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Top 5 Ways Predictive Analytics Helps Retailers on Back-to-School

Aaron

Back-to-school is one of the busiest seasonal events for retailers all year, as it serves as the second-largest selling season after the winter holidays. Yet, with thousands of customers rushing at the last minute to buy their child or themselves items – from clothing to computers – optimizing inventory, replenishing products, and keeping the right prices across multiple stores can make or break the business.

 

Understanding back-to-school shopping trends

According to the National Retail Federation, families are expected to spend $80.7 billion, with the average planned spending per household for back-to-school shoppers – elementary school through high school — to be $696.70, and the average back-to-college spending per household is $976.78. Despite all this talk about brick-and-mortar stores closing and the retail apocalypse hitting brands hard, an interesting Deloitte survey revealed that an overwhelming majority of people would actually be doing their back-to-school shopping in-store. Moreover, the amount that each student may spend would be a record high, with total back-to-school spending expected to reach $27.8B or $519 per student.

 

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When it comes to scheduling time to shop, the National Retail Federation found that families are now doing their shopping later in the summer, pushing it off a couple of weeks before school starts. Based on the comments of NRF President and CEO Matthew Shay, he stated that, 

 

Parents this year have been taking longer than usual to finish buying the clothing and supplies their children need for school.

 

This is definitely something retailers need to consider since it implies that they will have fewer chances to influence purchasing decisions, as many parents would be cutting it close to take advantage of promotional deals. According to Deloitte, 60% of shoppers were likely to begin shopping 4-6 weeks before the school year started. 

 

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In 2018, many households planned to spend an average of USD $292 offline instead of online. This number represented $16B in total spending, which was more than double that of the $115 (or $6B in total spending) that was meant to be spent online for back-to-school. In 2019, mass market retailers were still the number one choice for shopping, yet online sites followed behind, with one of the biggest growth.

 

When it comes to understanding what consumers are shopping for clothing and accessories were the most popular, with school supplies following close behind. However, compared to last year, electronic gadgets grew in sales, yet specifically from online. 

 

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Out of a group of respondents that were surveyed on their spending on back-to-school shopping, 12% stated they plan to spend more in the current year than compared to the previous year. Moreover, 31 percent of respondents claimed that back-to-school shopping was often associated with stress due to numerous factors such as scheduling, and sudden requirements for items they did not expect they needed.

 

How predictive analytics help retailers for back to school

When it came to retailers, one of the top issues they faced was related to managing inventory, as over 33% of respondents noted that the major complaint they received were out of stock items. To mitigate this problem, predictive analytics solutions that leverage AI can help retailers to make smarter, faster decisions to meet customer demands in one of the busiest seasonal events of the year. 

 

1. Identifying top-selling items

While certain categories like computers and clothing are guaranteed to see a higher comparable sales around the back-to-school period, a lot of times, retailers are left wondering what the next item will be a big hit. With predictive analytics, brands can dig deep into the data and discover hidden categories of items that have a high potential for being a top-seller. 

 

As many retailers may already know, even with traditional forecasting methods, taking a look at the previous year’s sales is not enough to understand a product’s true demand. Using predictive analytic solutions can help suggest actions on inventory that the retailer may have otherwise ignored. 

 

2. Analyze more time-sensitive data

Being able to analyze data as customers shop gives retailers the ability to see what products work and what doesn’t. With traditional forecasting methods, this is not possible. However, with predictive analytics solutions, and artificial intelligence, retailers are able to make actionable decisions from the timely sales data. They can adjust their strategies according to the results, and capitalize on a strategy that works best at the moment. 

 

Moreover, time-sensitive sales require retailers to adapt to consumer needs quickly. Based on a survey conducted by research firm IDC in 2017, they found that 77 percent of executives believed their lack of timely data prevented them from capitalizing on opportunities. 

 

3. Ordering the right quantities

Products that are high in demand can go out of stock instantly.. Or, as mentioned above, the hidden categories that retailers are unable to see may turnover  faster than expected. For this reason, it is important for any retailer to have the right products and units during such a busy season. Assortments must be in line with the demand for retailers to avoid overstock and markdowns.

 

Additionally, not all assortments may perform equally well at all stores, and not every store will perform the same depending on the location and demographics. Thus, with predictive analytics solutions, you can forecast a highly accurate analysis of different styles and colors for each location. Going deeper into the data using AI, you can understand how the introduction of a specific new school bag may affect the sales of the previous.

 

4. Greater omni-channel allocation

While knowing how many units to sell is important, it is only one step to maximizing predictive analytics to optimize your business. The next step is knowing where and how those items will sell. For example, some retailers may only fulfill orders through their retail locations, while others do at a distribution center. Since back-to-school shopping has high demand, and there is a short window for retailers to make sales, oftentimes, they do not have enough time to correct allocation. For this reason, using predictive analytics can help to allocate inventory as effectively and efficiently as possible, providing the right recommendations for the right products and stores. With AI-driven predictive analytics, you can recommend inventory allocation at a SKU/store level to maximize sales and in turn, decrease inventory costs.

 

5. Improved pricing and promotions

Not all products react to changes in price the same way. Using the right machine learning algorithms help to determine what triggers sales of the product. Examining a variety of data sources helps the system to identify and recommend the optimal price for retailers to put their items on promotion. This helps with promotional deals, as the AI-driven engine gives greater visibility to inventory data and takes into consideration potential sales uplifts from upcoming promotions or price changes. From there, it will provide suggestions on how to manage inventory better, and incremental improvements to maximize a promotion’s profitability.

 

Final thoughts

Since artificial intelligence pulls from multiple data sources rather than a single source, predictive analytics solutions like Chain of Demand can help retailers make faster decisions, with a smarter edge. This is especially important for one of the biggest retail events of the year – back-to-school. 

 

Not only will retailers be able to know which products to sell, but they can also receive recommendations at what price to put the items at for optimal profits, and when would be the best time to sell, along with what quantity to order. By doing so, everyone wins, as the consumer buys what they truly need, the retailer makes a profit, and the industry puts out significantly less inventory waste. 

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