Data hunters

“The ability to measure, analyze and present P.O.P. ROI as part of a sales presentation adds greatly to the effectiveness of the P.O.P. supplier’s pitch and success in securing an order.”

When customers enter a retail establishment, one of the first things they encounter is signage – something that directs them to the appropriate aisle or toward the product they’ve come to purchase. Similarly, while customers wait in the drive-thru of their favorite fast food restaurant, they’re scanning the promotional signs and menu boards to help make their purchase decision quickly.

Whether the focus is on retail establishments or quick-service restaurants (QSRs), marketing professionals and their printers include point-of-purchase (P.O.P.) signage as a vital component of any on-site sales or advertising campaign.

Given the importance of P.O.P. in driving sales results, P.O.P. suppliers must present more than price, design and quality in their client consultations. In today’s highly competitive retail environment, P.O.P. suppliers should also include each printed piece’s value in terms of return-on-investment (ROI).

The ability to measure, analyze and present P.O.P.
ROI as part of a sales presentation adds greatly to the effectiveness of the P.O.P. supplier’s pitch and success in securing an order. Converting the focus of the sales call from what a printed piece costs to how much it can make – the “return” – gives the client important and compelling data that leads to a results-focused purchase decision.

In order to provide this ROI data, it’s essential to have a robust process that can properly test the effectiveness of individual printed elements in the field, summarize the impact of the tested products and convert that data into a simple ROI calculation – incremental benefit divided by incremental cost – that the retailer can understand. The good news is that this process is easier than it sounds.

Setting up the ROI test
The purpose of the ROI test is to calculate how well-printed products perform in the field versus what these printed products cost. The process begins with the selection of a small group of test stores within the larger footprint of all locations. A few important factors must be considered when choosing the sample test locations.

First, in order to provide statistically valid results to the retailer, the test should run across at least 30 locations. This number is mathematically sufficient to draw valid statistical conclusions from data results.

Next, it’s important that the sample size includes locations that reflect potential market variations in demographics, traffic patterns, competition and, at times, even the weather. The goal is to have the selected test locations represent and perform like the total trading area – the geographic area from which a community or business generates the majority of its customers.

This allows the test store results to be extrapolated across the retailer’s total footprint. A smaller sample size or a concentrated grouping of test stores in a single trading area will likely not reflect the diversity and breadth of the retailer’s market footprint, making any claimed results suspect. Many franchise organizations have company-owned stores in addition to their franchised units. These company-owned stores often make good sample sets since they tend to be randomly dispersed within the total footprint and offer a single source, the franchisor, for all data collection.

In addition to the test stores, a control group of non-test stores must also be identified. This control group is usually all of the other stores in the chain not testing the P.O.P. product in question. Performance results from the test stores are then compared to performance results from the control group to measure the effectiveness of the P.O.P. element.

Another important aspect of the test setup is the requirement that all test locations have their P.O.P. test elements properly installed and installed on time. Poor or late installations will skew the results negatively and will not accurately reflect the P.O.P.’s true ability to deliver results. When possible, it is recommended that the P.O.P. fabricator supervise the installation of all tested elements to guarantee perfect field execution ensuring valid results.

Setting up the data metrics
Typically, a P.O.P. test will measure by how many additional profit margin dollars are generated versus the incremental cost – the increase in total costs – of the P.O.P. elements. It is important that the retailer agrees in advance as to what data will be required from each test location and also from the control group.

Some retailers are reluctant to share actual unit movement, sales numbers and margins. But this data is essential to the ROI calculation and there is no test without them. It’s better to have this discussion up front with the retailer rather than be disappointed later.

Getting down to business
The actual ROI testing period typically lasts anywhere from four to eight weeks from first identifying the test locations to completing the actual ROI analysis. This makes sense since most retail merchandising is promotion-oriented and changes every four to six weeks.

After the P.O.P. supplier receives margin figures from each test location at the conclusion of the test, the ROI analysis begins. ROI is typically calculated by taking the difference of average margin improvement across all the test stores versus the average of the control group and dividing that improvement difference by the cost of the P.O.P. element in question. The average margin improvement was hopefully significantly greater than the cost of the P.O.P. element.

And, because the test locations were set up to be representative of the market as a whole, this result then becomes statistically significant when projected across the retailer’s full system. When the testing is done correctly with necessary controls in place, the P.O.P. supplier can present these results to clients with confidence.

Why it matters
Many P.O.P. printers compete on price alone. As such, printing often devolves into a commodity. While retailers are concerned about cost, they get excited about ROI on their merchandising programs. They know that the lowest cost solution may not be the one that drives results.

Providing valid ROI test results isn’t just good for a P.O.P. supplier’s clients, it’s beneficial for the supplier as well. Armed with demonstrable ROI calculations, P.O.P. suppliers have the opportunity to win over new clients who can see the profit benefit the presented products will have for their organization. The testing process often provides the P.O.P. supplier with test store testimonials and pictures that can be included in sales presentations to other retailers.

Incorporating Big Data analysis into P.O.P. strategies
Earlier on, the need for taking into account – and testing – locations with demographic variations was touched upon. Incorporating Big Data analysis into P.O.P. strategies and ROI calculations is something that P.O.P. suppliers might soon be considering.

As P.O.P. suppliers develop new innovations, the possibility of quantifying how these innovations might become even more effective if customized store-by-store based upon Big Data exists.

While these short, Big Data-based customized print runs would be more expensive, they just might prove to be ROI winners when the results are calculated. This is an exciting next step in ROI analysis for P.O.P. suppliers.

P.O.P suppliers that go the extra distance for their clients – by helping them achieve maximum ROI on their P.O.P investment and introducing Big Data solutions into the client decision-making process – will be the companies that stand out as the industry continues to grow and change.