Retail analytics: Sodimac Homecenter Vs. The Home Depot
Wednesday, November 10, 2021
Retail home improvement franchises need to apply location intelligence techniques and foot traffic analytics to identify consumer mobility patterns, in order to maximize sales and generate more efficient expansion models.
The correlation between foot traffic, visits, sales, and the success of hardware home improvement franchises have been studied and proven, so the development of this type of analysis has become a priority in the site selection process and expansion modeling.

Case Study

Retail analytics: Sodimac Homecenter Vs. The Home Depot Mexico City, Mexico

At PREDIK Data-Driven we conducted a detailed study of twohardware home improvements in Mexico City, USA, Sodimac Homecenter and The Home Depot both located in Tlalnepantla.

In this case study, we analyze the mobility and pedestrian traffic inside and outside both stores, in order to understand the behavioral patterns of consumers visiting both franchises. This analysis aims to answer the following questions:

How are visits distributed in each establishment?



Through location analytics, we identify the points of interest and apply a heat map to visualize the in-store mobility patterns of the clients, which allows us to visualize the customer journey, the dispersion of consumers, and the distribution of visits within both establishments.



This provides very useful information when conceptualizing the design of the infrastructure and internal architectural plans that make up each establishment so that leaders can implement strategies that improve the customer journey and implement more efficient expansion models while maximizing the shopping experience of consumers.

Which of the hardware stores is the most visited?

Percentage distribution of visits recorded in December 2020:



By analyzing in-store mobility using the stated time period, we identified that 84% chose to visit The Home Depot, while the remaining 16% preferred Sodimac Homecenter, which correlates with store location and consumer preference when it comes to choosing coffee shops products.



These analyses allow businesses to observe the evolution of visits over time, which can be very useful to identify patterns of foot traffic customer behavior and market trends in high and low traffic seasons.

Identify consumer behavior: Which days of the week are the most visited?



One of the most interesting applications of location intelligence is that it allows gaining detailed knowledge of customers’ behavior patterns by day, hour, month, or year, offering valuable insights to design marketing campaigns and commercial strategies based on the power hours of the hardware stores.



This analysis is very useful to know what is the performance of the stores at peak and off-peak hours of the day.

What is the foot traffic mobility pattern around both hardware stores

Although foot traffic is related to the performance of any retail location, it is not the only key factor for success. Another fundamental aspect to be analyzed is the environment of the outlets, as it allows for comparisons and estimates of the number of visits, revenues, strategic and operational movements of the competition.

By gathering information on the competition’s potential customers, it is possible to carry out a more detailed benchmarking and generate strategies to capture the competition’s clients.



This analysis of the environment provides us with a detailed picture of the surrounding areas and the mobility patterns of people moving through the area. This data, combined with other factors, provides deep insight into predicting the revenues of any retail establishment.

What other insights can be gained by analyzing footfall at a hardware store?

Understand which customers visited bothhardware store

By analyzing data over a given period of time at a specific location, such as a clothing store, it is possible to estimate the percentage distribution of consumers who visited both stores.



These solutions benefit any type of business, an example of this is another case study that was conducted to compare two of the most popular hardware stores in the city of Dallas, Texas, USA, the findings were more than interesting. Read more about this case: “Competitor Analytics: Home Depot Vs. Lowe’s home improvement

Customer Analytics

With this analysis, it’s possible to infer in which other places (stores, restaurants, shopping malls, residential areas, among others) the people who were at a point of interest also visited. Thus, The Home Depot and Sodimac Homecenter can analyze how their customers behave, since they can look where and how long they were before and after visiting the supermarkets. This allows them to generate high-value insights to optimize the understanding of current consumers and search for new potential customers with similar behaviors.



Identifying ideal areas in expansion and site selection strategies

With mobility data, it’s possible to clearly understand the behavior of the people who pass through a given area, how they’re alike, their tastes, preferences, relative wealth index, and purchasing potential. Including an in-depth analysis of the commercial establishments in the area in question, becomes a crucial factor in determining the best locations for the opening of new stores.




What is the revenue potential of my competitor?

Through machine learning models, it’s possible to predict the revenue and visits of a competitors´ store. With these models, The Home Depot could get to estimate the revenue of its competitor Sodimac Homecenter in a specific week, month, or year. These models can also be used, for instance, to predict the potential success of an outlet that is about to open. This is ideal to complement feasibility studies for new stores in expansion plans.

All these insights are generated by applying location intelligence and mobility analysis, if you are interested in knowing more about these insights, we conducted a POI characterization case study in Bangalore, India POI Analytics: Uses and Applications.

DO YOU NEED MORE INFORMATION ABOUT YOUR BUSINESS SECTOR?
Request more information:
Name*
Last Name*
Email:*
Telephone (choose your Country):*


Company:*
Title:*
Size of the Company:*
Industry:*
Yes, I want to receive a call from a salesperson*
Comments:*
this site is protected by reCAPTCHA and Google's privacy policy and terms of service.
Need assistance? Contact us
(506) 4001-6423
 
More on this topic
How to increase foot traffic in clothing stores?
November 2021
Retailers can apply location intelligence techniques and foot traffic analytics to understand consumer mobility patterns, measure foot traffic at each store, understand the performance of their outlets, and estimate competitor turnover.
The correlation between foot traffic visitation, sales, and the success of retail apparel franchises have been studied and proven, so the development of this type of analysis has become a priority in the site selection process and expansion modeling.
Competitor analytics: Home Depot Vs. Lowe’s home improvement
November 2021
Retailers are already implementing Big Data tools such as location intelligence and foot traffic analytics to understand consumer mobility patterns, measure foot traffic at each store, understand the performance of their outlets, and estimate competitor turnover.
Footfall analytics
,
location intelligence
, and
POI analytics 
have revolutionized the way the
retail
and home improvement industries implement
expansion
, commercial and operational strategies.
How to Increase Foot Traffic in stores?
August 2021
In the digital age, location intelligence and foot traffic analytics based on mobility data are changing the retail business, giving many retailers an edge over their competitors.
Location intelligence is defined as a methodology for understanding and visualizing mobility data to help solve a wide variety of retail problems.
Location analytics can drive retailers to success
August 2021
Retail companies are already implementing Big Data and geolocation analytics tools to understand consumer mobility patterns, measure foot traffic in each store, understand the performance at their points of sale and estimate competitors’ turnover.
Big Data
techniques allow the recollection of large volumes of
geospatial
and anonymous data from various mobile devices such as cell phones, computers, tablets, etc., making possible to generate different detailed and general analysis that help to solve any kind of business problems in any specific sector.
Goods
Business Intelligence
Countries
Mexico
Insight
big data Business Intelligence foot traffic retailhardware geomarketing data science geospatial data expansion regional expansion consumer behavior Business Intelligence Logistics Business Areas Retail Wholesale
RELACIONADO
Business Intelligence
Retail
big data
foot traffic
geomarketing
Daily Update Government PurchasesDownload brochure (only in spanish)Trade Inteligence Subscriber Access NewsletterContact Us MarketDataMexico Español
2008-2022 © CentralAmericaData.com
Trade InteligenceWho we areContact Us