Understand the integrity of your data, create a data warehouse, and uncover your customer profile to sail the seas of data successfully.
In my last post, we took a look at how marketers could take a nibble of big data by examining internal resources and surfacing the right people to help manage and leverage analytics.
But solving the people problem alone won't make data work for your organization. Now, it's time to master the technology side of your analytics, which is one of the trickier parts of leveraging big data.
Here's why: most companies have enough data to fill seven seas. That means the challenge isn't about collecting more data. Instead, you need to understand which parts are the most valuable, and separate them from the deluge of other information. Here are three ways to sail the seas of data successfully.
1. Understand the integrity of your data. Since you likely have multiple data sources, there's a good chance more than one system is reporting on one metric. For instance, you might find your website analytics program is delivering information about the behavior of visitors who end up on your site after clicking on an email. However, similar data is likely stored with your email service provider (ESP). In some cases, you could have three or four different data sources reporting on the same metric, which can lead to internal confusion.
In order to truly make great use of your data, it's necessary to make decisions about not only the goals of your analytics projects, but also which data sources will create the single "truth" in your organization. Plus, some data sources will just be better than others for certain metrics. For a retailer, a content management system (CMS) or e-commerce platform will likely be the best data source for actual sales and transactional data, while a warehouse management system will likely be the best source for inventory or operational information.
But just as every business is different, so are its data sources. Review your disparate data systems, and then decide which one has the best and most accurate data. You want to be able to point to a certain system and say: "This is the metric the entire company is going to agree on using as we move forward."
Your own data shouldn't overwhelm the organization and drown the entire project. Think of it as snorkeling through the seas of your own data and spearing the biggest, most delicious fish. Those big fish are your most important data points. Find them.
2. Create a data warehouse. Now that you've drained the seas of analytics, and surfaced your most valuable and important data points to create a record of attributes for both customers and products, it's time to create a data warehouse. Compiling all of those important pieces of data means there's a business decision to make.
You have two choices: Are you going the technology route and creating a data warehouse to compile and manage those analytics? Or are you going to leverage your personnel resources by appointing a data scientist or analyst to query against rudimentary structures?
While there are vocal critics for and against either one of these strategies, it's really not that cut-and-dried. Every organization needs to take an honest look at its capabilities to determine what it is poised to do with data based on current systems and staff.
An older business, for example, might have enough staff to put one person in charge of querying the data warehouse. A newer company might already have the technology needed to create a data warehouse.
No matter which route you choose, don't underestimate the time and money that will need to be invested to get a data program off the ground. There's no way any company can just jump into their sea of data and expect results. Rather, it takes expertise, planning, and time to sail those seas to data success.
3. Uncover your customer profile. The goal of combining all of your most important data is ultimately to say: "How much information can we compile about our customers that's important to our business?" Typically, that comes down to understanding both your customer and your products.
So once you've emerged from the sea of your top data, fish in hand, it's time to put that information to good use. Start a checklist, and build out a full profile of what your customer and products look like.
Answering these questions with your data will create a long-tail list of attributes for both your customers and your products, as well as uncover new insights about buying/purchasing behavior.
Sea of Data image on home page via Shutterstock.
Join the Industry's Leading eCommerce & Direct Marketing Experts in Chicago
ClickZ Live Chicago (Nov 3-6) will deliver over 50 sessions across 4 days and 10 individual tracks, including Data-Driven Marketing, Social, Mobile, Display, Search and Email. Check out the full agenda and register by Friday, Oct 3 to take advantage of Early Bird Rates!
Nathan Richter is the global director of client solutions at Monetate, where he advises top enterprise clients on website optimization. A veteran of digital marketing and online retailing, Richter has extensive hands-on experience helping enterprise clients implement successful multichannel marketing campaigns. Richter has directed online marketing and site optimization programs for David's Bridal, QVC, The Franklin Mint, and dELiA's.
IBM Social Analytics: The Science Behind Social Media Marketing
80% of internet users say they prefer to connect with brands via Facebook. 65% of social media users say they use it to learn more about brands, products and services. Learn about how to find more about customers' attitudes, preferences and buying habits from what they say on social media channels.
An Introduction to Marketing Attribution: Selecting the Right Model for Search, Display & Social Advertising
If you're considering implementing a marketing attribution model to measure and optimize your programs, this paper is a great introduction. It also includes real-life tips from marketers who have successfully implemented attribution in their organizations.
September 23, 2014
September 30, 2014
1:00pm ET/10:00am PT
October 23, 2014
1:00pm ET/10:00am PT