In this special guest feature, Opher Kahane, CEO and Co-founder of Origami Logic highlights five key areas on which data professionals must work closely with their marketing counterparts to truly realize the promise of Big Data. Opher is a serial entrepreneur and executive, now on his third industry-disrupting start-up. His previous two start-ups, ClassX, a VoIP pioneer, and Kagoor Networks, a VoIP analytics pioneer, were market successes. ClassX merged with VocalTec and went public on the NASDAQ in 1996 and Kagoor was acquired by Juniper Networks in 2005. Prior to founding Origami Logic, Opher was General Manager and SVP of Juniper’s $1B routing division.
Never before have organizations had so much information about their marketing efforts and campaign results at their fingertips. Yet despite this surge in data, so many still operate in a world of siloed, unconnected systems and sources that make it difficult – if not impossible – to pinpoint the most meaningful marketing signals from the plethora of campaigns they are running and derive actionable insights. Organizations need to use data to figure out the “what” and “why” that matter most. To truly realize the promise of Big Data, data professionals must work closely with their marketing counterparts on five key areas:
Think Big Picture
When developing a standardized marketing measurement framework to more effectively harvest, organize and analyze marketing signals, it’s important to keep the big picture front and center. This means setting well-defined goals that map closely to the organization’s business objectives. To do this effectively, start by implementing just one or two specific goals that can be rolled out consistently and effectively across the entire organization, then grow the framework over time.
Look at Data Holistically
To understand the true business impact of marketing activities, it is not enough to simply look at basic metrics such as clicks, likes and opens. Big picture thinking must also apply to how organizations view their marketing data. To do this, it’s important to separate execution from measurement and utilize a dedicated system to correlate everything from efficacy of creative assets in driving desired actions, to targeting and audience segmentation information, to high-level business impact data, such as spend and revenue. This will eliminate manual cycles, correlate disjointed information and streamline resources.
Focus on the Customer Journey
It’s easy to get caught up in using raw, vanity metrics such as Facebook likes to gauge progress, since they’re so easy to measure. But this will only tell a small part of the story. Once again, it’s important to think bigger by analyzing comprehensive campaigns that follow the full customer journey – from awareness and intent to education, purchase and beyond.
Take a Team-Based Approach
Just as an individual data piece can never be as powerful as a complete set, nor can one person ever be as strong as a full team. There are a number of integral partnerships that data professionals must forge in order to succeed. For example, alongside the marketing team, they must strike a balance with the organization’s CIO to drive enterprise-wide change, uncover new insights out of marketing data, and deliver highly relevant customer experiences.
Never Stop Improving
Today’s organizations must be willing to listen, learn, adapt and change course dynamically. This means implementing a tight feedback loop to make regular adjustments across all campaign components, based on real-time feedback from the marketing signals most important to them. To stay agile, it’s important to never get too comfortable or satisfied with the status quo. Constantly seek new ways to improve – try new tools, take advantage of free trials, experiment often and ensure you’re allocating enough spend to these initiatives (five to 10 percent) to realize results and progress.
As Mark Twain said so perfectly, “The secret to getting ahead is getting started.” If you remember just one thing from this piece, let it be this: When it comes to building a data-smart organization, always think big, but don’t be afraid to start small.
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