• Written by Muhammed Abdulla NC, CEO

In theory, cost optimisation in e-commerce and retail should be straightforward: identify areas where costs can be reduced, and implement changes to achieve those reductions. However, in practice, the fast-paced and dynamic nature of these industries makes it difficult to achieve lasting cost savings. The constant flux of product assortments, high return rates, and complex promotional strategies all contribute to a challenging environment for cost optimisation.

Complexity in e-commerce and retail

E-commerce and retail businesses face unique challenges that make cost optimisation more difficult. Assortment decisions, for example, are critical in these industries, as they directly impact sales and revenue. However, the sheer volume of products and the rapid pace of new product introductions make it hard to optimise assortment decisions. Returns are another significant issue, with many companies struggling to identify the root causes of returns and implement effective strategies to reduce them. Promotion effectiveness is also a challenge, as companies must balance the need to drive sales with the risk of over-promoting and eroding profit margins.

A shift supervisor checks the maintenance log to identify potential issues that could impact production, while the category manager compares two suppliers to determine which one offers the best value. These decisions are critical in e-commerce and retail, where small changes can have a significant impact on the bottom line. Most people get wrong the idea that cost optimisation is simply a matter of cutting costs, without considering the potential impact on sales and revenue.

Unit cost vs total cost

When it comes to cost optimisation, many companies focus on reducing unit costs, without considering the total cost of their AI workflows. This can lead to decisions that appear cost-effective in the short term but ultimately drive up costs in the long term. For example, a company may choose to use a cheaper AI model, only to find that it requires more computational resources and personnel to maintain, resulting in higher total costs.

The difference between unit cost and total cost is critical in e-commerce and retail, where small changes can have a significant impact on the bottom line. Companies must consider the total cost of their AI workflows, including factors such as personnel, computational resources, and maintenance, in order to make informed decisions about cost optimisation.

Real levers for cost optimisation

So, what are the real levers for cost optimisation in e-commerce and retail? Caching, batching, and model routing are three critical strategies that can help companies reduce costs. Caching involves storing frequently-used data in a easily-accessible location, reducing the need for costly computations. Batching involves grouping similar tasks together, reducing the overhead of individual tasks. Model routing involves directing tasks to the most appropriate AI model, reducing the need for unnecessary computations.

These strategies can have a significant impact on costs, particularly in industries where data volumes are high and computational resources are limited. By implementing caching, batching, and model routing, companies can reduce their costs and improve their bottom line. For more information on how to implement these strategies, visit our website.

Token cost is not the dominant line

Token cost is often cited as a key factor in cost optimisation, but it is rarely the dominant line. While token cost can be a significant factor in certain industries, such as natural language processing, it is not the primary driver of costs in e-commerce and retail. Other factors, such as personnel, computational resources, and maintenance, are often more significant.

Companies must consider the full range of costs associated with their AI workflows, rather than focusing solely on token cost. By taking a holistic approach to cost optimisation, companies can identify areas where costs can be reduced and implement effective strategies to achieve those reductions. To learn more about how AsscherAi can help with cost optimisation, contact us.

Limitations of AI cost optimisation

While AI cost optimisation can be a powerful tool for reducing costs, it is not a panacea. There are certain situations where AI cost optimisation may not be the best choice, such as when data volumes are low or when computational resources are plentiful. In these cases, other strategies, such as process optimisation or personnel reduction, may be more effective.

Companies must carefully consider their specific situation and determine whether AI cost optimisation is the right choice. By understanding the limitations of AI cost optimisation, companies can make informed decisions about how to reduce their costs and improve their bottom line. Ultimately, the key to successful cost optimisation is to take a nuanced and informed approach, considering all the factors that impact costs and making decisions based on a deep understanding of the business.