You’ll hear it over and over again…
“Data is the new oil.”
“Data is the new gold.” (Technically, it’d probably be Rhodium but who am I to quibble?)
“There is no Artificial Intelligence (AI) without data.”
But what does that mean when it comes to your Marketing projects using Artificial Intelligence and Machine Learning?
Quite simply, it means that your data will be THE key to your success. Full stop.
The vast majority of AI/ML projects don’t exist unless humans set them up first.
The systems do what we tell them to do.
We give the goal.
We assign the purpose.
The systems figure out how to reach the objective(s) through lots of trial and error.
No matter how chi-chi-la-la the system is, it won’t work without data.
Data doesn’t do anything on its own.
If your data is bad, the outcome is likely to be bad/poor.
If your data is good, the outcome is likely to be good/useful.
“Good” data doesn’t need to be perfect. It needs to be clean, organized, properly labeled, and complete. Incomplete data tends to be difficult for systems to analyze. It’s not impossible. Just messy and often unreliable.
These days, most Marketing AI projects are designed to predict future outcomes. To do that, they use past (aka historical) and current (aka real-time) data.
IN THE WORLD OF MARKETING AI, THERE ARE THREE MAIN KINDS OF DATA
Bullseye Data (aka Source Data, First-Party and/or First-Tier Data)
This is the data that you’ve collected. You own it. It’s yours.
This is typically the most important type of data in your arsenal. As long as you can segment it, the more history you have, the better.
It’s important to note that many marketers think Bullseye Data is just buyer/purchaser data. It’s not. Bullseye Data includes buyers AND inquiries. Inquiries could be people who sign up for a free newsletter or to receive text messages; podcast and video subscribers; webinar watchers; whitepaper downloaders; etc. – anyone who has shown an interest in your products/services.
Bullseye Data also includes INTENT data: abandoned cart and/or lead form information; cookie information; time spent on site and page measurements; the number of pages looked at and specific browsing stats; app usage; viewing (video) stats; and so on. Anything that indicates the user’s propensity to purchase/interact with your business is Bullseye Data.
When you’re shooting at a target, you aim straight for the bullseye in the middle. It’s the #1 thing you want to hit. When we talk about data, your Bullseye Data is THE biggest thing that you want to get right.
2nd Party Data (aka Second-Tier Data)
This data is someone else’s first-party data. They’ve collected it. They own it. They’ve allowed you to use it. Before Co-Ops, the direct marketing world was built on this kind of data. Companies, even competitors, frequently exchanged buyer lists with each other.
3rd Party Data (aka Third-Tier Data, Compiled Data, Marketplace Data, Aggregated Data)
Third-party data is aggregated data collected through a vendor, co-op, profiler, or marketplace. There are often overlays (demographics, infographics, neurographics, psychographics, etc.) but in many industries, it’s just combined data.
Depending on the industry, the aggregator may or may not have a direct relationship with anyone on the list. When you’re working with an aggregator, it’s important to know how they got their data and what/how they’re following privacy and security laws and standards. Be sure to ask how they identify and parse fraudulent data as well.
Third-party data often gets a bad rap. It can be INCREDIBLY useful to you in your online and offline marketing efforts if you understand how it has been compiled and how current it is. (The freshness of the data often becomes more important when it’s not yours.) Just make sure you do your due diligence. There are big-name vendors doing smarmy things these days and you don’t want to get caught up in any of the drama, this much I can assure you.
“What type of data is best?”
It depends on what kinds of models and systems you are building. Many AI consultants like to push their clients to use 1st party data with 3rd party overlays. This is not necessarily a bad thing as long as you know why you need the 3rd party data. Are you using it for scale? To fill in gaps? To get information that you don’t have (competitive information, for example) and want? Or do you need it because your data is such a dumpster fire that it’d be easier to just buy someone else’s? If the latter is indeed the reason, you should go back to the drawing board and clean up your Bullseye (1st party) Data before you start mixing it with anything else.
As an aside, in the AI world, third-party data is often handled differently than old-school catalogers and direct marketers are used to. If you’re a legacy company and ruled out third-party data years ago because of how smarmy the industry was back then, I encourage you to look at it again with a fresh perspective. I’ve done a lot of AI projects where most of the success was due to the addition of outside data. You don’t need it for everything but for some stuff, it can be a BIG difference maker.
“What is Zero-party data and why isn’t it included above?”
Zero-party data is data that your visitors intentionally share with you. It includes things like their communication preferences (from your email preferences center, for example); survey information that the visitor has proactively shared; personal information that they’ve specifically offered you when signing up for offers or whitepapers; live chat information that they’ve consented to you using for marketing purposes; and so on.
It’s not included above because, for many companies, it’s difficult to segregate. For others, it makes things overly complicated. (This is especially true for companies with sales organizations.) Zero-party data is more transparent. It can build trust. And if it’s easy for you to capture and segment, by all means, do so. However, please don’t let it paralyze you if you’re not able to collect it at this time. (Please note: different countries have different rules for this. Consult with your lawyer for specific recommendations.)
“Does my data really all need to be in one place?”
For your best chances of success, you need rich and accurate data. The higher the quality of the data, the better the outputs. The highest quality marketing data usually comes from companies that have prioritized centralizing it.
Many companies have data that they haven’t compiled internally yet. For example, a lot of businesses don’t log all their incoming Customer Service data. This includes things like chats; emails to customer service and sales; support queries and downloads; and so on. Some companies don’t log all their activity data. Others house their sales data and/or their Voice data in completely different systems. I could go on, but you get the drill. Wherever your data is, it’s best to get it ALL in one place and then put a hierarchy together that ranks it from most to least important.
After that, you can figure out where you have missing gaps. Do you need more demographic overlay information? More behavioral data? Something else? Does your buyer information have recency, frequency, and monetary information along with affinity groups? Knowing what type(s) of info you need to supplement will also help you choose the right data vendor(s) for your needs. The amount of information offered these days can be incredibly overwhelming so it’s best to identify what you need before you start looking.
“Is there really such a thing as too much data? Seems like the more the better.”
If your data isn’t clean and/or isn’t prioritized, more won’t necessarily help you and it can hurt you, depending on how/where you are using it. (Think Garbage In, Garbage Out.) It’s important to curate just the right data mix.
In the old school world (catalog, traditional direct marketing, 2-step, etc.), it was easy to throw everything at the wall and hope something stuck. With AI/ML projects, that method is often the fast track to disaster. High-quality data allows you to build better models; saves time (less rabbit chasing), and it can make it faster to identify bias and maintain compliance.
In the interest of full disclosure, I’m one of those people who wants ALL the data. Every last speck of it. Once we get it, we prioritize it, often weigh it, and then test/train. I don’t use even a drop of the data till I am confident that it’s suitable for whatever AI-enabled solution that we’re using. If you don’t do this, and you just dump everything into your AI haphazardly, the system is forced to sort it out on its own. Once in a blue moon, this works, but more than not, it’s a hot mess. You end up having to disrupt too early or start over. Plus, there are often negative ramifications long-term. It’s worth the extra time to sort things out upfront.
Have a question about marketing data? Have a tip you’d like to share about using Artificial Intelligence Data in your business? Questions you’d like to ask? Tweet @amyafrica or write firstname.lastname@example.org.
A Down-and-Dirty Definition. (Read more about these here.)