30-second summary:
Assets that don’t work to tell a story don’t work at all. It’s important for marketers to be thinking about how stories are built and contextualized.
The more data you can examine from previous campaigns to see which stories resonated with buyers, the better you can plan a new campaign that engages and delights.
The more context that an organization can deliver to creatives the better decisions those creatives can make.
Companies need real-time analytics and predictive value measurement to understand which campaigns are delivering better returns than expected, and which ones haven’t been able to gain traction.
To make assets valuable beyond deployment, companies need to enhance their perception with predictive analytics
Assets that don’t work to tell a story don’t work at all. So when creatives are building assets and campaigns, how do you tell a story customers care about? It’s important for marketers to be thinking about how stories are built and contextualized. Let’s take a look at how stories move from idea to execution in the marketing world — and how storytelling strategy is important in every phase, to every role, in every tool.
Strategy #1: Keep it relevant with data
Before you can tell an effective, impactful story, you need to understand the audience.
Typically, in enterprise organizations, these functions are handled by strategists, using research tools to understand the most recent demographic and cultural trends that can be used to turn the next product launch into the next big thing.
Tools at this phase tend to be market-oriented, with only scant insight into how previous campaigns and assets fared or which stories resonated with potential customers.
While research teams pass recommendations on to creatives, this research often offers little visibility into the why of each recommendation, leaving designers and copywriters struggling to understand which parts are important — and which are just window dressing.
Getting it right at the research phase means keeping a laser focus on audience relevance. The more data you can examine from previous campaigns to see which stories resonated with buyers, the better you can plan a new campaign that engages and delights.
Strategy #2: Put it in context
Traditional digital asset management (DAM) tools often enter at the next phase, when designers and producers start to create assets using strategic recommendations.
At a typical enterprise organization, the teams putting together assets may live in ten different time zones, working on campaigns in seven different languages, using dozens of different tools.
With limited visibility into the most relevant work being done by other teams, creatives are limited to the recommendations that come from a small slice of the overall organization.
The more context that an organization can deliver to creatives — in the form of automated content recommendations, insights into similar campaigns, and value prediction — the better decisions those creatives can make.
Strategy #3: Adapt to thrive
As assets are deployed for a particular use, their performance can be measured. But for many companies, this kind of measurement happens only after, for example, a campaign has already wrapped up and spent its budget.
Realistically, this results in a lot of wasted money. Not every tactic in a major omnichannel initiative is going to succeed, and underperforming assets — perhaps those that tell a story that just doesn’t resonate — are unlikely to suddenly pick up steam after a tepid start.
Companies need real-time analytics and predictive value measurement to understand which campaigns are delivering better returns than expected, and which ones haven’t been able to gain traction.
By re-allocating campaign spends in a data-driven, agile way, organizations can get the most from their storytelling dollar while minimizing brand dilution from stories that don’t sell.
Strategy #4: Enhance your perception
The clicks are in — and the campaign is over. What happens now? At many organizations, the answer is that the asset is put into cold storage, never to be seen or used again.
That’s because most legacy tools restrict users to finding information they’re specifically looking for. To find that amazing asset that was so perfect in the campaign three years ago, someone has to remember it and go searching.
To make assets valuable beyond deployment, companies need to enhance their perception with predictive analytics. Aided by machine learning algorithms, predictive analytics can draw connections between old assets and new strategies, validating and improving storytelling choices.
How to fail tn storytelling without really trying
The easiest way to tell stories that fall flat: ignore what your customers are already saying loud and clear.
Companies today receive more direct feedback than ever about products and stories — from product reviews on retailer websites to social media comments on ads.
But this information is often decoupled from assets and campaigns, taking away vital context that could have made the next campaign better.
If your stories are being met with skepticism or hostility, or products are failing to live up to the stories you’re telling, continuing with the same tactics will inevitably lead to a loss of goodwill and sales
When your storytelling tools aren’t equipped to obtain information from all the channels your customers are using for feedback, you can’t get the full picture you need for continuous improvement.
Scott Bowen is the CEO of Tenovos. He is a results-driven technology executive and entrepreneur with a track record of building successful businesses whose expertise sits at the intersection of digital media and marketing, data analytics, and cloud. His operational track record ranges from blank-page entrepreneurial startup to global leadership teams of $1B+ enterprise software and ecommerce companies.
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Source: ClickZ
Link: The storytelling life cycle: 4 strategies for success (and one surefire way to guarantee failure)
