Michael Waldron

Reading Time: 4 minutes

Reading Time: 4 minutes

When marketers talk about data and analytics, we are most often referring to performance data, i.e., what was the outcome of the campaign in which our marketing assets were used, or how much engagement did we generate on a particular channel?

And while this end result is critically important to decisions about the kind of assets we should produce in the future, it only tells half the story. To identify areas where we can optimize across our content operation, marketers need to gather data across the lifecycle — not just at the end.

Data-Driven Marketing: Are We There Yet?

The concept of the “data-driven” marketing organization is not new, but achieving that status is still out of reach for many. According to statistics gathered in the Invesp infographic excerpted below, nearly half of marketers feel “significant pressure” to increase the role that data plays in their current marketing strategy. That stat is unsurprising when viewed in context with the next, which is that 87% of marketers consider data to be the most underutilized asset in the organization. 

But regardless of how much pressure organizations put on their marketing teams to increase the emphasis on data, that can be difficult to do without the ability to collect that data readily and accurately. While advertising platforms and CMSs have come a long way toward providing insight into the performance of completed campaigns, everything prior to that stage is still out of focus for most organizations.

What Is Story Lifecycle Data?

Before we jump into the stages of the Story Lifecycle, let’s back up a moment to define a “Story” as we think of it here at Tenovos. Digital assets are the building blocks of your content — photos, videos, logos, etc. — the kinds of things brands manage in a Digital Asset Management (DAM) platform like ours. These assets on their own are just the beginning. When you are able to augment those assets with data such that you get a 360 degree view of your content, that’s when we call it a complete Story. These data types include classic metadata, product and business data, all the way down to performance data, which we store alongside assets in our Story Manager platform.

As any content marketer or designer can tell you, a lot of time and effort has gone on in the life of a piece of content before it reaches its final, published form. In fact, we think of this Story Lifecycle as having six stages:

• Creative Ideation
• Design Iteration
• Review & Approval
• Campaign Creation
• Content Distribution
• Review & Optimization

Each of these stages is generating data that can lead to actionable insights for marketers that can improve their efficiency, effectiveness, and audience engagement. Below is an example of a Story Lifecycle and the kinds of data that can be captured at each stage if the right tools are in place.

Seeing it laid out this way, can you start to imagine the kinds of opportunities you and your team would have to improve your processes and outcomes if each asset in your content library was stored alongside this kind of data profile?

How To Collect and Store Lifecycle Data

Now that you’ve seen examples of the kinds of data your Story Lifecycle is generating, you might be trying to figure out how you could go about tracking and storing that kind of information within the structure of your organization and the tools you have available. There are as many methods for doing this as there are marketing teams out there, but most companies generally fall into one of three buckets when it comes to lifecycle data:

1. Not tracking it or storing it
2. Tracking some of it manually or in disparate systems, but not storing it alongside content
3. Tracking it automatically through platform integrations and storing it alongside content

Which category does your organization fall into? If you aren’t where you’d like to be, keep in mind that every move along the spectrum adds a great deal of value. That is, there’s a huge leap in value from not tracking data to manually tracking data, even if you haven’t devised a means of storing that data with the content it applies to yet!

The real magic happens when moving from bucket #2 to bucket #3. As those in #2 can tell you, manually tracking data and/or KPIs is time-consuming, and it can be hard to dedicate that kind of time when there’s no clear purpose to what you’ve collected. Without a way to easily attach what you’ve collected to your content and store it in a central hub for later review, it’s easy to let the tracking fall by the wayside when things get busy. 

But when teams are able to harness the power of automation and platform integrations to track their lifecycle data in the background and pair those insights with the content they apply to, those insights become actionable and can add real value to the organization. 

If you’re interested in learning more about managing your content library in a way that adds value and empowers your marketing team to make better decisions, click here to set up a personalized demo of Tenovos’ Active Story Management platform today. We’d love to show you what the modern, data-first DAM can do for your business.

Reading Time: 3 minutes

Assets that don’t work to tell a story, often don’t work at all. But when creative teams are building assets and campaigns, how are they supposed to know which stories will resonate with their audience the most?

At Tenovos, we’re always 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, and in every tool.

Strategy #1: Use Data to Ensure Relevance

Before you can tell an effective, impactful story, you need to understand its audience. Typically, in enterprise organizations, this insights function is handled by strategists using research tools to understand which demographic and cultural trends can be leveraged to capture attention and drive engagement.

Research tools available at this phase of content ideation tend to be market-oriented, with only scant insight into how previous campaigns and assets fared or which stories resonated with potential customers. While researchers pass recommendations on to creative teams, 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 coincidental.

Getting it right at the research phase means keeping a laser focus on 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 its audience.

Strategy #2: Put Content in Context

Traditional digital asset management (DAM) tools often come into the picture during the next phase, when designers and producers start to create assets using strategic recommendations.

At a typical enterprise organization these days, the teams putting together assets may be living in different time zones, working on campaigns in different languages, using dozens of different tools.

With limited visibility into the work being done by other teams, creatives are by default limited to the recommendations that come from a small slice of the overall organization.

The more context that an organization can deliver to creative teams — in the form of automated content recommendations, insights into similar campaigns, and value prediction — the better decisions those individuals can make.

Strategy #3: Be Agile and Make Real-Time Changes

As assets are deployed for a particular use (ad campaigns, social media posts, ecommerce websites, etc.), their performance in context can be measured if the right tools are in place. 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 spend in a data-driven, agile way, organizations can get the most from their storytelling dollar while minimizing brand dilution from stories that don’t perform well.

Strategy #4: Enhance Your Perception

Once an asset is ready to retire — whether its campaign has ended, or it’s time for a website refresh, or a social post has run its course — what happens to it? 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, taking serendipity and intuition out of the mix. To find that one amazing asset resonated so well in a campaign three years ago, someone has to remember it and go searching.

To make assets valuable beyond their initial deployment, companies need to power their search with predictive analytics. Aided by machine learning algorithms, predictive analytics can draw connections between old assets and new strategies, validating and improving storytelling options by surfacing content automatically that the searcher might never have thought of or know to look for.

Get Strategic About Storytelling

At Tenovos, we take storytelling seriously — at every stage of the storytelling life cycle. With the Tenovos platform, your teams can contextualize and collaborate more effectively to create stories that matter, using next-generation tools that create a strategic advantage when your content is competing for attention and engagement in the digital world.

Ready to learn more about enriching your storytelling capabilities with the newest cloud-native technologies? Read our white paper or contact us for more information.