Boosting retail store profitability: how advanced data analytics can help

Despite the rampant growth of e-commerce, there is still plenty of life left in bricks-and-mortar stores. The latest research by the Australian Bureau of Statistics (ABS) has shown that online retail turnover contributed just 3.1 per cent of total retail turnover in Australia during July 2015.

In fact, according to analysis by National Australia Bank, online sales in Australia fell by 1.4 per cent in July, compared to the previous month. Globally, the vast majority (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.

Despite these figures, bricks-and-mortar retailers don’t have the luxury to be complacent. Overall, online retail sales are growing in Australia. While physical stores remain dominant in the broader local retail landscape, they need to take action to differentiate the bricks and mortar experience and protect themselves against the encroaching prevalence of pure online retailers.

To continue to appeal to shoppers, retailers need to understand their customers and meet their expectations, regardless of where they are buying. Fortunately, retailers have an unprecedented number of digital tools with which to do this. Data analytics, along with mobility, are perhaps the most powerful tools retailers now have at their disposal to truly unlock the secrets of customer behaviour.

Retailers have an increasing amount of information to draw upon thanks to the data-capture capabilities opened up by online channels, electronic payment methods, social media interactions, beacon technologies and mobile device activity. This means they can increasingly know, rather than infer, how and why customers are behaving. The challenge, however, is to analyse that data effectively and act according to the insights that data delivers.

By using data analytics, retailers can begin to understand what customers want before they ask, and how they’d like to interact before they even walk into the store.

Professional services and analyst firm, PricewaterhouseCoopers (PwC), calls this ‘consumer adaptive retailing,’ where businesses provide a seamless customer experience across multiple touch-points by integrating all processes and business systems. However, without adequate data and the analysis tools with which to understand it, this approach could leave retailers fumbling over how to adapt their approach to each customer.

Big data, and data analytics in general, play a big part in PwC’s consumer adaptive retailing model. Although many traditional retailers recognise the importance and value of the data produced by digital channels and e-commerce portals, few are leveraging it to their advantage.

Retailers can integrate data analytics insights into business processes and systems to better tailor their product offering, increase relevance and adjust their customer service methods. This tends to engender better customer engagement, improve service level and, therefore, increase sales.

There are four key ways advanced data analytics can transform physical stores’ profitability:

1. Have the right stock in the right place at the right time

Data analytics can help retail outlets understand what stock they should have available. Data analytics has also emerged as a way to better understand where and when to place certain stock items.

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.

Advanced analytics can also help inform retailers’ scenarios planning, which provides detailed information for decisions-making as far-reaching as stock sizes based on customer demographics, outlet locations, and range. In the Australian market in particular, there is a significant opportunity to better optimise ratio pack provision to improve service levels and to reduce markdowns and wastage of over provisioned, irrelevant items.

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.

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. In store digital technologies can enable faster turnaround in on-shelf pricing.

3. Maximise the impact of promotions

One third of promotions don’t yield the results expected because of insufficient stock which continues to represent a significant area for optimisation. Promotions should first and foremost be relevant to the customers expressed or predicted need. In bricks and mortar retail, many are set around special events, such as Christmas, to take advantage of additional foot traffic at those times.

Advanced analytics can give retailers the fine-grain information they need to determine the most appropriate stock levels to align with promotions currently in market, be it Christmas, Easter, holiday seasons, or other special dates in the calendar. Traditional retail modelling techniques can now be significantly enhanced and run at much larger scale to enable wider and deeper data sets to be brought in. This enables retailers to modify analyses to allow for specific anomalies in market conditions that could exist at the time of promotion.

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 and attribute the value across the full lifecycle of a multi-channel campaign.

Retailers now have access to increasingly detailed operational data such as point of sales (POS) information and inventory requirements. When analysed at scale and 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.

About the author:

Alec Gardner is general manager, Advanced Analytics, ANZ, Teradata. Alec has 15 years’ experience in the Business Intelligence and Analytics market, specialising in customer performance management strategy and execution.

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