From the beginning, the data-rich nature of the retail sector has placed it at the forefront of the big data revolution. Retailers have access to an enormous amount of data, both internally and externally. We’ve got virtually every data-related virtue we could ask for – volume, velocity, variety and veracity. But with all this data, are we really mining it for all it’s worth?
There are many opportunities for big data across the grocery-retail/ FMCG industry. The key is understanding where they lie and what hurdles businesses need to overcome to maximize their value.
What products should we stock? What’s the best price? How can we improve the customer experience? Retailers have faced, and always will face, this same set of questions. But with technology rapidly amplifying customer expectations, there is less room for error. This is where big data comes in.
In the case of product range decisions, retailers have traditionally relied on supplier information, transaction and market-share data. However, the big data revolution has seen newer sources such as web data, competitive data and consumer-intent signals being synthesised to give deeper and more holistic insights to decisions around product ranges.
Determining the right price, as opposed to the lowest, is another concern that can be addressed with big data. Instead of competing in a race to the bottom, retailers can leverage data and algorithms to test and understand the price consumers are willing to pay. These investments need to be capable of uncovering intelligence and insights at scale and, in some cases, for near real-time applications.
Finally, big data offers ample opportunity to improve the customer experience. An oft-quoted example for big-data application is offering a text-alert coupon to shoppers when they are at a shopping aisle. But is this what they really want? And if the coupon is irrelevant, you may well have succeeded in greatly annoying your customer.
There are several opportunities in leveraging big data to provide better customer experiences both pre- and post-sale. For example, predictive algorithms can help with staffing, inventory management and stock-out problem-solving, thereby lowering concession rates.
Often, the greatest barrier to capitalising on these opportunities is the age-old organisation bottleneck. Bottlenecks originate from the top. Engendering data-driven decisions will inevitably require the attention and commitment of senior management. Unfortunately, in many cases senior leadership is either unaware of the possibilities of big data or simply relegates it to the bottom of their to-do lists.
Other issues with the adaption to big data-driven decisions include an uninviting organisation structure, lack of proven success, incentives, clean data or analytics resources, data silos, or an inadequate data processing infrastructure. These hurdles can be overcome from the top, which is why it is so important for decision-makers to be involved in this discussion.
The big data hype is over. It’s time for big data-driven decisions to start driving better business performance.
This blog first appeared in the May issue of Retail World, Australia. (pg. 21)