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Retailers: The secret to great customer experience is Big Data and analytics

by Monique Richards

Retailers: The secret to great customer experience is Big Data and analytics image
Retailers: The secret to great customer experience is Big Data and analytics

Big data and analytics offer retailers huge opportunities. This isn’t a new topic, but are we getting lost in the beast of information and the many options available to us?

Data Analytics and insights is not a fad — it’s here to stay and it’s going to change the digital marketing industry as we know it. Whether you like it or not, big data touches every part of the consumer journey and has the potential to transform nearly every aspect of retail marketing.

A quick guide on what makes big data so big

  • 90% of the world's data has been created in the last two years and most of it is on the go!
  • There are over 6 billion mobile subscriptions worldwide, which means that 87% of the population are engaged and actively contributing data
  • Over 1.01 billion Facebook users worldwide and 604 million of those log-in using a mobile device
  • There are over 4 million tweets per day, 84 million of these are mobile users
  • And it’s predicted to get bigger and more accessible with an anticipated 61% of people logging on via a mobile or tablet vs traditional desktop computers by 2016.

So this poses the following question:

How are retailers using this data to deliver a better targeted service to their customers?

All the current trends show that consumers want customised experiences — be it online or in store — they want the brand to know them, target accurate offers to them and make it easy to shop.

A perfect example of this is UK-based fashion house Karen Millen who use omni-channel interaction to develop brand advocates. This allows them to create tailored communications with loyal customers which in turn helps drive brand awareness and promotion through social media channels.

“The more we know about our customers – the way they shop and when, their preferred methods of communication, their buying patterns and habits – the more that allows us to tailor our client proposition.” - Andrew Ware, CFO, Karen Millen

Getting started with big data can be overwhelming, so where should you start?

When you look at case studies on big data, analytics and retail there is a theme:

  • Be clear on your objectives. You need to know what you want to understand before you start.
  • Don’t jump in headfirst hoping the answers will present themselves in a huge sea of data especially when you don’t know the questions. I promise you will get lost and come out more confused than when you started. This is costly both in time and money
  • Use predictive analytics to know and understand what’s next. It's no secret that retailers need to be quick to understand trends, and with great insights, brands can have merchandising teams ready for increased supply and demand of new or favourite stock items. This allows you to set trends not follow them
  • Increase sales. You just can’t guess any more and you cannot assume a customer comes to you based solely on your brand. You now have to get savvy and start using data to understand your customers preferences, their buying habits and how they like to interact with you. All of this can allow you to map the highest value customer segments and maximise ROI. By taking a deep look at how you can communicate effectively with all of your customers almost individually, you can look to build a genuine affinity between them and your brand.
  • Customer service. With a greater purchasing platform there is an increased risk of things going wrong - and there are so many channels for the customer to talk about their experience. Make sure that your online experience mirrors your in-store one. Can your customers reach you easily and is your response time rapid? Online chat or community managers can guide, support and resolve your customer queries effortlessly.

Retailers that leverage big data will design and procure products that are embraced more by consumers, better anticipate and respond to market shifts, and engage consumers with predictable results. This means fewer stockouts, higher visit to buy ratios, bigger basket sizes and other performance measures that can be improved with better data.

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