What Data to Collect and How to Put It to Work at Your Company

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One of the people I interviewed for this book had a long career at IBM before starting his own company. When he told me how storied IBM was, I asked him if he could tell me something about what it was like working there. He said, “It’s hard to put into words.”

I’m expecting that many readers might have the same reaction to my request that they read this post. It’s not that they won’t find it interesting or informative; rather, likely that they will struggle with putting their feelings surrounding data analysis into words. People are so enormously busy these days, and there are so many things vying for our attention every day, it would be challenging to spend time thinking heavily about one topic in particular unless you really felt it was important.

People are so overwhelmed with information that it’s hard to settle on one piece of data, no matter how interesting it is. Earlier this year, I read an article about the tracking of sneaker sales at Finish Line. This is a company that sells athletic shoes and apparel – my husband used to work for them – and after doing some analysis they were able to see a significant decline in interest in their products during the summer months. Now this conclusion seems fairly obvious – right? Who wants to buy sneakers when you can go outside and wear sandals or flip-flops? But they only came to this conclusion after collecting the data from all of their stores around the country and noticing that there was a pattern emerge over time.

Knowing that people are interested in their findings, but not wanting to cause them to actually spend time reading this post when there are so many other things vying for their attention, I decided to break it up into three parts. This is part one of the series.

It’s February 5th, 2015 and you have just read about the Finish Line story above in your local newspaper. It seems obvious that interest in athletic shoes increases during winter months when there isn’t much opportunity to wear sandals or flip-flops. But you were busy with all kinds of things this weekend – what did you do? You might even find yourself thinking about what you will do this coming weekend, which is less than a week away. Or maybe something happened over the weekend that you want to tell a friend about. There’s so much going on in your life, and although this article is an interesting one, it probably means very little right now.

If this article had been written nearly 50 years ago (1965), though, chances are that you would be eagerly reading every word of it. It was during the mid-1960s when the idea of using data to help companies make their decisions first came about. While data collection itself has existed since ancient times (think about tax records or census information), there wasn’t really anything like “big data” until computers were invented; once they became widely used for business purposes around the late 1950s people started thinking seriously about what kind of information could be pulled together and analyzed.

Since the time of the earliest computer programs that were built for this purpose, one of the biggest challenges with using data to inform business decisions has been getting people – employees and managers – to use it. People often say things like: “We know what we’re supposed to do.” or “This isn’t about me.” or “I know I’m doing my job; don’t bother me with details!” There’s a variety of reasons why people resist looking at data. It could be because they feel as though their jobs aren’t on the line (even if they are), they feel as though their manager is trying to catch them out (sometimes true) or they might even think that is what takes to be a good manager is the ability to spot problems without looking at data.

Now that you have a sense of what’s going on in 1965, I hope that if you are one of those people who “just knows” or feels as though they don’t need help from data, you feel a little more receptive towards it. Or maybe by this point, you’re thinking: “This is interesting, but I’m not sure how it relates to me.” If so, then read on…

The rest of this article will discuss three different examples where data has helped companies be more successful. The first two stories belong to larger organizations (Finish Line and Target) while the third involves an individual person (me).

I’ll talk about people use data at their company. The first example is about Target and their use of data to decide which products should be stocked on the shelves. The second is about Finish Line and their approach to using data to inform the decision of where new stores should be located. The final example is from my own experience as a school leader working with teachers, students, and parents.

1. Using Data at Target: Making sure products are stocked correctly

Target’s bullseye logo is one of the most recognized in North America, if not the world. It has become “synonymous with affordable style” where people go to find everything from clothing for children and adults to home decor items, groceries and various other things. The founder, George Dayton wanted Target to be a store that would undercut prices at other department stores like K-Mart (formerly known as “Kresge”), while also offering good quality items.

Since 2001, Target has used data analytics to help build their business; this was made possible by Gregg Steinhafel who was CEO of the company between 2008 and 2014. He believed that while many companies were proficient at collecting data on what they were selling, they weren’t always as adept at turning that information into actionable insights. In other words, there wasn’t a clear understanding of how best to use the data they had collected effectively.

In order to figure out what kinds of products should be stocked on their shelves and where those items should go throughout their stores, Target uses a program known as “demand forecasting”. This is a computerized system that examines all kinds of information – from sales history for certain items to weather forecasts – in order to predict what customers are going to buy during certain seasons or even later in the year. This helps companies like Target know what kind of inventory they should have as well as where particular items should be placed so as to not run out or appear undesirable.

Target has more than 2,200 stores in North America and 1,800 of those are located across Canada. Their revenue is over $72 billion annually with a net income of about $2.5 million each day. They have approximately 350,000 employees worldwide including at their headquarters which is located in Minneapolis, Minnesota (USA).

Today’s consumers are constantly connected to information via the internet or their mobile devices. There are millions of “shoppers” that visit Target every week looking for bargains on things they need as well as some items they just want to buy. The number of people shopping there on any given day means that it is quite difficult for Target’s buyers to predict what kind of products will sell well during certain seasons or months ahead of time.

That’s why they rely on good data and demand forecasting to help them stay competitive with retailers like Walmart and Costco.

2. Using Data at Finish Line: Deciding where new stores should be located

Finish Line is a US company that sells men’s and women’s athletic apparel, footwear, and accessories as well as products from various sports brands such as Nike, Adidas, and Under Armour. They also offer free shipping throughout North America as well as no-hassle returns which many people appreciate when making online purchases with the goal of saving money over buying those items in person at traditional retail stores like Foot Locker or Sport Chek.

The founders of Finish Line realized long that more customers would buy athletic shoes online if they had a reliable way of knowing which pairs would be most widely worn in the months to come. They also wanted those shoppers to have as much information as possible about those shoes – from price and size availability to shipping times and return policies – before purchasing them. That’s why Finish Line has been using data analytics since 2001, the year that it was acquired by JD Sports Fashion Plc for $558 million.

JD is a publicly-traded company based in London (UK) with over 2,200 stores worldwide including 1,165 locations across Europe. In fact, Finish Line now does much of its business overseas where US brands are well-established and highly respected for their quality and value.

Today’s consumers want access to quick shipping and returns which is why Finish Line uses data from its buyers, vendors, and customers in order to provide the “best-selling” styles of athletic shoes based on current trends. This has allowed them to be a major player in a highly competitive industry with a wide range of companies selling similar products including Foot Locker, Champs Sports, and Sport Chek.

3. Using Data at Sally Beauty: Sourcing high-quality hair care products

Sally Beauty is based in Denton, Texas (USA) and has been selling professional beauty supplies since 1964 when it was founded by Sam Sally as a storefront selling handmade items from local artisans. His wife Marcy applied product knowledge from her experience working at a salon where she used those same goods plus items from the stock room.

Today Sally Beauty has more than 3,300 stores in all 50 states and 20 countries and is a public company (NYSE: SBH) that earned $1.21 billion last year. They also continue to innovate their business using data – especially when it comes to sourcing high-quality hair care items – by acquiring competitors like Regis Corp, which added brands like Supercuts and SmartStyle as well as an additional 2,200 salons throughout North America under its umbrella.

Staying on top of emerging trends as well as what customers want from those products means constantly exploring new ways for Sally Beauty to source those items from suppliers around the world. This includes working with manufacturing companies in China where those goods are made. Data analytics is a core part of their business which helps them source the right products and ensure that they’re in stock at all times.

4. Using Data at PetSmart: Improving benefits for employees and customers

PetSmart was founded in 1986 and grew into America’s largest retailer of pet supplies with 1,500 stores nationwide by 2014. It also acquired Chewy.com, an online retailer specializing in natural pet food and healthy treats plus everything you need to take care of your pets such as crates, leashes, toys, and aquariums.

Chewy has helped PetSmart grow its e-commerce presence while providing an easy way for customers to order items online instead of going to a physical store like Petsmart or Petco. To help make sure that they have the right products in stock, PetSmart uses data analytics to stay on top of emerging trends for pet supplies including everything from which dog breeds are most popular to where cats prefer to scratch.

This includes finding new ways for employees at both PetSmart and Chewy to give back by using data about profits, productivity, and company spending that has helped them create initiatives like their Helping Hands 5K Walk/Run Event that encourages team members throughout North America to get out there and exercise while giving back through donations for local charities.

5. Using Data at Marriott International: having a great digital transformation

Marriott is one of the largest hotel chains in the world with 637,000 rooms, 30 brands, and more than 6,000 properties in more than 110 countries. Its portfolio of brands includes luxury hotels like Ritz Carlton, full-service business travel hotels like Courtyard by Marriott, and resorts like Marriott Vacation Club International.

It also continues to revamp its hotel rooms to meet the needs of customers by introducing digital re-vamping with voice assistants that can tell you about local attractions or even order food delivery from your room. This is just one example of how data analytics has made it easier for companies within the Marriott family to provide excellent service including Moxy Hotels which targets millennial travelers.

6. Using Data at Kroger: Being Strategic with Store Layout

Kroger was founded in 18 when Barney Kroger opened a grocery store in his hometown of Cincinnati. Today, it’s one of the largest retailers in the world with 2,793 supermarkets and multi-department stores across 35 states using more than 30 different banners including Kroger Marketplace, City Market, Dillons Food Stores, Frys Food Stores, and Smiths Food & Drug.

Like many other stores within its portfolio, Kroger is always looking for ways to optimize its business by ensuring that they have the right products at the right times so that customers can easily find whatever they need. This includes having enough items on hand during peak hours as well as investing in technology to help their front end work smarter like Kroger Ship which brings customer orders directly to store locations for the same pickup. Kroger also uses data analytics to keep track of customer behavior like what time they’re arriving at different locations and how many customers are coming in each day.

7. Using Data at Walmart: Optimizing shelf space

Walmart is the world’s largest company with 11,539 stores operating under a total of 63 banners worldwide that include Sam’s Club, Supercenters, Neighborhood Markets, Discount Stores, and more. In fact, it’s also one of the most valuable companies in history with revenues totaling $485 billion for 2016.

This means making sure that every single store has an optimal supply of products so that they can help as many people as possible from online pickup or delivery services to money-saving events throughout the year including Black Friday, Cyber Monday, and Clear Out Sale among others.

8. Using Data at Aetna: Forecasting Health Care Needs

Healthcare company Aetna is one of the largest and most-respected pharmacy benefits managers (PBM) in the United States with more than $60 billion in revenue for 2016. It’s also one of the highest-rated companies by consumers for employee satisfaction as well as their community involvement with initiatives like raising money to fight breast cancer and hosting blood drives.

To help its customers stay healthy, Aetna uses data analytics to predict health care needs including how much it will cost and every aspect from prescription drug trends to medical claims including disease management, wellness programs, and payments. This helps them make targeted recommendations like offering diabetes prevention services through a health program that helps customers get on the right track to prevent new cases of diabetes.

9. Using Data at KIND: Identifying Unhealthy Ingredients

KIND is a company co-founded by Daniel Lubetzky back in 2004 with a goal of making healthy snacking mainstream using bars made up of nutrient-dense ingredients like fruits and nuts. To do this, every ingredient goes through a screening process so KIND can ensure that it’s not only delicious but also has the best nutritional value possible.

Using data analytics, KIND keeps track of consumer preferences as well as emerging trends within different regions. This way they know exactly what people want to eat for breakfast, between meals, or even as dessert which helps them develop their newest flavors and products.

10. Using Data at Kohl’s: Getting in Front of Customer Needs

Every retailer has to keep track of their customers so they always know what shoppers want when they come to the store or check out online. One such company is Kohl’s, an industry leader with 1,158 stores in 49 states plus Puerto Rico and Guam that offers everything from apparel and home goods to athletic gear and even cosmetics.

To help its customers save money and get the most value for their dollar, Kohl’s uses data analytics to forecast customer behavior like where they’re going in-store or how much they typically spend per visit. Then it starts planning product assortments according to different age ranges, income levels, and more to make sure they have exactly what people are looking for.

11. Using Data at Unilever: Collecting and Acting on Consumer Feedback

Consumer goods company Unilever is a powerhouse within the retail industry with a wide range of household names including Dove, Lipton, Axe, Hellmann’s, and more that reach nearly two billion consumers each day. To help its brands stay competitive across all channels, it uses data analytics to collect consumer feedback and improve customer service like simplifying complex processes or communicating new product details.

12. Using Data at Intuit: Seeing What Comes Next

Intuit has been helping small businesses succeed since 1983 with financial software designed to remove stress from accounting so businesses can focus on growth instead. It also offers other services like credit card processing and payroll management to help make sure companies stay compliant with the law.

To manage all their customers and keep track of their financial data, Intuit uses big data analytics to identify customer needs and create products that meet them like Mint, an app that lets you track spending in real-time and receive personalized tips to help save money.

13. Using Data at Adidas: Optimizing Operations for Maximum Efficiency

Adidas is one of the most recognizable brands in the world thanks to its signature three-stripes logo which can be seen on everything from high-end designer sneakers worn by athletes before a match or even fans supporting their teams at weddings or birthdays. To stay competitive, it uses data analytics to optimize its supply chain and maximize operational efficiency so the retailer can ship the right products to the right places at the right time.

14. Using Data at Netflix: Creating Winning Content for Every Viewer

Netflix has revolutionized paring TV shows with online streaming services since it started in 1997 but now its main concern is creating content that engages viewers worldwide. To do this, data analytics helps them study viewership patterns across different regions to figure out exactly what people like to watch day-in and day-out which helps them decide what programs should stay or go.

15. Using Data at Starbucks: Getting More Customers Through the Door Each Day

Starbucks believes coffee isn’t just a drink, it’s a culture that drives its mission to connect with customers and provide a place for them to meet and chat. To help it do this, data is collected about customer demographics, behavior patterns, and preferences which helps the company track everything from who’s buying what to how many drinks they’re making in a day.

16. Using Data at Marriott: Customizing Every Customer Experience

From hotels that offer individualized services to resorts that have their own entertainment venues, Marriott is one of the most popular hotel chains worldwide with nearly 1.3 million rooms across more than 5500 locations. But staying on top takes a lot of hard work including using big data analytics to customize every guest experience possible so everyone gets exactly what they want when they stay with them.

17. Using Data at Raytheon: Creating One of the World’s Largest Weapon Manufacturers

Raytheon is one of the largest and most well-known weapons manufacturers in the world with a history dating back to 1922. It provides innovative technologies like missile systems, electronic warfare, defense, and cybersecurity for both government agencies and commercial customers which includes U.S. defense companies like Lockheed Martin, General Dynamics, and Boeing.

To optimize information about its products and services, Raytheon uses big data analytics to gather data from its vast network including employees, partners, and suppliers which helps it improve operational efficiency to see what works best or if changes need to be made along the way.

18. Using Data at Capital One: Reaching More Customers than Ever Before

Since 1994, Capital One has been a financial services company with an emphasis on credit cards, loans, and banking. But its mission is to help as many people as possible reach their financial goals by giving them the best tools for success no matter what those may be.

To accomplish this, data analytics helps Capital One get more insight into customer activity like purchases, account balances, and transactions which gives it information about customer behavior patterns. With that information, the company can offer relevant products based on what customers are actually doing instead of basing assumptions on personal histories or demographics.

19. Using Data at Walmart: Capturing Every Shoppers’ Attention in the Digital Age

Walmart has over 11000 stores worldwide which cover 28 million square feet of retail space alone meaning it’s nearly impossible to capture every shopper’s attention. To do that, the company has been busy developing its online shopping capabilities with fast shipping and reliable service which includes using data analytics to figure out what products are most popular in each location.

20. Using Data at Google: Becoming Human’s Most Trusted Assistant

Google is the world’s biggest search engine with over 3.5 billion searches each day but now it wants to help people find information about things they may not know they needed help finding themselves. Now, Google can automatically recommend restaurants, activities, events, and places for you based on your past searches through artificial intelligence (AI) which uses big data analytics to learn more about your habits. By doing this, Google hopes to become everyone’s most trusted personal assistant.

Data analytics is an incredibly powerful tool that can help you optimize your company in ways never before possible. For example, Marriott uses data to customize every guest experience down to the smallest detail while Raytheon leverages big data to improve operational efficiency and better understand what customers need. If you’re interested in learning more about how these companies are using this technology or want some guidance on how you could use it at your business, let us know! We have a team of experts who will partner with you to create a plan that takes into account all aspects of customer behavior patterns so everyone has exactly what they need when they come through the door. Which one of these cognitive neuroscience principles do you think would work best for optimizing your marketing?

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