(Note that in determining tenantsвЂ™ rent, a mall operatorвЂ™s primary goal shouldnвЂ™t be to maximize its own leasing revenue but rather to maximize the combined sales of the mallвЂ™s tenants. New tools-letвЂ™s call them вЂњright tenant, right locationвЂќ (RTRL) tools-can estimate each tenantвЂ™s omnichannel sales and the rent it should pay, in every potential combination of tenants and units. Visit our Big Data & Advanced Analytics page For instance, a tenant with low productivity (that is, low sales per square foot compared with the rest of the mall) but high cross-conversion productivity could potentially be resized or relocated to a smaller unit. By studying the toolвЂ™s outputs, the company can identify stores that may need to be resized, relocated, repriced, removed, or given marketing support (Exhibit 1). Such insights are valuable for optimizing tenant selection and placement. The tool can also quantify a storeвЂ™s вЂњhalo effectвЂќ on a tenantвЂ™s wholesale and online sales. For example, the company learned that its apparel-heavy anchor store tends to weaken sales at nearby apparel stores while strengthening sales at nearby accessories and cosmetics stores. In other words, it examines the performance of each store as a spend engine: it shows whether and how much a particular tenantвЂ™s presence increases or decreases sales at other stores and whether consumers are likely to shop at certain groups of stores during a single mall visit. The tool quantifies how each store affects overall consumer spending at the mall. They donвЂ™t have a data-driven way of answering important questions, such as: What types of stores best attract consumers who live or work in the mallвЂ™s catchment area? Are my anchor stores driving foot traffic and sales into my mall? Which tenants are unexpected вЂњspend enginesвЂќ (that is, tenants that are driving cross-conversion and thus creating value despite their own low sales performance)? Which combinations of stores or categories yield the highest collective mall sales?ДЄ mall owner in Asia uses an advanced-analytics tool that crunches sales data and transaction-level data to reveal typical walking routes within the mall and patterns of cross-conversion between categories. Some malls have a basic tenant-category segmentation but lack a systematic and analytical approach to prioritizing, prospecting for, and acquiring tenants. Most mall operators decide opportunistically which tenants to lease to and which units within the mall each store will occupy. ItвЂ™s paying off: malls using these tools have increased their leasing revenues by double-digit percentages.

TheyвЂ™re determining the best mix of stores, understanding and planning store adjacencies that drive higher consumer spending and longer mall visits, and engaging in more-informed rent negotiations with tenants. In this article, we home in on how malls are using advanced analytics in an especially critical part of their business: revenue management. TheyвЂ™re using prescriptive and predictive analytics-built into user-friendly tools with strong data-visualization capabilities-to make smarter business decisions. That said, a handful of forward-thinking malls are leading the way in advanced analytics. Most mall operators still make decisions based on tradition, experience, or intuition-thereby leaving value on the table. What they donвЂ™t typically have are the analytical skills and tools to generate insights from the data. But weвЂ™ve found that malls already have access to significant amounts of data, including data on shopper behavior, tenant sales, and category performance. One oft-cited explanation is that malls havenвЂ™t traditionally interacted directly with consumers, so they donвЂ™t have much consumer data to analyze. Unfortunately, many mall operators lag behind their tenants when it comes to using advanced analytics. Advanced analytics, in particular, has the potential to revolutionize almost all areas of the mall business.

New shopping mall project report