Annoyingly, your data lives in a bunch of disconnected systems. Data integration is the process of combining data sets that live in different applications so you have a unified view of your customer.
There’s a lot to think about. In addition to field-naming conventions (think “F.Name” in Data Set 1 vs “First Name” in Data Set 2) or alias values (CA vs California), you also need some way to recognize that Janet Smith (maiden name) and Jane Johnson (married name) are actually the same person.
The picture gets even more complicated when you want to start matching offline records to online data, like the digital ads that Jane was exposed to. To put these data sets together, you need a privacy safe approach to recognition and data connectivity that anonymizes your records before matching and linking your data.
This is the process of connecting the identities assigned to people and devices in one platform, the identities assigned to people and devices in every other platform. This is the heart of people-based marketing.
For instance, you could match cookie data from your website to CRM data about your customers and make sure Joe Customer gets treated like Joe Customer deserves, whenever he’s on your site. To comply with privacy regulations and best practices, offline data needs to be anonymized before it can be matched to online devices or digital IDs.
Data onboarding is when you match data collected offline to data collected online.
Your offline customer data can include first-party information, like purchase data (collected in-store) or call center data, and third-party information like demographics and buyer propensities.
This offline data is matched to online devices and digital IDs in a privacy-safe way, then delivered to the ad platforms, DMPs, and social channels where you run your campaigns. The process enables more strategic cross-channel and omnichannel campaigns.
There’s a 5-minute guide to Data Onboarding right here
The thing about data is that it’s usually pretty dirty. Different systems write dates in different ways. The addresses stored in your database become irrelevant when your customers change homes. There’s a whole lot of ‘erosion’ and variability that’s really important.
Data hygiene, or data cleansing, is the process of fixing all these glitches and making sure all your data complies with your own set of standards.
An evolving species with remarkable resilience; capable of withstanding immense technological fragmentation to give customers and prospects better experiences.
Adaptable, highly intelligent, and easily distracted by shiny new channel opportunities, the data-driven marketer feeds on the fruit of the conversion tree.
A ‘Data Management Platform’ sounds simple enough. It’s a platform for managing data, right? Pretty much.
DMPs evolved out of a need to analyze the data collected in the anonymous cookies that are collected by websites and DSPs. They allow you to build segments—using behavioral data from your own campaigns and/or a 3rd party—to target specific audiences with the right ads.
They centrally manage and present all your campaign activity and audience data to help you optimize your media buys and creatives.
DMPs can do all this because they use identity resolution to tie disparate IDs back to real people. (We hate to brag but *ahem*, the best ones use the best identity resolution service available – ours. Who are we kidding? We’re marketers. Bragging’s what we do.)
A ‘Demand Side Platform’ is software that automates the buying of display, video, mobile and search ads for you.
It automates the grizzlier bits like targeting the right audiences, buying the right impressions in real time, delivering the right creative, and finding the right publishers.
Imagine a normal creative and then explode it into all its component pieces—the copy, the background image, the size and color of the call-to-action button.
Dynamic creative is able to cycle through different combinations of these components, optimizing the whole thing in real-time for every viewer.
So you could have a banner featuring that 24- to 34-year-old male that loves wine transform into a 55- to 65-year-old female who loves it too.