For wireless retailers' business strategies to succeed, they need to incorporate feedback. Companies pursuing an omnichannel approach should test different tactics and measure their results.
If the feedback shows the retailers' goals are being reached, that's likely a good sign. If it shows the opposite, that's probably a signal that something in the game plan needs to change. What follows is a step-by-step guide walking wireless retailers through the process of testing the success of their omnichannel strategy.
There are, of course, many possible implementations of omnichannel strategies. Some attempt to entice customers into shopping by showing them targeted advertisements that relate to their niche interests. Others try to rearrange retail space to make the environment more conducive to purchasing, a field of practice known as visual merchandising.
Throughout this post, we'll use a simple, common strategy as our example: sending customers targeted coupons online to get them into brick-and-mortar stores. Imagine a wireless retailer wishes to increase foot traffic and is emailing out in-store discounts to do so. The company might want to know how effective this procedure actually is, and access whether it is driving conversions.
There are two types of data in question, quantitative and qualitative. Quantitative simply means numerical. Because numbers are so finite, quantitative data is often easier to process, especially with the help of a retail management system. Examples of quantitative data include how many in-store transactions have occurred on a given day and how many visits a webpage has scored. An advantage of quantitative data is that it is easy to process, especially with advances on the horizon such as machine learning.
Qualitative data, by contrast, is about experiences. Examples include customer feedback surveys, where customers share what they think about their consumer journeys. This form of feedback might reveal more insights, because patrons may express something that isn't easily captured in numerical format. That being said, this type of data may take longer to process.
For our coupon example, omnichannel strategists would want to collect both types of data with good analytics systems. The quantitative information would help establish if the discounts lead to higher sales. However, the qualitative data is needed for companies to stay aware of other elements that could influence numerical data. Perhaps, for instance, a weather disaster interferes with customers driving to stores in the first place—a factor that has nothing to do with the coupons. Gathering qualitative data can help wireless retailers ensure their data paints an accurate picture of their overall success.
Once all the information is in, wireless retailers will need to study it. Thankfully, analytics software such as point of sale systems and centralized commerce platforms do the heavy lifting. That way, individuals at companies don't have to be statisticians themselves. The data is presented in easy-to-understand ways that allow firms to ascertain what is happening at their brick-and-mortar locations and whether consumer habits are changing.
If the data suggests that a different course of action is needed, wireless retailers can adapt accordingly. In terms of our coupon example, the data could show, among other things, which categories of products the coupon-users are buying or particularly interested in. That would help retailers predict inventory supply for future coupon campaigns. Alternatively, imagine the coupon users aren't coming into the stores at all. Big data would reflect that, and wireless retailers could shift their omnichannel strategy away from coupons and toward something else, such as sending SMS messages to their customers.
Without collecting and analyzing data to inform changes and new strategies, wireless retailers are operating in the dark. Learn more about intelligently systemizing their omnichannel strategy by contacting iQmetrix today.