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Driving retail sales with big data

Visual merchandising 

Visual merchandising is an area where you can combine both sales and space planning data to produce highly efficient retail blueprints. Retailers can then work on a number of elements, including where products should be positioned, as well as the product you should present more of to customers. 

Overall, high-performing data for retail blueprints reduces customer frustration, as well as reducing congestion on the floor during peak shopping hours. And of course, for the retailer, this reduces the time taken to manually merchandise, as well as increasing sales. 

Buying

Big data pulled from a retailer’s sales and financial reports can assist retailers with understanding which products in existing stock are generating the most revenue, as well as new or related products they can introduce to their existing inventory. 

Retailers can also use data to choose products that encourage repeat buying, as well as online cart add-ons. They are then able to shift visibility to these products to maximise the shopping experience. 

Payments

Latitude’s Chief Country Officer for New Zealand, David Gelbak, says Latitude supports the retailers who partner with its Gem Visa and Genoapay products by providing comprehensive data about sales. 

The topics Latitude reports on include:

  • Anonymised demographic data about who’s buying what
  • Whether a particular promotion has boosted sales
  • How that promotion differs from previous promotions

This support means retailers can make more informed choices about their retail strategy and core sales strategy. Gelbak says Latitude can’t disclose specific details about individual customers, but it can use its data to effectively retarget shoppers throughout the consumer life cycle.

“So we might know that Consumer X bought a couch, and we know the best time to target Consumer X with another offer is Y number of days. We might say, ’Based on our predictive models, this might be a really good time to talk to Consumer X about a secondary purchase’, which really starts to bring the life cycle to fruition for our retailers because we’ve got a lot of behavioural data.”

Many of Latitude’s consumer credit products are also scheme enabled, giving it broader visibility across their day-to-day purchases. With more than 400,000 customers in New Zealand,  it can start to aggregate pools of data over time and provide deep, valuable insights to retailers.

Customer personas

Data is integral to figure out the different types of customers a retailer has, as well as what motivates these customers to buy. For example, some customers will choose to purchase from a store because it stocks a particular product, whereas other customers may only be motivated by regular deals.  

Data can also assess the obstacles that prevent a customer from completing a sale. For example, if the product materials don’t match a customer’s values, or if the cost of shipping is perceived as unreasonable. 

Thirdly, data can be utilised to gain insight into customer expectations when purchasing from retailers. This may be the way the product is presented to customers upon delivery or the longevity of the product. 

Importantly these insights then allow retailers to adapt their messaging to different customer segments.

Predictive pricing

Predictive technology uses data to optimise the pricing of products, for example, based on the market rates and whether competitors are selling the same product but on sale. 

Ultimately, the goal for retailers when using big data is to reduce inefficiencies and increase their revenue. Although these insights are produced by data, it’s about the human behind the data at the end of the day, reminding us that customer experience in retail is king. 

This story was created with the support of Gem powered by Latitude.

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