Is predictive modeling for you?
A guide for organizations at every stage: Predictive modeling to help organizations evaluate and select the right sites is poised to transform location-based decision-making. But are the latest in predictive intelligence tools right for you, right now?
The fact is, shifts in the site selection process are happening across industries and around the country, as a competitive landscape drives companies large and small to finely tune their efforts in order to help attract, serve and retain customers. It’s simply no longer enough to rely on gut intuition or assume that what worked in one location will be a home run in another.
So, no matter what size or stage of growth you are at, every company needs to be ready for transformation when it comes to location-based decision-making — through the right insightful analytics tools.
Here’s why predictive modeling, driven by modern A.I. and machine learning tools, is taking the site selection space by storm:
It mitigates risk
Growth is about location, location, location. That means reducing the likelihood of a bad location-based decision. Regardless of your size and expectations for growth, you should always be considering tools that allow you to more likely hit the location bullseye.
It helps deal with big data
These days, there is far too much data available for anyone to consume, digest and distill into something actionable. But, you can leverage intelligence tools that are architected to do just that.
It provides an objective narrative
All organizations should want an emotion and bias-free perspective when it comes to decision-making. The latest predictive tools allow data to support an objective narrative that may not always agree with your gut-level conclusion.
It boosts resource efficiency
Less is more in today’s competitive landscape, as organizations are driven to do more with fewer resources. Real-time analytics can help you streamline workflows and make decisions faster.
It helps in unfamiliar territory
Each new market brings new challenges. Even for mature organizations, current markets evolve over time and purchasing patterns constantly change. This requires up-to-date data and tools to synthesize into action items.
Evolving your story at every stage
Every organization has a journey-filled story to build and draw insights from. Leveraging a robust predictive model ultimately allows organization to make fast, dynamic, transparent and real-time location-based decisions supported by an objective narrative.
Predictive Power, however, is a process that evolves over time, with the right tools. To see ROI, you must develop your predictive model first, which requires you to set yourself up for success with the right data.
These are the three most common stages of growth and how location-based decision-making can be transformed with predictive at every step of the way:
Stage 1 Capturing predictive power
A small, growing business with just a handful of locations is likely just starting to gather the data it will need to build a predictive model — one that will help determine what aspects of its locations are repeatable and scalable, and what can be expanded outside of the home market or local/regional area. After all, the larger the sample size, the more predictability can be gained. As a company adds additional locations, more scenarios are encountered, from layout to geography to traffic. Now is the time to capture the right data and build predictive power.
Use predictive modeling to discover…
- How many units you can buildNow, your predictive model can help discover how many units are optimal in your market and beyond.
- The top markets you should focus onIt’s time to focus: how does the company organize and prioritize its market focus? How does it get started?
- How to optimize market planningOnce you have identified the top markets, now you can build them out. Your predictive model can show you what that can look like.
- Key differences between good and poor performersYou can start to identify key variables that are surfacing for highly successful locations.
- How well a site will performLeverage historical data to understand a prospective sites performance and build reliable performers.
The democratization of data means that small businesses have access to information only available to big retail chains like Starbucks and McDonald’s just a few years ago. Traffic count data, through anonymous GPS signals, takes the guesswork out of the big, expensive decisions surrounding site selection. It vastly increases sample sizes, offers typical data by time of day or week, and can help you figure out what your needs are as a business, from purchasing patterns to trade area — without just relying on the opinions of a real estate broker or sales rep.”
KEVIN FOREMAN | GENERAL MANAGER, GEOANALYTICS/BIG DATA, INRIX
Stage 2 Finding predictive ROI
As organizations grow, there is likely an inflection point where it may likely have initial capital infusion from a private equity investor as a growth investment. By this time, it probably has enough data to build a predictive model and is now seeking ROI. Not surprisingly, a firm making a brand investment will have plenty of questions for the management team and founders, especially if the organization is looking to build a regional or national brand. These are also the questions the company needs to ask itself, armed with support from a predictive model that is now robust enough with objective data to offer useful insights.
Use predictive modeling to discover
- How many units you can build
Now, your predictive model can help discover how many units are optimal in your market and beyond.
- The top markets you should focus on
It’s time to focus: how does the company organize and prioritize its market focus? How does it get started?
- How to optimize market planning
Once you have identified the top markets, now you can build them out. Your predictive model can show you what that can look like.
- Key differences between good and poor performers
You can start to identify key variables that are surfacing for highly successful locations.
- How well a site will perform
Leverage historical data to understand a prospective sites performance and build reliable performers.
With A.I.-driven predictive modeling, you can look at site characteristics, competition, or characteristics of people in the trade area that make them better or lesser as a customer — all with equal effect. Companies are now at the point where everybody understands that you have to have additional leverage of knowledge provided by these location research techniques, because you can no longer afford to make a mistake.”
JIM ROOTS | CEO, CAP LOCATIONS
Stage 3 Leveraging resources efficiently
A mature organization, with hundreds or even thousands of locations, has grown into a company with different needs and priorities. It has many layers of management and bureaucracy. It may need to visit hundreds of possible locations. It may have to decide what locations should close and which should be remodeled or relocated. For these “adult” brands, predictive modeling may no longer be simply about pushing growth to the next level. Instead, it becomes an essential tool in leveraging resources efficiently and communicating objective, useful insights that large teams can use to optimize opportunities and address challenges.
Predictive modeling can help with…
- Identifying differences among successful locationsNow that you are in multiple territories, have you identified geographical drivers of success and how they may vary?
- Assessing the site approval processYour organization has structure and workflow. Make sure you present the right information to the right individuals at the right time.
- Uncovering optimization opportunities in your current portfolioA cohort of your locations are at maturity. Do you have an opportunity to relocate, close, or optimize?
- Discovering the impact of existing locations on each otherAt a point where markets are becoming saturated, you need to understand the true size of trade areas and how existing locations affect each other within certain geographies.
- Identifying remodel prioritizationHow does a remodel package impact a location or portfolio? Predictive modeling can help you understand the impact and how to prioritize resource allocation.
Predictive location analytics should be ongoing, to understand profitability and evaluate all locations based on whether they are reaching their potential. In addition, a review of the portfolio is necessary to see if any relocations are necessary — sometimes, moving one or two blocks or moving from a strip mall to a nearby neighborhood mall can increase traffic and realize financial gains. Finally, established chains are always looking to grow – either expand in existing markets or cross state lines. Understanding where would be best can be done through screening new markets and identifying desirable attributes. You can discover new opportunities and score (match) them to existing locations to see what “potential” they have.”
DANNY HEUMAN | CHIEF ANALYTICS OFFICER, ENVIRONICS ANALYTICS
Predictive technology that grows with you
Today, you don’t need multiple tools or expensive, slow consultants to build and refine a robust predictive model that can help you boost your site selection success. The right technology tools, driven by A.I. and machine learning, are available to grow with you at every stage.
SiteZeus allows you to get all of your predictive modeling, from the infancy of your company to mature adult, done within one platform. As a data repository and warehouse, you can collect and gather all of your data as a small company with a handful locations. As you grow, other features will help you build and take advantage of your predictive model, from revenue heat mapping and Zeustimates to portfolio optimization.
With SiteZeus’ machine-learning predictive models at your fingertips, you will be in a powerful decision-making position, no matter what your size and stage — to grow, renovate, relocate or even downsize with confidence.
Want to find out more about how A.I. and machine learning can take your location-based predictive modeling soaring to site selection success?
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