If Data Is The New Oil, Why Are So Many Advertisers Squandering It?

(Article originally published on Forbes.com)

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Business leaders have become fixated with data, and for good reason. What Netflix show did you just watch? How long did you pause (to go to the bathroom)? These are just small instances where massive amounts of data are being collected about consumers that drive both business and advertising decisions.

Data has become such a contested issue that it has spurred the creation of legislation that protects consumers in the form of GDPR and the CCPA. These actions illustrate just how valuable data has become, especially its use in advertising and marketing. For all of the importance and money that advertisers have placed on data, they seem intent on giving it away and underutilizing it. If advertisers can figure out how to best utilize it, not only will they likely benefit financially, but consumers can benefit from better experiences.

Data is now a company’s (if not the world’s) most valuable resource. Just think about it. Unlike gold and diamonds, it’s easily mined; unlike oil, it is infinitely renewable. And did I mention that it is neither scarce nor costly to acquire? Many people and companies freely give it away. Businesses have continued trying to extract more and more value from their data — and yours.

There are all types of data, but one area that has been growing exponentially over the last few years and has the biggest opportunity to affect both businesses and consumers is advertising and marketing data. That famous John Wanamaker quote comes to mind: “Half the money I spend on advertising is wasted; the trouble is, I don’t know which half.”

Since then it seems that advertisers have been trying to find that elusive half. But advertising and marketing data are even more important than cutting waste, effective use of this data can drive innovation and deliver amazing customer experiences by not only connecting advertisers with consumers, but engaging with them.

When we think about the data we give away every day, we think about our Google search queries, Facebook likes, TikTok views and iPhone location. These are just small examples of the data consumers generate. In 2019, The World Economic Forum looked at this issue and begged the question: What if tech giants had to buy our data? And what about all of those free services offered to both business and consumers?

For example, businesses willingly give Google access to data on every website visitor, each page that they visit, and any conversions or sales that result from the visit. In this example, it’s likely Google knows more about a business’s website traffic then its business leaders will ever know. Google Analytics samples and aggregates the data it collects before presenting it to you, allowing users to view data down to the hour or minute level.

There are also gaps in data that exist for most advertisers, however, online customer journeys are more easily tracked and data generated. What about offline interactions? From TV ads to in-store visits, to phone calls and cross-channel engagement, few advertisers have this data. And if they do have it, they typically cannot easily integrate it with their digital data.

Advertisers need to level up and regard data collection with an omnichannel approach in advertising and marketing campaign strategies. Advertisers may not be able to easily extract themselves from giving data to the primary platform providers (and frankly may not want to), but they can create data sets that exist next to and outside of those platforms and develop insights truly unique to them. As Senator James E. Watson said in 1932, “If you can’t beat them, join them.”

However, before you can join them, you need data of your own. For those advertisers and agency leaders reading, let me share a few thoughts on how to get started. Your first objective should be to identify the data you need, and you can do that by following these five steps:

1. Map the entire customer journey from the time a consumer is first presented with the core problem that drives them to seek you out (influence), as well as awareness and ultimately to purchase.

2. Take each of the stages in the customer journey and then identify all of the environmental, offline and online influence, the message and the interaction points. (For example, you may not advertise on TV, but the weather may influence a consumer to seek out your brand via a search engine, which then may lead them to visit a landing page, make a phone call or visit a retail location.)

3. Identify all of the possible data points available for each influence, messaging and interaction. (For example, if you do advertise on TV, collect all of the ad insertion logs for each version of your ads, where they ran, date/time, run length, as well as website traffic data, paid search click data, call data, sales transaction data, etc.)

4. Start a spreadsheet with a list of the data points from step three and create four columns with these headings: that you currently have the data (yes/no), source of data, owned/not owned (i.e., Google Analytics data is not owned), and downloadable (yes/no).

5. Look over the spreadsheet for all of the data points that have a “no” in the currently have the data column or not owned in the owned/not owned column, or no in the downloadable column. Then identify which of the data points touch areas where you spend the most advertising/marketing dollars. These are your greatest risk blind spots. Start building your data collection by finding data that you can own and download for each of them, knowing that not owned and downloadable is better than not having it at all.

Ultimately, you will want to have all data points collected and owned (if possible). After you have the data, then comes putting it to use. You can use tools (or experts) to bring it together so you can connect the data journey that follows the customer journey and then run advanced analytics and attribution processes that will generate reports and dashboards you can use to cull out valuable insights to put into action, starting small and building from there as your data and experience grow.