The factor behind great marketing results

Picture this. You are on the sidewalk wearing your best smile, inviting people into your store. Some accept and come in, but most walk in and leave without buying anything.

Then you notice the store next door, your competitor, doing the same thing. Only with different people. Their visitors leave with heavy shopping bags. It looks like your competitor knows something you do not about the people they choose to invite.

Somehow they can see something you cannot: labels pinned to the people walking by, carrying information like "visited us 40 days ago", and others reading "has bought R$ 560 in the last 60 days". It is frustrating. With that much privileged information, inviting the right audience becomes easy.

Now, stepping out of that scene: this is how a marketing team should find prospects and build audiences. It should be easy and intuitive, like reading a label on the customer.

For most marketing teams there is no centralized information about all customers. Even when there is, the labels are simply not there. And when they are, they look like hieroglyphs the analysts have to decipher, cross-referencing data from different sources in different systems.

No marketing professional needs anyone to explain how a campaign runs. The recipe is reasonably simple: an organized site, well described content, considered copy, automation in place, email templates, a communication cadence, and tag review so multichannel interaction is properly tracked. The difference lies in easy, clear access to the audience.

That is where the pattern I observed in my career comes from, and it explains what looks like a contradiction. Teams of veterans delivering mediocre performance, while teams of junior analysts pulled results the experienced ones could not reach.

Good marketers know how to identify the right profile. That is the skill that most defines the final result, and it sits unused when exercising it depends on deciphering the hieroglyph first.

How to solve it

First, the unified catalog. Each person exists once, no exceptions.

Second, the fields that mark when each thing happened. One row per person, holding their current state. There are two kinds of field: some answer "when", others answer "what". For example: date of last purchase, date of last inbound contact, when they last reached out through any channel, and last product of interest.

Together, those fields describe a person situation in a single row. That row is where the decision comes from: talk to them today or later, and about what.

A real result, in a network with over one hundred stores

In a recent implementation at one of the largest dealership groups in Brazil, we built exactly this audience and used it in media. We took to the ad platform the people who historically buy vehicles very similar to a model being launched, and those who had already bought from the same brand.

Cost per lead fell 9%. In money terms, with R$ 500,000 invested per month and a cost per lead of R$ 22, the new cost per lead lands at R$ 20.02. Keeping the same lead volume, that is R$ 45,000 saved per month, or R$ 540,000 over twelve months. If the choice is to keep the budget, it is 2,248 additional leads per month.

The platform that makes the labels visible

The Databricks Data Intelligence Platform is our preferred tool for solving this. The reason is direct: the three problems Databricks names as the platform reason to exist are the three that show up in this article. Data scattered across silos, control and governance, and dependence on highly technical people to answer any business question.

That third one is the hieroglyph. Solving it is what Databricks calls democratizing data and AI across the whole organization, and for a marketing team it means one thing: choosing the right audience simply and intuitively, without depending on anyone.

In terms of capability, not license names, five things solve the problem in this article.

• Bring together what is apart. Service, lead intake, messaging and sales land in the same base without becoming a copy inside a closed tool. The customer base stays on the data platform, and the channel tools consume from it.
• Make the person exist once. Identity resolution combines exact rules, probabilistic similarity and agents for the edge cases: the name changes, the work email dies, the phone number gets recycled.
• Give the label traceable meaning. The catalog says what each field means, where it came from and how current it is. The hieroglyph stops existing because the legend now travels with the data.
• Keep everything alive without human work. The pipeline updates itself, the company systems keep running as they always did, and nobody redoes the reading by hand.
• Deliver the audience ready for the channel. It flows from the platform to media and to the communication cadence in both directions, and the campaign result comes back and enriches the label.

Put the five together and the sidewalk scene flips. The labels become visible on your side, and your competitor advantage becomes yours.

My team and I are official Databricks partners, in the consulting partner program.