Leveraging big data to improve customer experience

But what engine hides under the hood? As much as petrol gets our cars from point A to B, big data gets customers and companies to close the deal. As such, big data has become a truly valuable commodity. In the most recent Beyond Digital, the knowledge-sharing discussion club organised by digital expert Ariad, Sven Van Gucht, Chief Data Officer at online flash retailer Veepee, shed light on big data management. He was joined by other digital managers to discuss the impact of big data on the customer experience.

As Veepee’s CDO, Sven Van Gucht has introduced a data vision to transform Veepee into a data driven organisation. By providing easy to access data, meaningful insights and impactful data science solutions, the aim is to maximise the potential value of data for Veepee and its brands. “When data and analytical expertise are available in a secure and reliable way, businesses can adopt ‘data first’ as a valuable mindset,” says Sven Van Gucht.

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Impactful data science

Data science helps businesses to predict, forecast and take action. It serves as an accelerator for solving complex business processes:

Personalisation

The customer expects to be treated according to their specific needs and wishes. Therefore it’s important to make what you’re offering relevant. Machine learning algorithms help calculate and recalculate customer behaviour, context and purchase probability to optimise the positioning of banners and products to make the customer experience as personalised as possible, on desktops but certainly on mobile devices. Explains Sven, “on our platform we offer 250 sales but on a smartphone the customer only gets to see just four at a time. Scrolling down through all of them takes time so relevancy and displaying the right sales first is ever so important.”

Operational efficiency

Impactful data science boosts operational efficiency as insights can prove to be valuable in terms of pricing, production, logistics, finance, and beyond. Predictions allow for proactive and relevant decisions, for example to avoid over or underinvesting, which eventually leads to cost reduction.

Marketing

Member activation or churn avoidance needs models to target the right customers at the right time. Calculating purchase probability in order to align campaigns and increase margins and revenue is also crucial. Being able to determine the purchase probability of each customer helps monitor the interests of the best customers, increase purchases at a lower cost and avoid customer cannibalisation by optimising and targeting promotions.

“In the past we turned to voucher campaigns to activate customers who rarely made purchases. However, sending out vouchers bluntly to an entire customer base is costly because the people who are buying anyway also get a reduction. Through data science we are able to calculate purchase probability, which in turn, helps us better determine who receives a voucher and who doesn’t. As such the algorithm helps our marketing team to be as efficient as possible,” says Sven Van Gucht.

Different animal

What’s the best way to go about big data? According to Nicolas Bijvoet, Head of Product at Hello Customer, we are in fact all part of the same team. “Many companies feel the urge to reinvent the wheel in terms of big data and customer engagement. This is unnecessary because in the end, we all face the same challenges. If it were possible to map best practices and failures throughout all sectors and simulate scenarios based on heaps of anonymised data, progress would come so much easier. No more trial and error but guaranteed success stories by use of a proven approach. Also, companies always rely on their own data, but why not import outside anonymous data and patterns and learn from those?”

Jeroen Geleyn, managing partner at Ariad: “For me the overall message is don’t go for quick wins. Companies and organisations have to invest energy and resources in building a structure in terms of data governance and a solid and autonomous team. There is a tendency to go for low hanging fruit and aim for short term success but data is a very different animal. You need to have the courage to think in years and take your time to build a strong basis and scale up later.”

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