In the first blog of this series, we introduced a simple maturity assessment framework that could help to evaluate the performance and effectiveness of a machine learning project. The main pillars of that framework are centred around four different points: Data readiness, ML applicability, Business impact and Operationalisation.
In this new blog, we provide a simple and practical methodology to apply these principles in assessing, prioritising and identifying the right machine learning use cases to solve business problems, with a specific focus on the retail industry.
Problem Statement
Retailers have a multitude of objectives they strive to achieve, each of which contributes to their long-term success and profitability in a competitive industry. Some of the most common objectives are:
- Increasing sales growth,
- Improving customer satisfaction,
- Optimising operations and reducing costs,
- Gaining market share,
- Building brand awareness.
Achieving these objectives requires careful planning and execution, as retailers must balance their efforts across multiple areas to achieve their goals. By prioritising these objectives and developing effective strategies to achieve them, retailers can create a positive shopping experience for their customers, build a strong brand reputation, and achieve long-term growth and success in the marketplace.
By leveraging different types of data, retailers can gain valuable insights into customer behaviour, industry trends, and emerging technologies.
Some interesting machine learning use cases in the retail industry include:
- Personalised recommendations: Providing personalised product recommendations to customers.
- Customer segmentation: Segmentation of customers based on their purchase history, demographic information, and other factors.
- Churn prediction: Prediction of which customers are at risk of stopping purchases.
- Lifetime value prediction: Predicting the future value of each customer.
- Inventory optimisation: Optimisation of inventory levels for different products, locations and time periods based on demand forecasts, lead times, and other factors.
ML Maturity Assessment Framework
Applying this framework when we engage with customers can help enable purposeful conversations, which often lead to highlighting potential machine learning use cases. But, more importantly, it also allows us to prioritise them using a confidence scoring system, such as:
- +1: The customer provides a well-detailed answer
- +0.5: Further information is required
- 0: No answer provided
Data Readiness
Data readiness is an important step in ensuring the success of any machine learning project. As a machine learning professional, there are several questions you can ask a business to assess their data readiness for a machine learning project.
Some of the statements we might ask are summarised in the table below, which can also be used to score and rank different ML use cases within the organisation, using the scoring system outlined above.
# | Questions/Statements | Confidence Score |
1 | We gather data from a wide range for various purposes (E.g. transaction data, customer demographics, and inventory information) |
|
2 | We employ multiple ways to collect and store our data. | — |
3 | We have mechanisms in place to ensure the quality of our data both from an accuracy and completeness standpoint. | — |
4 | We store historical data. | — |
5 | Fairness is considered when building our data products using ML/AI. | — |
6 | Our legal team understands the ethical implications of using customer data in machine learning models. | — |
7 | Our tech team has the necessary infrastructure to process and analyse large volumes of data. | — |
8 | Any legal or regulatory constraints are considered when collecting or using customer data. | — |
Total Score |
ML Applicability
Assessing the applicability of machine learning to a retail business is crucial in determining if it’s a viable solution to specific problems.
# | Questions/Statements | Confidence Score |
1 | We understand the usefulness of ML to solve our pressing business problems. |
|
2 | We have exhausted all types of automation systems and decision support technologies before considering machine learning. | — |
3 | Using ML will help to improve efficiency, increase revenue, or achieve other outcomes. | — |
4 | Are these decisions primarily based on human intuition and experience, or do you already use data-driven methods? | — |
5 | How would the implementation of machine learning impact the business and its operations? | — |
Total Score |
Business Impact
Identifying the business impact of a machine learning project is a crucial step in determining its feasibility and return on investment.
Here are some questions to consider in assessing that:
# | Questions/Statements | Confidence Score |
1 | We have business goals that support the implementation of machine learning. |
|
2 | We have a set of KPIs to measure success in the areas where we are implementing machine learning. | — |
3 | We have identified any potential risks or challenges associated with the implementation of machine learning, such as data privacy concerns, lack of internal expertise, or infrastructure limitations. | — |
4 | We understand the costs associated with implementing machine learning. | — |
6 | We can deploy our ML solution across all our stores. | — |
7 | We have methodologies to measure the ongoing impact of the machine learning solutions once they have been implemented. | — |
Total Score |
Operationalisation
Operationalising machine learning solutions can be a complex process that requires careful planning and change management.
Here are some questions to ask from a change management perspective for any machine learning project:
# | Questions/Statements | Confidence Score |
1 | We have strategies and plans to implement ML solutions to impact current business processes and workflows. |
|
2 | We will be conducting workshops to ensure successful implementation of machine learning solutions within our organisation. | — |
3 | We have strategies in place to ensure that employees understand the benefits of the ML solution and are comfortable with the changes to their current workflows. | — |
5 | Oversight and governance are considered for the ongoing effectiveness and accuracy of the machine learning solutions. | — |
6 | We can measure the ongoing impact of the machine learning solution on business processes and employee workflows. | — |
7 | We have internal capability in machine learning within the organisation. | — |
Total Score |
Taking It For A Spin
In the previous section, we provided some example questions which can be asked as part of the ML maturity assessment for each pillar of the framework. We have also created a scoring system to rank different ML use cases within any organisation.
Once each statement has been scored, we summarise our findings in a matrix as shown below:
Data Readiness | ML Applicability | Business Impact | Operationalisation | Grand Total | |
ML Use Case 1 | Total Confidence Score | Total Confidence Score | Total Confidence Score | Total Confidence Score | – |
ML Use Case 2 | Total Confidence Score | Total Confidence Score | Total Confidence Score | Total Confidence Score | – |
ML Use Case 3 | Total Confidence Score | Total Confidence Score | Total Confidence Score | Total Confidence Score | – |
ML Use Case 4 | Total Confidence Score | Total Confidence Score | Total Confidence Score | Total Confidence Score | – |
Conclusion
In conclusion, a machine learning maturity assessment framework is a critical tool for businesses that are looking to successfully adopt and scale machine learning solutions to gain a competitive advantage and achieve long-term success in their respective industries. By assessing data readiness, ML applicability, business impact, and operationalisation, the framework provides a structured approach for evaluating an organisation’s current capabilities, identifying areas for improvement, and developing a roadmap for implementation that aligns with the overall business goals. It also allows business owners to:
- Ensure that they have the necessary data infrastructure in place to effectively leverage machine learning.
- Maximise the value of their machine learning investments.
- Integrate machine learning solutions into their existing workflows and processes.
