Competitor Analytics: Home Depot Vs. Lowe’s home improvement
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.
With these analytics, businesses get a more detailed picture of their store performance, while predicting or estimating brand positioning, customer behavior, market trends, turnover, and expansion models (site selection), both their own and those of their competitors.
By applying footfall analytics through spatial data mining, it becomes possible to collect valuable information such as the number and the classification of people who visits an establishment or area of interest, the hours and times of the day where there is the most foot traffic, the dwell time inside the stores, the number of visits and the market potential of points of sale or other points of interest (POI) that leaders use to make more efficient and concise decisions that generate greater profitability by maximizing revenues and optimizing costs.
The correlation between foot traffic, visitation, sales, and the success of home improvement franchises has been studied and proven, so the development of this type of analysis has become a priority in the site selection process and in modeling the expansion of retail and hardware businesses.
Study Case: The Home Depot Vs. Lowe`s Home Improvement, Dallas, Texas, USA
At PREDIK Data-Driven we conducted a detailed study of two home improvement franchises focused on hardware retail in the state of Texas, USA, The Home Depot and Lowe’s Home Improvement, both located in the city of Dallas.
In this case study, we analyze the mobility and foot traffic inside and outside the stores, with the objective of understanding the behavioral patterns of consumers visiting both brands. This analysis aims to answer the following questions:
How are visits distributed in each store?
Through location intelligence, the points of interest are located, then a heatmap visualization based on a mobility analysis is applied, letting us observe the internal distribution, the dispersion of consumer mobility, and the distribution of visits within both stores.
This provides very useful insights when conceptualizing the design of the infrastructure and internal architectural plans that make up each warehouse so that leaders can implement strategies that improve the customer journey and enhance the customer experience with the most efficient expansion models while maximizing the shopping experience for consumers.
Which of the warehouse clubs is the most visited?
Percentage distribution of visits registered between December 12, 2020, and January 22, 2021:
When analyzing mobility within both stores using the stated time period, we identified that 58% chose to visit The Home Depot, while the remaining 42% preferred Lowe’s, which correlates with store location and consumer preference when it comes to choosing home improvement products.
With this analysis, it’s also possible 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 peak and off-peak seasons.
Identifying customers’ patterns: Which days of the week are the busiest ones?
One of the most interesting applications of footfall analytics is that it allows gaining a detailed knowledge of the day, hours, months, or years of consumers’ behavior patterns, offering valuable insights to develop tailor-made marketing campaigns and commercial strategies based on the warehouse’s power hours.
This analysis is very useful to understand the performance during the most active hours of the day.
What’s the foot traffic mobility pattern like in the surrounding areas of the stores?
Although visits are correlated with the performance of any location, they’re not the only key factor for success. Another fundamental aspect that must be analyzed is the location’s environment since it allows identifying the competition, understanding its success, estimating the number of visits received, revenues, business strategies, etc.
By gathering information about the competition’s potential customers, it’s possible to perform a more detailed benchmarking and generate strategies that can attract the competition’s customers.
This environmental analysis provides us with a more general picture of the mobility patterns of people moving through the surrounding area. This data, combined with other factors, provides deep insight when forecasting the revenues of any retail establishment.
What other insights can be obtained by applying footfall analytics at a point of sale?
Understand which customers both home improvement franchises share
By analyzing anonymized and aggregated mobility data over a given period of time at a specific location, such as a hardware store, it’s possible to estimate the percentage distribution of consumers who visited both shopping centers.
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 supermarkets in the city of Phoenix, Arizona, USA, the findings were more than interesting. Read more about this case: “Visits battle: Whole Foods Market vs. Sprouts Farmers Market“
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 Lowe’s Home Improvement 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, socioeconomic level, 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 Lowe’s 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 of Zona Rosa in Mexico City.