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With U.S. consumers spending more than $700 billion over the holiday season alone, now is the time to determine your game plan for leveraging data to drive revenue. The reason? Marketing teams with big data at the center of their programs see 15-20% greater marketing ROI [1].

While data may seem daunting, companies only need to be focusing on 1.5% of the total data universe available to extract baseline value [2]. Within that 1.5% is the highest opportunity area for a brand’s bottom line of the 34% of total marketing data which is useful. For those who are just starting on their path to being an analytics powerhouse, this percentage of data creates a manageable set to start tackling and should increase over time. The challenge is, of course, identifying the data that will drive the highest impact to revenue.

In preparation for the holiday season, marketers who want to transform revenue generation should be taking a page from the playbook of the fastest growing online retailers. Just as they do, your marketing playbook should be leveraging your data to identify patterns within existing top customers. Who shows potential to become a top customer? Who will come into the holiday fold and exit just as quickly? By analyzing individual customer behaviors — if they’re connecting on all channels, engaged with your marketing efforts, converting across multiple devices, purchasing at the right pace (or not!) — you’ll have the foundation needed to structure a plan of attack that will ensure a strong holiday season.

Whether you’re a retailer who is just beginning to plan for data acquisition and analysis, or you’re a seasoned marketing analytics expert, you must identify which data is most important to you in both the short-term and long-term. Beyond your customers’ names, email and location, the approach that will net you the highest return is to combine explicit behavioral data and implicit interest data to built a complete, omnichannel interest graph on each and every one of your customers.

Explicit behavioral data is the known, quantitative individual attributes of a given customer based on user actions and purchases. Examples of explicit behavioral data points tied to a single customer might include: only purchases items on sale; follows you on Twitter; browses items on your mobile app; opens email at 3PM and 10PM; acquired via Google AdWords; accesses email via Android smartphone and iOS tablet. All of these attributes, and a seemingly infinite amount more, are demonstrated through their unique interactions with your brand, and help construct the story about each customer.

Implicit interest data is the inferred, qualitative individual attributes of a given customer based on browsing and purchase activity. Examples of implicit interest data: likes red; prefers leather over cotton; interested in Frye boots; prefers tote handbags to hobo handbags; enjoys DIY wedding content. These attributes, also collected during the data mining process with sophisticated platforms like Marigold Engage by Sailthru, yield extremely meaningful insights into your individual customers is and what they want from your brand beyond what their explicit behaviors have indicated.

The combination of these two forms of data will allow you to develop true 1:1 communications based on the individual’s specific engagement and purchase history and on their implied interests. It’s an approach that will allow you to build momentum during the holiday season and continue that momentum through the rest of the year. In the longer term, it will also help you become less fiscally reliant on the success of batch and blast holiday campaigns when serving targeted, relevant content to customers over time. If you plan to tackle data collection with your internal team, now is the time to start understanding where the data for each channel lives, how it is collected and the process for organizing and analyzing that information.


–Neil Capel, CEO & Founder


[1] McKinsey, “Big Data, Analytics, and the Future of Marketing & Sales”, July 2013.

[2] IDC, “The Digital Universe of Opportunities”, April 2014.