Tag Archives: Retail
Location analytics helps retailers breathe new life into old strategies.
Online shopping is well understood. We don’t only know how many people visit an online retail site. We also know that changing the size of a picture by a few pixels will generate more sales. We can even see if online shopping carts have been abandoned, what items people have viewed, and how long visitors have stayed on a page to calculate their interest in buying a product.
But when it comes to knowing how many people shop at a physical store, traditionally we scratch our heads. We’ve been trying to figure out those details for more than a hundred years. And don’t get me started on “dark shoppers”—customers that visit a store but don’t purchase. Unlike online shoppers, “dark shoppers” don’t leave an activity trail. There’s been a lot of talk about how in-store beacons will change this, but the jury’s out on how shoppers will respond. Continue reading
While their behaviors confound retailers and marketers, we’re starting to gain a better understanding of what makes this cohort click.
Do you know any Millennials? You might even be a Millennial yourself.
Milliennials are contradictions, alternately described as lazy, entitled, idealistic, close to family, and racially diverse. Pew Research notes that Millennials are not bound to organized politics or religion, support a more activist government, are linked by social media, carry debt, and are optimistic about the future.
Demographers disagree about the exact time frame this huge group encompasses. Some say that Millennials were born between 1982 and sometime in the early 2000s. Pew Research says that Millennials range in age from 18 to 33 years. Continue reading
Finding a balance between consumers and companies when sharing geolocation information in the age of big data analytics.
Recently we returned from a retail conference where we highlighted to attendees the differences in perception and attitudes they have toward location data, depending on whether they are using it in their personal or professional lives.
This was the type of conference where those big-box and household-name retailers you see every day send their people in charge. They meet and discuss different ways to sort out the massive amounts of data they capture from today’s digital world. Their main purpose? Turn that data into hard results. Continue reading
Technology and the great recession have changed retailing forever. Gone is “Clonetown USA” with its repetitive retail landscape replaced and redesigned to engage the customer on their own terms. Today, it’s all about doing business locally, bringing your store to the customer rather than thinking the customer is inclined to seek you and your products out at your store. AppFire caused a major media buzz when they announced in January 2011 that the average Smartphone user spends just over three quarters of their 84 minutes a day using maps, social networking, and other activities immersed in the Web. The least important thing we now do with our phones is talk!
Smartphones have empowered the tech-savvy consumer and as a result stores are porous. According to the Mobile Movement Study, 95 percent of smartphone users have looked for local information and 70 percent use smartphones while shopping in-store to price compare or find the best place to purchase a product. For the retailer the most important statistic is that about the same number visit the business they search and 53 percent actually purchase.
The importance of knowing your neighbor
Dorothy, this isn’t Kansas anymore. It could be Anytown, USA. On my last trip to Kansas, it wasn’t the wheat fields or flatness that amazed me but the repetitive retail landscape. It seemed that every town was a clone of the one I had just left—the same restaurant chains, grocers, drugstores, and general merchants. Was it an unholy alliance? Had real estate developers, government, and retailers reached perfect agreement on what every town needed and limited the choice to a small menu of options? However, the more I looked, the more I found exceptions. The harder I tried to quantify the way towns were similar to each other, the more I noticed the differences and came away knowing that local flavors dominate. Continue reading