Marketing Data Checklist: 8 Questions for the Data-Driven Marketer

Brad Ito: CTO, RETINA

Maybe you’ve heard that you need to map your customer journey or that all the real data-driven marketers are using a 360 degree customer view. If you’ve started looking into either of these things, you’ve probably uncovered a dizzying number of technology offerings, all claiming to provide similar capabilities. How can you cut through the hype?

To help you answer that question, I’ve created a checklist based on a over a decade of experience building marketing data platforms. The best thing about this list is that most of the questions on it can be answered with a simple “yes” or “no,” but you will immediately know where you are on the path to being fully data driven.

  1. Is your data in one place?
  2. Is your data checked for validity?
  3. Can a marketer answer his or her own questions from the data?
  4. Do you have an identity resolution solution / strategy?
  5. Can you correct past data?
  6. Is your data available at the speed at which you need it?
  7. Can you use per-customer data and segments in other systems?
  8. Can you enhance your data with advanced analytics?

Most folks I talk to end up answering “yes” to about half of these questions and realizing their data platform falls short on the other half. Don’t feel too bad if you don’t score perfectly.

1. Is your data in one place?

If you have one system for your marketing automation and one system for customer support, plus some top-of-funnel engagement occurring in Facebook and Google, it can be difficult to put the pieces together in order to really understand how customers experience your brand. Instead of trying to shoehorn data from one or more of these platforms into another (for example, putting all of your offline events into Google Analytics), you should take control of your data and the way in which it is stored. Use a data platform that is designed as a data platform; it will pay dividends with all of its pre-built connectors and features.

Marketers used to centralize all of their data in a CRM. But CRMs have difficulty scaling to the large volumes of traffic that come from the web; they also were never designed to handle top-of-funnel events that occur before you have a customer’s email address or phone number. Enterprise data warehouses (EDW) and data management platforms (DMP) collect all of this data, but these technologies often make you jump through a bunch of hoops to make use of it. What you really need is a customer data platform (CDP).

2. Is your data checked for validity?

Your data is only as valuable as it is trustworthy. Ineffective tracking and technical errors can cause significant problems, such as duplicate data, mislabeled attribution, and more. You need to have an internal process – think of it as a sanity check – that regularly fixes or mitigates any issues found.

A first step towards an effective data sanity check is minimizing the manual tracking involved when you launch a marketing campaign. Automatic tracking is an important part of keeping data clean. But even fully automated marketing systems will have technical hiccups; you don’t want to be caught with egg on your face for making an expensive decision based on bad data.

3. Can a marketer answer his or her own questions from the data?

Too many old “enterprise” systems force marketers to dig through obtuse user interfaces or use SQL queries to interact with a raw database. That results in the need for whole teams of analysts to translate marketing business questions into the technical language of data. Even under the best of circumstances, the additional turnaround time means marketers ask fewer questions and explore less new ideas within the data.

You need a modern system with flexible and powerful dashboards that makes it easy to explore and understand your data.

4. Do you have an identity resolution solution / strategy?

All of your different customer touchpoint systems use their own identity system to track your customers. Your website uses cookies and UTM parameters. Your email marketing system uses email addresses to track opens and clicks. And your customer support call system may only have a caller ID. Resolving all of those touchpoints into customer-level activity timelines is a complex undertaking.

Nobody has quite cracked this entire nut, but there’s progress being made by vendor consortiums. I’d recommend checking out some of the new innovations in identity solutions on the Advertising ID Consortium’s site. The essential takeaway for you is that you need to think about what is right for you — and select a single, unified solution or strategy to deal with it. Without a unified approach across your platform, uncovering answers to simple questions like “How many current customers do you have?” becomes impossibly complex and distracting.

5. Can you correct past data?

Mistakes happen, and you need the ability to correct them. It’s important that your marketing data platform be able to represent the best and most current representation of real customer experiences possible.

Perhaps an email campaign went out with bad tracking links and you want to correct that in your reporting. Maybe, after a 30-day delay, you got only some details about marketing spend in a channel and you want to record these preliminary numbers with the goal of updating them later. Or what if a channel or partner can only provide stats in the form of an emailed Excel document – and you realize it is full of mistakes? It’s vital that your data platform supports edits or rewrites of data history in a clean and simple manner.

6. Is your data available at the speed that you need it?

Customer data flows with different delays depending on where it comes from and how it’s being collected. For example, there’s likely a continual stream of purchases and pageviews from your ecommerce store, but maybe your advertising partner reports performance at weekly intervals.

Take a look at your data sources with time in mind. Ask yourself whether you are getting the right customer data in time to make the right decisions about it. Strategic decisions about the types of campaigns you should run can happen more slowly and thoughtfully, whereas cart-abandon marketing automation emails should happen the next day. On-page or in-app experiences may need to show different content or offers within less than a second.

Note that improving the speed at which information flows will often require a technology investment. Make sure that you invest strategically to meet your greatest needs first.

7. Can you use per-customer data and segments in other systems?

All of this data doesn’t do you any good unless you can do something with it. It should be easy and simple to come up with a new customer segment and deploy campaigns to that segment automatically. One-to-one marketing requires that you be able to easily pull up a complete view of all of the activities a customer performs as you are communicating with them.

Your data platform needs to unlock your data, not lock it. It should have connectors for marketing automation, in-app pushes, chat, social media audiences, and whatever other channels you are using.

8. Can you enhance your data with advanced analytics?

There’s a new wave of advanced analytics helping to predict, model, and explain customer behavior. Whether this wave goes by the name of data science, machine learning, or artificial intelligence, it requires the ingestion of large amounts of data in order to output everything from per-customer lead scoring and churn prediction to predictive lifetime value and product recommendations.

Don’t let your data platform limit your ability to take advantage of this new era of marketing intelligence.

About Retina

At Retina we obsess over increasing customer LTV and reducing CAC (Customer Acquisition Cost) through data science.

As the cost of customer acquisition skyrockets, it is important to get focused on LTV at the customer level. Companies who computed their LTV to CAC ratio wrong are failing in an increasingly competitive environment i.e. (Blue Apron, Wayfair, Chef’d). Companies that get it right are achieving sustainable profit and growth i.e. (FAANG companies, Dollar Shave Club, Ring, etc.).

The Retina Platform computes predictive-LTV and early customer behavioral drivers of LTV, at the customer level, using next-gen machine learning algorithms (Built on 30 years of academic research). Within a matter of days, Retina automatically builds audiences (Facebook, Google, LinkedIn, Snap) for your marketers to acquire new high-LTV customers and retain your existing high-value customers.

Interested in what we do? Go to https://retina.ai/story for a more detailed presentation about what we do, how we work and how we can help your business grow to the next level. If you’d like to see a demo of our solutions, go to https://retina.ai/schedule-demo/.