Gathering online data is an essential component of targeted advertising, but it is also a sensitive issue for many of your customers. Over recent years AI technologies have transformed the ease with which data can be gathered, processed, and shared. With increased capabilities comes increased responsibility to safeguard customer data and demonstrate transparency to maintain customer trust.
Privacy legislation, such as the EU Cookie law, exists to demand more transparency from businesses, but businesses shouldn’t rely on legislation to keep them in check. Transparency and privacy concerns must be considered an intrinsic component of company culture. It all comes down to respecting your customers. When it comes to targeted advertising, customers should feel facilitated rather than hounded to make their next purchase.
We discuss what targeted advertising is, and how to address customers’ data protection concerns.
What Is Targeted Advertising and How Does It Work?
Targeted advertising is a form of online advertising that centres around the traits, preferences, and online behaviours of a customer.
To gather the necessary information for targeted advertising, marketers may look at information that customers have explicitly shared, or they may infer information based on, for example, online behaviours, demographic traits, geographic location, etc.
Most of us have seen banner ads featuring a product we’ve recently viewed online: this is a more obvious example of a targeted ad that uses data from our behaviour on a certain website. Websites like Amazon also use your purchasing behaviour to send you ads for related items.
As another example, new home owners might find that they’re targeted by furniture stores based on their Google search history.
Advertisers at the forefront of their craft are even creating nuanced ads based on things like personality traits and communication style preferences. For example, marketers can create two adverts for the same product using different copy and imagery to appeal to two different personality types within their customer base. A study by Matz et al found that when users were sent targeted advertising based on whether they were introverted or extraverted, they were 1.54 times more likely to purchase from the retailer if they were sent messaging that matched their personality type.
Targeted advertising enables companies to make their marketing budget go further by sending ads to the people who are most interested in receiving them. Done well, targeted advertising also works in the consumer’s favour by only sending them content that is relevant to them. If done effectively and with care, it’s a win-win situation.
However, businesses must take care not to overstep the mark and maintain a balance when it comes to using private data.
How Not To Be Creepy With Targeted Advertising
While many of us “accept cookies” without much thought, most people are still suspicious about how companies use their data. A Pew report into US consumers found that 79% are concerned about company use of online data, 41% regularly delete cookies and 30% have installed an ad-blocker.
The more marketers overstep the boundaries, the greater the call for regulators to intervene. So it is vital for advertisers to understand what makes consumers uncomfortable, and respect their boundaries to avoid an eventual total ban on targeted advertising.
Aside from the wider implications of disrespecting data privacy, it also doesn’t make sense from a business perspective. Research by the Harvard Business Review found that when people don’t like the way their information is shared, purchase interest decreases. In short, targeted advertising works best when consumer privacy boundaries are respected.
So, here are our tips for creating targeted ads that won’t creep out customers.
1. Make sure users understand how their data is used
Customers should be able to opt out of (or ideally opt into) use of their data for advertising purposes.
2. Avoid using over-generalised data
While demographic data (such as sex and age) can be useful when used in conjunction with other data, it is not advisable to use it exclusively for targeted advertising. Using demographic data alone can cause you to make assumptions that some people may find offensive.
For example, not all men in their 30s are interested in investing in cryptocurrencies, not all women in their 40s are interested in childcare. Misfires like this are jarring and can cause the user to question the use of their data more than if they are show ads which are genuinely relevant to them and their interests.
3. Avoid overly personal information
Sensitive information such as someone’s health conditions and sexual orientation are off the cards. Limit your data collection only to the things that are relevant to your marketing campaign.
4. Don’t listen in where you’re not welcome
It’s one thing to gather data from your customers on your website and through your interactions with that customer, it’s a whole other thing to collect data as a third party.
For many customers, browsing websites is still a private activity, and third parties are not welcome. Companies are even less welcome into personal conversations with friends and family, and most customers are still highly uneasy with advertisers using smartphones to listen in on private conversations.
It’s best to focus on data you have collected through your website or through public channels. For example, when someone comments publicly on social media channels or on a review site, they’re explicitly sharing information in the public domain. However, in these instances, it’s still important to use this data sensitively.
Using Symanto AI for Collecting Publicly Available Data
Our technology is capable of gathering and analysing thousands of social media comments and reviews within minutes. Get insights from publicly available information that doesn’t rely on cookies from third party websites or browsing history data.
Symanto technology can give you insights into the psychographics of your customer base just by using information that they’ve shared publicly. Our advanced natural language processing technology analyses texts for topic detection, customer sentiment and psychographics to give you actionable insights into the needs and values of your consumers.
Use this information to enhance your targeted ad campaigns without using common data collection techniques that some consumers find invasive.