Strategy for Information Markets/Gathering Market Information

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In this chapter we will go through gathering customer information from the perspective of a retail business. We will show why it is beneficial to the growth of your company to collect consumer data by yourself as well as researching any available data so that you can make the best decisions. There will be comparisons of the many ways to go about collecting data, as well as how to do it. Then, more importantly, we will discuss the many options you have in utilizing that data, whether it be discounting certain items together or sending coupons to a specific set of people.

Why collect data?[edit | edit source]

There are many positive things retailers can do with a massive database that would help the business as well as the consumer. If they see that people who buy cookies usually buy milk as well than one thing they might want to do is arrange them in the store so that if you go to get cookies you pass the milk section. This helps the business sell more as well as suggest to the consumer something they may want but did not think of. In classical economics when we consider what will affect the quantity demanded of a product the only factor we consider is the price. However when we realistically look at the retail industry of a supermarket we know this is not true. There are many other factors that influence consumers purchasing behaviors such as product placement, display size, appearance of label, and many others.

Product appearance is more specifically aimed at which item will sell, not if it will sell. For instance if your going to buy frozen vegetables you might be more inclined to buy the one that is wrapped in a green wrapper than a brown wrapper because it gives you the idea that it is healthier since green is associated with healthiness. This however can affect the supermarket since many times the supermarket offers there own generic brand of many items, and if they can realize that the product appearance can be improved than there item will have a considerable advantage in appearance and price. There are so many variables when it comes to how consumers behavior will respond to different things. How can we tell if a consumer will be less likely to buy fruit if there's only a few there opposed to the entire display being full. The only way to make an educated decision on something with so many variables is to physically test it over and over and see what works the best. There is a need to collect data on this so that we can understand what works best and what needs to be done, but that leaves us with other questions. What is the best way to collect this data and turn it into information, how can we know that the data we collect will translate into results that work.

How do we physically collect the data?[edit | edit source]

If the retail industry you are assessing is a supermarket there are a few different ways we can collect data. Manual counting and a computer generated system don’t compare closely in ease of use and price to use. A manual count of everything that happens would be extremely time consuming and would be completely inefficient. Because of the advances in the technological world there is a way to compile this data automatically every time something is rang into the register. After you compile the data there are many different ways to compile it.

You can compile data specifically for each consumer by using a membership system that you must sign up for. An alternative option would be to employ an open membership policy, in this system you would compile data that spoke to the supermarket as a whole. We will go into the different ways you might want to sort the data later.

Consider fictional consumers Nancy and Sally, Nancy has no preference to which supermarket she goes to and usually bounces around to many different ones. Sally on the other hand always goes to the same supermarket because it is so close to her house and she has always been satisfied with it. Nancy at some point might sign up for a membership to one of the supermarkets but she is unlikely to do so because she has no habit of going to the same one. Nancy however is very likely to be a member of her supermarket because she always goes to the same one and she would be foolish to not take part in the discounts offered for being a member. This example shows that regardless of what system you use its not going to be the best for everyone. One important aspect of understanding how you would like to collect data is to first collect data of which type of customers you have more of, Nancy or Sally. Are the consumers dominantly one time shoppers or loyal customers. If you can understand the type of business you are getting than you have a better chance to use a system that will be effective for you.

Which way of collection is right for you?[edit | edit source]

To understand which data collection method you want to implicate you need to understand which type of information you want to receive. If you collect your data on a customer basis instead of an overall database for your store you will get data that can be used to give personal rebates and coupons to boost sales. When you analyze what your company needs, you can than decide which way is the best for you. There are a few guidelines that would benefit you to follow when deciding how to go about collecting and analyzing your data.

Pre-data collection steps[edit | edit source]

  1. Clearly define the goals and objectives of the data collection so that you know your purpose for the study, this is one of the most important aspects of the research. Even if you do everything else right without having the correct aim for implementation of your data could make it entirely useless.
  2. Clearly define the data collection plan such as when to record the price of a product bought, or how to quantify location of a product.
  3. Ensure data collection repeatability, reproducibility, and accuracy so that the data can be reliable.

What do we do with our database?[edit | edit source]

Sometimes when we have a large data set we simply use algorithms to find patterns that we can use to our advantage in a few different ways. Other applications of this information would be utilizing not just what is bought, but the behavior of the consumer. A great example of this would be to look at some airline companies, they change the price of the same exact ticket depending on how you search for the flight. They did a lot of studies that showed people who search for flight based solely on date and price don’t care about amenities such as paying for food. A smart utilization of this information was to charge you to book your bags. People don’t realize to factor in that cost when comparing flights so you end up paying that airline the same amount of money as the other one without realizing it. Amazon and a lot of other online retailers show another example of behavioral analysis when you search for an item, down below it they have a few options of other things you might like. This way when you are shopping for something you are more likely to buy another product because they know that they usually go together. So how can we get from our database to the utilization of our information? With a process called Data Mining.

Data Mining[edit | edit source]

Data mining is the process of extracting patterns from data. As more data are gathered, data mining is becoming an increasingly important tool to transform these data into information. While data mining can be used to uncover patterns in data samples, it is important to be aware that the samples of data may produce results that are not always accurate. You need to make sure you have a large enough sample size before deciding to make decisions based on the information you have received. Likewise you must realize that there are patterns that may not appear in your data set even if they do exist, just because a pattern does not show up does not mean its not there. To start “mining” your data you must first go through your data set and ensure that there are no inconsistencies. Eliminate mistakes from your data set to reduce the possibility of coming to wrong conclusions once you analyze the data correctly it is up to you to use the information as best you can.