How Retailers Can Separate The Right Data Sets From The Clutter

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In this opinion piece, Cameron Curtis (pictured below), general manager for Australia and New Zealand at Near, explains the three factors retailers need to consider when they are identifying the right data sets from the clutter.

Cameron Curtis

From understanding sale slumps to deciding on strategies that will likely push revenue through the roof, data helps to explain why certain actions and events take place in the world of retail. Yet, analysing data itself isn’t enough. It’s vital to have the right data from the start.

Approximately 90 per cent of the world’s data today was generated within the last decade, and at a rate of 2.5 quintillion bytes per minute, that’s a ton of data to analyse. So much data creates a heavy load, or ‘data dump’. This data dump makes it difficult for most retailers to make sense of consumer behaviour. But, all data is not created equal. It’s vital for retailers to leverage the right data and analytical platforms to enhance planning and make smarter decisions.

Thanks to social media, location information, connected devices and other technology, there are tons more sets of data to mine. But what is the right data? It essentially boils down to the information that your enterprise requires to understand your customers or potential audience, and drive specific decisions.

Here are the three factors retailers need to consider when they are identifying the right data sets from the clutter:

  1. Your retail business

 Retailers in different businesses define the right data in different ways. For example, a retailer in the fashion industry requires data that unlocks purchase history, spending power, interests, purchase frequency, competitor store visitation etc. A mix of first-party data and offline consumer location with context will help fashion retailers get the answers they are looking for. The automotive industry may include return per vehicle and defective unit rates to the above and may have store conversion as a number they want to track. For a supermarket, the SKU volume is of importance and they would like to measure impact of promotional sales on inventory movement to capture ROI. 

  1. Product pricing 

Pricing of the products you are retailing impacts the data since it defines your audience. Consumers have varying income levels, so pricing has a major role to play in decisions such as store placement and stock inventory movement. Retailer of luxury items may consider the average sale price per unit sold as relevant data, while retailers selling premium items may include retail conversion rate, and retailers of budget items who are concerned with the volume of purchases may look at customer traffic data.

  1. Store formats

The type of analytics that a retail business needs differs according to retail formats. For example, standalone stores, malls and supermarkets may all be within the grocery or retail industry, but their analytical needs will vary. One prime example is identified in a recent Near study, which found that Macquarie Centre had double the composition of high-income shoppers compared to other Australia-based shopping centres in the study, making it the shopping centre with the largest amount of consumers in this category. So, their need for analytics could mainly revolve around the spending habits of high-income shoppers, brand and product preferences among others.

On the other hand, the study also revealed that the main group of Broadway shoppers are middle-income customers who are very connected, tech-savvy and young. With access to this information, brands can gain the necessary insight to make important decisions online expansions or launches. These insights and actions will vary for smaller formats like stand-alone stores as they may need to push extra promotions to capture footfalls.

There are certain data sets which all retailers need to analyse such as weekday versus weekend comparison footfalls to make staffing, promotion and product assortment decisions. For example, brands can use the insight that Macquarie Centre sees less foot traffic to its stores on Sundays in comparison to other days of the week to give their sales staff Sunday as their day off or go heavy with promotions to increase footfalls.

Conclusion

Data reveals a ton of insights that retailers can leverage to make smarter decisions to enhance their profits. It’s vital for retailers to streamline the vast amount of information gathered via connected devices, social media and other forms of technology, and leverage the right data and analytics to stay competitive and enhance their profits.

 

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Cameron Curtis Data Near reailers retail analytics

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