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Your marketing budget is likely one of the largest budgets in your business. It’s also one budget where you may later determine that you wasted a lot of money for very little gain. The problem is that it can be very difficult to anticipate market trends and what customers will respond to. Even the most amazing product can fail if you don’t market it in the right way. To avoid as much waste as possible, it’s very important that you gather as much data about your customers and potential customers as you can. Analyzing this data and putting it to work for you will help you better focus your marketing campaigns, which in turn results in less waste.

Gathering Data

Before you can put data to work for you, you have to first gather it. This can be done in a number of ways. Some businesses ask their customers for specific information when they check out, most commonly their zip code. Others may ask customers to sign up online for an email newsletter or other types of messages. They can gather certain information at this point. It’s even possible to use credit card charges to gain some information. Customer satisfaction surveys are another option. It’s even possible to buy a list of potential customers and their demographics, saving you a lot of work and time.

Wal-Mart is one of the earliest users of big data. With more than 10,000 stores and over 245 million customers, the company’s effective use of data is one of the reasons it brings in $36 million dollars every day, and that’s just from their 4,300 stores in the United States. They have spent years building up their database, which contains information related to almost 145 million customers. Every hour, the company collects 2.5 petabytes of data from around one million customers.

Using Data to Segment Your Target Audience

One of the biggest problems with marketing is that it often feels like you’re yelling your message out into the void. You have no idea who it’s actually reaching. Your message about a line of clothing for teen girls may be heard primarily by single men in their 30s. These people are very unlikely to care about your products because they’re not your target audience.

One of the very first things you should do with the data you’ve collected is to segment it. Create groups for each type of customer you want to market to. The easiest way of segmenting information is to do so by gender. But you can do so much more than just divide customers into men and women. You can fine-tune your segmentation until you can create specific marketing campaigns aimed at women between age 25 and 35 who have children and live within a specific zip code. This allows you to greatly narrow your focus and create marketing campaigns that these specific customers will respond to.

In short, you’re no longer yelling into the void. Instead, you’re talking directly to people who are going to want your products.

Going back to Walmart, the information the company gathered from their customers allows them to segment their customer base in a number of ways. They look at customer preferences in order to create local and regional segments based on what customers wanted and purchased. The stock of products or brands that customers didn’t buy in one region or at one store can be shifted to those locations that more frequently sell out of those products. Once this system was in place, Wal-Mart didn’t purchase less from suppliers, but instead simply redistributed their stock based on what customers wanted. The result was a large improvement on their investment.

This affected their marketing budget, too, since the company now had a much better idea of what people wanted from each of their stores. They could better focus the advertisements in those areas.

Learn How to Analyze Data Quickly

Walmart pulled ahead in the big data race partly because of how quickly they began analyzing the data. As their senior statistical analyst stated in an article on Forbes, if you’re analyzing data for a month, that’s a month you’ve lost sales you could have been making based on that data. It’s also a month where you could be spending money trying to market something in a way that your data shows is not effective.

Fortunately, your business likely doesn’t have hundreds of petabytes of data to analyze, which will help. You likely do have a large amount of data coming in, though, so you need to look at the different methods of analyzing it to find the one that works best for you. That method will depend on the data you’re collecting, how you’re storing it, and what information you need from it.

Buy and Collect Responsibly

If you decide to purchase a list of potential customers, you’ll likely notice that the larger the list, the more it costs. That’s because the companies who compile these lists usually base their price off of the amount of data you’re buying. If you go in and purchase a list of all the people who live in a target city, you’ll spend a good amount of money to purchase a large list of people. But how many of those people are truly potential customers?

Even without data, it’s possible to determine your customer profile. Look at your products and determine who the ideal user is. Those are the people you want to target, so that’s the customer data you need to buy. This method means you spend less money to buy data, get better data that allows you to narrow your marketing focus, and see a higher conversion rate because you’re marketing to people who want your product. All in all, you save money and increase your customer base.

If you don’t purchase a list, consider what Walmart did. They began collecting data from their store, through their website, their app, and other sources. You likely don’t need this much data, so in addition to buying responsibly, you need to collect responsibly. If you don’t need certain data, don’t collect it. Some businesses gather all possible data because they’re afraid of missing out. While Walmart might do this, they also have the resources to store petabytes of information and the staff to analyze it. You likely don’t. Decide what information you need to collect so you’re not wasting time (and, of course, money) on analyzing data you have no real use for.

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