— By Pranav Tyagi —
Enhancing your store location strategy with technology.
Retailers and restaurants live and die by the addresses they call home. Yet, over the last few years, rapid digital transformations have all but forced some retailers and restaurants to re-think their physical store strategies and embrace new consumer behaviors. This shift, however, also bent the other way. Brands with strong e-commerce and direct-to-consumer approaches, like Amazon and Warby Parker, also saw the value that comes with opening physical locations.
Pranav Tyagi, Tango
Then COVID-19 happened, putting an even higher premium on what a business’ physical location (or lack thereof) means for its continued success. According to Coresight Research, nearly 8,750 stores closed in 2020. However, they also reported that roughly 3,300 stores opened in 2020, showcasing that while retail is not yet dead, it needs to be resuscitated. Many research estimates claim we have accelerated the transition to e-commerce by almost 10 years in just a few months, proving that a retailer’s location matters less. Yet, I would argue that location is more important now than ever before.
A retailer or restauranteur may find that not all of their locations will outlive the COVID-19 pandemic but having a good store lifecycle management (SLM) system in place will equip them with the data, insights and strategies needed to optimize their store portfolio and maximize profitability.
Forward-Thinking Data
Hindsight is 20/20, but for retail and restaurant firms, looking back at business and sales models from last year is no longer useful. Traditional models for store sales forecasting rely on historical observations to determine future outcomes. While the data coming out of 2020 may be historic in a sense, it’s certainly not data to model future sales projections given today’s rapid change of pace and evolving consumer behaviors. In an increasingly omnichannel world, businesses need data that reflects these rapid environmental changes in real time.
SLM utilizes advanced technologies like artificial intelligence (AI) and machine learning (ML) to more quickly understand the new normal and enable retail and restaurant businesses to make faster, more informed decisions. A sales forecasting model driven by AI and ML is quick to respond to changes, efficient and able to draw impactful conclusions far beyond the capabilities of a strictly human analysis. By sifting through new and old data sets and applying several different algorithms to find the optimal model, business managers can work with data that reflects the moment they are in. These systems can also support understaffed market research and data science teams by combining several jobs — data monitoring, collecting, compiling, aggregating and analyzing — into one.
Win the Location Long Game, Beat the Location Short Game
Just as Kenny Rogers sings in “The Gambler,” retailers better know when to hold a location and when to fold a location. They also have to know when to remodel a store and when to consider operational changes. There’s a lot to consider, and the stakes are high. A 5- to 10-year lease term is a long time to gamble on, so it is imperative that retail and restaurant chains have a reliable system feeding them up-to-date information with which to make these informed decisions.
SLM can help retail and restaurant firms optimize their current spaces and develop smarter location strategies to ensure they are maximizing their long game, while simultaneously reacting to short term changes. This could mean turning a customer-facing brick-and-mortar location into a mini-fulfillment center or increasing drive-thru and pickup capabilities at QSR units. Retailers are constantly cycling through new store prototype models to see which to adopt and which to avoid, so having an SLM on hand can help these businesses make more informed choices by comparing those models to ever-changing customer behaviors.
The way people shop and dine has been forever altered. Retail and restaurant executives must adapt. Investing in a SLM system can help to recalibrate data sources that will reflect changing consumer behaviors, reorient around new customer personas and recalibrate store strategies.
— Pranav Tyagi is founder, president and CEO of Tango, a leader in store lifecycle management (SLM), which Tyagi founded in 2008. Tyagi works with a wide range of clients across retail, restaurants, REITs, healthcare, and banking/financial services. For more information, visit www.tangoanalytics.com.