The Retail industry has always been one of the strongest users of analytical techniques. However, there is always room to reconsider processes and take advantage of the latest advances. Basically every aspect of a retail business has a lot of data being collected, and has opportunities for improvement. From forecasting demand and optimizing inventory, to targeting customers for marketing campaigns, or tracking loyalty and customer value, analytics can reduce costs, increase margins and efficiency, and assist with planning for growth.

Here we can touch on just a few of the applications where analytics is most suited to help your business. We are always happy to have a chat about your unique challenges and priorities.

Forecasting is one of the most critical activities for a retail business. Variable forecast accuracy has flow-on effects on budgets, supply-chain planning, and eventually results in lost income from shortages, or excessive inventory costs. Even businesses with a sound statistical methodology sometimes struggle with modelling the impact of promotions, seasonal and holiday cycles, and the effect of competitor activities.

Often businesses who think they have a robust forecasting process still struggle with forecast accuracy, and this is usually due to poor model validation and monitoring. If you aren't tracking how accurate your forecasts are at various time horizons (1 month, 6 months, 2 years, etc), then you definitely need to review your methodology.

The physical aspect of retail stores has many opportunities for optimization. First, facility location. Where should new stores be located, based on current store locations, competitors, population density and demographics, and transportation networks?

Next, facility layout. How should stores be laid out? Which products should be placed next to each other, based on customer buying patterns? What sort of queueing system is most appropriate? For a supermarket, what is the optimal shelf-space allocation and layout?

Marketing is crucial to retail businesses. There are many analytical aspects to this. Campaign management is about choosing and tracking which customers you send which campaigns to. You don't want to spam your best customers constantly and ignore the rest. Marketing optimization is connected to this and is an analytical way of selecting the best mix of customers for each campaign, given their individual propensity to respond, expected value, channel constraints on how many of each campaign you can send, and contact rules on how often you can contact customers.

Individualised marketing has traditionally been unattainable, but now with more information known about customers collated from loyalty programs, linked transactions through credit card matching, and online behaviour, it is within reach. Real-time analytics, with alerts and automated systems can help you deliver the perfect offer to each customer at the most approriate time, and even deliver differential pricing and discounts.