What You Can Learn From Your Average Retail Selling Price

Last week, I had the opportunity to work with two different customers analyzing their sales at The Home Depot.  One sells products in the paint department and the other sells into the building materials department.  The analysis goal for both customer projects was to deconstruct their YTD sales and identify the factors contributing to higher than expected comp sales increases.  Both customer’s sales for a set of key SKU’s were up about 2.5% YoY and the question they wanted answered was, “What is driving the higher sales, and will it continue?”

As we deconstructed their respective sales into its component parts of distribution, price, and rate of sale, we uncovered interesting data regarding average retail selling price.  The first customer’s average retail price was pretty consistent across Home Depot stores at the expected $22.95 retail price.  There were some fluctuations, but the data was pretty clustered, as expected.

In contrast, the second customer’s average retail price was very inconsistent.  Customer #2 believed that The Home Depot was pricing their core SKU at $59.95 or $65.95, depending on the market.  However, as you can see in Figure 2, the pricing was very inconsistent across stores.  In fact, we identified stores in the same Home Depot market with a selling price variance of $28.35 per unit. 

While there can be good reasons to price a product differently by market due to shipping costs or competitive pressures, it seems irregular to have different prices within the same market.  Take a look at the average retail price of your products. It’s an important exercise that can yield some interesting insights.  It presents an opportunity to have a conversation with your merchant about pricing strategies.  And, It also provides an opportunity to identify markets with similar demographic profiles, but different selling prices, thus providing insight into what price point drives the highest sales. 

Does your average selling price match your expectations?  The answer may not be as simple as you think and could reveal some interesting insights.

US BEAUTY MARKET EXPECTED TO GROW TO $90 BILLION IN 2020, AND ACCELERATED ANALYTICS COSMETICS BRANDS ARE IN THE NEWS!

Today’s $80 billion US beauty market will continue to grow according to a report by Euromonitor International. This growth rate will mean the beauty market will grow by 45% in 10 years. In 2015, the premium beauty category grew 7%, with premium foundation sales increasing 14.2%, compared to just 3% the prior year. Consumers have been shifting to premium products, looking for high quality products. Many luxury beauty brands who use Accelerated Analytics POS reporting to optimize inventory levels, track promotions and drive sales are in the news with big headlines this week, such as Parlux Fragrances, Shiseido, Inter Parfums, Estee Lauder, Elizabeth Arden, Coty and L’Oreal. Click Here to learn more about our solutions for beauty brands.

Data Analytics Among Strategies to Combat Loss in Omnichannel

With new technologies hitting the market on a regular basis, the retail industry is constantly evolving. Most retailers now operate in a technologically sophisticated omnichannel environment that includes strategies to enhance selling, including online, mobile retail and social media marketing.

In a 2015 retail industry survey conducted by PricewaterhouseCoopers, findings revealed that technology is making it more difficult for retailers to prevent, detect and manage loss.

“At the core of omnichannel is the concept that a customer can securely purchase and receive their product in any manner they desire,” Zawoyski says. “For many retailers with legacy pre-omnichannel operations and systems, ensuring secure and dynamic customer purchasing and fulfillment is a significant challenge. Beyond the well-documented data breaches and information security risks, many retailers struggle with ensuring supply chain inventory accuracy.”

According to the PwC report, many companies haven’t fully integrated new technologies that are needed to combat loss in omnichannel effectively. One key recommendation for the report: Retailers should embrace the process of root cause analysis while increasing their use of data analytics to better spot and mitigate shrink. Zawoyski says 59 percent of respondents reported they are employing data analytics and end-to-end root cause analysis as a primary tool to identify losses.

One of the many strategies that PwC suggests to enhance root cause analysis is to leverage data analytics.

“Effectively leveraging advanced analytics and end-to-end root cause analysis allows retailers to assess, strategize, plan and deploy risk mitigation programs,” says Zawoyski.

Sources: National Retail Federation: STORES magazine, June 2016, PricewaterhouseCoopers

Retail Sales Rose More Than Expected in May

According to the Commerce Department, U.S. retail sales rose 0.5% in May, beating a forecasted increase of just 0.3%. This followed a 1.3% increase in April and lifted sales 2.5% from a year ago.

The second straight month of gains was fueled by Americans purchasing automobiles and a range of other goods, suggesting economic growth despite a slowdown in job creation. Sales at clothing stores rose 0.8%, the largest gain in six months. Online retail sales, sporting goods and hobby stores all rose 1.3% last month and restaurant and bar sales climbed 0.8% Electronics and appliance outlets also saw a rise in sales with a 0.3% increase. Not everyone saw an increase in retail sales. Sales at building materials and garden equipment stores fell 1.8%, and furniture store sales dipped 0.1%.

The growth in retail sales could impact economist’s second quarter GDP growth estimates which are currently around a 2.5% annualized rate.

TAKEAWAYS FROM ACCELERATED ANALYTICS’ GS1 CONNECT 2016 SESSION: ‘The Importance and Value of Data Sharing – Using Point-of-Sale Data to Deliver Outstanding Customer Experiences’

I had the opportunity to lead this session last week and came back with some takeaways that I wanted to share, as I feel they are reflective of the state of retail today and how retailers and vendors are using point-of-sale data to manage their business in this time of OMNI-channel, customer-experience driven retail.

Customers want a single-vision of brands and be able to have a consistent, complete and winning experience every time they shop, in whatever channel they shop in. To ensure products are available when and where the customer shops, retailers need real-time inventory visibility, a seamless order management system and the ability to deliver. Retailers and brands realize they need to work together to have both a single view of the customer and a single view of their data in order to be successful.

Some interesting, yet not surprising, statistics were gathered from the retailers and vendors represented in the room. When asked about managing their POS data week to week, 66% were using POS data that was not provided via an EDI 852 file, eliminating their ability to automate the collection and processing of the data electronically, and instead having to work with multiple sources and formats of data. This process of data management and then trying to create usable reports to have meaningful partnership conversations between retailer and vendor is extremely time consuming: 83% of the retailers in the room spend 11-50+ hours per week managing and processing their POS data. More staggering was 95% of the vendors in the session were spending 11-50+ hours per week: 30% spending 11-20 hours, 40% spending 25-50 hours and 25% over 50 hours per week!

The good news is over half of the attendees in the room felt they are getting better at managing and using POS data each week, but 30% still admit to ‘Barely Using’ their POS data. Recognizing that heavy resources are needed to use POS data, especially on the vendor side, is making vendors ask, “Do we build an in-house solution to manage this, or outsource it?” CLICK HERE for an infographic detailing the pros and cons of each, and the differences in resource and financial investment.

Based on the time and effort being made by most of those represented, it is clear that POS data sharing is important for effective collaboration between retailers and vendors to “get it right for the customer”. POS data can be used to not just track units sold overall, but can give product/store level details on out-of-stocks, weeks of supply, sell thru %, average sales, geographic trends, inventory investment and lost sales opportunities. CLICK HERE for our industry sell thru % guidelines infographic.

We need to do everything we can to exceed our customers’ expectations and deliver an outstanding experience for them when they come across our brand. Sharing POS data and then using it to partner together to analyze it will help shape the customers’ experiences and give us inventory visibility and fulfillment across channels to meet customer expectations.

Want to learn more? Contact Jennifer@AcceleratedAnalytics.com to start a conversation. CLICK HERE to download whitepapers on analyzing POS data like a pro.

- Jennifer Freyer, Director of Sales and Marketing, Accelerated Analytics

Build vs. Buy: Outsourcing your POS data-reporting and analytics is faster, easier and less expensive than building an in-house solution

Retail POS data holds the key to understanding how your products are performing at a store level. But it can be a daunting task, not to mention a drain on resources, to gather, analyze and report on EDI 852 and POS data from all of your retailers. Our newest infographic compares building an in-house solution to outsourcing your POS data-reporting an analytics. While there are beneifts to each approach, outsourcing the solution is faster, easier and less expensive. Complete the form below to request our FREE infographic!

 

Three Factors Contributing to Continued Weak Retail Traffic

The second week of April continued to see sluggish retail traffic. According to reports by Citi Research analyst Kate McShane and Cowen and Co. analyst John Kernan, total U.S. retail visits for the week ended April 16 declined 6.58 percent year over year. Total U.S. retail same-store traffic slid 8.75 percent year-over-year for the week while year-to-date retail visits are down 3.4 percent. In a report released this week, Cowen and Co. outline several factors contributing to the decline.

E-commerce and Mobile Growth

According to the Cowen report, holiday e-commerce sales increased about 14%, while mobile currently captures 61% of consumers’ time spent shopping online. The increased competition from e-commerce and the pressure to maintain brick-and-mortar locations contributed to several large retailers filing for bankruptcy in recent months.

Lack of Trends 

Retailers continue to be plagued by what Cowen and Co. termed “product malaise”. Either brands and retailers are not creating /offering enough fresh and exciting product to compel consumers to spend, or consumers simply aren’t as interested in purchasing “stuff” these days.

Election Year Politics

Retailers have notoriously blamed sluggish traffic and sales during an election year on the hype and uncertainty surrounding a presidential election. The campaigns and debates may draw attention away from retail shopping, plus high-end shoppers may scale down their spending before an election if they fear an increase in taxes when the next president takes office.

One tactic vendors and retailers can use to weather the storm is remaining focused on inventory and consumer buying trends using POS data and analytics. Research continues to show that when people find a product that they really like, they will buy it. With comprehensive data and reports readily available, vendors can make informed decisions that help ensure their customers can find and buy their product.

Source: footwearnews.com

WARM WEATHER HELPS HOME DEPOT SALES RISE ALMOST 10% IN 4TH QUARTER, AS HOME PRICES INCREASE AND DEMAND FOR NEW HOME CONSTRUCTION STARTS 2016 WITH SLOW GROWTH

The Home Depot’s fourth-quarter sales included an 8.9% sales increase in US stores that have been open for at least one year. This is despite the challenges facing the rest of the retail industry. Sales were strong the entire quarter, but warm weather in December brought in a sales spike – Home Depot attributes $100 million in sales growth to the milder weather. High sales were seen for power washers and pressure-treated lumber by homeowners building decks.

Home prices also rose in December, showing the strongest gains since July 2014. The Home Price Index showed a rise of 5.2% in the 12 months ending in December, versus a 5.2% in November. The national median sale price for a home in December was $213,800, 8.2% from the previous year. There is a limited inventory of homes for sale, and demand for new homes is likely to drive more new construction and help keep a ceiling on the rising home prices, although January 2016 was a slow start for new home construction for the year.

Accelerated Analytics helps Home Depot vendors, such as WM Barr, Hillman Group, Alexandria Molding, and CPG Building Products to manage their sales and inventory data to drive results. Our vendors use our analytical measures, such as sell through, inventory shortages, out of stocks, weeks of supply and consumer purchasing trends, to take quick actions to ensure they are optimizing inventory and growing sales.

Source: Wall St. Journal

Calculating Sell-Thru

Sell through (or sell-thru) is a very useful metric for vendors to use in evaluating item performance, because it provides a composite measure of sales and inventory. But like many business measures, there is more than one method of calculating sell through.

The most common calculation is: Sell Thru % = Units Sold / (Units On-Hand + Units Sold). Sell thru is typically evaluated on a daily basis for fast moving products or weekly for slower moving or replenishment based products.  A higher value is better, indicating your sales velocity is good and your inventory is appropriately forecasted. If sell thru is low, this indicates either poor sales or too much inventory. In most cases, sell-thru for an item is compared in recent periods like current week and last week, as well as in aggregate across several months or even a year.

When evaluating sell-thru, it is also useful to group together products which have been selling for a similar period of time and/or which are sold into the similar store types. For example, comparing sell-thru for a product with 5 weeks of selling activity against a product with 20 weeks of selling activity most likely will not produce a useful comparison. In the same way, comparing sell-thru for a product in a group of stores in a highly affluent area is not likely to compare favorably to a group of stores with a low income level.

Most retail buyers have a set sell-thru percentage they use to judge vendors based on product category or department.  It is important for vendors to discuss the sell-thru expectations with the buyer in order to align with those objectives.

For reference, we've compiled sell-thru percentage data that you can use as a benchmark. The complete infographic includes the sell-thru percentage for eight retail categories each at 8, 13, 26 and 52 weeks. To download the complete infographic, simply complete the form below and we will e-mail you the link to download it.