Almost every element of retail has been transformed by digital technologies and by the disruption of the pandemic. Now retailers are grappling with changed demand patterns, inflation, a heightened battle for customer loyalty, all while balancing evolving ecommerce expectations with great in-store service. Stocking and staffing right is essential for happy, loyal customers so retailers are constantly seeking to improve their forecasting and planning capabilities.
The importance of accurate demand forecasting itself is not revolutionary, but thinking beyond historical data and weekly or monthly rolling averages is a boon for retailers, which are leveraging dynamic, predictive data to better understand what’s driving consumer demand. Despite the fact that forecasting models have evolved substantially, most companies are yet to have zeroed in on how real world, external intelligence dramatically improves forecasting accuracy. But it equips businesses with the insights needed to make future-looking decisions without relying on singular, ever-changing variables and with confidence.
The Importance of Dynamic Forecasting
Planning and forecasting decisions are the heartbeat of every organization – big or small. If you miss the mark on demand forecasting, you end up dealing with either too much or not enough supply – both in terms of physical product and staff. Yet, many retailers rely solely on historical sales data and seasonality to inform forecasting, failing to consider incidents occurring in real time as well as in the future that will have a massive impact on demand for their business’ goods and services. In our increasingly dynamic world, making decisions based on last month or year’s data doesn’t cut it anymore. From scheduled conferences to severe weather conditions, to unexpected school closures – there are countless external forces that impact demand, and there is no “one size fits all” solution to planning for them. Businesses that fail to consider these types of external forces and their impact on demand end up losing billions in revenue.
These types of events vary in impact, so your event data source needs to be verified and enriched. Retailers must think beyond when an event is occurring and what the venue size is, and instead consider what the actual attendance is likely to be (spoiler alert – the venue’s capacity rarely equates to the true number of attendees); whether there are other smaller-scale events taking place in the same area simultaneously; where foot traffic will likely lead following the event, and more. Going beyond date, time and title is what really helps to eliminate guesswork and enable accurate forecasts for every location you operate in.
This kind of dynamic demand forecasting gives businesses the ability to not only track these anomalies, but gain a holistic understanding of their impact and prepare accordingly. Leveraging modern solutions for generating forecast grade data that delivers this comprehensive view unlocks powerful insights that can ultimately save a business significant time and revenue.
Why Retailers Need Smart Data The Most
For retailers that often have multiple locations nationwide that face varying demand indicators, having responsive forecasting mechanisms in place is absolutely vital. It is a balancing act. Retailers must have enough inventory to stock their shelves in order to keep their customers happy, but must also be sure not to over forecast, driving needless cost of excess stock and staff hours.
This kind of visibility goes beyond “we had a spike here last month”. That spike could have been driven by a single large-scale event like a music festival, which is fairly simple to track, or the collective impact of a series of smaller-scale events like community events, concerts, a college’s students returning to thunderstorms, and much more. The latter type of impact is significantly harder to predict, which is why it is so important to track events and demand indicators of all kinds, and at all times.
For example, a snow storm in Milwaukee may have very little impact on a consumer’s decision to visit a store for in-person shopping, so you don’t need to adjust staffing levels for that store. However, that same weather event in Nashville could very well cause a major dip in demand as residents less familiar with snow decide to hunker down at home rather than venturing out – and without that demand intelligence, you’ll end up overstaffing your Nashville store and losing revenue in the process. That’s why you need to source clean, granular data that can help your forecasting models truly understand what’s happening in every location in which you operate. Knowing why your demand fluctuates – both surges and drops – is critical for optimizing staffing, merchandising, and marketing decisions, and it all boils down to having the right data.
Dynamic Forecasting For Business Longevity
If the question is – can a major retailer get by without sourcing this kind of dynamic, real world data to feed into their forecasting models? The answer is maybe, for a little while. Would skipping this expensive step save your organization money? Only if you’re after false economies.
You must take a look at the bigger picture and your long-term goals, which then begs the question: what are you losing without having access to this kind of data? Over time, you are incurring massive losses with wasted products due to over forecasting. You are losing previously satisfied customers because their favorite product was out of stock in your store. You are losing employees because they feel underprepared and understaffed during a rush. So while it might seem like a hefty upfront cost, investing in an external data solution to improve forecasting accuracy will pay dividends in the long-run by improving core business functions like inventory and staffing management.
To put it simply: The world is only going to continue to be dynamic and more technologically driven, which means that the companies that exist within it must adapt accordingly. Using enriched, dynamic data to inform forecasting decisions is a smart and cost effective step in the right direction. Remain resilient, minimize the risk, and take the guesswork out of forecasting.
The author, Campell Brown is CEO of PredictHQ.