In the UK alone, the effective use of data could create £66 billion of new business and innovation opportunities, as noted by the Department for Digital, Culture, Media & Sport in its 2017 policy paper. What’s more, much of this could be made possible by leveraging the data that companies already hold.
Many companies are now waking up to the potential big data has to inform their business strategies, but they may not understand how this could work in practice. They may be sitting on a wealth of data but lack the knowledge and skills necessary to unlock it and put it to work. As the same paper says, the vast majority of existing datasets are nowhere near fully exploited, with most companies estimating that they are analysing just 12% of their data.
We may not realise it, but we’re already surrounded by innovative uses of big data and data analytics, many of which enhance and individualise the consumer experience. We’ve taken a closer look at some recognisable ones below.
Data analytics and the customer experience
Customised promotions through Ikea’s Family loyalty scheme
Like many retailers, Swedish furniture giant Ikea runs a loyalty scheme to encourage repeat and regular custom. It’s Ikea ‘Family’ scheme grants access to exclusive offers and promotions, in-store events and expert advice, but there’s more to it than simply providing the same perks to every member.
Card holders earn benefits with each store visit and purchase, and Ikea are able to learn more about them and their shopping habits with every new transaction. By analysing and combining this data with demographic and preferential information customers submit when signing up, Ikea creates individual and real-time data profiles on each member of the scheme. They then use this data to tailor promotional vouchers specifically to members via email and text marketing, offering each one personalised discounts on products they actually buy.
Ikea Family members get a bespoke customer experience, while the scheme has given Ikea a set of customers that are 35% more profitable than non-members for relatively low investment.
Spotify and its ability to recommend new music
It’s not just retailers that can benefit from big data analytics; service-providers can also use it to strengthen consumer relationships and keep them coming back. Music streaming service Spotify employs data and machine learning to customise its offering in a similar way to Ikea, taking hyper-personalisation to another level.
Spotify is able to recommend undiscovered musical preferences to its users with startling accuracy. Artificially intelligent algorithms capable of deep learning, analyse data points that the music users choose to listen to, as well as those they skip and replay, and then use these to curate playlists of similar, yet never-before-played songs.
Every week, Spotify Premium users get a unique, personalised playlist of fresh music (Discover Weekly), as well as a daily updated playlists of music from favourite artists and those that are similar (Daily Mix). Particularly popular is the Time Capsule feature – a bespoke collection of older songs that Spotify hopes will take users down memory lane, having used data from their profiles to identify music that they were likely to have listened to as teenagers.
Many users also log in to the system via their Facebook accounts, which gives Spotify another source of preferential data to mine. This data-led approach is definitely working; as of August 2017 Spotify had 60 million subscribers worldwide, compared to Apple Music’s 27 million.
How the British Museum optimises visitor experience
Based in London, the British Museum spans 94 galleries across 807,000 square feet. That’s a lot of ground to cover, so museum managers have used data to track how most of its 6.5 million yearly visitors choose to make their way around the building.
Using anonymous information gathered over time through features like their audio guides (with visitors’ consent) together with freely-available open data from Wi-Fi hotspots, the British Museum can now see which exhibits visitors spend most time at and which routes they take. They used powerful interactive dashboards and visualisation tools to identify these trends and patterns in the data, and then ensured that the museum was providing the right exhibit information in the right places.
This initiative is part of a drive to make the British Museum a completely data-driven organisation by 2018, one that uses data to create the experiences visitors really want.
Adding value through data analytics
Each of these examples has allowed the organisations in question to offer their consumers a more relevant, customised service, pinpointing the kind of offering they want and making sure they provide it.
Manipulating data in this way is an important aspect of Deriving Business Value from Data Science. You can discover the methods businesses are using to make sense of data, and how to deploy this to reveal opportunities for adding value, improving customer service and increasing return on investment. In many cases, getting the most from big data requires a shift in organisational and operational approach for a business so understanding key business processes such as Opertations Management and Stratergic Change Management is essential to effectively utilise your company’s data.