How a content supply chain merges content with Big Data
BY DOUGLAS ELDRIDGE
(Published in The Produktkulturmagazin issue 2 2018)
In The Group of Analysts’ recent EDEN study, the topic of Big Data is broached as a major trend that will help advance digitalisation. The study highlights the vast volume of data across the internet – likely to reach 35 zettabytes come 2020 – and the challenges that companies have with managing only fractions of that amount of data. Advanced software is the only way that Big Data can be managed, but data without content is as useful to a marketer as content without a means to disseminate it. Big Data and content need to merge to get the most out of both. This is done with a content supply chain.
Before one can solve the business challenges of Big Data, one must understand what Big Data actually is. Big Data is not the number of contacts in a database, but rather the size of each dataset in a database. For example, a million people can fill out a form and include their name and phone number, but those million contacts do not equate to Big Data. Big Data is the term used to describe the vast amount of information that can be collected on a single contact, that when extrapolated correctly can understand target prospects and even predict their behaviour. Neither Big Data nor data interpretation are new concepts to marketers. The term Big Data was first used in 1997 and data interpretation has been a part of marketing since long before the term Big Data was ever used.
As early on as 2011, the US retail giant Target was able to accurately predict if someone was pregnant based on analysing the purchase history of 25 objects. By having the ability to understand how those items correlated they put themselves in a position to win expecting mothers with special offers and coupons. With great power comes great responsibility, and Target had to refine their marketing approach as it became “creepy” when they were able to predict pregnancies with such accuracy. This is just one example of predictive marketing based on Big Data. Big Data is collected by tracking online use, by apps that know where you are at any given moment, by what you watch, and an array of other possibilities based on industry need and user behaviour.
Retail has a lot to gain with the proper use of Big Data. In an era with declining visitors to brick-and-mortar stores and the need to stay ahead of the competition, the use of Big Data can not only lead to better marketing opportunities but better yet, an improved customer experience, which could be the differentiator that keeps them in business. Retailers that have both brick-and-mortar locations along with an ecommerce platform have an added advantage when it comes to Big Data collection, because not only can they track online behaviour and collect in-store purchases with loyalty programmes, they also have the added advantage of utilising geofencing, which means when a person enters a Wi-Fi zone with their mobile device the person can then be sent messages or their shopping pattern can be tracked so that they can be sent messages later. This data, like all data, can be used to target individuals or can be used to understand whether store displays are working.
Geofencing is a form of real time marketing that wouldn't have been possible without the recent advances in technology and the Big Data sets that preceded it. Now real-time communications, while in its infancy in offline marketing, is the standard in online marketing. Companies such as Amazon and Google know what you’ve recently seen online down to the second and are able to conveniently and subtly put relevant content in front of you at a microsecond’s notice. Both online and in the store, real-time communications can lead to a better customer experience by leading a prospect on a real journey with alternative products and possibly better offers rather than just a normal ‘click here or pick something off the shelf and checkout’ experience.
While Big Data analysation is wonderful in theory, practically speaking most companies are not equipped to handle the vast data sets that they likely have, and their content, as a result, cannot be fully utilised. According to Forrester, between 60 per cent and 73 per cent of all data within an enterprise goes unused for analytics. How much does this unused data cost companies? According to Baseline Magazine, a 10 per cent increase in data use can mean an additional 65.7 million Dollars in net income for a typical Fortune 1000 company. If the low end of the Forrester stat is accurate it can be assumed that roughly 400 million Dollars annually is lost to unused data per company.
Not only does unused data cost a company revenue, with increasing government oversight beginning with GDPR and almost certainly moving swiftly across the Atlantic, then eventually to the rest of the world, the risk of having ill-gotten data will become more than most companies can take. The penalty for GDPR can be up to 20 million Euros or 4 per cent of a company’s annual revenue, which would bankrupt many companies. Every company has the ability to overcome the challenges of Big Data. Artificial intelligence (AI) and machine learning, two buzzwords of today, will help sort the data. But, despite the autonomous nature of AI technology, it's still only a tool that needs to be both integrated properly with the rest of your technology stack and controlled and monitored by proper data analysts (experienced humans). Data analysis software is only the beginning of a broader suite necessary to accurately deliver content, both online and offline.
In marketing terms, data and content complement each other like two trouser legs, without the other one the entire thing is pointless. It should be mentioned that Big Data in certain fields can be analysed and used without content, such as doctors diagnosing diseases and insurance companies assessing risk, but for the purposes of this article, content and data require total orchestration. The only way to do that is to produce your content and turn your processes into a content supply chain.
Much like a physical supply chain which gets a product off an assembly line and to the shelves, a content supply chain gets content off the digital assembly line and to the target audience. A content supply chain requires several solutions to get the content to the right place. While content has to lie at the centre of the solution, the infrastructure that is required has to be well thought out and unique to every organisation. In most cases a digital asset management (DAM) system is the entry point. A DAM allows a company to streamline their content upon ingestion with accurate metadata fields and allows for easy content retrievals, workflows, and more and more a platform for creatives to work in.
A product information management (PIM) system is often practical as well, especially with products having replacement parts, different pricing and even varying products based on upgrade potential. While DAM and PIM are not interchangeable, when integrated properly, both can be used seamlessly with one interface.
After the DAM and PIM, content needs to go through proper workflows for an array of possible approvals, creative, legal, corporate, and so on. How content is disseminated is the next layer of the infrastructure, whether it be on social channels, email, retargeting ads, push notifications, or any other means you want to publish by. This is the disconnect for many organisations. When a company sends a tweet or sends a real-time push notification, the data has to be sent back to the same broad platform. It’s easy to have social media data in one place and email data in another and so on, but by siloing the return data the entire content supply chain is compromised.
Data and content are two of the main drivers for every organisation, by investing in infrastructure to integrate content management with data management and allowing them to function together along the same supply chain, companies will be able to manage Big Data accurately.
Censhare develop a universal, smart content management platform and provide content and marketing solutions to global brands such as Dyson, Jaguar Land Rover, Kohl’s and Hearst Media Group in order to enable digital transformation and multi-lingual personalised communication across all channels.
Picture credit © Barcroft Medi-a/Kontributor/Getty Images