In this article we will discuss:
200-page annual content plans are dead
Marketers are more focused on ‘filling in’ their ‘annual content strategy’ instead of being obsessed with creating real value for their readers based on real-world occurrences:
Target audience interest in terms of what they are searching for, and where they are consuming their content (social media, digital magazines, blogs)
Changes in search engine algorithms, ranking methods, keyword trends
Competitor activity including a reactive, and preemptive approach
Current events which may use topical, highly trending stories as a hook for a product, service, or thought pattern we wish to plant in our target audience’s mind
An agile, data-driven approach can help you implement this into your day-to-day workflow.
Creating content in a vacuum doesn’t work
The agile approach is not ‘against’ having a plan, it is, however ‘for’ quickly assessing the space in which we wish to act in order to:
- Grab attention ( ‘Awareness’)
- Create a relationship (‘Interest’)
- Help customers make an informed decision (‘Consideration/Intent/Evaluation’)
- Successfully sell a product or service (‘Purchase’)
When we successfully assess our space both from an ‘audience perspective’ (Who they are?, What their pain point is?, Where they ‘hang out’ in the digital realm?), we can move on to evaluate their ‘current interest’ (What content they are engaging with on social, and actively searching for).
This will allow us to take an ‘educated guess’ as to the content we should be creating in order to get our desired result (awareness? interest?). When we start putting out content we can use our predefined Key Performance Indicator (KPI) metrics to determine if a piece of content was successful in achieving its goal, or not. If yes, then we can create more of what works, and keep emulating that model. If not, then we need to iterate, and try again.
This process of ‘agile content creation’ can be taken to the next level using data to inform all parts of the content creation, feedback, and iteration cycles.
How data is driving content marketing that resonates
Let’s take a moment to evaluate the above content strategy using a data-first approach:
Step one: Map your audience
Mapping your audience using data may differ based on your industry, and goals. But generally speaking you will want to know, and collect data about:
Major search engine trends and keywords i.e. what topics interest audiences in specific geolocations. A good example of this in the travel sector would be discovering that Australian-based customers are searching for ‘best places to stay this summer NYC’, around which you could then create tailored, targeted content clusters.
Social media sentiment – This would entail crawling groups, influencer profiles, and the like in order to see which posts/stories/articles are getting the most engagement, and traction (likes, shares, comments etc). For example, an eCommerce business selling kitchenware may discover a social media based chef who uploads short cooking videos, and posts of popular recipes. This insight garnered through data collection can be the starting point of a joint effort to create content, helping you tap into new, relevant, high intent audiences.
Step two: Track your competition
Knowing where your competition is investing efforts and engaging with consumers can be super important when choosing how to approach specific audiences. You don’t want to be a ‘blatant copy cat’ or be ‘passively reactive’, but you do want to make better informed data-driven decisions. Here’s a prime example:
Paid promotion – One of the best ways to keep tabs on the competition is vis-a-vis their paid advertising. You can collect information regarding their messaging, imagery, and consumer engagement in order to discern what is working for them without spending time, and money on ads yourself.
For example, a marketing agency looking to promote a financial analysis, and trading platform may see that their competition is successfully converting customers by promoting their ‘financial academy’. This may lead them to seriously invest time in cross-platform creation, and promotion of educational materials. They may start doing webinars, record YouTube videos, and write blogs/e-books, all based on the insight that their competition was able to engage audiences on the premise of self-education. No further Proof of Concept (POC) necessary. Just data.
Step three: Monitor interest, and trends
Many platforms have built-in tools to monitor such things, but alternative data, for example, can be much more efficient in providing real-time insights. Here’s an example:
Consumer feedback, and purchase trends – A marketing team concerned with promoting a makeup brand, for example, may choose to collect:
- Customer reviews
- Transactional data
By doing so, they may discover particular interest in eyeshadow that creates a very dramatic effect, and find that women are leaving competitors negative reviews, and are disappointed with the end result their products are achieving.
This is a data-driven opportunity in disguise, you have just discovered explicit consumer interest in a niche, as well as discontent over competitor performance. Not only can this inform your content strategy in the form of a video or image-heavy social media tutorial of how to properly apply said eyeshadow, this can also help inform product line choices, branding decisions, as well as messaging/creatives for paid advert campaigns.
Step four: Get insights – ditch or repeat
Whichever data you ultimately choose to collect, and use to inform your content strategy, you are going to want to gain access to a data-driven feedback loop as well. If data points to high-engagement, and desired results (purchase? consideration?) then you can repeat this approach, if not then you can ditch this type of content or iterate with a fresh angle. Here are some examples of data you can use to gauge how your content is performing:
Collect link data – One way to find out if a piece of content is gaining traction is to search the web for links to your piece of content (either backlinks or shares). You can create a chart and update the amounts of links from across the web to discern which pieces of content are absolute winners, and publish more of that.
Crawl for content – More often than not, if your piece of content is good, you will find sentences, paragraphs, and shockingly, sometimes even the entire piece republished on another site or blog. Besides being a great way to perform brand protection, this is also a good way to see how valuable your ‘peers’ think the information you provided is. The more your content is plagiarized, the better it is performing, using grade-school logic, i.e. ‘average kids only copy from smart kids’.
Collect web traffic data – You can do this for your blogs, and landing pages as well as for competitor blogs, and compare web traffic as your content strategy evolves to become more agile. The more traffic a page generates over time, the more engaging the content must be, seems like a logical deduction.
Collect search engine data – This can be data pertaining to your ranking in search results, how your posts are being listed for specific keywords, as well as how many clicks, and impressions your posts are getting (largely indicative of the quality of the title, and metadata but also of ‘time-on-page’, and how well your content was indexed by Google/Bing/Yahoo ‘spiders’).
The bottom line
Content strategies still work, and are an important component of any marketing strategy, especially when looking to create ‘evergreen’ content that will always provide value to your target audiences. But in a world where audiences, competition, and algorithms are constantly in flux, it is prudent to use data to power a marketing approach in-tune with what is happening online, in real-time.
