Editorial
Using advanced customer analytics — and especially predictive analytics — to improve the ROI of a digital environment is a promised land that many businesses strive for but few have reached successfully.
According to Forrester, “despite heavy investment in advanced analytics and emerging big data technology since 2012, business satisfaction with analytics is still going down.” In fact, the research firm cites a 21 percent decrease in satisfaction (paywall) among data and analytics decision makers from 2014 to 2015.
Why Predictive Content Poses a Challenge
Predictive analytics solutions promise to help predict customer behavior in any given digital experience.
While some applications of predictive have met with relative success — such as in retail environments with product recommendations and offers — others present more challenges. In particular, predictive content analytics is a much more challenging beast, as it requires more complex inputs, analyses and interpretations than many other systems.
When it comes to content, serving visitors the exact information they seek requires:
- Actually having the content visitors want
- The ability to understand and interpret metadata about the content (which needs to be high quality) in combination with data about the customer (situation and context, behavior, history)
- The ability to intuit whether the search ended successfully, as visitors may not always tell you.
Perhaps the most complex challenge of predictive content analytics is figuring out how to track the success of a given piece of content, as engagement metrics are notoriously fluffy and difficult to interpret, particularly when trying to measure ROI or other hard numbers. For example, someone clicking a lot around a website could indicate high engagement with the content … or a lot of trouble finding information.
Are Predictive Analytics Worth the Bother?
In spite of the frustrations marketers feel towards predictive analytics, they're worth implementing.
A good predictive content analytics system will help marketers understand how their audience will experience the content they create. Marketers will see the effect their content has on their audiences as they are creating it.
Good predictive analytics can become a powerful tool in the marketing process to continuously help marketers make better decisions about the content they serve their customers. This leads to greater visitor engagement, customer satisfaction and ultimately, ROI.
Considering Predictive? Ask These Questions
There are no perfect predictive content analytics solutions yet. But for businesses looking to apply these practices to their marketing process over the next few years, consider the following questions when evaluating how best to implement them within your business:
1. What purpose do predictive analytics serve?
Knowing what purpose predictive analytics will serve is key. The purpose will evolve as capabilities of the analytics solutions improve; but by deciding now, in broad strokes, what goals you are looking to achieve you will, by extension, understand how to measure success. The best goals are those that link to bottom-line revenue (which is the biggest challenge with content analytics in general).
Tip: Budget owners want to see effects immediately, while effective content marketing builds up its effect gradually and over repeated interactions. Eventually, smart businesses will understand how to think in terms of programs rather than projects. This will mean measuring content marketing success via longer-term metrics, such as end-of-year revenue, customer satisfaction, upsell and renewal numbers, and so on.
2. Where will the analytics live?
Marketing managers often don’t have direct access to all the analytics needed to perform meaningful tests and optimizations quickly. Instead, analytics live with a "numbers guy" who struggles to communicate actionable insights to the marketing department. The result is only slight changes, if any, being made in the digital environment based on the data.
Even when marketing has access to a full spectrum of analytics, these are often segregated by channel. A good predictive analytics solution will comprehend and optimize the customer journey across all channels.
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Tip: Due to the complexity of an excellent predictive content analytics system, the best-of-breed solution may exist outside a CMS solution. For this reason it’s important to ensure that the CMS implemented by the business supports interoperability with a wide range of tools and services.
3. How do we ensure that marketing is actually using the analytics?
The best solution in the world is worthless if it’s not actually being used. And this poses a big challenge for marketers who are either not yet comfortable in the use of digital analytics, or as per above, not granted access to the data. The right predictive analytics system will bring analytics to the end user in the form of actionable insights, helping content creators tap into the analytics with lower effort.
Tip: Using analytics — whether predictive or retrospective — requires practice, but it also requires giving users the power to quickly make changes based on the analytics without a long approval process. Giving marketing managers, content creators and editors the power to make decisions based on the data today will help businesses develop two-way trust and confidence between the C-Suite and marketing managers around content marketing, as content quickly and inevitably becomes a key driver of revenue and business success.
Another valuable strategy is bringing young talent into the organization — they can act as a source of new ideas and fresh approaches to data-driven marketing. This raises the knowledge and skills of the marketing team across the board, and helps ensure that your business is using the analytics solutions and capabilities to their full advantage.
The Bottom Line
Over the next few years, businesses will become more adept at measuring difficult-to-grasp, but increasingly important content success metrics. Content marketing managers will take their place in the spotlight as key drivers of business revenue.
For this reason, predictive analytics will become a hugely powerful — and unavoidable — force in content marketing, capable of helping marketers make better decisions more quickly.
Remember though, that development continues on these systems. They will continue to evolve and improve over the coming years.
Over the next two years, predictive content analytics will provide smart businesses a means of gaining better insight into customer's interactions with content. And by equipping their marketers with better access to analytics and more decision-making power, businesses will reap the benefits.
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