• Written by Sonali Subhadarsini, Product Manager

A finance team in e-commerce and retail often finds itself waiting on a report to answer pressing questions about the business, such as the current state of cash flow or the profitability of a particular product line, and while these reports can provide valuable insights, they can also be slow to arrive and based on outdated data, which can hinder the team's ability to make informed decisions in a fast-paced and competitive market.

The finance questions that currently wait on a report

The finance team in an e-commerce or retail organisation typically has to wait for a report to be generated before they can answer questions such as what is the current level of expenditure on a particular cost centre, or what is the total value of outstanding invoices, and this can cause delays in decision-making and planning, as the team has to wait for the report to be compiled and distributed before they can access the information they need, and even then, the data may be outdated or incomplete, which can lead to inaccurate conclusions and suboptimal decisions.

For instance, a category manager may need to know the current level of sales for a particular product line in order to make informed decisions about inventory levels and pricing, but if they have to wait for a report to be generated, they may not be able to respond quickly to changes in the market, and this can put the business at a competitive disadvantage, as other companies may be able to react more quickly to shifts in demand and customer behaviour.

The data behind them: general ledger, budget, open commitments and purchase orders, receivables and terms

The data that underlies these finance questions is typically stored in a variety of systems, including the general ledger, budgeting software, and accounts payable and receivable systems, and this data can be complex and difficult to access, particularly for non-financial staff, such as category managers or shift supervisors, who may not have the necessary training or expertise to navigate these systems and extract the information they need, and even for financial staff, the data may be scattered across multiple systems and require significant manipulation and analysis before it can be used to answer the questions that the business needs to ask.

For example, the general ledger may contain detailed information about the company's income and expenditure, but this data may be difficult to access and analyse, particularly for non-accountants, and the budgeting software may contain information about planned expenditure, but this may not be easily reconcilable with the actual expenditure data in the general ledger, and the accounts payable and receivable systems may contain information about outstanding invoices and payments, but this may not be easily accessible or analysable, particularly for staff who are not familiar with these systems.

Sector constraints: assortment decisions and returns and their root causes

E-commerce and retail businesses face a number of unique challenges and constraints that can make it difficult to answer finance questions and make informed decisions, such as the need to manage complex and fast-moving assortments of products, and the need to respond quickly to changes in customer demand and behaviour, and these businesses also have to deal with the challenge of returns, which can be a significant cost and can also provide valuable insights into customer behaviour and preferences, but can be difficult to analyse and understand, particularly if the data is scattered across multiple systems and requires significant manipulation and analysis before it can be used.

For instance, a shift supervisor may need to know the root cause of a high level of returns for a particular product line, in order to take corrective action and reduce the number of returns, but if the data is scattered across multiple systems and requires significant analysis and manipulation before it can be used, this can be a difficult and time-consuming process, and may require significant expertise and training, and even then, the insights gained may be incomplete or inaccurate, which can lead to suboptimal decisions and actions.

What improvement looks like: variance to budget, commitment coverage, days sales outstanding

Improvement in this area can take many forms, but some common metrics that finance teams in e-commerce and retail use to measure their performance include variance to budget, commitment coverage, and days sales outstanding, and these metrics can provide valuable insights into the financial health and performance of the business, and can help to identify areas for improvement and opportunities for growth and optimisation, and by using AI for finance to analyse and understand the data that underlies these metrics, businesses can gain a deeper understanding of their financial performance and make more informed decisions.

For example, a category manager may use variance to budget to identify areas where the business is overspending or underspending, and to take corrective action to get back on track, and a shift supervisor may use commitment coverage to identify areas where the business is at risk of not meeting its financial commitments, and to take action to mitigate this risk, and by using AI for finance to analyse and understand the data that underlies these metrics, businesses can gain a deeper understanding of their financial performance and make more informed decisions, and can also contact us to learn more about how AI for finance can help their business.

What this does not do, or when it is the wrong choice

While AI for finance can be a powerful tool for e-commerce and retail businesses, it is not a panacea, and there are certain situations where it may not be the best choice, such as where the data is incomplete or inaccurate, or where the business lacks the necessary expertise or resources to implement and use the technology effectively, and in these situations, other approaches may be more suitable, such as traditional reporting and analysis, or manual data manipulation and analysis, and businesses should carefully consider their options and choose the approach that best fits their needs and circumstances.

Most people get wrong the idea that AI for finance is a replacement for human judgement and expertise, and this is wrong, as AI for finance is a tool that can be used to support and augment human decision-making, but it is not a substitute for it, and businesses should be careful not to rely too heavily on AI for finance, and should always consider the limitations and potential biases of the technology, and should use it in conjunction with human judgement and expertise to make informed decisions.