How your data can turn Black Friday into a White Christmas Returns are a huge area of concern for many retailers; indeed, the sale has not been completed until the customer actually decides to keep the product. But with Black Friday around the corner, and all of the flash buying that entails, can you predict what products are going to cause you the most problems, or even each which customers could give you a headache?
In the run up to Christmas 2013, what would have influenced you to purchase items on your local high street, rather than online? The UK’s shopping habits have changed beyond recognition over the past decade. The high street’s continuing decline at the expense of global online retailers is well documented. Even with recent and much-publicised government initiatives, such as the Mary Portas-led review, which are intended to drive footfall back to local centres, most are still struggling with shop closures and high vacancy rates. Acxiom polled 2,582 online shoppers in late October and asked what factors would influence them to make purchases on their local high street rather than online in the run-up to Christmas 2013. The results reveal that local authorities […] Continue Reading
Big data is now a commonly used term within the business world and many companies are implementing solutions to make the most of the untapped data within their systems. It’s no longer ‘new,’ but how businesses are choosing to use this data is changing. Beyond traditional uses of big data there are exciting ways for organisations of all size to better leverage the information gleaned to make more informed decisions and ultimately, run their business better. Typically, big data is seen as a huge mass of information which businesses can mine to better understand customers, make financial predictions and improve sales forecasting. These are of course obvious uses and ones which remain hugely important and fundamental to business success. However, […] Continue Reading
Data generated by online retail is very different, and much more difficult because the ‘inputs’ of online retail are both more numerous and more changeable. Online there are fewer upfront constraints on scope or reach. Combine this with the almost limitless possibilities for adjusting the hundreds of day-to-day activities – keyword bids, navigation, delivery charges, sort orders, prices, marketing, promotions or customer emails. The consequence is that the data is considerably more complex – there is more data, more often, from more interconnected sources. The ‘outputs’ – sales, conversion rate, average order value – are as a result unhelpful, as each can be affected by a wide variety of causes. The outputs online tell you what’s happened but give no […] Continue Reading