When it comes to advertising, targeting has always been important
From the earliest days of newspaper advertising, to old-time radio jingles, to the television ads during Saturday morning cartoons, companies have always needed to know their market to know how to best craft their message to connect with consumers.
What is new in the age of digital advertising is the recognition that many of the elements that allow advertisers to know their markets are, in fact, data. And with the understanding that data are available, comes the knowledge that data can be collected, manipulated, and analyzed, to get a deeper understanding of your consumer base.
This increased understanding of data and its place in advertising has exploded along with the explosion in the digital work and play space. As more and more of our lives are lived online, we leave a trail of easily accessible data in our wake – what sites did we visit, how long did we stay, did we leave the site without making a purchase?
All of this without ever entering any data ourselves. The opportunities for businesses to collect and analyze our shopping and browsing habits have never been greater. A wise company would know that the insights gained from data analytics from their digital channels can be combined with the data holdings gathered from their more traditional lines to both understand and serve the needs of their buyers.
Big data has become something of a fad in many industries, the advertising industry included. But in terms of understanding the customer, it is not simply the sheer amount of data that a company holds that will offer the greatest insights into the needs and wants of their customers, but having the most relevant, most reliable, and timeliest data that will offer the best results. Advertisers who are skilled in using big data understand that hoarding data does nothing but take up server space. The other half of the big data equation is analytics, and it is in that area where advertisers are seeing the most returns.
It’s All About Targeting
It is this principle that the use of the data held rather than the amount of data held is what has the greatest effect on the success or failure of an advertising campaign is what makes big data and data analytics successful. Content produced by advertisers has to connect to the audience they intend to reach. But what is that audience? Who would be most likely to purchase this product or service? To be return customers? Who would tell their friends to buy? In their data holdings may be answers to those questions. The larger the amount of data held, and the more specific the data fields are, allows the answers to those questions to also become more specific, even person-level specific.
Having a large amount of data about an individual consumer allows the greatest targeting and the most relevance in messaging. The most exact things about a person can be pin-pointed by analyzing their shopping habits, but there still must be intelligence in how those analyses are made. Consumer A may spend a great deal of time looking at luxury vehicles online, which at first glance may suggest that advertisements for a Jaguar would be a relevant. But, if Consumer A also is searching for the location of the nearest payday loan company, the relevance of that ad seems much less likely.
To reach that level of specificity, advertisers must master the push and pull of privacy versus precision. Not only because protecting a customer’s data is the morally right thing to do, and in many cases, legally required, the ill-will produced from a data breach can hurt the brand and affect future sales. As there have been a number of high visibility data breaches recently, including those with sensitive financial information – like the Equifax hack – consumers are understandably concerned about the security and privacy of their personal data. Any use of big data or analytics that has the appearance of leaking personal data by advertisers will likely lead to a very big push back from consumers resulting in lost business.
Even less than a full breach can be thought of poorly by consumers. Obvious data mining, tracking, and targeting can feel very intrusive when not done in a sophisticated manner. Most people have had the experience of having researched a product online, then for the next few days, have been inundated with sidebar ads for every conceivable make and model of the same type of product. It is a very un-subtle example of tracking based advertisements, and leaves a bad taste in the mouth of a buyer.
One of the advantages to big data is that not only can it be used to target relevant advertisements but to track response to those advertisements. An agile and responsive advertiser can quickly respond if their analysis shows that a consumer would rather close out of a YouTube video altogether than to sit through their newest ad, or that is a consumer notices that, as in the example above, after visiting a certain site, they receive many tracked ads based on their visit, so does not visit that site anymore. This kind of real-time response allows advertisers to make changes and fixes on the fly, rather than having to go through weeks of focus groups or surveys, and then respond to the issues.
As many advertisers know, digital marketing is hard to get right. There a balance that needs to be maintained so that consumers feel as though they are understood without feeling as though they are being spied on. If an advertisement pushes too hard, the consumer will simply respond by using ad blocking software. This balance is one where the “old-fashioned” skills of marketing and the newer skills of analytics can meet to find a middle ground where the customer is happy. While big data has certainly given advertisers new and very powerful tools to work with, there needs to be something more than algorithms driving the business decisions, otherwise companies may risk driving their customers away in a way that wasn’t accounted for in the math.