How advanced retail analytics facilitates the selection of successful retail outlets

After more than a year of pandemic, large retail chains have announced aggressive expansion plans in different parts of Mexico. This generates great competition among different retail categories such as supermarkets, convenience stores, gas stations, banks, coffee shops and restaurants. To make their investment successful, when opening a new store or branch they need to choose an ideal location. How do advanced business analytics and machine learning help?

Retail present

In the current context, the retail industry has shown strength in the face of the health contingency, the update of the T-MEC and the volatility of the markets due to the confrontation between China and the United States.

According to data from the Asociación Nacional de Tiendas de Autoservicio y Departamentales (ANTAD), retailers in Mexico contribute close to 20% of Mexico’s Gross Domestic Product (GDP).

It is worth mentioning that the following sub-industries stand out among the retail sub-industries:

  • Food
  • Consumption
  • Chemistry
  • Technology
  • Automotive
  • Energy

Retail expansion plans

According to ANTAD data, the retail industry will invest 2 billion dollars in Mexico during 2021. This investment will be divided as follows:

  • Remodeling – 35.1
  • Construction of new stores – 34.2%.
  • Logistics and distribution – 16
  • Systems and technology – 9% – 9% – Systems and technology – 9% – Systems and technology – 9%
  • Human capital development – 5.7

In other words, approximately US$680 million will be invested this year to open new stores or branches. Among the companies undergoing expansions are:

RetailerExpansion plan
Carl’s Jr.This fast food chain aims to reach 400 branches in the country by 2024 and 500 by 2027.
ChedrauiAt the beginning of 2021, they announced that they would open eight new stores during the year: two Super Chedraui, five Supercito and one Chedraui store.
KFCDuring 2021, the company is looking to open 30 new restaurants and remodel 40 with walk-in kiosks and home deliveries.
La ComerThis supermarket chain expects to reach 100 branches by 2023, 28 more than at the end of 2020.
LiverpoolIt plans to open two stores this year and three in 2022. In addition, for Suburbia, its fashion and footwear brand, it plans 15 to 20 openings per year from 2022 to 2026.
Mail BoxesIt has an expansion plan that aims to double its stores in Mexico from 64 to 130 in the next five years.
Pizza HutBy 2021 it aims to open 49 stores and by 2022 it plans to open more than 100 new stores.
SorianaIt plans to invest 750 million pesos to open a Sodimac and four more stores.
3B StoresThanks to a 25% increase in sales in 2020, the chain plans to add 150 more stores to reach 1,350 by the end of 2021.
ToksIt intends to open new restaurants, including of its fast food brands Panda Express, Shake Shack and El Farolito, with the aim of reaching 15 new branches per year, as in 2019.
WalmartOf the 22.2 billion pesos that this company will invest in Mexico during 2021, 25% will be allocated to the construction of new Bodega format stores.

<< Build your company’s future: apply advanced analytics >>

Choosing the ideal space to sell

Although there is a certain consensus in the commercial sector as to which are the main streets in each locality to establish a store or point of saleOn those streets there are countless segments where the sales result can be very different even in terms of which side of the street the new location is placed. Many times retail companies make use of scouts or real estate companies, but their results can be slow and inaccurate.

Geospatial predictive analytics models help forecast what and how much will be sold in a given space before it is chosen or built. Such models are run on Big Data platforms with multiple variables for all street segments where a new store can be placed.

Thus, the internal databases of a business are cross-referenced with geographic, population and economic data from the environment and from private sources such as those from NoTrafficare processed by means of machine learningand with the help of operational applications, not only the best points in Mexico are providedbut quantifying the sales potential and the ideal product/service portfolio.

The Predictive Location Intelligence(PLI) technology of geospatial predictive analytics models increases the value of internal retail data – sales, CRM, Revenue Management – by combining it with data from the micro-markets where a business competes.

This, in turn, provides the possibility of reconfiguring or more precisely defining the offer in terms of products, pricing, promotions and point-of-sale execution, in order to make resources more efficient and increase sales.

In addition, PLI’s advanced analytics offers the following possibilities for the retail sector:

  • Expand and optimize the coverage network
  • Evaluate and manage the performance of established branch offices
  • Create promotions for the specific micro-market
  • Organize products according to space and business objectives

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With such a tool, retailers can adapt in a more agile and flexible way to changes in both the market and their micro-market, which is essential to face the challenges of the present and the future.

In addition, with the implementation of PLI, retailers acquire sufficient capacity to specialize, which provides competitive advantages at a time when users have so many options to choose from.

Does your company have concrete, real-time data on its stores and branches? What actions do you take to evaluate whether your offer corresponds to the demand of each location where your points of sale are located? What tools do you use to understand the micro-market in each of your stores or branches?

Comment in the space below and subscribe to my blog to learn more about other topics of innovation and scientific technology applied to business.

Originally published in Jorge Pérez Colin Blog

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