Throughout the ages, multiple planning tools have been used while discussing business strategy and direction. In many cases, these planning tools are lauded as critically important to the process of management, development, and improvement. While the tools are used during the planning process, seeing any “playing time” as businesses are being actively managed can be rare. As I am sure there are many others some might reference, two such tools often relegated to the bench after being central to the “pre-game” are the SWOT analysis and comp shop. A few years ago, I had the fortunate opportunity to participate in a planning process where I was coached on how to not only fill in a SWOT but how to pair each box with other boxes in the 2x2 grid to create meaningful and actionable plans. How I saw the SWOT analysis was forever changed. The last couple of years have been an iterative process on how Paradygm creates and communicates via a comp shop. A recently completed proprietary comp shop tool has unlocked hidden gems and breathed new life into the potential of comp shops.
With some of the recent seismic shifts to value orientation, curbside pick-up, e-commerce, etc., it seems ever more critical to maniacally understand the layout of the retail landscape, how your own products fit within that landscape, and where the gaps are to most easily benefit the consumer, customer, and manufacturer. The traditional comp shop is a two-axis process, with features down the left side, items and retailers across the top, and most often sorted by retail from high to low. As the retail landscape has become hyper-dynamic, the tools around it sometimes seem to be changing at a snail’s pace. The traditional methods for conducting comp shops fall short of the multi-dimensional manner product managers, account managers, channel managers, merchants, and many others need to manage. We hope to take a look at how comp shops can provide increased value.
A starting principle behind comp shops is to conduct a deep-dive into a product category by retailer and by SKU to provide a snapshot of what is on the retail shelf. This sounds simple on the surface, but some additional considerations need to be made when developing a comp shop. Some key components to consider in such an analysis are:
Role of the category
Brand representation
Retail pricing
Size/Quantity
Feature set
Retail progression
Brand/manufacturer share
Intellectual property
The traditional format in which comp shops have been conducted can lack depth. In many instances, comp shops are completed by doing store walks and select online audits, putting the findings in a table of some format, and then submitting the finished product. Like the SWOT analysis has been for so long, historically, the comp shop generated few observations of what the data meant to the overall market, and even fewer ways how it could be used in the decision-making process or what needed to change to improve a category’s performance. This is not to say ensuring retails are being considered is not a valuable conversation, but this is often the singular focus of many comp shops. As the list above suggests, there are several factors with influence on a category’s performance. In today’s comp shop paradigm, the depth of data is often lacking as well as flawed. With the dynamic tools retailers have at their disposal, 100% similarity across 100% of stores is unlikely. Many retailers vary assortments across markets based on a plethora of parameters (local building codes, consumer incomes within a geography, freight constraints, market styles, size of market, competitor proximity, etc.). Select stores may also have programs and/or products being tested. These are things difficult to quantify by walking a sample of stores. In most cases, these nuances go undiscovered like an archaeological structure deep within a jungle, while in other cases, after being discovered, they are assumed to be ubiquitous.
With each passing month, retailers become savvier on how to succeed with their customers and their competitors’ customers. There is an arms race for information via an ever-increasing number of tools and becoming more systematically driven, creating an increased lean towards fact-based decision making. With the comp shop’s traditional format, the data can be relatively shallow compared to what retailers can collect on their own. With imperfect data, the innate shortcomings of the comp shop process can create doubts in the value of the data compiled, causing others to discount the hard work involved in a thorough comp shop. So what can be done differently?
To help answer this question, let’s examine comp shops by breaking them down into three key stages: data capture, turning data into information, and using information to take action.
Data Capture
It’s tempting to skimp on the data capture stage and move straight to information and decision making, especially when bias is involved and assumptions are made. But, if the process starts with incomplete data, the information will be imperfect, and any actions taken will end up being off from the needed path. Even worse, such actions could have adverse effects on a category’s performance. Taking a multigenerational approach to data collection creates the ability to demonstrate underlying trends and improve the data set’s completeness. Data points to collect could include: specifications and features, retail price points, items added/deleted, and brands. Looking at this data over time gives the ability to help understand the trajectory of a category. But data needs to be captured not only across a broader spectrum of fields, and it also needs to come from an increased sample size, thus increasing the accuracy of the data. The traditional process of capturing data for a comp shop and then arranging it in an ascending or descending fashion would be like drawing the 2x2 of a SWOT analysis and not formulating takeaways from the thought exercise. This is done in the information phase.
Information
The fundamental goal of the information stage is to make data digestible and actionable. By taking a multigenerational perspective of the data, one can index the change over time to get a general feel for the market at large. For example, if a data component of a category has changed consistently over time and across retailers, this would provide valuable input into both the macro environment and what steps to take in the future. Visualization is also an essential aspect of information; in other words, how the data is presented so opportunities are clearly apparent creates opportunity when making business decisions. We have achieved this by incorporating innovative cross-indexed iconography into comp shops so the data is easily digestible. Here are a few examples of how data can be turned into information:
Share of wall can lend to a better understanding of the role of the category.
Retail tiers can be determined by examining the price data.
Pricing can be utilized to measure the value, not just the number, being communicated to the consumer.
Showing how special features, such as IP, create value within a category
Cross-referencing two items to isolate the value of certain features
Now that the data has been transformed into digestible information, it’s time to create action.
Action
The action stage is where it is determined how to best react to the information. Along with the information, it’s important to consider external context, such as where the market is headed and has been, to help make sure decisions are logical. For example, some of what is occurring within a category can be driven by forces external to consumer demand (moves to increase or decrease private label, industry consolidation, dependency mitigation, etc.)? Improved data quality can aid in actionable decisions around brand shifts, mix changes, space allocation, packaging, new category introduction, and pricing shifts. For manufacturers, information can be applied to deciding where to invest, gaps in product offering, investments to make, throughput optimizations, costing, and new product specifications, to name just a few. It’s equally as important to approach actions holistically. It may be clear from the information which SKU needs to be removed from a category, but the comp shop can also support decisions around what will be added to replace it and why.
If you want to use comp shops to get an advantage in the marketplace, it starts with extensive and consistent data collection. The quantity, quality, and accuracy of the data directly relates to the information observed from the data and the actions suggested. At Paradygm, we have a unique tool to capture multigenerational data and aid in visually interpreting the data into easily digestible information to help take market-conscious actions. Reach out if you’d like to find out how our internally developed software can help elevate your comp shops and revenue.