As a marketer born in the fringe years between Gen X and Millennials, my early professional experiences coincided with the rise of the dot-com boom. “Digital transformation” was well over the horizon as I patiently endured the whine of dial-up modems and moved production files around via clunky, temperamental zip drives. Likewise, the way we planned campaigns at the time reflected a dearth of audience insights and a limited selection of overly broad channels by today’s standards.
Today, zip drives are gone and web connectivity seems as ubiquitous as oxygen. We also have exponentially more audience data to fuel marketing across an ever-expanding selection of channels. However, despite these advances, too many brands plan and execute campaigns using methodologies similar to those I dutifully employed in my days as a copywriting intern.
I blame an attachment to old-school campaign thinking–perhaps a hangover from the golden era of TV and print planning. It is in marketing’s DNA to boil an audience of millions down to three or four segments because it’s easier than getting to know people based on a hard look at data.
When attempting to push any new initiative, it’s important to remember old habits die hard. For example, email, despite the newly evolving sexiness of inbox innovation, has its roots in old-school campaign planning. “Batch and blast” is the bad word everyone throws around when they want to draw a distinction between data-driven messaging and a more generic form of bulk messaging. It would be tempting to say that “batch and blast” has become an over-used marketing boogeyman, except that the boogeyman doesn’t exist and “batch and blast” still does.
So why are some brands exceling at data-driven personalization while others stubbornly batch and blast away? It’s because customer expectation hasn’t yet reached the point where email recipients automatically unsubscribe for anything less than an ideal interaction. But they’re getting closer to doing so every day.
Sweeping metamorphosis doesn’t happen overnight. A caterpillar requires 28 days to transform into a butterfly–but only lives a few weeks afterward. This means the transformative portion of his life makes up a significant portion of his overall existence. Similarly, a company doesn’t simply shift from batch and blast to individually personalized email experiences at nearly the pace one would desire. Getting IT, marketing, and executive leadership on board with major shifts takes time and requires incremental wins to prove the concept.
‘Service As A Software’ Approach
One of the most practical aspects of software is that it unfolds in releases. A 1.0 or a 2.0 is considered a major release, often defined by wholesale changes in structure. A 2.0 version of software could look significantly different than the 1.0 version.
Conversely, a software update might not warrant a completely new release. A 1.1 or 1.2 might be reserved for improvements, meaning, after a certain period of observed behavior, the software architects decided to issue a change to the code to make it better–but aren’t quite ready to fully replace the existing underlying architecture.
This has immediate and practical application to today’s e-CRM campaign planning. It acknowledges that with a greater number of data inputs and measures, those responsible for building campaigns have compelling evidence to make improvements to campaigns more often. Rather than simply reacting in an ad hoc manner, services are provided as if thet were software. This approach to campaign planning provides a disciplined way to accommodate new insights in future campaign releases.
But results from automated marketing will only be as good as your understanding of the customer and the creative assets powering the campaign. The better you become at this now, the better artificial intelligence will be at giving you what you want in the future. Here is how:
- Capture more data on an ongoing basis: Combine efforts to build better integrations of existing data with efforts to increase audience insights through behavioral data.
- Integrate data into campaigns more frequently at predictable points: Log available but unused data and set a schedule for incremental integration into future campaigns. Set learning objectives for behavioral data and absorb all you can.
- Keep your eye on the long-term dollar: Resist calls to squeeze more revenue from your database in the short term as it kills the future potential of your list.
- Set a date for the next campaign release: Ad hoc updates don’t work. Set a date for diving deep into data 180 days after launch and plan for a significant 1.1 update.