Calculating Digital Signage ROI: The Ground Rules
Author: Bill Gerba on 2005-03-16 23:55:33
Given the obvious benefits and proven returns of in-store marketing and POP advertising displays, a large portion of the digital signage industry is perpetually trying to up the ante by offering dynamic signage to retailers and in-store merchandisers. From new digital signage start-ups to old POP display manufacturers, everybody is looking for the next big thing that will increase revenues and improve customer satisfaction on the sales floor. Whether or not digital signage will be the next big thing still remains to be seen, as a number of challenges must be overcome before the medium is widely accepted by retailers and marketers alike. Of these, the most important (and perhaps the most difficult) is determining the return on investment of the digital signage network. Without a quick and easy way to calculate ROI, no amount of flash and sparkle is going to save our beloved plasma screens and networked media players from the scrap heap.
Because there are so many different business models that a digital signage network owner could subscribe to, it would be impossible for me to cover all of the different ways to calculate ROI. So instead, I'm going to focus on techniques for coaxing the number out of the most common kind of network: ad-driven or ad-supported retail networks. I'm also going to avoid interactive kiosks and interactive signage that have more obvious, feedback-driven methods for deriving ROI numbers for now (these will be the subject of another article). In this, the first in a series of articles covering the basics of calculating digital signage ROI metrics, I'm going to start by introducing a couple of concepts to get you in the right mindset for determining your signage network's return on investment. In subsequent articles, I will try to provide some ideas and methodologies for accumulating and analyzing your data to come up with the magic number. So, without further ado, let's get started.
First, Know Thy Metric
Do you know what you're going to try and measure? Ok, the obvious answer here is "how much did we lift sales?" But depending the focus of your project, how tightly integrated it is with the rest of the store/product/brand marketing, and the size of your data set, you might be trying to measure one of several different things. To explain, I'd like to introduce a little diagram called the retail marketing funnel:
/ \
/ \ <- SALES
/_____\
/ \ <- IDENTIFICATION
/______\
/ \ <- PREFERENCE
/______\
/ \ <- PERCEPTION
/______\
/ \ <- RECALL
/______\
/ \ <- RECOGNITION
/______\
/ \ <- AWARENESS
/______\
There are probably a hundred variations on this diagram (which I think everybody has seen in Marketing 101), but I'd like to do a quick overview of what I believe each of these stages to mean:
Awareness is simply the cognizance that a brand or product exists. Awareness marketing targets the largest groups of people, and simply strives to "get the brand name out there," wherever "there" may be.
Recognition is the stage where a consumer can mentally link a brand to a product or marketing slogan. Many marketers like to correlate recognition numbers with sales numbers, so there's a lot of recognition data out there.
Recall, the next stage of the funnel, tests the opposite of recognition: given a particular product, can the consumer remember the particular brand image or redeeming qualities of the product? Most TV commercials for consumer products strive to make it to the recall stage, but in reality, they're typically relegated to recognition or awareness status.
The perception stage relates to how a brand or product is generally received and comprehended by consumers. The notion of "positive" or "negative" brand image and equity commonly comes from marketing efforts at this level.
The preference stage is the first funnel stage where marketing has solidly influenced the customer's beliefs. This level of marketing promotes specific features and benefits of the brand or product (often at the expense of competing products) to try and become the customer's brand of choice.
Identification, in this case, is literally the point at which the consumer visualizes him or herself as a user of the brand or product. Marketing at this level will often involve showing the brand or product being used by a particular class of individual that (hopefully) the target consumer will be able to identify with and relate to, thus creating a bond between consumer and product, and motivating the sale.
And last, but not least, is the sales stage itself. This is the point where the customer makes their active purchase decision, whether that means choosing between brands, or deciding whether to make the purchase at all.
The funnel shape is used for a couple of reasons. For one, it shows how you can pump a ton of resources into brand awareness campaigns but only see minimal sales increases. Also, it demonstrates how you often have to walk a shopper through all of the funnel's stages before converting him into a buyer. However, if we look at the diagram as a pyramid instead of a funnel, it suggests that increased sales can only be built on top of a foundation that includes brand awareness, recognition, recall, and so on.
Now that's not to say that every advertising tactic needs to focus on every stage of the funnel. In fact, quite the opposite is true, since an ad campaign that tries to do everything probably isn't going to do anything particularly well. Let's think about a retail digital signage network (perhaps an in-store TV network) in this context. A retail digital signage network is probably going to be most effective between the awareness and perception stages, although there are certainly cases where it could influence perception and preference as well. As a customer moves down the aisles of a grocery store, for example, he is already primed to make purchases. A typical grocery store aisle might contain 1,000 different products, and unless your product is the one the customer has come to buy, it's probably not receiving much attention from him. However, if the customer happens to pass a digital sign displaying an ad for your product as he walked through the store, he is now mentally primed with your product's image, and is more likely to notice it during his walk down the aisle. In fact, according to a recent study by Arbitron (and sponsored by digital signage provider CoolSign), "having the [digital signage network] in a mall seemed to raise the average recall for an advertiser by 18 percentage points." On the other hand, if your network is narrowcasting long-form content in a captive audience network, the priming effect isn't really in play, since your audience won't be able to buy the advertised product immediately. In these cases, your network's content will probably serve to better improve awareness and recognition.
When trying to decide which stage of the funnel you want to improve with your digital signage deployment, think about the immediate goal of the system. For example, do you want to:
- Drive more traffic into your store from outside
- Drive traffic to specific locations within your store
- Encourage "up-sells" to higher margin products
- Encourage cross sales of complementary products
- Improve awareness of a particular brand, service offering or product line
Each of these goals targets a different stage of the sales funnel. So the goal of your network is going to determine the method and metrics that help you determine your ROI.
I'm going to stop here for now, but I'll pick up soon with the next installment of this article: Understanding the Limits of your Data.
Calculating Digital Signage ROI: Understanding the Limits of Your Data
Author: Bill Gerba on 2005-03-30 12:02:13
This is a continuation of a previous article called Calculating Digital Signage ROI: The Ground Rules. In this article, we're going to focus on some things NOT to do while performing an analysis of your digital signage data.
Understand the Limits of Your Data Set
We've all heard the saying "numbers don't lie," and that's true. However, it's unfortunately also true that people do lie. Methodically. And perpetually. And in fact, even the best-intentioned people can get into trouble when analyzing data because their own hopes and beliefs will alter the way they interpret their numbers. So I'd like to point out a couple of common pitfalls and misunderstandings about data analysis that will hopefully spare you the embarrassment of being called a cheat and a liar the next time your customers ask you for the hard numbers.
First, correlation DOES NOT imply causation.
In other words, just because two events happen to occur at about the same time, you can't automatically assume that one of them caused the other -- or will continue to do so in the future. Without a control case, you'll never be able to accurately measure the impact of your digital signage network. You might like to think that the 30 second spots that you're running for cotton tube socks were responsible for the 15% up-tick in tube sock sales, but in reality, other factors -- perhaps unusually cold winter or a new fashion fad that idolizes 1970s basketball stars -- could have caused the surge as well. The only way to be sure is to measure your signage-enhanced numbers against numbers from a known control group, for example a store with similar demographics and geography, but no signage network. If I've confused you with my explanation of this concept, you might want to check out this link, which gives a brief introduction to some common statistical notions.
Next, work blind when you can.
As I said before, even the most honest people can be influenced by their own subconscious desires. That's why any serious scientific analysis takes place under "blind" or "double blind" conditions, where researchers are not aware of whether they're analyzing their target dataset or that of a control group. Digital signage analysts can work much the same way by simply removing any identifiers from their data set and having more than one person analyze the data. If you can afford it, you might want to consider having an independent auditor come in to check your numbers. The bigwigs like ACNielsen and Arbitron have all sorts of clever ways for measuring traffic in your locations, and years of experience in determining the effectiveness of marketing campaigns in retail locations. Also, paid auditors can lend an air of legitimacy to your data, and they have (slightly) less incentive to cook the books (Arthur Anderson consultants notwithstanding, of course).
And finally, don't affirm the consequent
When people say something like, "you can't prove a negative," what they're probably talking about is the 3,000 year-old proof that you cannot prove an argument true by affirming it's consequent. Consider the example:
[ 1 ]If the digital signage network is effective then we will see an increase in sales of promoted items.
[ 2 ]Sales of promoted items have increased.
[ 3 ]Therefore, the digital signage network is increasing sales.
These are perfectly logical-sounding statements, and a great way to "prove" the same kinds of things that I mention in the "correlation does not imply causation" example above. The problem is, you actually can't prove this argument. Even though the premises might all be true, the conclusion is not necessarily implicated by the first two clauses.
I think this is a good place to leave off for now. In a future article, I'll start looking at different ways to model ROI, as well as some techniques for getting the data you need to tweak your network's performance.
Calculating Digital Signage ROI: 3 Metrics that Matter
Author: Bill Gerba on 2005-04-07 08:14:27
Picking up where we left off last time (in "Calculating Digital Signage ROI: Understanding the Limits of Your Data"), I'd like to focus on some specific metrics that might be useful to digital signage network owners trying to discern the true value of their systems. While there are probably thousands of different variables that you could choose to analyze, I've picked 3 tried-and-true marketing statistics that exist outside of the digital signage world to show that even though our medium may be new, some of the ways to analyze it are as old as the hills.
Metric 1: CPM
Ah, CPM, the granddaddy of marketing statistics. A three-letter acronym for Cost Per Thousand viewers, CPM shows its age by having part of its name written in Latin ("M" is the Roman numeral for 1,000). While many people debate the value of CPM metrics in today's era of Tivo ad-skipping and Internet clickthroughs, I maintain that CPM is useful simply because there is such a vast quantity of CPM data available that we can use to establish baselines and normalize our data. Basically, the way to calculate CPM goes something like this: get the average weekly traffic numbers from your venue. For the sake of this example, let's say that our sample retailer has 5,000 customers each week, based on register sales and quarterly audits. To keep the example simple, we'll also assume that the average customer spends 10 minutes in the store, and our digital signage content loop is 10 minutes long, consisting of 100% advertising. Thus, on average, every customer in the store would see each ad in the loop about once. Finally, assume that a single spot in the loop costs $150 per week. We can thus calculate that each impression costs about $0.03 ($150/5,000), so the cost per thousand is about $30.00.
Metric 2: Impressions
Very similar to CPM is the notion of impressions. Instead of tracking only the number of unique people that see an ad, impressions is a way of measuring the number of times that any given person sees the same ad (even if they've seen it more than once). For example, let's use our scenario above. Once again, our sample retailer has 5,000 customers each week, and the average customer spends 10 minutes in the store. Our digital signage content loop is 10 minutes long, consisting of 100% advertising. Once again, each impression costs about $0.03 and our cost per thousand visitors is about $30.00. However, what if the average visit length was really 20 minutes instead of ten? In that case, each viewer would see each ad an average of twice. Now, even though our CPM is exactly the same (since it's the same number of visitors as before), our cost per impression has been halved, to only $0.015 per impression.
How do we tell whether it's better to use CPM or impressions? Well, like everything else, it depends. In this case, it depends on what you, the digital signage network owner, are trying to sell. If your network places screens in high-traffic areas with short average visit duration, a CPM-based pricing system probably makes the most sense for you, since it more accurately describes the kind of traffic that you have and the kind of impact that you're going to be able to make. On the other hand, if you have a captive audience that tends to stay in the venue longer, you'll be able to present the same (or similar) content to your audience several times in a single visit. In this case, it might make sense to measure impressions, and price your network out that way.
Metric 3: Immediate feedback response
There's no generally accepted term for what I call immediate feedback response, or IFR, even though it's an extremely common method of measuring different kinds of out-of-home marketing and advertising campaigns. In an IFR system, the marketer uses some kind of simple but measurable feedback system to record the presence of a user, and then uses that conversion number (e.g. the number of viewers who were converted from passive viewers to active participants) as the basis for other measurements, like sales conversions, brand recall, and so on. For example, imagine a digital POP display that instructed the viewer to take a free recipe card from a stack beneath the monitor. By recording the number of recipe cards given out, and the frequency with which the stacks need to be replenished, a retailer can gauge (with reasonable accuracy) how many people are paying attention to the signage. This technique can yield more accurate results than just using baseline traffic data, and more importantly, it helps demonstrate who's looking at the signage, instead of who's just walking past it. The key to a successful IFR experiment is to keep the feedback loop short, simple and attractive to the viewer. If it's challenging or time-consuming for your viewers to respond to your promotion, they won't.
These are three of the quickest and easiest metrics to investigate, and they require relatively little activity on the part of the store owner (which can be critical, since getting owners or managers to do extra work for you is not always going to be an option). As I mentioned before, the metric that you choose to track will be highly dependent upon the type of network that you run, the frequency and duration of your content, and the kind of audience that you are hoping to attract and appeal to. And in some cases, you might need to track something completely different than the metrics I use above. However, these should serve as a good baseline, and we'll use them as examples in the next installment of this article, Calculating Digital Signage ROI: Methods to Gather your Data.