5 Ways Retailers Can Use Predictive Data Analytics to Prepare for Warmer Weather

May 7, 2020

Retail businesses have the ability to generate an abundance of data points on their customers daily. With all of this data being collected, an opportunity arises where retailers can use predictive data analytics to better understand consumer wants and navigate future market trends. By doing this, retailers can derive the best value of all the data collected and ensure better sales outcomes for their stores.

With predictive analytics, retailers can use data from the past to predict future sales growth and prepare their stores for warmer weather and the transition to summer-focused merchandise. This preparedness can help retailers stay ahead of the curve and compete effectively within the market.

Here are five ways predictive data analytics can bring value to retail as the summer season begins:

5 Ways Retailers Can Use Predictive Data Analytics to Prepare for Warmer Weather

Inventory Management

Historical data can help in developing a retail strategy and ensuring there is enough inventory in stock for warmer weather. This includes analyzing what products have been popular during past seasons and what channels customers are using to purchase these items. For example, smaller items may sell better in stores and larger items that require delivery might sell better online. Retailers should be cognizant of these trends and stock retail locations for in-store purchases or warehouses for online purchase and delivery accordingly to ensure order fulfillment.

In addition to trend analysis, retailers should try to avoid overstocking inventory. U.S. retailers collectively lose an estimated $130 billion dollars due to overstocking. Retailers without a sound strategy will often overstock inventory because they know shoppers will be making more purchases come warmer weather, but they fail to capitalize on specific items that sell well during the summer months.

Customer Behavior

Location data provides valuable insight into how customers interact with retail brands and engage with products. For example, are people traveling to the local pool from the store? If so, there is an opportunity to workout a partnership with the pool to drive up awareness and business for both parties.

Retailers can gain an even deeper understanding of the customer with a further application of location data – one that reveals consumer intelligence. With verifiable, accurate data, a retailer can discover even more about their customers by learning their interests. An individual who likes to go snorkeling frequently would be more inclined to shop for water gear, as opposed to someone who simply enjoys sitting by the pool. Understanding a customer’s interest adds the ‘why’, giving context to other types of data, such as demographics and customer spending habits.

Pricing Decisions

Artificial intelligence and predictive analytics can track store inventory levels, competitor prices, and collate demand to determine what prices should look like at a given store during the summer season. Being proactive in moving prices can help differentiate store analytics and give you better control over promotions while staying a step ahead of the industry. Retail has become as much about anticipating customers’ needs as it is about simply stocking nice products. Companies that innovate with the times and harness analytics can optimize their efforts and garner better results thanks to proactive strategies emerging from real-time insights.

Customer Service

As predictive analytics helps companies in the retail industry further understand their customers, data also helps them develop a marketing plan. Before talking to the customer, it is important to know about their interests. By leveraging predictive analytics, retailers can help their customers make informed decisions, which can help brands improve their social media responses and answer on-site queries.

Retailers can also send customers information about potential products of interest. Customers who previously abandoned their cart may be more inclined to complete the purchase. However, retailers don’t always factor in seasonality to the relevance of offers. Instead of using simple retargeting and remarketing techniques that show the same products of interest, retailers should look toward shifting their strategy to focus on products that might be of interest based on the time of year.

In-Store Personalization

To optimize merchandising tactics, in-store personalization can greatly help retailers. They can personalize the in-store experience to establish and drive loyalty by giving offers to incentivize frequent consumers to make more purchases thereby achieving higher sales across all channels.

These insights, which can be obtained from sources like loyalty cards and mobile apps, can help increase promotional effectiveness and drive cross-selling. Using predictive analytics, retailers hold the ability to personalize in-store services for customers by using their online purchase and browsing history to identify their needs and interests. This is especially useful to drive impulsive purchases.

When you’re ready to learn more about how data analytics can benefit your retail business, contact a Gravy location intelligence expert today.



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