Finding ‘huge’ content marketing opportunities in ‘big’ data – #SESNY

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by Brafton Editorial
Brands now have access to more data than ever before. SES NY experts offered tips on leveraging metrics for smarter content marketing.

“Big data” is a big buzzword in the content marketing world. But marketers can’t be intimidated by the phrase.  At SES New York, Jim Yu, CEO of BrightEdge, and Jacob Hagemann, CEO of Hoosh Technology, helped marketers break down big data into actionable ideas for their content marketing campaigns.

Big data is really just “data,” but “big” is often applied as an adjective because marketers have access to an overwhelming amount of new metrics that can have significant implications.  Ninety percent of global data has been produced in the last two years. The changing marketing landscape (and the general media market) gives brands access to new types of information.

The overwhelming amount of data available on the web

Search, social, local, mobile – each of these channels is constantly evolving, and with just about every new development comes new metrics. There are more figures at marketers’ fingertips now than ever before. Yu shared some stats about the ridiculously high levels of web activity that translate into marketing figures:

  • 2.7 zetabytes of digital data available
  • 2.4 billion internet users across the web
  • 634 million websites online
  • 1.2 trillion Google searches in a year
  • Millions (if not billions) of active social users

The volume of consumer activity on the web constantly drives new data. It’s helpful to categorize data into different buckets to determine how easy it will be to leverage figures into strategies. Yu suggested three data buckets:

  1. Structured data: This is information with a high degree of organization, found in databases, warehouses and enterprise solutions. Google is a good source of structured data, he said, and it’s easy for marketers to turn these insights into results-focused marketing activities.
  2. Unstructured data: This is raw data that has been extracted from applications on the internet, but it has not been processed into productive or meaningful formats. This data isn’t easy to organize or measure at scale, and splicing and dicing is necessary to analyze any sort of information.
  3. Semi-structured data: This is information you might gather from social media tools available on the leading networks. It’s likely somewhat organized, but hard to act on without applying or comparing it to other (broader) insights.

Order out of chaos, and focusing on the bottom line

One of the most important lessons for marketers is ensuring measured data correlates with the bottom line: What do rankings mean for a brand’s reach? How do returning visitors translate into leads or sales?

When companies consider the real-world insights at data intersections, they stand to drive the strongest results. Yu offered anecdotal insights on Tiny Prints boosting PageRank for targeted keywords by 47 percent after the company effectively identified which content (and related keywords) received the biggest search lifts from social engagement and Twitter links. Similarly, Feeding America combined search ranking reports (used to reverse engineer top content strategies) with converting traffic patterns to develop a longtail SEO strategy. It ultimately saw a two-fold boost in conversions for the longer key phrases.

How every brand can leverage big data

For any brand looking to leverage big data, Yu recommended marketers seek insights (and correlating patterns) around:

“Data-driven marketing is here to stay, but so are we, the people.” – Hagemann

  • Keyword progressions
  • Universal search standing
  • Local SEO
  • Social SEO
  • Mobile SEO
  • Global SEO
  • SEO ROI 
  • Content marketing
  • Compliance with search engine guidelines

For those seeking more detailed or actionable tips, Hagemann offered some specific examples and insights on using data to refine content marketing campaigns. Of course, for any source of data, he reminded attendees that data doesn’t do the work, marketers do. He asked attendees to reflect on a well-worn idea: “Advertising is fundamentally persuasion, and persuasion is not a science, but an art.” Metrics may be science, but winning campaigns require the artistry of an analytical, creative mind.

“Data-driven marketing is here to stay, but so are we, the people,” Hagemann said. He then proceeded to share tips on what’s worked for him.

Applying data to local content strategies

There isn’t one SERP for a given query anymore: There are 25 result pages , or 125 result pages, largely influenced by location, Hagemann reminded attendees. He referenced an example of a query in Switzerland that resulted in maps and images. The same query in Germany was heavily dominated by AdWords. Italy SERPs, on the other hand, contained no paid results, and more organic text results than images. In the UK, video results were the most common, rewarding brands with video content marketing in place.

Local data indicates the need for international content strategies, but it also applies to marketers focusing on U.S. audiences. The SERPs are dramatically different according to country, and there’s also a difference – especially in terms of specific results’ ranking – that can be observed within a single country.

If content type presents itself as a trend for reaching different markets, consider how users in different regions demand different formats and diversify the content portfolio. Working on a tight budget? That doesn’t mean a company can’t use different content types… Still, those who want to be cautious with their investments can prioritize regional markets and invest most heavily in the content types local audiences want.

Want to try this “at home?”

  1. Examine SERPs in different countries or regions

  2. Identify content trends

  3. Invest in content formats for different keywords that are responsive to audience demand (and SERP prominence)

Fueling business development with social media intelligence

Next, Hagemann showed data breaking down a jewelry brand’s social comments. Rings were clearly the most commonly mentioned product. By subsequently analyzing the social comments according to occasion, the company learned “engagement” and “wedding” were the most popular events (at least for the studied period). When looking at the data, the brand realized its rings were the most popular category for engagement or  Christmas chatter, while necklaces took top prize in comments about birthdays.

This information enabled the brand to refine its social content to market different products during different seasons. More, the business decorated its offline stores seasonally according to the social themes it uncovered on the web.

To get product insights from social metrics:

  1. Discover which products fans and followers talk about most, and compare to seasonal or “time of day” data.

  2. Get ideas for new website themes or offline location marketing based on non-commercial buzzwords.

  3. Pair products and themes.

Uncovering competitive insights through data

With all the “big data” talk, Hagemann advised marketers to remember good old-fashioned SERP analysis. Rankings will naturally shift over time, but observe the trends in how a competing brand moves in organic and AdWords spots.

Some of his top tips included monitoring AdWords position. If one brand appears more frequently than the next, don’t assume it’s paying a lot more… Explore whether the competitor refined the keyword positioning within the ad copy, or if the related landing page has been updated for more relevance. He also emphasized the need to do SERP analysis on organic spots. (Matt Cutts – and Brafton – have recommended that content marketers not underestimate the metadata and descriptions that appear on SERPs. That copy can impact click rates, which will influence rankings.)

For smart SERP analysis:

  1. Know a company’s keyword position in the market and where competitors rank.

  2. Understand the drivers for success and failures in search (often driven by content).

  3. Respond quickly if a strategy works.

Of course, all of Hagemann’s tips are only effective if the studied data is reliable. To avoid marketing missteps due to faulty metrics, Yu had some ideas for data quality assurance.

Four tips for finding big data you can trust 

Big data is a new weapon, but it needs to be wielded by an experienced professional. – Hagemann

  1. Know the data is accurate. Be sure the data source is reliable: Is it coming from partnerships? What quality standards are upheld for data coming from multiple sources or parties?
  2. Make sure data is secure. The technology that used to measure and merge cross-web data should be private and protected, as the metrics can reveal how a company generates revenue.
  3. Ensure data is accessible any time it’s needed. Having high availability allows marketers to slice and dice the data according to timely ideas, campaigns or business needs.
  4. Get data at scale. Scale matters even for brands not self-described as big business. As marketing outreach evolves to incorporate new channels or target new markets, companies must ensure the data tools can grow with their efforts.

Hagemann ended the session offering marketers a valuable reminder: “Big data is a new weapon, but it needs to be wielded by an experienced professional. Dare to add big data to the marketing mix.”

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