Ever wondered how some businesses keep their Ad spend low and still record high ROI numbers? Some even run their Ads for a short period but get most of their target audience to take action.

Well, it all comes down to strategic data-driven advertising. Unlike instinct-based or gut-driven advertising, data-driven advertising relies on data analysis to match the right target audience with the right Ad at the right time. So, what data is used in data-driven advertising? Here’s a comprehensive guide for you.

What Data is Used in Advertising?

While there are several data elements used in advertising, most fall under these seven data categories. You can obtain advertising datasets from third-party providers or collect the data from the web or internal business databases to prepare custom advertising datasets. For this piece, we’ll explore these data categories and how they influence decision-making when curating advertising strategies. Delve in! 

Customer interaction and engagement data 

Rather than guessing what your potential and existing customers care about, what if you could tell their preferences based on data? Yes, analyzing customer interaction and engagement data like social media likes, comments, and shares can help you tell whether your content resonates with a specific target audience.

Take reviews and ratings, for instance. This data shows you how satisfied your existing customers are, influencing future purchase decisions. Potential customers also rely on such data  when making decisions. That’s why it is critical to filter through positive reviews and constructive criticism to spot improvement opportunities. 

Other customer interaction and engagement data elements include email click-throughs and open rates, time spent in-app, push notification engagement and more. Essentially, they are the signals buyers and future clients send back to your business when they come into contact with your messages, content, or products. 

Behavioral data 

While customer interaction and engagement data is all about the feedback you get from customers, behavioral data focuses on customer online activities or actions. And, other than revealing customer preferences, behavioral data reveals actual buyer intentions. 

For instance, you track website activities like product clicks and views, time spent reading a product’s description, and time spent on watching product tutorials. If a customer clicks on a product and spends more time reading the product description and watching tutorials, there’s a high likelihood that they are interested in the product. In response, you can show them a personalized Ad that includes a discount to encourage them to make a purchase.

Other than influencing one’s actions based on previous behavioral data, you can also use the data to understand the reason for drop-offs or friction. For example, a large group of potential buyers adding an item to their cart and not completing the purchase may indicate an issue with pricing or delivery concerns.

Psychographic data

At a deeper level of understanding existing and potential buyers is psychographic data. This includes data on their values, passions, goals, struggles, attitudes, and lifestyle choices. You are literally making an effort to know your target audience at a human level by digging into their unique desires, stories, and motives.

A good example is tracking one’s meal orders. A health-conscious person would choose natural and healthy meals over junk. This signals you to compose personalized emails or Ads highlighting groups of people sharing healthy meals or even participating in workouts. Such Ads or emails align with the customer’s inner world, nurturing loyalty. 

Overall, psychographic data helps you speak directly and deeply to your customers’ preferences and lifestyle choices. It helps you create a story that resonates with your prospective customer, encouraging them to choose you over and over. However, avoid being intrusive while collecting psychographic data. Doing so may push customers away.

Search query or intent data

Search data, either from search engines like Bing or Google and built-in website search engines, reveals what a customer needs or wants at that moment. For example, if someone is searching for, ‘vegetarian meal delivery near me,’ they are likely looking to purchase a vegetarian meal. 

Search query data mostly comprises keywords or phrases that people use to search for answers, products, or services online. These keywords or phrases can tell whether one wants to learn about a thing, find a specific brand, complete a purchase or action, or compare options before making a decision.

Use SEO tools to find search queries relevant to your advertising strategy. Apart from tailoring the Ad to a group of people keying in certain phrases, you may uncover a developing trend. This presents you the opportunity to cash in on the trend before competitors notice it. 

Demographic and location data

While demographic data can tell who your customer is, location data can reveal where they are. Both data categories are especially critical for brick and mortar businesses. For demographic data, you got gender, age, income bracket, occupation, marital status, and education level. Location data includes ZIP codes, city, country, or even actual real-time GPS data. Combine these data elements and you can know who to target and within what radius.

For example, a home for the old can create an Ad targeting those aged 60 and above, within 15 miles from their facilities. This way, they exclude people who are outside their service radius and who don’t match their service. Ultimately, they reduce waste and optimize ROI on Ad spend. 

Customer membership data

Customer membership data is not just about the members that have subscribed to your product, service, or content, it is also about the people who choose your brand or business frequently. For example, customers that purchase frequently from your e-Commerce store count as members. They are more likely to respond positively to your promotions than new customers. Upselling such customers is often cheaper and more profitable than targeting new customers. 

If you have both free and premium groups, it is strategic to focus more on the premium group whenever you want to promote your products. Why? These are members who have shown loyalty already. So, they are more likely to respond better to tailored discounts codes or other sales moves. 

Purchase history and transaction data

Purchase history and transaction data reveals a customer’s preference based on previous or past transactions. You don’t have to guess whenever you are deciding whether to retarget them or not. You are to collect data around what customers buy, how often they buy them, and at what price point. This allows you to not only decide what product or service to include in your Ad but to also know what they are more likely to spend on next. Based on the customer’s purchase frequency, you can also know what products to stock up during a specific season. You can also use the data to know when to launch a new product. 

Conclusion 

And there you have it! Seven advertising data categories you can use to optimize your next advertising campaign. Combine the data elements from different categories to create rich and more effective advertising datasets. Nonetheless, always respect customer privacy while collecting, processing, and using the data elements.