Cure Your Data’s ‘Gollum Syndrome’
The shift to data correlation means different business units have to be less possessive of their insights.
The world has seen an explosion of data collection. Our locations, behaviors, interests, patterns and interactions are becoming "datafied." In the past, data was very expensive to collect, but today, the ability to do so easily and at reduced cost, makes it meaningful.
We have moved from a world of causality to an exploration of correlations. For example, Walmart uses data to forecast buying trends. When there was a storm coming, Walmart knew that flashlights sold well, and anticipated that with increased stock. But by putting together their Big Data they also discovered a surprise – an increase in the sale of Pop Tarts. Causality is, ‘when it’s stormy, people buy flashlights.’ But a correlation of data is, ‘they also buy these sweet snacks.’
To truly unlock the power of data, you need to fight against human territoriality and break data silos within corporations.
To explore correlations you need to bring together as many data sources as you can: customer, prospect, web, behavioral, transactional, marketing, third-party, and so on. These are typically owned by specific business units and people tend to be obsessively possessive, struck by the ‘Gollum syndrome’. You can hear them whispering, “my customers, my contacts… my precious.”
A Corporate Asset
We know that big data is a strategic asset, and fast becoming part of a company’s valuation. There used to be a phrase: ‘brand matters’. If you looked at the value of a company on the world stock market, around 40% of its worth was apportioned to brand.
In 2014, 45 percent of Coca Cola was valued by brand. But, here’s an interesting thing: Google’s brand value was 28 percent, Apple and Amazon came in at 16 percent and Facebook, just 6 percent. Do we think any of these companies—of GAFA, as the acronym goes—have a brand problem? Absolutely not. Do they have a problem with market valuation? Maybe.
But, the power of these companies is in their ability to monitor, collect, and make sense of data on a large scale.
Impacting the Bottom Line
In-house, we leverage product, audience, customer, consumer and campaign insights as well as targeting and personalization capabilities to be better marketers but also to provide our advertising partners with better marketing solutions.
It helps us grow our advertising revenue, promote our brand, reduce our acquisition cost and retain our customers. It can also be leveraged to identify new business and revenue streams and product opportunities.
Where to Start?
With big data you have to think big, but start small. Ideally, a publisher would begin the journey with data capabilities that earn direct associated revenue: advertising.
With the rise of programmatic and real-time bidding, the advertising industry is turning into an automated and data-driven market place. CPMs are going down due to the explosion of supply, and the concerns regarding viewability and non-human traffic, and brands themselves want to become publishers. Data can help publishers turn these trends into opportunities.
A Data Management Platform (DMP) capability lets publishers sell media, combined with data, in a programmatic environment, and also target and monetize their audience beyond their own platforms. They can leverage their first-party data about their audiences as well as tap into third-party data from the data marketplace. With the ability to retarget on social media, video and mobile marketplaces, publishers with valuable audiences can aim for bigger advertising budgets. They can also produce and promote content for brands, as well as be measuring a campaign’s effective reach and engagement.
All these capabilities can then be leveraged for customer acquisition and The Economist was recently awarded with a bronze Lion in Cannes Lions for Best Use of Data.
The aim of this campaign was to expose our content to new prospects, to win their attention, change their perception of our brand and turn them into readers and subscribers.
To identify our prospects we first looked at our core audience. Data helped us map their interests and identify their profile attributes. We then targeted ‘lookalikes’ in relevant context with relevant creatives. For example, a prospect keen on liberal causes and tech, when reading an article about the NSA on another newspaper website, would be displayed the following creative: “This ad knows who you are. How do you feel? a/ special b/ spooked.” After clicking, the prospect was directed to a specific landing page with an article from The Economist on internet privacy and data tracking. The prospect could then decide to register for more or buy a subscription.
Beyond identifying prospects and personalizing creatives, we also use data to optimize the conversion funnel. We were able to understand what attributes and creatives linked to a higher conversion. For those who did not subscribe, we built pools of ‘hot prospects’ that we could retarget with special offers.
For this campaign we had to bring together campaign analytics, web analytics and CRM data as well as our Bluekai DMP. We also had to bring together our agency and internal circulation and data teams.
When it comes to big data, connectivity is paramount. Bring together not only the data sets and tech, but the people.