Issue link: https://resources.tangoanalytics.com/i/1465853
SMARTER STRATEGIES. FASTER EXECUTION. I www.tangoanalytics.com I PAGE .5 And like all standalone solutions, they are only one piece of the puzzle. While they provide some answers, without the logical connection to predictive analytics and store execution development, you're making decisions based on parts of the story – and missing the full plot. Retailers require their GIS systems to move beyond mere visualization, to be responsive to shifts and ensure rapid and fully informed decision making and execution alignment. Predictive Analytics Ironically, most of the predicative analytics suppliers are not forward thinking when it comes to their solutions. Being able to anticipate changes and adapt the way you think and operate provides distinct competitive advantage. This type of agility can only come with rapid _eld deployed access to market intelligence and decision support tools – a paradigm that is foreign to most traditional analytics vendors. They approach the analytics space from the perspective of "selling fish to a customer, instead of teaching them and then enabling them to fish on their own". Traditional vendors direct their attentions to narrowly focused studies and analytical projects, whose resulting deliverables provided in either document format or deployed on bare-bones mapping tools, have little application. Their modeling approaches are limited by the lack of understand- ing of current trends in both analytical methodology and enabling technology. Their finished products are of limited use to retailers because these vendors simply lack the ability to operate and integrate with complex IT and data environ- ments that exist in retail operations. Therefore, they deploy their models and analytical results on third party tools with hard coded / black box models, making these already limited solutions accessible to a select few, and only within the confines of the office. Additionally, the fact that the models are hard coded by third party contractors, makes them impossible to maintain, subject to upgrade cycles of these vendors and certainly not adaptable to changing market realities. Aside from the lack of underlying technology, the analytics they o_er are incredibly limited from the standpoint of a retailer. Beyond sales forecasting for new store development, they do not provide models to help inform any other parts of the store lifecycle. Key strategic decisions need to be made constantly – should we renew a store's lease, close the store, remodel it, or maybe relocate it further down the strip center like Dunkin did to increase sales by 50 percent. Without integration to the entire store lifecycle, these standalone predictive analytics solutions fail to provide the intelligence required to inform all strategic decisions across the entire real estate strategy and store development process and realize the impact of these decisions through to strategy execution. STRATEGIC STORE LIFECYCLE MANAGEMENT