In our previous blog entry (Pottery, Painting, and … Real Estate Analytics?) we talked about the importance of the right blend of art and science in retail real estate investment decisions. We also created profiles of two types of art that are often practiced in most companies: “pottery art” (field real estate people) and “paint by numbers” art (real estate analysts).
Let’s take a closer look at the “science” part of the equation. The science consists of information systems that help decision-makers visualize important data about markets and sites as well as predictive models that guide the development of successful store networks.
Although it’s great to philosophize about the blend of art and science, at the end of the day there is a cost-benefit analysis that must guide any decision to invest in software, data, and modeling services.
There are four key factors that must be considered:
1) The marginal impact of better science on the financial performance of stores.
For most companies, the opportunity to maximize profitability by putting the right number of stores in the right places is a compelling case for investing in more science.
If decision support tools reduce the number of underperforming stores by a small amount, the return can be very large. However, there are some cases where this may be less true: sales forecasting for stores with very low capital outlay (kiosks), market planning for stores that require highly specialized real estate (zoning, parcel size), and market planning for stores that are “opportunistic” and sign short leases (e.g. Christmas decorations).
2) Ability to define and measure critical success factors.
Predictive models must be able to quantify the key drivers of the business.
If success is predicated on the presence of a trade area population with certain demographic characteristics, daytime population, and competitors that can be identified and rated, a model can reliably estimate sales. However, stores that depend on specialized venues such as tourist attractions, sporting events, or interstate highway traffic will rely more heavily on “art,” and mathematical models may not be worth the investment.
3) Consistency in historical data.
Science is a data hungry beast.
The better the data, the better the tools, whether they are based on visualization or mathematical models. Most companies have pretty good data these days with the exception of customer transaction data, which can be acquired through surveys and other methods. However, if there are few stores, or a totally new concept is being launched, the best approach might be to defer the investment in science until a critical mass of data can be developed.
4) Culture of senior management.
There is a VERY broad range of management cultures among chain facility operators when it comes to analytics and the perceived value of science.
Although the trend in the past 10 years has been significantly in the direction of greater emphasis on science, it only takes a CEO who operates very fast by the “seat of the pants” to squash any attempt to capture the value of investment in visualization tools and analytics. CFOs are usually the most supportive because of their quantitative view of the world, and if you are a visionary who sees this opportunity first in your company, you might want to take him or her out to lunch to begin building support.
Each company can be profiled on the basis of these four key factors in order to generate a “high level” ROI assessment. If the preliminary indicators are favorable, the financial returns can be estimated more precisely and compared to the expected cost of implementing a system to capture the value. Companies like geoVue and others can assist in this process which includes identifying such line items as staffing, software, data, and professional services.
Jim Stone
