Contributed by Dave Sanderson
It’s clear that the volume of data is increasing, and shows no signs of slowing down. As such, brands will have to move beyond using big data just to create internal reports to satisfy internal stakeholders; they need to start leveraging cross-departmental data to deliver insights and shape the customer experience better.
This comes at the back of companies undergoing a digital transformation in the Asia Pacific, where the first wave of change was a migration towards the cloud and expanding their digital capabilities. With the right tools, the next wave will see marketers using both public and owned data to drive measurable outcomes, and enhance customer experience through personalisation.
It all sounds great, but where can companies start? Here are some big brands that have successfully managed to embrace data analytics to fuel efficiency and growth, with proven results:
The L’Oreal Group – Spearheading customer engagement through social listening
As L’Oreal sells via retailers, it led to a large gap between the beauty brand and its customers. While it may be understandable given the size of the beauty conglomerate, the cosmetics giant is aware that customers are key, and were looking to change the dynamic with its new customer command center.
Using CRM technology, L’Oreal started analyzing its social media platforms – from tweets to Facebook posts, as well as product reviews and news stories. When necessary, for example, a bad review is flagged, posts are routed internally to an appropriate employee in the command center who can engage with the writer. This has led to a shift in brand awareness and customer loyalty.
“Your most unhappy customers, are your greatest source of learning ” – Bill Gates.
Dr. Pepper Snapple Group – Driving contextual relevance through machine learning
We have all heard about manual processes and legacy systems. The sales staff at Dr. Pepper Snapple Group were still referring to large binders filled with customer data and notes on sales and promotions; a massively painful and inefficient process. Fast forward to today, instead of a binder, sales staff are armed with iPads that tell them everything they need to know, from what stores they need to visit, to the right offers to give retailers and many other crucial metrics. How is this possible?
The platform they use is equipped with machine learning and business intelligence analytics on an app. When launched, the app funnels recommendations and a daily operational scorecard that workers can use to gauge their performance against their expected projections, meeting or missing their KPIs, including insights that will guide them on how they can change course to achieve better outcomes.
Burberry Group plc – Personalising customer experiences with RFID
Burberry decided that they would create a richer shopping experience for customers through radio frequency identification (RFID) technology in its stores.
When a customer walks by a display screen with an item in hand, the RFID tag is triggered, and a video showing how the item was made will be shown, together with complementary products. For example, if a customer picks up a signature trench coat and triggers a video, accessories such as a matching scarf or another best-selling item would be proffered up as well. Plus, if the customer wishes to have their tastes catered to specifically, the RFID tags can also help create a customer profile by keeping records of what they have previously tried on.
Combined, it helps the store through soft selling items that customers are actually interested in, without customers feeling they are being shadowed by sales assistants, thus allowing them to browse and try at their leisure.
As these brands have shown, there’s a number of ways that technology can be utilized to collate big data and then applied based on the insights gathered. The trick is to first identify a focus before implementation.
While CMOs and CIOs alike are embracing data analytics to drive efficiency and growth, in order to produce measurable results, efforts must first be made to get the foundation right. This will require a lot of inter-department cooperation and clarity across all business units. Once focused KPIs are set and implementation complete, successfully tapping into data analytics and machine learning to boost revenues becomes second nature as opposed to a pleasant surprise.
This post about big data and customer experience originally appeared on the Nugit website and is republished with their permission.
To read more about big data and other technology verticals, read Voices.
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About Dave Sanderson
Dave established Nugit after 13 years of experience leading Digital Marketing teams in Sydney, Hong Kong, and Singapore. It was during this time running digital campaigns for some of the largest brands in the region that he grew frustrated by the limitations of dashboards and spreadsheets, and the amount of data that was wasted. Quitting the corporate life to solve this problem with a new approach, he spent the first few months building Nugit on the beach in Malaysia, where his vision of the Nugit Data Storytelling platform materialised. He’s passionate about utilising AI-technology to do tasks previously reserved for humans and enable data analytics to scale infinitely in the form of stories that have a proper narrative, context, and purpose. Leading a team of 40+ data scientists, designers, engineers and storytellers, Dave’s goal is to build a world-class SaaS company born in Singapore.