Lucernex expert and President Joe Valeri (see Joe’s management summary here) discusses how integrated sales prediction modeling improves an IWMS.
In my last Blog I discussed use of Lease Analysis integrated into IWMS to provide the cost side of an ROI prediction tool. Cost is only half the data needed however, an accurate and trusted prediction of sales is equally important.
Sales prediction modeling is a tool used by real estate organization with multiple revenue producing sites whether it is direct sales, cell phone contracts, walk-in patients, investment clients, or tuition payments. In all cases a picture of “the customer” must be produced, usually based on past location results or a client defined picture of their customer.
Building these prediction models is an art and requires a sophisticated statistician as well as knowledge of the industry and available data. There are a number of companies that specialize in producing these models and some, like the market leader Buxton, have software tools that include their models providing a visual, usually map-based, view of market opportunities. Many clients also have analysts inside their walls to build and maintain their models. In-house models, like cost estimation models, are very often built in MS Excel and then loaded into some mapping technology like Streets and Trips for use by a few users with seta licenses for that software. In-house models, while likely accurate at some moment in time, generally do not take advantage of the most up to date data and rarely utilize the most up to date technology for disseminating the results of the model.
To come up with a trustworthy and accurate ROI Prediction for any site you need to have an accurate sales prediction model. The quality and accuracy of the model comes from years of experience building models for companies in retail, restaurant, hospitality, healthcare or whatever specific sector your company is in. There are few companies who are capable of providing a really meaningful and accurate picture of “the customer”. However when you invest in developing a model that fits your organization you should make the most use of it. Most real estate organizations will use the sales prediction model and, using a combination of tools usually involving MS Excel, compare the sales prediction per site to estimated cost to figure out the ROI or IRR for that site. This process is rarely real-time, usually involves multiple steps and is often open to error through human keying and re-keying.
As far as I know there is no real estate software solution on the market that has both a time-tested cost projection tool and an accurate sales prediction model fully integrated into it in a real-time manner. If it does exist it is likely custom built for the customer (which means expensive).
Having a real time ROI on every site under consideration would allow for quick, effective decision making on site acquisition and would allow those negotiating leases to see the real-time impact of a change in lease terms on the site ROI. This would give any company a strong advantage in good or bad economic times and, if used over the long term, should substantially increases overall location profitability.
My last Blog discussed how Lucernex had labored to overcome the common problems associated with simply integrating desktop MS Excel based financial models for lease cost analysis and sales prediction.
Having solved the cost side of the equation with Lx LseMod online, we set our sights on overcoming the sales prediction side. Last week we announced a partnership with Buxton, the premier predictive modeling firm in retail, restaurant, hospitality, healthcare and government.
One of the most important goals of this relationship was to bring the technology containing predictive models inside the Lx IWMS Location Performance Management Solution allowing Lucernex to provide a real-time ROI prediction tool for every site under consideration. In its first version, the ROI predictor will focus on leased properties followed quickly by an ROI predictor for owned properties.
To learn more go to the Lx Location Analysis page.
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