Few organizations are big enough to satisfy the needs of an entire market. Most companies are forced to split the total demand into several segments and select only those that they are best equipped to handle.

Market segmentation can help businesses of all kinds and sizes to make better decisions.

Experts suggest that a market segment should be:

• Easily and clearly identifiable
• Measurable
• Accessible by promotion, communication and distribution channels
• Different in its response to a marketing mix
• Stable (not changing too quickly)
• Appropriate for the company’s policies and resources
• Substantial enough to be profitable

The basis for segmentation is a factor that varies among the groups of a certain market, but is consistent within each group. All markets can be broken down in different ways, and although many of the bases used to segment a consumer market can also be applied to businesses and organizations, the sheer nature of these eventually leads to other specific segmentation bases. The following is a comparative chart of the criteria or basic strategies to segment both market types:

The identified segments are profiled and the attractiveness of each one must be evaluated before choosing a target market. However, a question that stands after selecting the criteria to break down the market is: what methodologies are followed to build up a segment in the first place?

There are rigorous analytic techniques used to organize consumers into groups with related needs, wants, and attitudes. With these, the size and potential market of each segment can be determined, along with the positioning and the appeal that will be used to cover it. For example, a K-Means Cluster Analysis attempts to identify relatively similar groups of interviewees based on selected characteristics using an algorithm capable of handling large amounts of people. Essentially, this method tries to separate n observations into k clusters, and each observation belongs to the cluster with the closest mean.

A very well-known industry in which this sort of analysis is frequently used is the mobile telecommunications industry, where competition is becoming stronger around the globe. Customers are demanding a higher quality at a lower price, and carriers’ profits and average revenue per user are facing tremendous challenges. In this case, the aim of clustering is to categorize prospective customers into unique groups for distinctive contact strategies and new offerings, by managing billing system data and information. This data, along with call records, describe customer utilization and spending behavior, allowing operators to approach customers in a more effective way. China Mobile Limited, the largest mobile carrier in the world with over 600 million subscribers, is well known for using this analytic technique.

Apart from playing a major role in developing new marketing efforts to attract new customers, market segmentation can also help a business to discover ways to reinforce existing customer loyalty. For instance, a firm may request some kind of feedback through questions specifically addressed to a certain group with the purpose of obtaining practical suggestions on how to make a product better. The customer will then know that the company is truly aware and the bonds between them will grow stronger.

This was the premise of the study by TIS Consulting Group to provide recommendations to the Secretariat of Agriculture, Livestock, Rural Development, Fishing and Food (SAGARPA, Spanish acronym), which seeks to promote tourist centers and support agricultural producers in Mexico. The most important touristic centers in Mexico, being the main tourism-based income generators are: Cancun and the Mayan Riviera in Quintana Roo, Puerto Vallarta in Jalisco, the Nayarit Riviera and Los Cabos Corridor in BCS. These centers were the subject of study.

Among other objectives, was the use of analytical optimization tools to determine the potential competitive advantages presented in these chains. One of the main findings of the study is that agricultural producers find difficult to accomplish demand and financial requirements of the centers of tourist consumption, by themselves. This causes an excessive involvement of intermediaries which add costs of supply for the touristic centers, and a limited relationship between producers and buyers; making the traditional supply chain inefficient, inflexible and raising the costs of products on the market. As a result, touristic centers become less competitive.

The underlying objective is to move from traditional to modern supply chains. Through the design of an efficient logistics network between the producer and the client (in this case in the tourist area) using optimization tools. The modern supply scheme proposed a consolidation facility closer to production, with a direct link to distribution centers located in the different tourist areas.

Example of the expected flow volume of fruits and vegetables from production zones to the main touristic centers considered in the study (scenario of demand at 50%)

To assist in the network design a mathematical mixed integer programming model was used, to find an optimal result given a certain number of decision variables. This model generates the lowest possible and feasible cost based mainly on p-median plant location model and transport model. The decision variables considered to minimize these costs are:

• The opening of a storage and consolidation facility in a specific Rural Development District (RDD) (based on their agricultural production).
• Which consolidation center should supply which tourist distribution centers in the given time periods.
• The number of pounds shipped from each consolidation center to each destination.

The model follows some assumptions and several restrictions; such as RDDs’ production capacity, tourist centers’ demand coverage, and the minimum shipment from the RDDs, among others.

The proposed logistics strategy is based on the optimization model and was created to get results for different scenarios. These were developed based on the agricultural products demand. One of the analyzed scenarios covers 50% of the demand generated by the consumption centers. In this scenario the results were:

• 30 RDDs are required to provide 53,000 tons of agricultural products.
• In detail, 35,000 tons of fruit and vegetables would be supplied by 10 RDDs,  while 9 RDDs would supply 10 thousand tons of chicken and meat, and finally 7,000 tons of seafood products by 16 RDDs.

A feasibility analysis was done, and it provides an economic interpretation of the benefits to be obtained from the distribution strategy mentioned. These benefits would not only be for farmers and fishermen but for consumers in the touristic centers as well, thus contributing to the competitiveness of both sectors.

Based on the model’s results recommendations and conclusions were made for a gradual implementation of a logistic network and a modern supply chain. SAGARPA’s objective was supported by the used of analytical tools to create public policies that boost agricultural producers benefits derived from the tourism consumption in Mexico.

[*1] La Salle University in Cancun. 2002. Analysis of the Structure in the Demand of Products for the Touristic Sector in the Mexican Caribbean. Touristic Investigation Institute. http://www.touriststats.com.

There is no ideal or standard pattern to the dilemma of business location. This is because the variables involving every type of business according to their characteristics, company profile, among many others, create a gap between their needs and therefore the way they approach the problem. For example, the location considered good for a basic necessities store, may not be so for a luxury brand clothes store. This given that it is more likely that a customer is willing to devote a greater amount of time to buy a luxury garment that meets the specifications required, than to spend the same time seeking to find a loaf of bread or a gallon of milk. This is why although it would be appropriate to establish a convenience store where there is traffic on residential areas; it would not be the same for a clothing store which can generate the customer traffic it needs.

The location is usually one of the most important strategic decisions

Currently, the location of commercial establishments can be very diverse. From inside a supermarket or mall, to independent retailers in touristic areas or in the center of the city, among other places. In either case you may find advantages and disadvantages relevant to your specific situation.

Other questions that arise in the process of locating stores include: Which sector of the city would be more convenient?, Does the crush of people and lack of parking affect my business positively or negatively?, Is it preferable to be within a shopping center?, How can competitors located next to me affect my performance?, What are the cost benefits of locating my business in a busy street or a corner?, Should I then consider the direction of traffic at different times of the day?, What kind of factors should I focus on and which of those really add value to my business?

Vehicular or pedestrian traffic is mentioned in many studies as a significant factor in the decision making in regards to location. However it is important to consider that attracting customers not only depends on the flow but this should be considered as part of the target market as well. A location that has a considerable level of traffic would not be beneficial for sales if this flow does not consume the product.

The decision on locating a business can be conducted using various methods such as analog, financial, statistical and regression analysis, and mathematical modeling, among others. The variables to consider in these methods generally fall into categories such as socioeconomic, demographic, situational (external environment), site, competition, establishment’s conditions, its staff and the products and / or services it offers. In particular, the use of statistical methods has become very popular for making decisions in this area, since it provides a mathematical support to the decision -that is, the analysis based on hard data and not just opinions-. Nevertheless, experience in the type of business and target market knowledge will always be useful in addition to any other method when selecting the optimal location.

In addition to the common analysis procedures used for selecting locations, in recent years a procedure based on Geographic Information System (GIS) was introduced. GIS is an information system that works through spatial referenced data and geographical coordinates (Star & Estes, 1990), designed to solve problems of location, routes, trends, models, etc.. This system depends on a geographic information database, which through a digital map identifies relevant data of a specific point on the map. This way, GIS provide relevant information assisting in the proposed site location problem; however the availability of such systems is still limited to only a few countries.

Clarkson, R. M., Clarke-Hill, C. M., & Robinson, T. (1996). UK supermarket location assessment.
Star, J., & Estes, J. (1990). Geographic information systems: An introduction. Englewood Cliffs, N.J.: Prentice Hall.
Wheeler, A. K. (1997). Competitive Facility Location Strategies for Retail Firms. University of Georgia.

As part of the Federal Government’s efforts towards healthier eating habits in schools, TIS Consulting Group acquired the task of undertaking a project to identify the needs of supply and procurement of fresh produce (fruits and vegetables) for public schools in Mexico or farm to school programs. The purpose of the project is to design a distribution network between the producer and final consumer (public schools); reducing the number of intermediaries in the network in order to benefit the producer as well as the consumer.

The above project aims, among others, the following objectives:

• Identify the food requirements of the student population in basic public education (elementary and middle schools), where the focus would lie in fresh produce (fruits and vegetables) that could be supplied locally and regionally by the producers in Mexico.
• Analyze barriers that refrain from achieving a satisfactory, economic, quality, timely and reliable supply to schools.
• Designing an optimal distribution network that meets the needs of the market (demand) and given the conditions of production in the country (supply).

As part of the activities, 30 public elementary and middle schools were visited in the states of Aguascalientes, Queretaro, Sonora, Veracruz and Yucatan by members of TIS Consulting Group. From the field visits, information provided by the Ministry of Public Education, and interviews with a physician who specializes in child nutrition; an estimation of the potential demand for fruits and vegetables was made for two scenarios, which refer to cover 50 % and 100% of the volume required by the student population. Also, monthly estimates of production for fruits and vegetables were obtained from the Agrifood and Fisheries Information Service (SIAP, by its spanish acronym) for year 2009.

Correspondingly, a network of consolidation-distribution of fresh produce –for the farm to school program consumption- was designed. This design was based on optimization models that determine the location of consolidation centers, and distribution and processing centers throughout the country, in order that the healthy food guidelines published by the Ministry of Public Education are met at minimal cost.

The optimization model is divided into two phases:

• The first phase is a maximal covering model, which seeks to find the optimal location of distribution centers and its zone of influence.
• The second phase is a mixed integer model whose objective function seeks to minimize transportation costs from collection centers to distribution centers.

The maximal covering model results are valid for the two scenarios studied and cover 100% of the student population. To achieve this, 300 distribution and processing centers must be established throughout the country. Therefore, any shortage in the solution is directly related to the lack of production of certain fruits and/or vegetables (at certain times) –and not with the distribution process.

In contrast, the mixed integer model was solved for each of the scenarios described above. As part of the results, a design of distribution network necessary to collect and distribute the product volumes was developed. The flow of the distribution network is shown in the figure below:

Example of a distribution network

Where:

• Consolidation Centers (CC): it is a center for receiving, packing and consolidation, which should be established near the crop since it is the entrance of the produce to the cold chain.
• Consolidated Transportation: it is the transportation between the consolidation centers and the distribution and processing centers.
• Distribution and processing centers (DC): it is a center where the products from the CC are received. Its main function is the reception, final packaging, in some cases processing (washing, cutting and individual packaging) and distribution to schools.
• Distribution: it is the transportation required for retail distribution to each school.

The distribution network was designed with the objective of minimizing total costs and obtain the most efficient and feasible network. The outcomes of the two scenarios are shown below:

• To cover 100% of the demand based on menus, 3 million 415 thousand tons of products need to be distributed through the creation of 165 collection centers, obtaining a service level of 91.85%; this collection centers need to supply 300 distribution and processing centers that will supply to all Public Schools in Mexico.
• To cover 50% of the demand based on menus, 1 million 796 thousand tons of products need to be distributed through the creation of 136 collection centers, obtaining a service level of 96.58%; this collection centers need to supply 300 distribution and processing centers that will supply to all Public Schools in Mexico.

An analysis of economic feasibility of the proposed distribution network was prepared, which concludes that the development of the distribution network is profitable and attractive, to both, private investors and farmers who will participate in the supply of fruits and vegetables to schools.

CNNMéxico.com. (2011, June 20). México debe declararse en emergencia ante epidemia de sobrepeso: ONU – Salud. Retrieved from goo.gl/Ko2xa

Organisation for Economic Co-operation and Development. (2010, October 11). Health: Obesity rising in developing countries, warns OECD. Retrieved from goo.gl/uNQea

Pricing is a difficult task and it must always reflect the relationship between supply and demand. Furthermore, it should take the following into account:

• Fixed and variable costs
• Competition
• Company objectives
• Proposed positioning strategies
• Target group and willingness to pay

A company can choose from a great variety of pricing strategies depending on its aims and objectives. Some of the most widely used strategies are penetration pricing, premium pricing, competition pricing, cost-based pricing and value pricing.

In addition, there is a very specific strategy based on discounts and promotions, with which it is important to experiment, given that a small change in prices can have a big influence on sales. The first step in developing a better promotion strategy is segmenting the market into different groups, based on customers’ shopping preferences, for instance.

The main benefit of a discount pricing strategy lies in its ability to drive traffic and sales short term. However, this strategy should be used cautiously, for it might have some negative effects on market position and brand loyalty in the long run, or it could begin a downward pricing spiral that may eventually damage your ability to sell the product at full price.

Price elasticity of demand is precisely a measure used to show the responsiveness of the quantity demanded of a product to a change in its price. Price elasticity models are handy tools in the development of pricing strategies, as they allow you to see the impact of a marginal change in price and they also evaluate how pricing can drive sales to support a business. In short, they attempt to express through a curve how much unit sales will increase or decrease if you raise or lower the price of your product by a small amount. Ideally, price elasticity models will help you determine the optimum price, or the price point after which a price drop by 1% will not allow volume increase by more than 1%.

Price Elasticity Chart

Some multi-variable regression models can analyze the impact on sales volume caused by changes in certain independent variables, such as price, distribution, and promotion activities of your company, as well as those of your competitors.

Multi-variable regression model

A very distinctive model is the Van Westendorp Price Sensitivity Model, in which price sensitivity relates not to absolute price, but rather to the perceived value of the good or service. Consumer price expectations and tolerances are measured by asking a set of price perception questions that are key to the model and the price at which the product or service is…

• So cheap that product quality/value is questionable (too inexpensive)
• A bargain (inexpensive)
• Beginning to seem too expensive  (expensive)
• Too expensive to consider (too expensive/prohibitive)

The answers to these questions are then graphed and the Range of Acceptable Prices is defined. Consequently, the model allows us (as many other models do) to set a price that enables sales maximization and minimal loss in a world full of commodities, where competition is increasing by the day and product differentiation is becoming more difficult to establish.

Sometimes making a good decision can be like finding a needle in a haystack.

Nowadays 15 thousand terabytes* of information are generated daily. The adage that “information is power” seems less valid as time goes by; since data by itself does not make decisions. To take effective and even better decisions in all business you need to know how to analyze, manage and manipulate the data to reach the results you need.

Analytical Intelligence – or advanced analytics – is an exhaustive way of managing the information to define strategies and take optimal decisions. It is based on analytical tools that allow you to run statistical analysis, either quantitative or qualitative; as well as different types of predictive, descriptive and optimization models. The purpose of this is to optimize the strategies and decisions in an organization. There is not a specific line that divides what could be considered as traditional analysis and advanced analytics; however, the use of statistical tools, data mining, stochastic processes and mathematical algorithms, will lead to decisions based on advanced analytics.

Different authors suggest three specific functions of advanced analytics:

- Descriptive analysis: In this kind of analyses the objective is to organize the information either through segmentation, categorization, or data mining techniques.
- Prediction analysis: Identify trends, patterns and relationships in the data in order to understand the causes and anticipate what will happen.
- Optimization analysis: Aid decision making to choose the best possible options from hundreds of available possibilities.

Different industries base their competitive advantage on advanced analytics, and they range from consumer products industry, retail, e-commerce, transportation services, telecommunications, to financial services and sports team management among others. Some of the companies currently using these tools are: Procter & Gamble, CEMEX, as well as various retail stores such as JCPenny and Wal-Mart; sports teams like The Boston’s Red Sox and The Athletics from Oakland are also known to apply advance analytics.

Advanced analytics create an opportunity for companies to build a sustainable competitive advantage based on differentiation that allows them to improve performance over the competition. Through these exhaustive data analyses, a company could offer its clients the products they require or develop a marketing program to get the best results with the proper budget. It can also be used to optimize prices based on the expected utility, improve inventory control, or evaluate a potential store or distribution center location, among many others.

* 1 terabyte = 1000 gigabytes, storage unit.
Philpott, Sara. Advanced analytics: Unlocking the power of insight. April 2010. http://www.ibm.com/us/en/.
Thomas Davenport y Jeanne Harris. Competing on analytics: Inteligencia competitiva para ganar. (México: Alfaomega, 2009).

Corridor's Map

In an article titled “Incentives of an Alternate Route: Finding the logistics capacity of a multimodal corridor” (cited in the footnote), collaborators of TIS Consulting Group published general results of a study developed for the Arizona DOT, about the logistics capacity of the commercial corridor between Guaymas, Sonora and Tucson, AZ. Next, a brief extract of the article.

Due to the high utilization of ports on the west coast of the US, the Port of Guaymas has brought attention as an alternative route for containerized trade between the US and the Far East. The Port of Guaymas is situated on the Sea of Cortez (or Gulf of California) in the northwest coast of Mexico, 258 miles from the nearest border crossing point at Nogales, AZ.

The article addresses the question of whether a competitive container service can be established in Guaymas as part of a multimodal corridor for cargo bound for the US through Guaymas to Tucson, AZ. The Port of Guaymas has unused capacity that could be very attractive to shippers located in the states of Sonora in Mexico, Arizona in the US, and other states close to the area of influence. Of particular importance is the idea of using the Port of Guaymas as a relief port for the congested ports of Long Beach and Los Angeles, CA.

For the purpose of analysis in the article, the Guaymas-Arizona multimodal corridor starts in the Port of Guaymas, in the Mexican State of Sonora and connects in Tucson, AZ with access to Interstate 10 and the Sunset Line (Los Angeles-El Paso) of the Union Pacific railroad.

In the article, through various analysis tools (as queuing theory and stochastic simulation, among others) and assumptions, it was determined that the maximum capacity of the corridor, considering an overall perspective of the multimodal corridor and analyzing all the links and nodes, is approximately 175,000 TEU per year, if both rail and truck services are available.

René Villalobos, J., Sánchez, O., Maltz, A., & Ahumada, O. (2009). INCENTIVES OF AN ALTERNATE ROUTE. Industrial Engineer: IE, 41(7), 39-43.

ProModel Screenshot

One of the benefits of discrete event simulation models compared to other deterministic modeling –e.g. linear programming or MIP; as well as compared to other stochastic modeling –e.g. Markov chains or dynamic programming; is that the problem doesn’t need to be relaxed (a technique used to overcome strict restrictions of NP-hard models so they can be solved in polynomial time) and hence can be kept more ‘real’. Furthermore, it allows a graphic and visual representation of the system, which facilitates the understanding of it and the validation process. Once a model is programmed in a commercial software (e.g. ProModel or Arena), a process of verification and validation is followed to ensure the model behaves as expected and compares closely to the real-life system. This gives assurance that the model accurately represents the process being analyzed.

Rockwell Arena Screenshot

At this point, bottlenecks can be easily identified and an optimization process can be followed to find the best set of actions –based on variables in the model- to follow in order to have the highest improvement on the overall system. Results aid to create a cost-benefit re-engineering and investment plan for the continuous improvement of the system being analyzed. For example, a queuing process of a bank might require different staffing schedules, but it could rather need a different system to handle customers upon arrival. Previous to making costly decisions or high investments without assurance of success –and generating more cost in a system by ‘playing’ with the different options- a simulation model enables us to test all possibilities and prioritize without disturbing the actual process.

This tool can be used for evaluating, planning or designing, as well as in manufacturing, warehousing, logistics and other operational and strategic situations. If you are interested in learning more about our simulation services do not hesitate to contact us. We’ll be glad to help you.

As globalization makes its way into every aspect of our lives, the commerce and relation between countries accentuates. In the case of Mexico and the U.S. –given the vast binational trade– the relation is extremely complex. For TIS Consulting Group this complex relationship represents opportunities to draw from our binational experience to work closer with industry, organizations and governments on both sides of the border to find solutions to complex problems.

We hope to add more projects from Mexico to our portfolio and leverage our bi-national background to deliver excellent results. Maintaining the same focus of bringing the people in the forefront of knowledge to companies, we expect to keep doing so but now with a dedicated team to operate south of the border. We can provide the same services and quality we do in the U.S. but for Mexican organizations –that have the same needs as any other.

The new office is located in Hermosillo, Sonora. For more information please see the contact us section in this website.

The truth is far from that. In statistics reported by the Bureau of Transportation Statistics, the volume of personal vehicles (POV as in privately owned vehicle) crossings at Nogales is lower than it was 14 years ago. The decrease started just after 9/11. From 1995 to 2001, there was a 38.99% increase (from 3’368,337 to 4’590,933 car-crossings/year); but from 2001 to 2008, there was a -34.74% decrease (from 4’590,933 to 3’026,767 car-crossings/year) or -341,570 car-crossings per year less than in 1995.

On the other hand, the number of pedestrians crossing the border remained stable from 1995 to 2001 with a slight 3.76% increase (from 4’698,049 to 4’874,738 pedestrian-crossings per year); but from 2001 to 2008, there was a significant 34.74% increase (from 4’874,738 to 6’568,207 pedestrian-crossings per year).

The reasons behind this behavior, the impact of the decrease in total crossings and the economic consequences of people crossing more by foot that by car; are interesting questions yet to be answered.