With the festive season nearly upon us, the last thing buyers want to see are empty shelves – both on the high street and online.
You don’t need to be told that the festive season is critical for retailers – with many generating a huge chunk of their annual sales during this time.
But the purse strings of consumers are tighter than ever, which presents a big challenge. In fact, GlobalData predicts UK retail growth of just 3.4% this Christmas. Compare that to 6.2% in 2022.
Between the glum closure of high street stores and the rising cost of living for more frugal customers – there’s already so much stacked against the retail industry this Christmas.
With all that in mind, the last thing any merchant needs right now is to be faced with stockouts. To prevent this festive faux pas, retailers are having to be smarter and savvier than ever with forecasting to prevent replenishment emergencies.
Luckily, there’s a whole lot of data solutions, AI tools, and machine-based resources that can step in and put a stop to stock outs.
A multitude of stresses
Let’s face it – stockouts are bad news all around. Firstly, they can cause lost sales, as customers may turn to competitors when the desired product is unavailable.
They also lead to frustrated customers, fuelling negative word-of-mouth and causing reputation damage. Lack of stock can result in missed opportunities for cross-selling and upselling, with customers unable to purchase complementary products.
As if all that wasn’t bad enough – they can seriously decrease customer satisfaction, leading to dips in customer loyalty, with customers going elsewhere to more reliable (and replenished!) competitors.
Why do stockouts happen?
There are several reasons why a retailer might experience stock shortages. Often, supply chain hiccups can mean product availability takes a real hit. There are other factors though. These can include:
- Sudden shifts in customer behaviour
- Discrepancies between digital and physical inventory data
- Inaccurate sales forecasting
- Logistics problems with suppliers
- Just-in-time supply policies
Whatever the reason for a stock shortage, when they do happen, it’s time to act fast. And with the fast acting, intuitive nature of AI technology – more and more retailers are turning to the power of AI to help prevent stressful stockouts in the lead up to the December madness.
Why turn to AI for help?
Machine learning, a branch of AI, offers a solution by analysing data and making accurate forecasts.
AI not only combats stock-outs but also addresses overstock, which poses a different kind of financial and environmental risk. By enhancing sales forecasting, machine learning can lead to improved sales performance, better inventory management, insight into customer behaviours, optimised delivery and supply chain management, and increased customer satisfaction. What’s not to love?
The process of stockout prevention with AI
Preventing stock-outs requires establishing a robust process. This includes anticipating stock needs and good communication within the supply chain. When the two are combined, you’re sure to minimise errors.
Many big player online retailers like Amazon have built their business on investing in this approach and base their ways of working around these algorithms.
But implementing AI is not as simple as it sounds. In fact, for AI to do the trick, you need to think about some key steps first and set the stage for the machines to do some heavy lifting.
It's important to think about the following:
- Start with a goal
Before AI can really do its thing for businesses this Christmas - companies must set clear goals. Is the aim to optimise inventory and prevent stock-outs? Understand evolving customer buying behaviour? Avoid overstock and financial losses?
To really reap the full benefits of machine learning, an analysis model that fits the company's objectives is crucial. And the more this model aligns with the company's structure and goals, the more precise the results it’s sure to yield.
- Lay the groundwork with data
The core principle of AI for stopping stock-outs is (no shock) based on data analysis. Whether this data is external or internal to the company. Before choosing a solution to optimise supply, retailers need to have enough information – or make serious moves to collect it, if they don’t already.
Internal data can cover product details, prices, promotions, and points of sale. External data can involve things such as seasonal trends (Christmas, Valentine's Day, etc.), economic and health contexts, weather patterns, and competitor insights.
- Training AI with acquired data
After obtaining internal and external data and determining the company's objective, the next step involves training the AI and the chosen model. For instance, to teach AI to recognise a house, it must analyse numerous property photos.
Similarly, when aiming to prevent stock-outs or overstock, the AI operates on the same principle. Inputting the collected data into the appropriate algorithm allows it to "train" itself, ultimately providing compelling results. Only after this process can machine learning generate predictions for the specific issue. In essence, AI-driven inventory optimisation is a time-consuming process that cannot be rushed within a few days.
- AI Prediction analysis
After AI generates predictions, they need thorough comprehension and examination to draw the right conclusions. This bit involves real experts and specialised tools for result visualisation and interpretation. Adapting this data to the company's expectations and underlying issues is also crucial.
This step demands advanced skills and expertise, particularly in data and forecast analysis, rather than improvisation.
Putting AI on the wish list
All in all, retailers can gain a significant competitive edge through AI - especially when it comes to the prevention of stockouts during this upcoming festive season. It’s my belief that we’re likely to witness a clear distinction between those rapidly advancing their data capabilities and embracing AI and those falling behind.
The upcoming results will demonstrate the effectiveness of decision makers, and the proof will most certainly be in the pudding when the festive rush is over.
But as the industry gears up for the retail rush - considering a partnership with data intelligence experts is probably the first thing to ask for this Christmas.
About the author: Pete Denby is chief commercial officer at decision intelligence platform Hyperfinity.
