top of page

Sales Network Expansion with the help of advanced Data Analysis and Machine Learning tools.

It's time to expand your sales network to new locations.

The question you have to answer is how to choose the right geographic locations to take full advantage of your new investment.

Data Analytics for sales network expansion

The solution lies in advanced Data Analysis and Machine Learning tools.

These require data from internal sources of your business but also and most importantly from external sources. So, the decisions you make will be knowledgeable and based on reliable and well " processed" information.

But what should be the information that will feed the analytical tools and how will it be used to produce the desired result?


1. Use information from internal databases related to your existing sales network:

  • Sales sizes by code and Product/Good category

  • Profiles and Demographics of existing customers (e.g. age groups by product and goods category, spend by customer category, etc.)

  • Customer Satisfaction Surveys

2. Analyse the above data to better understand your customers. Identify their preferences and behaviours based on their profile and sales metrics to understand their buying patterns.

3. Use data from external databases. Below are some examples of information to consider and combine with your internal data:

  • Demographics of the areas where your current Points of Sale are located

    • Statistics of reported income by zip code

    • Consumer spending by area

  • Geographic distribution of your existing customer base. Population density and mobility

  • Competitor data

    • Balance sheets,

    • Advertising campaigns,

    • Other reports

  • Local economic development

    • Employment rates,

    • marginal propensity to consume by population group,

    • disposable income

4.        Combining the above information (1 and 2) with the use of Artificial Intelligence & Machine Learning tools will lead to conclusions about how the buying patterns of your existing customers are shaped by demographic data, the geographic areas with the highest customer density and the financial data of each region of your existing sales network.

At this point having a clear picture of your existing sales locations and existing customers gives you a good starting point. But what are the next steps and what data from external bases can be used to develop "what-if" scenarios and evaluate the potential and alternative areas you have in mind to establish new sales points?


1.     Demographic data

  • To understand population density, income levels and age groups in different areas.

  • To identify the locations with the highest concentration of your target audience.


2.     Geographical Area Distribution

  • To identify areas with a high density of potential customers.     


3.     Vehicles and pedestrian traffic data

  • To locate the busiest areas, both for pedestrians and vehicles.

  • And the areas with high visitability, as they generally have more potential customers.


4.     Economic data

  • To consider local economic growth, employment rates and disposable income.


5.     Competition data

  • To assess the competition in each region, taking into account the number and importance of competitors


 6.     Data related to potential risk factors in each location such as:

  • Regulatory requirements,

  • Climate factors

  • Seasonality

  • Crime

7.     Data related to cost factors such as for example:

  • The level of property prices and rents,

  • The cost of utilities,

  • The cost of personnel.


 Having all the information you need you can proceed with Predictive Analysis to identify the best locations to expand your network based on potential sales and consumer behaviour.


At e-On Integration we have developed Data Analysis and Machine Learning tools that can help you in all the stages we describe above, in order to make informed and objective decisions that lead to the best possible performance of your business.



bottom of page