Last week, Ad Age published an article where the following comment was made about the convergence of shopper marketing with digital technologies:
“The killer app of mobile media may yet be in the store, as more consumers use their mobile devices to scan barcodes and get product reviews, coupons, or other promotional offers.”
Think about that for a moment. When the Internet came along, it had a tremendous impact on shopper behavior; we did much of our researching and browsing online (even if we didn’t always purchase online). Now, in a short time, with our mobile phones, we’ll be able to can scan a barcode and get reviews, coupons and other offers? Wow.
As stores move to visually de-clutter (a big move of late), imagine creating your shopping list using a special app and then walking into a store where the app would scan the store and the store would send you all the specials in the categories of your choice. Any manufacturers coupons that week? Send those too. Oh, and to save you time, we’ll tell you what aisle to find the merchandise – maybe even map a shopping plan for the store to save you time. Finally, at check out, your mobile device would send the correct coupon information to the register via Wi-Fi or IR; other promotional offers could be sent to your phone if you’d like.
How to get that killer app to market fast? Also last week, Mob4Hire announced the first “crowd-sourced mobile market research” service. They have more than 20,000 application testers (mobsters) operating in 105 countries. How this works is that when a new application is ready to be tested on the more than 330 mobile network carriers around the world, the mobsters download the test app to their mobile phone, test it on their network, and provide feedback to the developer. Fast, comprehensive, and very cost efficient relative to how this is typically done today.
Where does this take your imagination?
When the focus is on metrics or statistics, there are really three things that marketers must know to use them effectively.
#1: Observed and Expected
Statistics typically are based on a simple equation: “Observed minus Expected.”
Ex 1: Comparing the target audience (the Observed) against the total population (the control or Expected). High or low indices can inform marketing strategies and tactics.
Ex 2: Assessing company sales performance, where the actual sales (Observed) are compared against prior sales (Expected).
Even as statistics get more complex, we see this. An “average” (aka “mean”) is another way of saying “Expected.” Conjoint (trade-off) analysis, a complex multivariate technique, works under the assumption that at the start, all attributes have an equal chance of being selected.
Most marketers don’t know it, but they “talk” in “Observed minus Expected” all the time: “If we do A, I hope to see B change by X.” Measuring that goal becomes the foundation for designing research, for the statistics used in analysis, and for seeking unexpected insights.
(For those of us into social media/discourse analysis and data mining, a special note: there often isn’t an “Expected” with these methods, which is one reason why they’re harder to analyze. The goal, moving forward, is to start establishing benchmarks against which findings can be assessed – or it will always be difficult to separate new, critical insights from the undefined norm.)
#2: Correlation vs. Causation
Because two things are related doesn’t mean that one thing caused another. We know that over-eating (unfortunately) causes most people to gain weight. However, most smokers – some say 90% of them – don’t get lung cancer. Smoking doesn’t cause cancer, but it’s clearly a (correlated) high risk behavior because many more of those who smoke will get ill when compared with those who don’t.
How does this impact marketers? You can’t really assume that doing A will cause B to happen. However, business models built on connections between operations, consumer attitudes, behaviors, etc. are very powerful in finding ways to reach business goals.
#3: Compound Findings
Independent facts can’t be compounded or strung together to simplify the “story” being shared with others.
Does this example tell us that our target should be women, age 25-34, who participate in outdoor sports? Not necessarily.
Assuming a perfectly distributed sample, we’d estimate that the target is 25% of our population (65% x 65% x 60%). But what if all the men – this is a hypothetical! – said they were into outdoor sports? That would mean that only 25% of the women said the same – reducing our estimated target size to 11% (65% x 65% x 25%).
While compounding or grouping results makes for a clearer story, marketers need to know that the story may not be accurate.
I recently saw a really great article in More magazine. Judy Jones interviewed John Medina, PhD, director of the Brain Center for Applied Learning at Seattle Pacific University.
To me, the most interesting part of the article was the description of the four-step learning process:
- Encoding, where information enters the brain through the senses
- Storage, what we do with the encoded information
- Retrieval, being able to get information “out” when it’s needed
- Forgetting, which is often overlooked, he says, but critical to learning:
“Human learning is primarily subtractive. It’s controlled forgetting, learning how to forget in a smart enough way so that you can focus on what’s left in your cognitive landscape…[The brain has to figure] out what’s relevant and irrelevant, and then subtract the irrelevant…[this is] some of the most important work the human brain can do at any age.”
Medina goes on to say that as we get older, what we actually lose is our “filtering ability.” What’s happening is that so much information is going into the brain and we can’t “turn down the noise” – so we forget why we went into a room or where we left the keys, for example. We can retrieve information, but filtering is harder.
The findings suggest that, in our personal lives, exercise is most helpful in keeping our “filter” intact. (Just one more reason to get that 30 minutes in three times a week!)
From a marketing perspective, I wonder how we can use this information to break-through the competitive clutter. This is particularly critical in communication strategy, where the use of verbal and non-verbal cues, coupled with relevant associations, might create a more impactful execution.
What do you think? If your target audience is older, how can you envision using this new finding?
