ESB
Nominated Award: Best Use of AI in a Consumer/Customer Service Application
Website of Company: https://esb.ie/home
At ESB, we have been Ireland’s foremost energy company since our establishment in 1927. We are driven to make a difference by achieving zero carbon emissions by 2040. Our commitment to providing clean, affordable, and reliable energy directly supports the achievement of UN Sustainable Development Goals.
We are a strong, diversified utility operating right across the electricity market: from generation, through transmission and distribution, to supply. We also work in other related sectors where we can make a difference, including telecommunications, electric vehicle charging, home retrofits and more.
ESB Data & Analytics sits in the strategic CIO organisation. We are arranged in a Hub and Spoke Model; Data Science, SAP BI, and Data Engineering sit within the Hub and each of the ESB Business Units has an Analytics Spoke which includes – Customer Solutions, Enterprise Services, Networks, Generation and Trading and Engineering & Major Projects, made up of a team of data analysts. With this model, we get to sit close to each business area, working collaboratively with them to understand and help solve business challenges through data.
Reason for Nomination
Project Strategy and Objectives
The objective of this project was to simplify Electric Ireland’s complaints reporting and review process by reducing the amount of manual reviewing of complaints. Once this was achieved, a further aim was to identify potential triggers to allow the business to anticipate an increase of complaints and potentially stop them before they occurred.
Prior to this project, Electric Ireland had a very manual process for reviewing complaints. A selection of complaints was taken every month from the customer service database, each entry was reviewed and categorised to identify problems and process errors leading to complaints. This could take two people up to two weeks per month to review and put together analysis of the previous month’s complaints. The complaints team approached Analytics for help with automating this process to reduce the time taken to review complaints.
ESB Data & Analytics engaged with the complaints team to understand their current processes and identify ways in which the workload could be reduced. The complaints team provided data showing the categorisation of complaints, their reporting metrics and the final report produced. Their process consisted of extracting data from the customer relationship management (CRM) software in the form of Excel spreadsheets, manually reviewing the data and recreating the same charts each month for presentation. It was determined that this review process could be simplified by extracting the CRM data into a Hadoop data lake and producing a dashboard displaying the information. A natural language processing model could be developed to analyse the text recorded by the agent and categorise the complaint.
In addition, the business sought a way to become more proactive in addressing customer complaints. In order to enable this, they provided a list of metrics that they thought might influence complaints numbers and Analytics undertook a study to identify the relationship between these metrics and complaint numbers. A regression model was built based on these features to predict the number of complaints per month.
Project stages:
• Set up project team and project plan
• Understand the existing process for logging and reviewing complaints.
• Develop categories for types of complaints and processes leading to complaints.
• Identify the data required to categorise and review complaints.
• Work with Data Engineering team to have complaint data fed into the central Data Lake from the customer relationship management system.
• Explore relevant metrics that might impact complaints, including:
– Customer billing history
– Customer activity in company online portal
– Customer satisfaction survey results
– Call centre metrics (number of calls transferred, wait times, repeat calls, etc.)
• Produce a Power BI dashboard showing monthly summary of complaints data.
Systems/Technology
• Log of complaint notes taken by call centre agent were taken from CRM system and fed into central data lake (Hadoop).
• Sklearn python package was used for the following tasks:
• Create a natural language processing (NLP) model to categorise complaints and to identify if a previous call from the customer could be associated with the complaint.
• Correlation analysis was carried out to identify key metrics that were impacting complaint numbers.
• A regression model was built to predict the number of complaints based on these metrics.
• Power BI was used to create a dashboard displaying the number of complaints, their categories, call statistics and the errors in processing that were leading to complaints.
Impact for the Business
The manual workload for analysing complaints has decreased due to the creation of an automated Power BI dashboard showing complaint data. This dashboard created better visibility of complaint numbers and the main categories of complaints. The time to put together a complaints report was reduced to one week and could be conducted by one person, as opposed to the previous requirement of two weeks and two people.
Natural language processing revealed that process errors such as lack of agent knowledge, the manner of the agent or an error in the company’s system could be attributed as a cause of the complaint. This allows the business to focus training of agents to reduce incorrect information and errors in process flows.
There was an assumption within the business that the customer would always call to make a query first, then make a subsequent call to complain. This was revealed to be incorrect as only 1% of complaints could be associated with a previous call/query. In practice, the majority of customers will lodge a complaint without any prior contact. The monitoring of metrics, such as customer billing and repeat call volumes, along with regression model allow the business to anticipate the number of complaints that are likely to be received and address these issues before they lead to complaints.
ESB project 2021
Nominated Company: ESB Data Analytics
Nominated Award: Best Use of AI in a Consumer/Customer Service Application
Electric Ireland is the retail division of ESB (Electricity Supply Board). ESB was established in 1927 as a statutory corporation in the Republic of Ireland and most shares are held by the Irish Government. Previously known as ESB Customer Supply and ESB Independent Energy, the retail division of ESB has been rebranded to Electric Ireland in 2012.
Recognised as Ireland’s leading energy provider, Electric Ireland supplies electricity, gas and energy services to over 1.2 million households and 95,000 businesses in the Republic of Ireland and Northern Ireland. ESB is now supplying electricity and gas to homes across Great Brita in. ESB Energy launched as Britain’s newest energy provider in 2017 and our pledge is to be the most innovative, responsible and easy to deal with electricity and gas supplier in the British market. ESB has been working in Britain for over 25 years and invested nearly £2 billion in projects across the country. Whether you’re a residential or business customer, our ambition is to always offer you the most relevant, affordable and innovative energy solutions that answer the way you use our electricity or gas services. We call this ’Smarter Living’ – a way of understanding and answering your needs so you can enjoy our energy with more control and in sustainable ways. ‘Smarter Living’ also defines our approach to supporting community initiatives and sponsorships.
Reason for Nomination:
Project Strategy & Objectives
As part of the “Smarter Living” approach a project team was put together in order to understand the driving factors behind the Repeat contacts across the Electric Ireland Residential customer base. A review of all methods of contact by customers was required to determine contact method preferred, analyse what the most frequent reason for contact is and look at suggestions to improve. This was always with a view to how to interact with our customers in an agile and timely manner in order to provide a better-quality service.
Approach: A single source table was designed to be a view for every customer and included all their incoming & outgoing triggers and contact activities. An Impala table was used for the single source table by creating union of queries that draw data from tables in our Hadoop infrastructure across multiple data sources, which included Telephony data, CRM and Billi ng transactional data and customer segmentation model. The queries have been constructed to allow changing date ranges to be amended to create versions of the table for different time periods, and in a manner conducive with the addition of new triggers and contact methods. The resulting table is structured with a row for every occurrence of each trigger and contact method and can be sorted by customer and activity date to get a chronological view of each customer’s activity.
Feature Engineering: By utilising Cloudera Data Science Workbench and Hadoop, the process of feature engineering was made easier and enabled us to understand more about how each trigger relates to contacts. This assists in analysing and discretising continuous features into buckets. For continuous variables, feature engineering enables the selection of discretisation buckets that capture the variation in propensity across the range of continuous values. This means that when running the conditional probability and profiling code, the various facto r levels provide visibility of potential variation across propensities for different regions of the continuous variable. We can do this by plotting the propensities across the continuous variable and then using the visual output to decide on where to discretise from. In plotting the data for positive and negative value it can appear to be symmetrical, the buckets contain the absolute value of a feature selected during the study. Were the positive and negative values to show quite different trends, separate buckets for positive and negative values could be created to incorporate the different trends.
Feature Selection: The feature selection process involved the analysis of the model running when a feature isn’t present. If a feature’s removal led to an increase in the predictability of the model, the feature was removed from the model. The code recommended the feature that, when removed, caused the greatest increase in predictability. The feature was then removed from the dataset, and the feature selection code run once again. This process was continued iteratively until all features that caused a decrease in predictability have been removed from the model. Hidden nodes can be generated as an additional way of increasing predictability.
These are new nodes that can be created as an amalgam of existing nodes that have a link between them in the Bayesian Network. If a combination of nodes increased the predictability of the model, the two nodes can be combined into a hidden node, and their originals removed. The code tested the change in predictability of the model with each combination of nodes that have a link in the Bayesian Network to see whether the joining of any pair s increased predictability. The combination of nodes that increased predict ability the most were then selected and combined.
Model Visualisation: The model code outputs the Bayesian Network links into a connection matrix file. This file was then inserted into the visualisation code alongside the parameters to create the visualisation. The visualisation can be modified to amend the visual elements of the nodes and links to ensure that the output is visually coherent, and the sizing of the various elements is conducive with the business area where the visualisation will be utilised. Th e main element to define within the visualisation is the node size. This should ideally be defined based on the relative opportunity sizes for each node. The node colouring is also an element that was considered as this can also add meaning to the visuals.
Additional Information:
Conditional Probabilities: The conditional probability code calculated the propensity for each individual feature to drive repeats. The generated probabilities were then outputted for each feature. These were then used to calculate the additional yearly repeats for each feature.
Profiling: The impala query for the profiling table created a row for each customer with each column denoting a customer profiling element. The profiling elements were overlaid on to the customer data to provide visibility of the repeat propensity for various customer segments for each trigger. The profiling prep code conducts the preparatory work for this process by pulling in the profiling elements and joining them onto the observations. The profiling code then calculated the propensities for each profile overlaid onto each trigger, and outputs this for each trigger. These outputs were then used to calculate the additional yearly repeats (opportunity sizes) for each customer segment for each trigger.
Impact for the Business: The model can be run and compared for different date ranges allowing the Electric Ireland business team to identify different triggers and events based on time dependency. This can assist in analysing the change in propensity for trigger to drive repeats when a change is made to the customer experience/engagement process. This can be done by re-running the single source creation code for different date ranges, or by creating a multi-variable matrix with large date range and selecting specific dates to draw a single sample. The modelling, profiling and conditional probability code can then be run on the samples from each date range to yield which triggers were most impactful in
each date range.
Evidence of Success: Improved understanding of the drivers to repeat contact means the ability to determine who, when and how customers will contact Electric Ireland. This means an increase in proactive outbound contacts through cheaper and more efficient channels, reducing cost to serve and increasing the customer satisfaction and improving the journey for the customers. Understanding of the customer journey for those who repeat contact improves the services provided for all customers as Electric Ireland can place the critical and necessary resources where needed and at the right time.