Manufacturers, licensors and distributors who are looking to gain more insight from point of sale (POS) data face the challenge of determining what data they need, how to get it and extracting actionable information from it. Because of these challenges many companies rank it low on their priority list, yet there are significant benefits and often high ROI to doing it. Using the data to determine what products are in demand, which design elements sell best (or worst), learning the geographic and demographic characteristics and being able to communicate more confidently with the buyer are just a few. All these lead to improved inventory productivity and higher revenue and profit, because you are reducing risk in producing products that won’t sell and selling more of what you make money on. While there are many outstanding buyers out there that know who their customers are, which stores are the best and how to allocate optimally, many simply don’t have the time, tools or people to do the job well. Suppliers that can aid the buyer in this way generally do better and are awarded more programs because they become a trusted advisor. The ultimate collaboration is best accomplished through vendor managed inventory (VMI), which uses the supplier’s resources and tools to allocate and write orders for the retailer. It’s a mutually beneficial situation.

Besides weekly reporting and improved demand planning, another often overlooked use for POS data is SKU rationalization. A client who is a licensee for a very strong apparel brand in Men’s, Women’s and Children’s often asked how to reduce the cost of product development. He noticed that season after season, only 70% or so of the line that was designed would get picked up. If they could close that gap both productivity and profitability would go up.  The time and energy of the designers and sales team, the cost of making samples, modeling, photography, etc. ultimately pushed prices up to the retailer. Our idea was to analyze what sold profitably and use the learnings to help designers from the beginning of the product development process. He made sure not to stifle creativity- after all, newness is vital to any brand. But very quickly the designers could see what the fringe items were. All the work they did to create them and then see they were unprofitable. Many of the same mistakes were made season after season. The analysis had a profound effect on the organization- and the next season designers had a much better idea of where to focus. Not surprisingly, more of the line was picked up by retailers and the profitability went up an average of 10%.

Many manufacturers pay top dollar for market data (NPD, Nielsen, etc.) which is good for broad directional information. However, store level POS provides far greater ROI because it zeroes in right on your merchandise. Ask your buyer to share their category information which you can use to compare against your data.

So how does a company get started? It all begins with the data. Retailers offer POS in a variety of flavors. Some allow you to select the data fields, others don’t. The most common POS data sources are:

  • EDI 852: can be at store, chain or channel level depending on the retailer. We prefer this option because it can be completely automated end to end. But use of a VAN, knowledge of mapping and pushing to a database is required. We offer it as a service.
  • Web portals: many retailers have their own vendor portals with varying degrees of sophistication. It is generally less automated, downloaded data needs to be re-formatted, and not a great solution for many SKU-store combinations. But it is usually free. Walmart and Target are the best. Amazon also offers reporting from their portal on “fulfilled by Amazon” orders.
  • Spreadsheets or custom created files: There are still some retailers out there that email weekly Excel files with data. It’s usually only chain level, not great for deep analysis and if not automated from the retailer, doesn’t provide a consistent flow of data.

Once you get the data, consider these options and factors:

  • Chain or store Level: We are big believers in store level data because it offers the truest picture of sell through, store execution and allows to integrate 3rd party data such as weather or demographics.
  • Fields: At a minimum you want unit sales and on hand by SKU. Then add dollar sales, attributes such as description, color, size, etc. Actual average selling price may be provided, but the MSRP or ticketed/full price usually is not. Adding this into the data set enables markup and markdown calculations.
  • Item Identifiers: Most retailers transmit by UPC, but some have their own item number nomenclature. Linking to a vendor item or style ID is helpful.
  • Business Rules: This can get tricky. Understanding what shows up on the data feed- like only items with activity for the week- is important. In these cases, if there was previous on hand, you must carry it forward. In other cases, you might have to calculate a perpetual inventory. These are advanced tasks and easy to have inaccuracies if you don’t know what you’re doing.
  • Channel: Don’t forget eCommerce which is growing in importance. Some retailers report their ecommerce sales as a store number in their data set. Others offer a separate feed. Find out in case special handling is required. Also, if you drop ship, you can integrate your orders from your ERP system into the data set as well.
  • Timing: For most purposes weekly data is fine. For perishable or other urgent type products daily sales may be available. For weekly data, the date and time data become available should be noted, as sometimes retailer’s systems report differently. That could generate small differences in sales figures.
  • Platform: Generic data visualization tools such as Domo or Tableau can be configured with programming knowledge to get what you need in terms of reporting. However, retail-specific platforms such as intelligentretail.net offer a far greater set of actionable functions and reports curated for retail and wholesale. They also focus on inventory productivity and the metrics that relate to that are usually baked in. Smart systems such as RetailNarrative go even further by using AI to do the analysis for you. As for the do-it-yourselfers- we’ve seen very few successful home-grown systems that are built to last. With the low price of today’s data tools, it makes sense to use them and configure to fit your business.

There are many other factors to take into consideration, but you can start small and grow your data set over time. We recommend writing up a data strategy with your team- determining what the actual uses of the data will be, what questions it will help answer, how it will be used by both sales and planning teams, etc. Once you have a company goal, it will be easier to implement because the answers to the key considerations will answered.

For more information on POS data gathering, management and use, contact ERS today.

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