The modern shopper has come to anticipate highly-personalized marketing communications that speak directly to their interests. The good news is that targeted data is abundant, whether compiled in-house or purchased from a reliable list firm. But how should we put that information to use? There are several types of data, and each has its own set of potential applications. Let’s analyze four types of information and the roles they play.

Demographic Data:

Marketers can use demographic information, which is among the most specific data available, to target their messages to certain segments of the population. To give two simple examples, the types of apparel you offer teenagers will vary from those you sell to adults, and the landscaping services you recommend will change depending on whether a person’s home is on a half-acre lot or a 15-acre farm. Demographics can only tell you so much, but they’re a good place to begin the investigation.

Geographic Data:

The person’s place of residence can be a valuable piece of information. When compared to customers in the Southwest, individuals in the Northeast are more receptive to a variety of alternatives in the months of January and February. Customers in the city have different concerns and expectations than those in the country. Many different geographical “slices” exist; you should target those that make sense for your goods.

Interests:

Data based on customers’ interests can be cross-referenced with your existing customer data for even more insight. To further segment your target audience, it would be helpful to know whether or not people in your database make charitable donations, buy hunting or fishing equipment, or participate in ultra-marathons.

Prior Buys:

You may learn a lot about your clients and their wants and needs by keeping tabs on what they buy. For example, if someone bought a pair of all-purpose walking shoes in January, they would probably need to replace them in about a year. However, if they invested in a premium pair of trail running shoes, they are probably putting in a lot of kilometers and will need replacements in three to four months.

Segmenting:

When a marketing team first begins customer segmentation, typical starting points include age, gender, and geographic location. These are the cornerstone components of Google Analytics, and they’re prominently shown right on the home page. Plus, this categorization seems reasonable at first glance: if you’re marketing a product for ladies (in this case lingerie), you’d naturally focus on women as your primary audience, right?

If you define your “segment” as all women on the planet, then that number is 3.8 billion. There are 175 million potential customers if your target market is American and Canadian women. It doesn’t sound like much of a segment if you’re still marketing to several million individuals even if you narrow it down to persons aged 18 to 35.

Beyond demographics and psychographics, more nuanced characteristics such as a desire to purchase, interest, income, and personality are crucial for effective segmentation. Identifying and targeting potential customers need not be difficult. Make sure you’re collecting and using the right information for your marketing objectives.

Acquisition:

In terms of client information and patterns, the acquisition tab is invaluable. You may observe which campaigns are successful, which landing sites visitors find annoying, and which search phrases they respond to. The SEO section that displays search queries is particularly useful.

You can learn what people are looking for that you have yet to emphasize if you remove the obvious terms, such as your brand name, from the search results. Perhaps it has been unusually warm this year, and people are looking for swimwear even before you’ve begun advertising it.

Perhaps sales of Eggo waffles skyrocketed due to the popularity of the Netflix series Stranger Things. (It’s true!)

Swapping the line chart at the top for a motion chart is the most effective method of determining audience subgroups. Over time, you should see increases in tangential terms (which may have promise) and a clearer image of your core client groups emerge.

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