This article will show you how marketers are trending towards data analysis, which can provide useful insights regarding your target market’s Cell Phone Number List behavior and preferences, enabling you to redesign your marketing strategy. Data analysis has played an important part in marketing since the 1980s when the first million-dollar advertising Cell Phone Number List campaign started. This is when bigger companies like mcdonald’s or coca-cola started to gain power and dominance over the smaller companies who did not pay much attention to using data in their marketing strategies. As more new technologies, like computers, were introduced to businesses,
Marketers and advertisers began to use their Cell Phone Number List amazing potential for data analysis and promotional techniques that would optimize their marketing. What is data analysis? The better you know your customers, the more you can Cell Phone Number List tailor your marketing to them. Data analysis is the data-driven process of discovering new information. First, you collect data, whether through surveys, focus groups, or analyzing customer transactions. Next, you analyze the data, looking for patterns and relationships. Finally, you decide what action to take. Data analysis can be used for discovering trends, predicting probabilities, and measuring outcomes.
Marketing companies use data analysis to help customers choose what to buy when to buy it, and where to buy it. Today, data analysis is less expensive Cell Phone Number List than ever. (in 2007, home depot launched a program that allows regular customers to download information about their purchases in a format they can analyze with a spreadsheet.) with data analysis, companies can capture and tailor marketing not only to their individual Cell Phone Number List customers but also to their potential customers as a business growth strategy. The 5 big changes data analysis brought to marketing we all know that data is important. But data is only valuable to the extent that it enables better decisions. Too often, data is treated as a number. For example, suppose you study how people use a product. You ask people whether they did x, y,