
Scan nearly any technology publication and you’ll find an article about Big Data. Big Data’s omnipresence not only makes it easy for companies to believe they can readily collect it, but also unlock its secrets to expand their marketing to produce results.
I’ve personally been using data-driven marketing techniques since 2002, developing marketing programs that were at the time new to the tech space. My background ranges from founding eBay’s telemarketing platform to localizing marketing programs internationally. Today, my team and I provide advanced data modeling for our clients, including algorithmic targeting and propensity models.
What I’ve seen is that many businesses don’t know how to capture their data in a way that reveals future performance. This is because data capture on its own doesn’t make companies clairvoyant, and the sheer volume of data gathered doesn’t guarantee deeper revelations. Ultimately, it’s what you do with Big Data that makes it a useful marketing tool.
At its core, marketing is about relationship building, and Big Data should be used to help companies craft messaging that really speaks to people. My team pulls from the largest commercial datasets warehoused in the world, and below is some of what we’ve learned on how to successfully use Big Data for marketing.
Avoiding Information Over-Think
Make no mistake, Big Data can cause analysis paralysis. This sort of information over-think is common. While Big Data offers great potential, marketing teams can end up paralyzed by seemingly endless analysis, examining the wrong things in the wrong ways.
From my experience working in the corporate world — and as my company has continued to guide enterprise and startups in using their data — adhering to three basic guidelines can help marketers think about how to use their data:
- Don’t let decision-making get derailed by nonessential analysis simply to satisfy curiosity.
- Continually elevate decisions based on value over cost at every level.
- Deploy data-driven marketing to personalize, not just customize, marketing campaigns.
Let’s consider these principles using an example from my past: At eBay, we developed marketing programs that hadn’t previously been considered — ones that involved scaling to millions of users worldwide. To be successful, we needed to stay on point and wring as much value as we could out of our data analysis.
Data became a key element of identifying the right user and the proper talking points to capture that person. (I admit it was only much later that I got smarter about using data to identify the channel to which they were most likely to respond.) However, the third principle was complicated for us. In the face of large data sets and complex sales scenarios, you need to create a sophisticated customer profile while also understanding the data’s appropriate use.
We marketers know there’s a lot to be said for wrapping your strategy around an open, flexible customer data model, but there are tradeoffs. Learning how to apply relevant Big Data at the right level, at the right time, for the right reasons is anything but guesswork.
The Building Blocks of Positive Outcomes
Even with these guidelines in mind, there are practical steps to overcome obstacles posed by data-driven marketing, particularly for fast-growing organizations or marketers with complex messaging initiatives.
It’s most productive to think of the following as building blocks to produce positive marketing outcomes, with Big Data as your underlying advantage:
1. Measure Only What’s Actionable
Actionable data is information that directly affects your decision-making. If metrics such as customer acquisition, time between purchases and saved sales are the most important to your business, be sure to limit your field of view to these primary objectives. You want data that works for you, not volumes that bury you in extraneous, irrelevant information.
2. Understand How and When to Use Data
While data-based insights may help you find a strategic direction for your marketing campaigns, they may only be marginally helpful on an individual customer basis. It’s important to recognize when and how data should be applied to campaign- or customer-level analysis.
3. Use Data to Optimize Marketing Channels
Use targeted insights gleaned from your data to refine how you execute your strategy. For example, defining customer targets based on propensity to respond to messaging is best built and supported by actionable data. It shouldn’t come out of the blue.
The ability to contact a customer through various methods — email, direct mail, phone, website, or another channel — makes a company accessible and versatile. Measure the results of your campaigns within the channel itself in addition to a broader summary performance report.
The performance results can be used to drive specific messaging strategies, campaign types or delivery timing. In all likelihood, you’ll also want to take advantage of immediate insights gleaned from high-touch channels such as the telephone to help you modify your marketing message across all channels.
4. Focus on Your Customers
Tailoring your marketing messages to meaningfully personalize every communication becomes more challenging as your customer base expands. Still, with attention to detail, it’s an obstacle you can manage.
Clearly defining success for your customer as they engage with your products or services will allow you to frame the type of data you’ll need to collect. When this data reflects customer behaviors and outcomes, the results can feel like magic: the type of content and level of personalization required to communicate your message becomes clear.
Remember, adding depth to marketing programs through data science is just another tool to increase program efficiency and results. No matter how deep your data, how insightful your findings, and how focused your efforts, you can prevent Big Data information over-think by concentrating on opportunities to improve your marketing.
This article was originally published on www.forbes.com and can be viewed in full


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