As an industry, retail has relied deeply on data, across a variety of applications, for many decades. With the heavily optimised supply chain, distribution, and marketing processes that are common in retail today this dependence takes on significance as a strategic pillar of business competency. With that in mind, it’s imperative that retail leaders look beyond day-to-day operational efficiencies and keep an eye out for “red flags” or early indicators of changes/risks affecting this important company asset.
Further, with dramatic advances in technologies such as Artificial Intelligence opening up unforeseen opportunities, it is important that retailers re-invigorate their data and analytics programs to maintain differentiation and respond to increased competitive pressures.
On this basis, one might assert that data (and insight) consumption, as well as data governance, are well-established and mature undertakings within retail. So, what then might red flags look like? Several prominent ones come to mind, including:
- Data as a liability: Data security and privacy have always been non-negotiable considerations for retailers, particularly with regard to customer data. With increasing cybersecurity threats (and incidents) and more vigorous (and punitive) enforcement by regulators, the risks of handling data have increased to the point where it can be considered both a strategic asset and simultaneously a liability. A clear red flag is when data’s burden outweighs its utility, and it is incumbent on retail data practitioners to objectively balance those priorities.
- Unreliable benchmarking: The success criteria of modern retail are based to a large degree on comparative performance and the seasonality or “momentum” of the market. Well-established metrics include comparable store performance, sales growth rate, gross margin, and customer measures such as acquisition cost and lifetime value. What happens then if those comparisons are upended by extraordinary circumstances such as what happened with the Coronavirus pandemic? Suddenly benchmarks disappear or become meaningless, with forecasting impossible. This has immediate bearing on commercial performance, with retailers suffering further with a downstream impact across the entire supply chain. While hard to foresee, the red flag here is when there is too much focus on using data and insights to drive optimisation, at the expense of resilience and flexibility.
- Pricing complexity: Pricing is an area where sophisticated retailers use a range of customer and market data to optimise their market offering, possibly even in real-time. It’s a tempting practice, given the breadth of relevant data available, and the potential gains in sales and volume. Arguably, granular, and dynamic pricing are simply additional dimensions of customer personalisation, however the red flag appears when pricing complexity circumvents clarity and customer trust/acceptance. Pricing needs to address emotional considerations including fairness, status, and value in addition to data driven elements like cost or margin.
- Disingenuous personalisation: Often considered by retailers (and marketers in particular) as the holy grail of customer engagement, data-driven personalisation remains a work in progress to my mind. For all the touchpoints and data sources available it remains frustratingly complex to construct meaningful and accurate (individual) customer journeys. Programs that operate at an aggregate or segment level can be rendered irrelevant or counterproductive when applied singularly. Unfortunately, the final mile of attribution remains a long and arduous one with the red flag in this case being the assumption that the consistent data, models, or algorithms, apply at any scale. There are no easy answers on this front – the promise of hyper-personalisation is so high that data and technology will continue to be increasingly applied, just incrementally.
- Real-time relativity: Finally, one of the major considerations for retailers is the use of real-time data to facilitate responsiveness to the market, and efficiency in supply chains and distribution. Speed can eliminate cost and enable greater opportunities and when applied to data processing, can allow more sophisticated insights at scale. That said, the challenge for retailers, or perhaps more specifically merchandisers, is to consider the relative speeds of each stage of the customer journey (or in the supporting back-end process) and implement accordingly. Real-time data is costly to manage and to consume, with the red flag being a tendency to want to do everything in real time, even when circumstances don’t justify it.
So where does this leave us? Retail is, and always will be, a heavily data-driven industry. The ease, and relevance, in which data can be applied to all aspects of the retail value chain are clear and will certainly expand and improve as time goes by.
Why worry about red flags then? As with any endeavour, especially one as lucrative as retail, it is unavoidable that practitioners must think clearly and make the most of the tools and capabilities available. In this regard, data is no different to any other input, and demands thoughtful, continuous review to ensure it is realising its potential as a crucial retail capability. The red flags above are a good starting point to ensuring the ongoing relevance and business impact of this vital corporate asset.
Brad Kasell is principal technology strategist at Domo Asia-Pacific.