March 22, 2022

“Mathe-Marketing” – Product line Optimization through Data Analytics

Many people are constantly fixated on the abstract nature of mathematics and whether it is evidently applied to everyday life, most importantly its contributions outside academia. A very practical example of the application of mathematics to industry especially in the production of goods and services to consumers is through product line optimization based on data analytics.  

How can an enterprise optimize a product line? 

The idea of creating the best or most effective market situation for both producers and consumers such that it is a win-win for both parties should be the goal of optimizing a product line. In the development of a new product or in assessing the possibility of revising an existing product within a company, an entity would need to first know the conduct of a product or how a combination of products are performing on the market and the reaction of consumers to these products. Here, product line optimization in data analysis becomes necessary in determining the profitability and reach of a product in order to realize the lucrativeness or potential success of a product line.  

In product line optimization, there are various mathematical approaches in data analysis and analytics which are used as tools to understand the market niche and demands for optimum results.  

Optimizing a single product line is determined by data analysis. This involves finding the best or “optimal” blend of products to make up a product line. Best would refer to the service or product that gets the attention and widest reach of consumers thus drives high demand and purchase. Total Unduplicated Reach and Frequency Analysis for example, is able to generate the percentage of consumers that constantly target purchase of a specific product in the line. This allows for only one option and works best if the aim of the company is to find out the best-selling product. 

In comparing near-optimal solutions, the next alternative in the product line is considered aside the best. Here once this is realized, the goal might be to develop or refine that product.  

Data analysis also allows for comparison of near-optimal solutions — giving the opportunity to identify the most practical solution, rather than the most ideal. Here instead of showing the one optimal product line solution, it is able to generate every possible combination of a certain number of products – permutations of existing products to realise which ones are closest to the optimal mix. This can also derive the average number of products that reach consumers within each combination as well as how frequently this occurs. 

In a 2018 market study for instance, the Nestle product line of dairy products showed Ghanaian consumers preferring Ideal full cream evaporated milk to the Carnation Tea Creamer which was a lighter alternative, with the former having a 70% reach. 

Both the reach and frequency of product line options offers extra insight in enabling marketers determine when and where to make trade-offs. 

Another approach would be to find the impact each item brings to a product line. Here every possible combination of the items is run, to see how much each one individually contributes to total reach. Based on that, an individual item’s contribution to reach is determined by noting the difference in reach when that item is included in the product line vs. when it is excluded. This method can provide strong directional insight to compare items and determine which ones may be worth keeping versus which ones you might want to remove. Employing this method can significantly impact your decision-making by providing the tools to get you closer to that optimal product line. 

At the end of the day, each and every institution, industry or company involved in trade is looking to maximize product line potential, cover large markets and even influence stocks. Data analytics and analysis remains a crucial tool to reaching this goal. This why AIMS has established initiatives such as the AIMS-ESMT Industry Immersion Program and the AIMS-Ishango Data Science and Artificial Intelligence (AI) Fellowship Program. Boosting the potential of mathematicians in all fields including research, academia and industry is the way to ensure that Africa can ride on the wave of technological advancement as well as contribute to global scientific breakthroughs and discoveries. 

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