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Incredible noise. Powerful signals. The results of all of the virtual testing showed no difference between the two conditions. The virtual experiment both cast doubt of the practice based on the theory, and prevented us from any expensive upheaval at the shelves.
One defends virtual shopping by using the same argument for surveys. Yes, VR is not real. But despite those seeming limitations, shopping decisions in a virtual store mostly correlate with shopping in a real environment.
Item shares match. Trial and repeat patterns validate. Even purchase dynamics largely agree. These validations are regularly published. Properly trained and prepared, virtual store shoppers provide us with valuable and valid data to guide decisions. What limitations we know are pretty easily avoided or managed. Purchase decisions involving heavy items, and shopping where any sensory experience besides visual is important can be a limitation.
But for the most part virtual reality shopping is a valid, indispensable tool, for experimentation. At their best, models are ways of thinking as much as they are an approximation of reality. Whether physical or mathematical, models are a way to make sense of a complex world, and shopping is a complex world.
There is lots of evidence that the use of models improves decisions. In fact that is evidence that using lots of models helps even more. Models help us organize information, make better and faster decisions, and develop and adopt more effective strategies. Given the amount of data in the shopper discipline, it should be no surprise that models are useful.
However, there is no question that among all of the described ways of knowing about shoppers, model building is the most complicated and requires much technical skill. Pepsi built a model of workplace beverage consumption that illustrates this. The workplace shopping environment is data-poor. Beverage sales can only be captured through survey or diary methods. We modeled a employee business whose office was in an office park to understand potential influences of consumption and purchase.
The model development allowed for continuous comparison between our hypotheses from survey analytic work, real-world observations, and the model. Model assumptions were adjusted to gain harmony among the sources. We then simulated the effect of different marketing and sales tactics on outcomes. Prior to modeling, most research identifying characteristics of workers, demographics, and consumption patterns implied that the only way to grow was to make access to beverage consumption as ubiquitous and easy as possible.
Modeling simulations revealed a surprising tactic. As few as 2 machines in the lobby accounted for as much consumption and sales as 4 times that number on the office floor. The maximum exposure to building traffic three times a day drove more consumption and sales than the convenience of having the machine on the floor.
Subsequent field-testing confirmed the strategy. One model overcame years of assumptions about the best way to deploy vending machines. Frankly, it does not always work that way, but models remain an essential tool for shopper work. In some cases, we might prioritize accurate prediction over understanding. This is the situation for many digital A B tests. An algorithm or a test uses the data to predict whether one alternative is better than another.
The prediction may produce no insight as to why the result is true, and in the world of just in time digital ad delivery, the results of an A B test run by a computer, evaluated by a computer, and automatically selected by a computer, the prediction is often judged enough.
While this process may be efficient, it is a big change from the past where management understanding was a key to success. The need to understand is a foundational reason for market research itself. Prediction and understanding are related but independent goals of Market Research, and it is possible to separate them. It is a business decision. The complexity of Big Data and the algorithms that read it have increased the frequency with which predictive models take precedence as more marketing occurs in digital environments where automation is possible and desirable.
Striking the right balance between prediction and understanding is still required for shopper insight. While accurate and timely prediction is often essential to leveraging opportunity, understanding is as often the foundation for innovation. Ancient Babylonian mathematicians were extremely good at predicting eclipses and other astronomical phenomena, but it was not until years later that Ptolemy began to understand how the planets and sun functioned in the solar system. Understanding why a result was achieved is important.
These overall processes and questions of shopper insight endure, but smaller, tighter, seemingly important, and urgent questions appear, driven by perceived crises, fire drills, and novelty. Despite the trend, eCommerce is still in its infancy for many categories, and its knowledge base is fairly shallow. At an advisory meeting for the fast-growing e-commerce arm of a large retailer, the head engineer reported finding they were continuing to deliver ads to customers who purchased expensive products post-purchase.
The engineers had no exposure to wider learning about shopper behavior. The current situation is forcing the accelerated adoption of online shopping tools and generating much anxiety about the collective uncertainty of the future. Although we will never know everything, we can use these fundamentals to ask good questions and to know enough to make good decisions.
Fundamental 2: Shopper v Consumer is an Important Distinction This definition also implies a clear delineation between shoppers and consumers and between shopping and consumption.
There are two common questions about this distinction: 1. Fundamental 3: Questions Are Really Important The questions generated by that curiosity are the key drivers of valuable insight. These are my BIG strategic shopper insights questions : 1. Traditional market research methods are quickly becoming an outdated marketing tactic. You need shopper and consumer insights immediately to stay one step ahead of your customer at all times.
Your shopper and consumer data should be delivered with sufficient volume to easily identify patterns and trends in both quantitative and qualitative data.
But, data alone is not enough to propel your business into success. Insights, or more specifically actionable insights, from your data collection is the key to maximizing the return on your investment. Without insights, data is just… data. At Premise we understand that time is money, and we built our robust global marketplace with speed and volume in mind.
With a network of verified contributors in over countries, we can almost instantaneously create a network, leverage our existing network of contributors, or partner with our clients to create a private respondent network that provides the data you need, when you need it. Interested in learning more about how Premise can help you to gather shopper and consumer insights to improve sales and gain market share? Visit us at www. What You Need to Know: Shopper vs.
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