Personalised search calendar yields data for high-performing content

By Lovisa Bergström

Dagens Nyheter

Stockholm, Sweden


Mapping the monthly search patterns of your readers is something that can be useful for several reasons. The patterns can be condensed into a tool illustrating findings that are significant for each specific month for your organisation.

Imagine a tool similar to the “rising” search queries in Google Trends Explore, but with annually recurring topics specifically chosen with regard to your users and their search patterns.

The Google Trend Explore function shows search queries with an increasing share of searches.
The Google Trend Explore function shows search queries with an increasing share of searches.

At Dagens Nyheter, the search calendar was built a couple of years ago. It serves both the newsroom and the advertising department with data and insights. There are several primary use cases of the search calendar.

First, it is a planning tool for articles with high search traffic potential. Search traffic is one important source of new readers to the Dagens Nyheter site. By looking into the search calendar, we get inspiration of what to write about and when it’s relevant to publish the stories. By planning ahead and making sure to write articles around topics we know in advance will receive large amounts of search traffic, and then publishing them at the right time, we optimise our chances of gaining as much search traffic as possible.

Additionally, the search calendar provides data as input in idea generating sessions. The idea is to identify what and when different themes are especially relevant to the readers based on their common historical behaviour.

Finally, it provides input for data-informed sales calls made by the advertising department. The search calendar has also proved useful when it comes to finding the right time for different ad campaigns as well as a tool to target advertisers and sell ad placements.

The search calendar can contain anything from large sporting events and national holidays as well as words that symbolise the spirit of each month. For example, in January, a few of the topics named in our calendar are: flu, health and fitness, newly enacted laws, pre-packaged grocery bags, and ski resorts.

This is similar to what the search calendar looks like at Dagens Nyheter.
This is similar to what the search calendar looks like at Dagens Nyheter.

The search calendar at Dagens Nyheter is interactive and visual. You can easily navigate between the months and expand the list of topics to see more details.

The search calendar can build on different types of data, depending on the purpose of the calendar and what data you have available. At Dagens Nyheter, we were able to access monthly search queries from search engines to our Web site during the last three years. We also analysed the search queries from our built-in search box on our site. Additionally, we got some help from Google News Lab and Google Trends to extract national data into the search calendar.

How to build your own search calendar

You need at least three years of data, split into years, months, and search quantity or pageviews for each topic. Do whatever data cleaning is needed.

For each month, calculate an expected value for each topic. The expected value is the mean value per month of the topic for that year.

Next, compare the expected value per topic and month to the actual value. That will make it possible to identify which topics “overperform” during specific months. Do a year-on-year analysis and save topics that overperform consequently each year during the same month. Once you have this data, present the topics per month.

This is just a basic description of how it can be done. Dig into your own data, and be creative with what you can find! The search calendar can be as sophisticated as you wish, based on time and competence available.

Maybe you can get hold of external data, like sales of e-commerce companies in your region, to see if you can find patterns that are useful to your search calendar. Data extraction and cleaning can be improved by using named entity recognition (NER) tagging, for example.

About Lovisa Bergström

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