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Five Best Practices for Managing the Transition to Omni-Channel Marketing, Measurement & Optimization

Volume 4, Issue 5 - May, 2014

Editor, IQ Advisor

Omni channel marketing, which is based on the concept of creating personalized, contextually relevant experiences for each customer across all channels and devices, is quickly becoming a mission critical competence for sophisticated marketing organizations. Yet implementing an omni-channel approach is contingent upon an ability to measure the impact of every marketing touchpoint experienced by every individual on their path to a conversion, and then translate that measurement into individualized optimization strategies to maximize the performance of your overall marketing ecosystem.

Advanced algorithmic attribution is the cornerstone of a successful omni-channel strategy, as it enables marketers to collect marketing performance data from multiple, disparate sources and devices and turn it into accurate, actionable insights. But as with any transformative technology, there are often sizeable cultural, procedural, and technical chasms to be crossed prior to successful adoption. Here are five tried-and-true practices to help you manage the transition:

  1. Communicate for buy in: In today’s connected world, marketing is a team sport. Marketing attribution isn’t about picking winners and losers among existing channels. Rather, it’s about gaining a more nuanced understanding of the combined contribution of all channels to the successful execution of your company’s omni-channel marketing strategy. Change is hard, so make sure each channel manager understands how attribution will help facilitate individual roles by helping the team as a whole become more effective.

  2. Include the value of information discovery in your ROI calculations: Beyond its financial gains, attribution management offers numerous supplemental benefits to which it can be difficult to assign a dollar value. Attribution replaces your marketing magnifying glass with a microscope, providing your marketing team with orders of magnitude more information than previously available to assist in making better media optimization decisions. Acquiring a more detailed understanding of how your media works across all channels not only lowers uncertainty and risk, but also helps build a foundation for greatly improved media planning efficiency and predictability.

  3. Plan for quick wins and frequent value delivery: With marketing attribution, it’s important to focus on keeping implementation complexity low and delivering results early and often. An iterative, or “agile,” implementation approach is typically best, as it allows you to see initial results that demonstrate the system’s performance and builds momentum for expansion. The initial rollout can be defined by a specific pair of channels, business units or product lines, or a precise time period. Once some initial results have been realized, those early successes can be used to build a data strategy and roadmap for expansion across additional channels, business units, geographic areas, and more.

  4. Actively support adoption amongst your team: Adoption is only going to happen if the attribution solution is understood and easily accessible. Managers and end users of your attribution solution should receive hands-on practical training tailored to help meet your immediate tactical goals and objectives. Becoming comfortable with the solution and extracting incremental value from it will greatly help encourage adoption and lead to better long-term results.

  5. Develop a testing strategy: Successfully adopting marketing attribution requires building trust that the solution is delivering the right recommendations about your marketing programs and spend. Fortunately, algorithmic attribution allows you to validate the efficacy and accuracy of the model by testing media planning predictions. By setting up simple controlled experiments, it is possible to simultaneously control exposure to risk, validate the accuracy of the solution, and realize incremental revenue from optimizations. As you become more comfortable with this process and identify areas where the model is consistently accurate in its predictions, you can start to implement more aggressive optimizations and realize bigger gains.

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