also small, medium or large shop. But their items are items that are branded goods to sell at discounted prices, Target could also be considered as a discounted store. This allows customers to buy items without customers being burdened with paying fully for the cost of production in the items they want. Department stores are a large store with a wide range of goods and services, segmented into departments which can be managed by the department store itself of salespersons from outside retailers that have distribution through the store. An example of this would-be Target, Walmart, JCPenney, Dillards, Macy’s. Lastly, we have online only stores that sell directly to customers through an e-commerce website. This has taken the retail industry by storm. This allows for a company to no longer have to pay for a brick and mortar and give them the opportunity to pour into the products and increase revenue based on operating margins. If you don’t have to pay the traditional fixed cost that is associated with building such as rent, electricity, plumbing, Wi-Fi, software and hardware, and mass amounts of employees and possible benefits. This can save you a ton on capital that can be used everywhere. Example of retailers like this is Amazon, Fashion Nova, ASOS, and Shoe Dazzle.
Today’s retail has expanded whereas, most retailers are not just pigeonholed into only being one thing to customers. Companies like Target can be a convenient, discount, grocery, and discount, and an online store. “The U.S. Commerce Department reports that total retail sales in 2015 were around $5 trillion, The vast majority of sales that make up that $5 trillion dollarss are in store purchases. In 2015 e-commerce only accounted for 7.1 percent of revenue. It is forecast that this percentage will grow to about 9.8% by 2019. More than 25 million people in the U.S. are employed by retailers (Comfort, 2017).”
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Description of the Company’s Business: Target
Target Corporation is the second-largest department/discount store retailer in the United States, behind Walmart. “Target began expanding the store nationwide in the 1980s and introduced new store formats under the Target brand in the 1990s (Fortune, 2018)”. The company has found success as a cheap-chic player in the industry. “In 2018, Target operates a total of 1,822 stores throughout the United States. The company is ranked No. 39 on the 2018 Fortune 500 list of the largest United States corporations by total revenue (Fortune, 2018).” Target has always been known for its emphasis on the desires of its younger, stylish, image-conscious shoppers, whereas Walmart relies on their strategy of always low prices.
Motivating Factors (What changed and Problem Statement)
As stated previously, customer behaviors play a huge part in the success of a retail company. Target realized that “most shoppers don’t buy everything they need at one store. Instead, they buy groceries at the grocery store and toys at the toy store, and they visit Target only when they need certain items they associate with Target, (Duhigg, 2018).” The Targets store was essentially a one all store and could satisfy all their needs. The issue that Target faced is convincing customers that the only store they need is Target, and breaking brand loyalty is a difficult task to do.
To break brand loyalty, there are a variety of aspects that should be addressed. Target would have to have a great environment (Brick and Mortar), the convenience of getting multiple items in one place, and catching customers during major life events, which in turn is considered a time-saving. In the process of breaking brand loyalty, Target would need new age technology, data mining and market segmentation. This case will attempt to address the following questions regarding Target Corporation:
- How does Target use predictive analytics to increase revenue and sales?
- How can Target use Market Segmentation to assist with ad marketing to potential customers?
Description of The Study
When consumers shop for everyday items they tend to shop based on history. If as a child your parents always visited Kroger’s store for fresh baked goods and groceries, visit Walmart for clothes and stationery items, and visited Home Depot for household repair you will follow suit, unconsciously. These trends that are ingrained into our lives are considered consumer buying behaviors. This is defined as the “sum total of a consumer’s attitudes, preferences, intentions, and decisions regarding the consumer’s behavior in the marketplace when purchasing a product or service (Grimsley, 2018).” This concept is great when it works in your favor because you are grandfathering in customers based off of habits of the generation before them. However, when you are attempting to garner more customers breaking their loyalty is tough. Target Corporation recognized this phenomenon as only being able to be addressed during a pivotal time in consumers lives. This is when they are welcoming a new child into the world. There are transitory periods in a person’s life when traditions fall apart and buying habits are suddenly changing. When parents are, tired and overcome with stresses and worries their shopping patterns and brand loyalties are now considered up for the taking. In order to capitalize on this change in dynamics, Target would have to be strategic on how they present ads and commercial to garner new customers. One would also ask, how would you even know if someone was pregnant? This is where Targets genius comes into play.
Strategy for Change
The business strategy and solution for Target is to customer market segmentation and predictive analytics to increase sales. In addition, create a great environment for customers to enjoy shopping and get all their items in one place.
It is vital to understand your core audience and how that is broken down by demographics. Segmentation is essential because it does just that, breaking down a larger target audience into smaller, more concise groups of patrons. This effort is needed because it allows you to funnel your marketing efforts into ads, campaigns, and sales that connects with customers most likely to buy your product. In addition, this assists you with all potential markets that you may want to pursue and assesses the markets based on their possible for viability to your company.
Segmenting isn’t a small feat because if done incorrectly finances and investments will be spent in the wrong places causing potential bankruptcy for some companies. In efforts to better segment the market, it is based this on 4 traits; Demographics, Behavioral, Geography, and Lifestyles. Demographics play a huge part in any business because what a 20-year-old Hispanic males needs are can vary from the needs and expectations of a 55-year-old Caucasian female customer. When addressing demographic segmentation age, race, gender, marital status, income, education, and occupation will all play its role. Some having more weight than others based on the products that are being marketed.
Behavioral Demographics are huge because it studies trends, usage, and decision making. Trends are important because they can be classifying as trendy and short-lived or trends that have been established over time based on loyalty to products. For example, in the early 2000s younger adults preferred baggier jeans vs older consumers preferred fitted jeans. Another example would be a preference in deodorant depending on your daily duties, whereas a sports player would need a stronger deodorant vs a person who doesn’t do any active sports. You see this clearly being done with Target and its competitors.
Geography demographics are based on where your customers are located, which require varying needs. Target stores located up north like Chicago would certainly need to carry a product like a snow shovel whereas a Target in Texas would not. Understanding this difference can limit you in wasting resources in places where it is not profitable.
Lifestyle segmentation looks at from the angle of how you like to shop. Online shoppers are different from shoppers that go to the stores. Typically, online shoppers know what they are looking for, don’t have much time, and cost conscience. They want coupons, things delivered to their doors without interruption in their daily schedules. Shoppers in-stores usually make the time to go to the store and may not clearly know what they want in all cases. For example, if a woman that isn’t a frequent makeup shopper, she would need to go to the store to test out pigmentations, and how the makeup feels to them. This wouldn’t be the case for an online shopper because most of the time they are re-ordering based on prior uses.
All of these factors play a major role in predictive analytics. If you have all the variables captured in one place, now, the task is to figure out how to use this information.
Technology and Additional Distribution Infrastructures
With market segmentation gathering valuable intel, now it is time to address what target views as catching customers during the transitional years. As mentioned before, Target realized that when customers are welcoming a new child into the world, traditions fall apart and buying habits are suddenly changing. This means that they can now garner new consumers. Because birth records are traditionally public, the moment a couple has a baby, they are almost immediately flooded with offers and ads from all sorts of companies. Target realized this and wanted to be ahead of the curve and catch them earlier. Targets marketers stated that “they wanted to send specially designed ads to women in their second trimester, which is when most expectant mothers begin buying all sorts of new things, like prenatal vitamins and maternity clothing (Duhigg, 2018).” They figured if they could get them at this point then they would have them for years to come.
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For years Target, along with other chains have been using analytics to increase sales from shoppers. Using predictive analytics, Target has been able to categorize customers based on anticipated buying patterns and influence those customers with advertisements or coupons. Target capture this data by “assigning each customer a guest ID number which tracks any and all information available like name, credit cards used, email address, purchase history, if they’ve clicked on email advertisements, where they shop online, etc. Target also purchases “demographic information like where the customer lives, their job history, estimated salary and where they went to school (Julie, 2018).” This all is used for predictive analytics.
What is Predictive Analytics and why it is Important
As stated before, when some customers go through a major life event, such as graduating from college, getting a new job, moving to a new city. Studies have shown that when someone marries or moves in together, that couple is more likely to start buying some new things. This is considered being a compromise between the two. For example, when my fiancé and I moved in together we no longer shopped at Target or Walmart, we began shopping at Costco due to the change in lifestyle.
Predictive analytics is a form of advanced analytics that uses both new and historical data to forecast activity, behavior, and trends. It involves applying “statistical analysis techniques, analytical queries and automated machine learning algorithms to data sets to create predictive models that place a numerical value — or score — on the likelihood of a particular event happening (Rouse, 2018).”
As we stated, the life-changing event that we were going to focus on was when our customers become pregnant and having babies. Now, although it sounds as simple as identifying customers that are now pregnant, it’s not as easy at it sounds. A researcher utilized Targets Baby shower registry information, although limited, it provided great insight. They were able to help understanding how pregnant women shopped and navigated their sites, by collecting and tracking consumer online shopping behavior. Upon studying the results of running it through software, Andrew Pole, the lead researcher for this study noticed that women on the baby registry were buying unscented lotions around their 2nd trimester. Another trend that was noted was that women around their first 2 weeks, they began to purchase large amounts of supplements. It was also recognized that when they began to buy scent free sanitizers, lotions, cotton balls, and hand rags it could be a sign of them nearing the end of their pregnancy.
As a result of the allorhythmia data and regression analysis that was run through multiple computer programs, target found that there were 25 products that could be used to predict the likelihood of a customer being pregnant and her associated due date. This was groundbreaking because now, with this predictive data, Target could then send coupons to mothers-to-be for products before she even knows she needs them. By doing this, Target could capture some value from customers by now starting the process over, by creating brand loyalty at a time when shopping habits are changing.
By conducting this research and using the tools that were developed, Target found that there were 25 products that could be used to predict the likelihood of a customer being pregnant and her associated due date. This is brilliant for the facts that were stated earlier, with the use of predictive analytics, it optimizes marketing campaigns. Predictive models assist Target on how to further attract, retain and grow their most profitable customers and push merchandise. The by-products of this are that you now are improving operations in the process, as well as giving the customers a tailor-made shopping experience. As a result, “Target’s revenues grew from $44 billion to $67 billion from 2002 to 2010 (Duhigg, 2012)”.
Using data that the customers didn’t know that you were privy to can be a public relations nightmare. Target experienced a scare of this when the model that they’ve developed had generated and add to send to a mother-to-be home. However, the conversation In the home had not been had prior to receiving the add. This caused an angry father to go to the nearest Target store and confront management about promoting pregnancy to his teenage daughter. Little did the father know, his teenage daughter was indeed pregnant he just wasn’t aware? This was done in poor taste on Targets end. Although it correctly predicted her pregnancy based on their model, it came off as intrusive and creepy.
As a business, it behooves you to understand and have complete control over your target audience. This is essential to success as a business. This analytical tool is used to explore large amounts of data for the purpose of decision making (Stair & Reynolds, 2018).” But, this can also be seen as intrusive and exploitive. Essentially, you’re honing in on a demographics without their knowledge. Target marketing is done every day to understand the potential in markets and products, but using this information in the way that it could cause strife in a home just doesn’t sit well with most. Doing things tastefully with great timing can take you far.
Retailers all know that “experience” matters, and now we’re seeing more innovation in this area than ever before. Target’s Predictive Analytics has changed the way that retailers use and obtain data. As stated by a former president, “Gregg Steinhafel, explained that the reason for the intensified raise in revenue was due to a “heightened focus on items and categories that appeal to specific guest segments such as mom and baby (Duhigg, 2012).” This is the result of being able to predict shopper’s experiences based on shoppers trends and behaviors.
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- Duhigg, C. (2014). The power of habit. New York: Random House, p.43.
- Target Using Predictive Analytics to Increase Value Capture – Digital Innovation and Transformation. Julie (2018). Retrieved from https://digit.hbs.org/submission/target-using-predictive-analytics-to-increase-value-capture
- Rouse, M. (2018). What is predictive analytics? – Definition from WhatIs.com. Retrieved from https://searchbusinessanalytics.techtarget.com/definition/predictive-analytics
- Stair, R., & Reynolds, G. (2018). Principles of information systems (13th ed., pp.394).
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