From generating insights to predicting sales, from safeguarding reputations to driving engagement strategies – with a little bit of creativity, data can play a leading role.
Featuring initiatives by Netflix, Taxi Stockholm, Alibaba, McDonald’s, meteolytix and The New York Times.
1. Netflix: Data to generate local insights
For its launch in France, streaming service Netflix worked with Ogilvy Paristo create the first digital OOH campaign made entirely of GIFs. They installed a series of over 100 digital posters at bus shelters and other public spaces across Paris, featuring iconic scenes from popular movies and shows that can be accessed on Netflix.
What’s innovative about the campaign is not only its use of GIFs – which are popular among younger generations – but also the way the GIFs change their content in real-time, depending on what’s happening locally. For example, a rainy day at a bus stop will see the billboards feature King Leonidas from the movie 300 taking shelter under his shield and the message “Stuck in the rain at the bus stop? Console yourself with a good film on Netflix.” An elimination for the French soccer team will result in the GIFs encouraging people to get over the setback with a nice movie on Netflix!
How did they manage to react so quickly, within a span of two hours? It’s all in the listening. Netflix tuned in to local news, weather changes and locally relevant events to determine which events could be leveraged, and linked to their content offer.
2. Taxi Trails: Data to generate local insights
Taxi Stockholm, Stockholm’s local taxi company, did used the large amount of GPS data it had collected from its fleet’s daily trips, to create an innovative app for tourists flocking to Stockholm. (Stockholm ranked number 10 on the list of the most attractive destinations in Europe in 2013).
Positioned as the tourist guide that shows ‘where the locals really go’, the Taxi Trails app highlights the most popular destinations across the city on an interactive map. The insights are sourced by analyzing data from the 8 million trips its taxis make each year, including which places people are visiting, how often and so on.
Tourists also have the option of filtering the map to show only results from certain kinds of neighborhoods, and can discover the more offbeat destinations as well, by opting to see places that are not visited as much. The app also enables tourists to preview the destinations on Google Street View.
Taxi trails is a good example of how insights from large data sets can be turned into smart – and useful – ideas!
3. Alibaba: Data to protect reputation
E-commerce giant Alibaba accounts for about 80% of the $390 billion Chinese e-commerce market. But given its vast inventory, the company faces a higher risk of fraud: In 2014, Chinese quality authorities found that sellers on some of China’s e-commerce websites were offering counterfeit products.
In a bid to eliminate such goods from their inventory, Alibaba used big data to track down sellers with counterfeit goods.
The company invested heavily in this project, recruiting a 300-person team dedicated solely to tackling the problem of the manufacture and sale of counterfeit goods.
The team developed image-recognition software to scan products by their brand logos and thus detect fake goods. The system performs about 300 million visual checks each day. Alibaba also relies on data-mining technology to locate the suppliers of fake goods in an effort to weed out the goods from the source itself.
Since the introduction of this initiative, over 90 million products have been taken down from Alibaba platforms and about 90% of them were found to have IPR infringements.
Alibaba’s strategy brings to light how big data can be used beyond insights generation, to ensure security and safeguard reputation.
4. TrackMyMacca’s: Data to manage reputation
How exciting would it be if you knew where the ingredients in your food comes from, – if it was responsibly sourced?
In 2013, McDonald’s enabled customers in Australia to track ingredients and products, for a limited time, with the TrackMyMacca iPhone app (named after the Australian nickname for the brand, ‘Macca’).
Customers could aim their iPhone at QR codes on specially marked packaging, and the app would show where and how the ingredients in the box were processed before being prepared onsite.
People could track the food product as a whole or pick each ingredient, and the app accessed McDonald’s vast supply chain data to find the answer. Details differed by restaurants and region, ensuring people were able to track the source of the product they were actually consuming. Details included information about the farmers, bakers and even fishermen involved in the supplying of the vegetables and meat.
To make the data more engaging, McDonald’s used animation and 3D-augmented reality. Have a look at the promo video.
5. meteolytix: Data to predict sales
Businesses that have the best intelligence about fluctuating consumer demands and behavior are best poised to win. Increasingly, data is helping drive such intelligence, and can lead to better and more accurate sales predictions based on multiple variables – like the weather.
Can big data predict how many cupcakes a bakery will sell on a specific day?
For German data analysis company meteolytix the answer is yes. The company is a collaboration between WetterWelt GmbH, a supplier of location-specific weather forecasts, and analytix GmbH, a market research and data analyst company.
meteolytix uses this data to share daily sales forecasts with clients, down to the number of pieces per product. The company also considers factors such as the brand, product, branch locations, vacation dates, holidays, the local competitive environment, current market trends and so on.
According to meteolytix, its approach has helped clients improve efficiency – returns for goods have reduced by approximately one-third.
*meteolytix’s clients include a German baker who used the system to predict sales of pieces and improve efficiency in production and distribution
Data is changing the game for online publishers. Today, media houses use data analysis to guide the topics they cover, their distribution strategies and even their engagement strategies.
In March 2015, The New York Times (NYT) columnist Frank Bruni published an op-ed piece about the college admission process and its effects on parents and students alike. His piece inspired a large number of comments and social conversations, in which Bruni himself participated – responding to reader’s questions on NYT’s Facebook page.
NYT’s social media tracking team noticed this engagement helped keep the conversation active throughout the weekend, and directed Bruni’s attention to another site of social conversations.
NYT’s audience development team had noticed an unusually high amount of referral traffic was coming from political commentator Laura Ingraham’s Facebook page. They asked Bruni to engage on Laura’s page, and interact with her audience.
The result? The article stayed in the Top 20 most-visited articles on the NYT website for five days, with approximately 70% of all visits coming from social media.
Real-time listening helped the publisher identify where the influential conversations were taking place, and which conversations to engage around.
This post is part of our People’s Insights report Data In. Data Out. Transforming Big Data into Smart Ideas.