Despite the hype around e-commerce, businesses must understand there is still plenty of life left in bricks-and-mortar stores: 93 per cent of total retail sales worldwide still take place in-store, and customers are willing to pay 50 per cent more for items they can touch and see*.
This doesn’t mean retailers can ignore online channels. Retailers need to understand their customers and meet their expectations, regardless of where they are buying. To do this, it is important to understand customer behaviour. Data analytics are powerful tools to unlock the secrets of customer behaviour.
Alec Gardner, general manager, Teradata, said, “Retailers have an increasing amount of information at their disposal thanks to the data-capture capabilities opened up by online channels. The challenge is to analyse that data effectively and act according to the insights that data delivers.”
Teradata advises four ways advanced data analytics can transform the profitability of physical stores:
1) Have the right stock in the right place at the right time
Data analytics can provide the tools to help retail outlets understand what stock they should have available.
Alec Gardner said, “Most retail chains develop assortment plans using high-level historical sales with some rules-based customisation across store clusters. Unfortunately, this often results in poor inventory performance and customer dissatisfaction because the plans don’t account for variations in demand.
“Data analytics has emerged as a way to better understand where and when to place certain stock items.”
Advanced analytics can also be used to help inform retailers’ scenarios planning, which provide detailed information with which to make decisions as far reaching as stock sizes based on customer demographics, outlet locations, and range.
2) Price it right
Despite the availability of granular data around price and sales, many retail pricing decisions are still based on past experience and spreadsheet analysis of summarised data.
Alec Gardner said, “If retailers use price sensitivity analysis on their granular data, they could be far more precise about which prices should go up, by how much, and at what time. This approach to pricing could yield incremental revenue for the company.”
3) Maximise the impact of promotions
Alec Gardner said, “Promotions should be set around special events, such as Christmas, to take advantage of additional foot traffic at those times. You should also make sure there is enough stock on hand to cope with the increased demand: one third of promotions don’t yield the results expected because of insufficient stock.
“Advanced analytics can give retailers the fine-grain information they need to determine the most appropriate stock levels at the most appropriate time of year, be it Christmas, Easter, holiday seasons, or other special dates in the calendar.”
4) Get value for money on marketing spend
Extra marketing spend doesn’t always translate into additional sales. Businesses must understand the factors that contribute to the success of marketing efforts. Data analytics can work to understand how much Return on Investment (ROI) there is for marketing dollars spent.
Alec Gardner said, “Retailers now have access to increasingly granular operational data such as point of sales (POS) information and inventory requirements. When combined with other data, such as catalogue promotions and media consumption, and viewed through the lens of the right analytics, retailers can understand customer buying behaviour at stores much better.”