Added Value Edits: Activating the Data Ecosystem
10 Jun 2016|jhall
Big Data has been a hot topic for companies for some time now, but recent surveys show how disillusioned marketers and insights professionals have become, as the promised riches have failed to emerge in the ways and at the speed people had hoped. But the principle of leveraging interlinked datasets into a powerful ecosystem still holds true: to paraphrase Bill Gates, we’ve just overestimated its impact in the short term. Which companies have made strides in this space and what are the lessons we can draw? This month’s Edits moves the conversation forward.
A Holistic Data Ecosystem
Brands need to connect meaningfully with customers, beyond the core product or service being delivered. Marketers need to develop a view of the whole person and deliver a better experience to them. Integrating disparate datasets can be the answer to building this view. It’s not easy, but it can be done.
Click to read more…
Top Tips to Win When Creating an Actionable Data Ecosytem
Building a Data Ecosystem that enables the business to take more real-time, actionable insights is the new challenge for the marketing and insight community. At the start, this can seem like a daunting and complex task due to the number of sources available.
Click to find out key guiding principles which businesses follow to create a successful Data Ecosystem.
IBM and Weather: the next big data thing
IBM’s recent acquisition of the Weather Channel is designed to enrich their analytics offering with a new source of insights for business decision-making processes – when aggregated and smartly analyzed along with other variables. From retail to commodities, they see endless application: in the case of a forthcoming weather event, an insurance provider may send targeted alerts to customers who could experience material damages.
Spotify provides artists with actionable fan analytics
Ongoing controversies make it critical for Spotify to provide artists and management companies with unique services that can support their businesses. They now have access to a high quality Fan Insights dashboard that offers a comprehensive view of fan bases. Spotify is working hard to make the information truly actionable, rather than just providing raw data. A function enabling to launch highly targeted emailing campaigns is also under development.
Starbucks to rely on multiple data sources to tailor customer experience
Customizing the customer’s experience is Starbucks favorite hobby horse – and rightly so. They are already making the most of various sources of data such as the Starbucks Rewards card, the mobile app, and everyday transactions. But Starbucks is looking beyond this, to the aggregation of external data – such as the weather or local events – to get to know their customers’ preferences better, and further customize the experience they provide.
Starwood Hotels implements dynamic pricing strategy
The Starwood chain invested in a powerful Revenue Operation System that unceasingly crunches a huge number of data coming from a variety of sources – both internal and external. The self-learning ROS is able to constantly update and optimize the hotelier’s rates. Crucially, the system is accessible to any Starwood property’s manager, so they can manually recalibrate prices if they have additional information not taken into account by the system.
Aetna delivers data-driven lifestyle recommendations
The health insurance provider recently conducted a large-scale experiment, aiming at identifying and monitoring the risk factors of metabolic syndrome – a cluster of conditions that increase the risk of heart disease, stroke and diabetes. Based on the aggregation of data such as medical and pharmacy claims, lab tests and demographics, they created a predictive model which allows them to deliver specific and customized recommendations to the patients at risk.
Luxottica targets most profitable customers
When Luxottica crunched the data they had accumulated about their 100 million customers, they segmented the huge dataset with a view to identifying their most profitable type of consumers: the omni-channel customers, who typically shop both in-store and online. The analysis has enabled the company to customize their campaigns and target these specific customers for greater marketing effectiveness.
How big data helped Marvel produce the next blockbuster
The Marvel universe has been created – and is still being enriched – from hundreds of comic books, films, and TV series. About a decade ago, in response to growing competition from DC Comics on the movies front, they decided it was time to dig deeper into that richness. Marvel compiled 70 years of character-related data and created a 3D map from which scriptwriters could draw inspiration. More importantly, they were able to decide which characters had the most potential to become the next blockbuster!
Macy’s implements consumer-centric assortments in store
The retailer figured that several variables can help understand the consumption patterns and local preferences of their customers. By conducting in-depth analyses and crossing data from sources including sales, inventories, and consumer behavior, Macy’s has been able to adopt a consumer-centric strategy in regards to stores assortments.
Get in touch if you’d like to hear how Added Value can help you think about creating and activating your data ecosystem. And visit our website for more topical news, stories and client case studies.
Written by Jonathan Hall, President North America Consulting, Added Value
Follow Jonathan on Twitter @HallCJonathan
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