With advanced analytics based on Big Data, it’s possible to develop solutions to optimize marketing and distribution strategies and thus maximize the profitability of any business.
The conventional distribution channel is one of the most complex to optimize in terms of marketing and distribution. The variability in size, characteristics and location makes it difficult to analyze and measure the real potential of each point of sale. In addition, changes in consumer buying patterns make it even more difficult to estimate the real potential of each establishment and area of interest.
In order to maximize the profitability of any type of business, an analytical technique known as POI analytics which is based on advanced analytics and Big Data is applied, when it’s combined with foot traffic and location analytics that work through spatial data, it’s possible to:
- Profiling consumers in each area of interest or point of sale, according to their demographic characteristics, income level, interests and preferences, among others.
- Performing a comparative analysis of visits trend in the channel versus the others, such as the modern one.
- Benchmarking states points of sales (ideal for those seeking to open new points of sale in other regions or states).
- Analyzing population spending on the product of interest.
- Characterizing all points of sale according to their location and the population that travels around it.
These analyses answer the following questions:
- Should my product be in all points of sale?
- Which are the most suitable products to promote and in which establishments?
- In which areas of the country should my product really be present?
- Which zones in each state or city are the most profitable?
- Is the potential of any point of sale being wasted?
- Which points of sale or zones are being over served?
- What are my consumers like and what are their characteristics?
Also Read: “Branch expansion with predictive analytics“
These are the steps to take to maximize the profitability of any type of business:
- Profile the consumers and potential customers in each area, know what their similarities are and how they differ from other channels.
- Identify and characterize the floating population around each point of sale or commercial area, according to their income level, purchasing capacity, interests and demographic profile.
- Estimate the real turnover potential in each area and at each point.
- Calculate the purchasing potential index of the product of interest to the customer in each point of sale or zone.
- Analyze all available information variables and estimate the level of opportunity in each zone or point of sale.
This is an example of a case study conducted by PREDIK Data-Driven about detergents in Mexico City:
Identification and profiling of each point of sale
We identified all grocery stores, and convenience stores throughout the city of interest.
This analysis can be disaggregated to the level of counties, neighborhoods, blocks and even streets.
The floating population is categorized according to its purchase capacity, identifying the consumers circulating around each point of sale, and then classify them according to their income level.
This can also be performed by gender and age.
With all the information collected, we estimate the expenditure of consumers in the surroundings of a point of sale in a week with predictive analytics. (This can be done monthly or yearly depending on the amount of available data)
Any business leader who makes the decision to integrate this type of technology into their business decision making process will be able to:
- Optimize their merchandising strategy to focus on the outlets with the highest turnover potential.
- Identify outlets that may be over-served or under-served.
- Redirect marketing and product positioning efforts to the outlets with the greatest potential.
- Maximize business profitability by reorganizing the commercial strategy in the traditional channel.
Each point or zone of interest is analyzed and characterized according to variables such as:
- Foot Traffic affluence
- Vehicular traffic
- Floating population
- Demographic characteristics: gender, age and Rwi.
- Consumer interests and preferences.
- Proximity to places of interest (hospitals, schools, museums, etc).
- Population expenditure on the product of interest.
At PREDIK Data-Driven we help businesses maximize their profitability through Big Data techniques and advanced analytics, to improve decision making and base it on facts.