MCM Analytics: Bright Future Despite It’s Challenges

Contributed by:

Senior Director of Analytics, Ogivly Health World, part of Ogilvy CommonHealth Worldwide

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Analytics is an integral, inseparable element of multichannel marketing (MCM), which is an increasingly essential approach to pharmaceutical marketing. However, we are still far from realizing the full potential of empirically driven audience-level multichannel marketing. This piece highlights the challenges against the full realization of analytics in the MCM arena and encourages the maximization of the imperfect, but commendable progress.

QUOTE: Iyiola Obayomi Senior Director of Analytics Ogilvy Healthworld, part of Ogilvy CommonHealth Worldwide The future of empirical marketing will be the combination of evidence gleaned from large audience-level dataset integrated with forward-looking, rapid cycle feedback based on customer interaction.

These Are Great Times for ­Empirical Marketing
We are living in a great time in the history of marketing. We have never been privy to these levels of unprecedented access to rich arrays of audience-level dataset that allows marketers to understand their customers’ needs, preferences, intents, and satisfaction levels. By applying appropriate analytical skills to data, marketers can begin to understand their customers, predict their behaviors, and deliver finely customized communications that generate the desired responses. We are in the era of advanced empirical marketing, the place where what customers do, and not what they say, powers marketing. Multichannel marketing, the continuous adaptation of the right mix of channel, content, and context to individual customers, has been a key driver (and benefactor) of this customer-level analytics.

In healthcare professional marketing, this personalized, non-field marketing gained popularity in healthcare communications because of seismic changes that impacted the industry. Almost everyone working in this space can recite these drivers by heart — aggressive cost controls due to patent cliff slashing revenues, the need for marketing efficiency, and a shrinking sales force. These changes are paving the way for expectedly lower cost, scalable, and easy to optimize personalized non-field promotional marketing.

Data are the valuable byproduct of MCM and have created a boon for MCM data and analytics because of the diverse form, speed, and scale of the information generated at the intersection of customers and MCM tactics. By engaging with typical channels deployed in MCM programs — emails, direct mail, display advertising, paid search, Web, tele (phone), and mobile applications — customers generate tons of data that hold the secret to exposure, engagement, interest, intent, and action. Marketers have every reason to be excited because the current trend suggests that we are heading towards nirvana, where we can deliver the right message through the right media at the right time to the right audience to get the right results.

Challenges Lie Ahead
However, a closer look also suggests the need to be cautiously optimistic. There are formidable barriers in this journey, primarily the inability to access most of the necessary dataset required to develop the optimal analytic models. For instance, we cannot or may never be able to:

» Have all the datasets. Direct mail and print, for instance, do not provide confirmation of exposure nor enable full capture of all ­responses occurring as a result of exposure. Email and digital print may have to ­supplement their off-line counterparts to ­address this limitation.
» Trace back all interactions of our audience, especially for display, paid search, and most website visits. Even third-party data match rates are typically less than 70%, leaving us to rely on look-alike guesstimate for a good portion of our targets.
» Understand the full context of our audience. Mood, location, time, and prior competitive exposure will always exert ­confounding ­effects that may mask good ­audience-level marketing signals.
» Replicate learnings easily because of the complexity of the interactions between ­marketing drivers. A well-developed ­learning for our audience may be too ­context-specific, changing with time, new messages, and new channels.

The Future Remains Bright
These boundaries should not limit our excitement about the future of empirical marketing. The work-around is to acknowledge the inevitable limitations of the accessibility of audience-level marketing data, as well as the breadth of data-driven recommendations we expect to identify.

As we increase the share of targeted tactics, we will be able to understand the cross-channel effect of targeted drivers. We can bring non-targeted tactics into the equation using look-alike models, as well as proxy measures, such as geographic location of customers, to estimate the likelihood of exposure, interactions, and engagement.

But the true maximizing potential of empirical marketing comes from a hybrid approach: the integration of the analysis of massive retrospective datasets with rapid cycle testing, learning, and optimization. The prospective “doing and learning” will round out the nuances we may never be able to pick up from data limitations discussed earlier.

In essence, the future of empirical marketing will be the combination of evidence gleaned from large audience-level dataset integrated with forward-looking, rapid cycle feedback based on customer interaction. We may never get to the point where we have full historical data to inform a plan that delivers the right message to the right audience at the right time with the right outcomes, but we will make great progress in the coming years. What we can achieve creatively within these limitations can be quite powerful. The future remains bright.

Ogilvy CommonHealth Worldwide — the health behavior experts of Ogilvy & Mather — is committed to creativity and effectiveness in healthcare communications, everywhere.
For more information, visit ochww.com.

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