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Whitepaper | Empowering Your Team: A Guide to Successfully Rolling Out Microsoft Copilot Part One: Piloting Copilot
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As AI comes to dominate the world of technology, generative AI tools are becoming the key to staying competitive and efficient.
Enter Microsoft Copilot, an AI-powered assistant integrated with Microsoft 365. By automating routine tasks and providing intelligent suggestions, Copilot has the potential to revolutionise your team’s productivity and streamline your workflows.
However, a successful implementation takes more than purchasing licences and adding desktop shortcuts. A well-planned and structured rollout of Microsoft Copilot will ensure that your organisation reaps maximum benefits from this world-changing tool, while minimising disruption and wasted resources.
This guide outlines the essential steps for businesses to successfully implement and maximise the benefits of Copilot
To discuss how you can best protect your data and IP, contact us, and an expert will get in touch with you.
Email: sales@advance.net.au
Or call us on +618 8238 6500
Whitepaper | Your Employees Are Probably Using ChatGPT – Are You Ready for the Risks?
The past two years have seen the meteoric rise of a technology that many experts hail as world-changing: generative artificial intelligence (AI). AI chatbots like ChatGPT, developed by OpenAI, have become a nearly ubiquitous tool for individuals and businesses across many sectors. In fact, one in 10 Australians now report using ChatGPT for work 1 —regardless of their workplace’s official stance on AI usage.
To use ChatGPT effectively and safely at work, employees must be educated about its features, risks, and limitations. Key training topics are covered in our free whitepaper below!
To discuss how you can best protect your data and IP, contact us, and an expert will get in touch with you.
Email: sales@advance.net.au
Or call us on +618 8238 6500
Supercharging CX with Customer Journey Management
Source: inQuba
The Value of Optimised Customer Experiences
It's difficult to argue that positive customer experiences don't provide benefits to both customers and business. When customers have a seamless, rewarding experience, the result is greater loyalty, improved customer retention and customer lifetime value (which invariably means greater customer spend). Having worked with businesses in a variety of industry verticals, it has always been exciting to demonstrate these principles when correlating CX scores with operational metrics, such as revenue or profit. And in reality, the points generally hold true.
Better customer experience also translates to lower operational cost to service the customer - this can be via lower complaints or through more efficient handling of customer queries/issues.
And because experiences are more difficult to replicate, than say a product feature, they can also be the ultimate competitive differentiator..
Source: inQuba
Where Traditional CX Falls Short
There is a "but" however. Simply measuring customer feedback, obtaining scores for your key metrics, and even correlating these with business outcomes, is no longer enough in today's hyper-connected world where an omni-channel approach to the customer journey is mandated.
I'm often reminded of my grandmother's ISP, who only offers support via a single channel, which isn't easily accessible for many people like her. But efficiently delivering a well-orchestrated omni-channel customer journey isn’t all that easy. Which begs the question; is it valuable to know that CX metrics for a given channel are on-target, if the customer is traversing many other channels to reach their goal? Or how do we know where the customer is dropping off and which journey path is actually the best for that individual customer?
And how does one bring together all experiences, touchpoints, and channels along the customer journey to ascertain (with a strong degree of confidence) where the pitfalls and opportunities are... all in real-time so that the customer can be engaged appropriately?
Add to this that often it is difficult to demonstrate tangible business value from traditional CX programs, within a reasonable lead time to ROI. Business leaders aren't merely interested in a CX-metric uplift; they want to understand what improvements mean in terms of increased acquisition, retention, and upsell revenue.
The need is for a better understanding of causality and improved control over customer outcomes along the journey i.e. Customer Journey Orchestration or better Customer Journey Management.
Customer Journey Orchestration
What really excites me about Customer Journey Management/Orchestration is that it provides a mechanism to help customers reach their goals, all while making it easier to demonstrate tangible business results to leadership. It all starts with a clearly-defined goal, followed by a mapping of the key journey points that customers traverse towards that goal (which are often done via customer Job To Be Done and Journey Mapping Workshops).
Like any CX or Customer Journey Management methodology, good data is the cornerstone of a successful program. However, where many methodologies and technologies miss out is the customer's context - there's a reason they say that "Context is King".
But context needs to extend beyond behavioural context (i.e. what the customer has done or what they are likely to do). It must include the customer's emotional context (what they think/feel), and this often requires a "conversation" or dialogue with the customer.
A good example of this was with an insurer's onboarding journey, where we were able to ask customers what they wanted to achieve in retirement. This simple additional emotional context allowed us to better understand the customers' context and thereby personalise the customer's journey accordingly. It facilitated personalised customer nudges along the onboarding journey, and the results spoke for themselves; with the test group cohort outperforming the control group by 34%.
Example Customer Journey: Acquisition Journey (source: inQuba)
Finally, Customer Journey Orchestration needs to extend beyond a single channel. Where customers need or want to switch channels, the methodology should allow them to do so. Think about my grandmother with her ISP. Alternatively, think of a customer that is going through the Application Journey and hits a snag when completing online forms. Typically, this customer might drop off if the effort is too high, but by managing the customer journey we can identify when the customer has gone idle and nudge them, perhaps by offering support via a different channel such as the call centre. There might be other points along the journey where the customer goes idle, and where different nudges/engagements would be appropriate, but the compounding effect of these incremental, contextual nudges adds immensely to the final result - customers reaching the goal.
Journey Summaries, including Journey Duration, Status, and Sentiment Overview (Source: inQuba)
Demonstrating Value
To my earlier point, business leaders need to demonstrate causality in order to prove the value of initiatives. Customer Journey Orchestration is no different, which is why an experimental design is best when first testing your program's impact. Experimentation in business is nothing new, with Harvard Business review writing about it back in 2014 already.
To ensure that you can more easily demonstrate the value of your journey programs, I find that it is best to consider the following design principles:
Start small by selecting a customer journey (what others might often call a "micro journey") that has high business impact
Embed test and control groups in the program design, at least until you've proven that your approach adds value
Embrace Outside-In input by leveraging the expertise of people outside your domain to help uncover blind spots in your Customer Journeys and Engagements
Be bold; to innovate by definition means to do something different. Many of our customers have realised immense value by trying new digital channels and nudges/engagements to help customers along their journeys
Getting Started
Customer Journey Management and Orchestration is as much a methodology as it is a technology-based approach. That is why we recommend starting with Journey Workshops, where we identify customer journey points, drop-offs, goal(s), as well as opportunities to engage and nudge the customer.
If you're as excited about Journey Management and Orchestration as I am, look out for one of our webinars or round-tables. Or, get in touch for a coffee & chat.
Hook, Line, and Sinker Building Phishing Resilience in Australian Businesses - Whitepaper
Hook, Line, and Sinker Building Phishing Resilience in Australian Businesses.
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The threat of phishing cannot be underestimated—nor can it be entirely eliminated. However, with diligent attention to education, preparation, and response, Australian businesses can build a resilient defence that significantly reduces the risk and potential impact of phishing attacks.
We’ve created this whitepaper to help raise awareness so you and your business can avoid being compromised.
To discuss how to protect your data and IP, leave your details below, and an expert will contact you.
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Use Case | Australian Healthcare Provider Maximises Efficiency and Success Through Data Integration, Business Intelligence and Modern Reporting
Where do you begin with a data integration project? Data is the lifeblood of success, but it's also overwhelming, and people often ask us where to start with a data project. Usually, it's the place that is causing significant pain.
Business owners, CEOs, managers, and employees all face decisions daily that impact the success of our organisations and individual roles. It is crucial to use data to make the best decisions and focus energy on getting the best return on time investment. We have lots of data, that is a certainty!
One of our clients in the healthcare sector had a significant problem with data integration and effective reporting. While providing medical devices for patients, they were unable to make data-driven decisions due to lagging and inaccurate reporting. This impacted their appointing setting across the entire business and across various locations in Australia.
Reporting challenges faced were:
A lack of insight into the business, proving difficult to make good decisions on staffing and resource planning.
Difficulty determining which clinics were busy and the number of reserved appointments/placeholders available within the calendars.
Inconsistency across the retail brands.
Different diary management systems and appointment definitions.
Operational data entry issues.
The business needed to run more efficiently, and management had to make effective decisions.
To address these challenges, we used a Business Intelligence tool (PowerBI, Qlik, and Tableau are popular examples) to create a holistic view of business performance and leveraged the data warehouse for the brands.
Data integration and Business Intelligence tools allowed us to:
Create reports according to exact Key Performance Indicators (KPIs).
Create a mapping table for appointment types and a traffic light "dashboard" for quick identification of low/high-performing areas, with a drill-down function for further investigation.
We can now examine the percentage of booked/busy areas by business unit, state, clinic, and medical practitioner to make changes in real time.
Determine the number of reserved "test" appointments available.
Check for free space within the calendars.
Enable reconciliation and check for any data entry errors.
Using this approach provided the following business and efficiency benefits:
A holistic view of the business.
Management can make informed decisions on where to deploy resources. Call centre staff can be tasked with booking appointments for clinics with a high vacancy rate or moving appointments forward if needed.
Connecting various data sources highlighted the inconsistent format/terminology used across different clinics, which was then improved.
Reduction in errors due to manual data entry.
You may already have access to Microsoft PowerBI or have used it before, but are you getting the business benefits discussed above? We work on new projects to make clients more efficient and effective with better data integration, reporting, and insights.
Phone: 08 8238 6500
Email: sales@advance.net.au
Website: www.advance.net.au
DATA: Data Debt - A Silent Challenge in Data Engineering
Where do you begin with a data integration project? Data is the lifeblood of success, but it's also overwhelming, and people often ask us where to start with a data project. Usually, it's the place that is causing significant pain.
In the realm of data engineering, the spotlight often falls on technological innovation and how to manage burgeoning datasets efficiently. Yet, a critical but less discussed aspect is 'data debt.'
What is Data Debt?
Data debt arises when shortcuts in data handling, such as collection, storage, and management, are taken. Driven by the urgency to produce results, compromises like embedding transformations directly into SQL, instead of utilising variables, are made. Although this might offer immediate time savings, it can lead to poorly structured datasets and overlooked documentation, causing long-term complications.
Real-World Impact
The repercussions of data debt are significant and widespread. While no one intentionally codes a server’s IP address only to change the production server pre-weekend with a Monday board meeting looming, such scenarios are not unheard of. This often results in teams spending excessive time deciphering value calculations and dependent data sources, causing project delays and data inaccuracies.
Strategies to Mitigate Data Debt
Documentation: While documenting data sources, transformations, and assumptions can seem burdensome, it's invaluable for managing complexity. Balancing the speed of development with adequate documentation is key. Encouraging new hires to contribute to documentation and code improvement can also be effective. This not only ensures the upkeep of your data processes but also fosters a learning environment.
Data Governance: Implementing a solid data governance framework is essential. This involves establishing clear policies and procedures for data management to minimise inconsistencies and inefficiencies, ultimately reducing data debt.
Regular Audits: Conducting regular audits is crucial. Surprisingly, some ETL/ELT processes yield inconsistent results upon repetition. Regular audits help identify data quality issues, code alterations, and state-dependent calculations, ensuring reliability and consistency in data processing.
Looking Ahead
As Joel Spolsky aptly put it, "code doesn't rust," but in our interconnected world, even code can 'rust.' Adapting swiftly is crucial, yet challenging when burdened with significant data debt.
One lingering question remains: Is managing data debt a technical challenge or a strategic business issue that requires proper budgeting and management?
Have you encountered similar challenges? How does your organisation address data debt?
Use Case | Unlocking Efficiency: Data Integration Transforms Travel and Accommodation Business
Where do you begin with a data integration project? Data is the lifeblood of success, but it's also overwhelming, and people often ask us where to start with a data project. Usually, it's the place that is causing significant pain.
Data is the lifeblood of success, but it's also overwhelming, and people often ask us where to start with a data project. Usually, it's the place that is causing significant pain.
In the example below, we discussed the existing process and the challenges this causes and applied a process and software to automate and simplify the process.
Invoice/File Processing
Challenges
1. Invoice Overload
Travel and accommodation businesses deal with a high volume of invoices daily. Manually processing and approving these invoices can be overwhelming, leading to delays, errors, and inefficiencies.
2. Visibility Gap
Without real-time visibility into invoice commitments, businesses often find themselves in the dark until month-end or the next reporting cycle. This lack of transparency hampers decision-making and financial planning.
3. Manual Data Entry Woes
The tedious task of manually entering invoice details consumes valuable time and resources. Errors are common, and the process is prone to bottlenecks.
Solutions
1. Streamlined Approval Process
We’ve transformed the way invoices are handled. By implementing an automated scanning and approval process, we enabled swift and accurate invoice management. Say goodbye to paperwork overload!
2. Web-Based Portal
Our user-friendly web portal empowers clients to track invoices in real-time. From submission to approval status, everything is at their fingertips. No more waiting for month-end reports!
3. Budget Alerts
Our system proactively alerts departments and projects when invoices threaten to exceed budgets. Timely notifications prevent financial surprises and allow for course corrections.
Benefits
1. Workload Reduction
Significantly reduce manual effort. Staff can focus on strategic tasks, knowing that routine processes are automated.
2. Informed Decision-Making
Real-time visibility enables better decisions. Businesses can allocate resources wisely, optimise spending, and stay ahead of financial commitments.
Data and system integration is not just about solving problems. This type of work empowers businesses to thrive. Efficiency, transparency, and informed decision-making are the cornerstones of our success.
Interested in transforming your business? Reach out to us today below.
Email: sales@advance.net.au
Phone: +61 8 8238 6500
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Cybersecurity Myths Debunked - Whitepaper
Cybersecurity is no longer a new topic for most organisations.
Digital transformation creates incredible opportunities in almost all industries, but it brings with it unique and modern challenges. While those challenges are generally understood, several myths still surround them. In this whitepaper, we take a look at some of the common ones and check the facts.
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Cybersecurity is no longer a new topic for most organisations.
Digital transformation creates incredible opportunities in almost all industries, but it brings with it unique and modern challenges. While those challenges are generally understood, several myths still surround them. In this whitepaper, we take a look at some of the common ones and check the facts.
To discuss how to protect your data and IP, leave your details below, and an expert will contact you.
Or call us on +61 8 8238 6500
Data Security - ASD Cyber Threat Report 2024
Using cloud software like Xero, MYOB, or Salesforce? Our free whitepaper provides a foundation of cloud cybersecurity considerations to make sure your data is secure.
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Every business needs to protect its data. The Australian Signals Directorate (ASD) releases a Cyber Threat Report every year that covers the state of cybersecurity, cybercrime, and cyber resilience in Australia. In this whitepaper, we summarise and analyse the most important findings that are essential to small and medium-sized businesses (SMEs).
To discuss how to protect your data and IP, leave your details below, and an expert will contact you.
Or call us on +61 8 8238 6500
Cybersecurity: Cybersecurity Incident Response Plan
Using cloud software like Xero, MYOB, or Salesforce? Our free whitepaper provides a foundation of cloud cybersecurity considerations to make sure your data is secure.
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A CIRP empowers business leaders to take a proactive, predictable approach to cybersecurity incident management—rather than waiting for an incident to occur and trying to determine how to respond under intense pressure.
In this whitepaper, we’ll delve into the essential components of a CIRP and how to create one that suits your organisation’s specific needs.
To discuss how to protect your data and IP, leave your details below, and an expert will contact you.
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Machine Learning with Unstructured Content: Text Analytics
When we think of machine learning, we think of models that predict an outcome... we don't often think of how these models can be used to predict the sentiment and themes of text data.
What is Text Analytics?
Often when we think of machine learning, we think of models that predict an outcome... we don't often think of how these models can be used to predict the sentiment and themes of text data. Text Analysis leverages machine learning and natural language processing capabilities to help businesses extract insight from text data. By converting raw text or unstructured data to structured content using machine learning algorithms, allows business users to understand the key constructs in their underlying text. Below are some of the key functions that text analytics performs:
Text Preprocessing: Cleaning and formatting the text data to prepare it for analysis. This may include removing stop words, punctuation, and stemming words to their root forms.
Sentiment Analysis: Determining the emotional tone and polarity of the text (e.g., positive, negative, neutral) to gauge customer sentiment. Good Text Analytics tools will also provide a numeric sentiment score so that users can ascertain the extent of the polarity (i.e., very positive or very negative).
Entity Recognition: Identifying and categorising entities such as names, locations, and products mentioned in the text. Text Analytics can extract entities such as brands, countries, products, etc.
Topic Modeling: Discovering prevalent themes and topics within the text data. This is a critical component of Text Analytics, as it allows businesses to identify key themes, as well as define rules for the classification of words and/or phrases (sometimes referred to as "Queries").
Note: Text Analytics is not a word cloud. Word clouds are a very simple representation of commonly mentioned words in a given text data set, but does not provide the richer capabilities mentioned above. There are better ways to analyse and visualise outputs from Text Analytics; please get in touch with our team if you want to learn more about these.
Why Analyse Text Data?
Text analysis plays a pivotal role in three key areas for businesses: Customer Insights and Feedback Analysis, Competitive Intelligence, and Operational Efficiency and Risk Management. Firstly, it enables organisations to gain valuable insights from customer reviews, feedback, social media mentions, and customer experience interactions. These can help with facilitating informed decisions to enhance product development, marketing strategies, and customer experience.
Secondly, text analysis aids in monitoring competitors by analysing public sources, allowing businesses to identify market gaps and refine their strategies. Understanding what your target customer perceives about your competitors and/or their products is incredibly valuable, especially if this data can be obtained from mechanisms that carry less bias (such as review sites for example).
Lastly, it can improve internal operations by analysing employee feedback and communication, while also serving as a tool for proactive external risk management by monitoring news articles and social media for emerging threats.
These applications collectively empower businesses to make data-driven decisions and improve overall performance, using unstructured text data that would have otherwise been cumbersome or impossible to analyse.
Text Analysis for Customer Insights and Feedback Analysis
Understanding customer sentiment and preferences is paramount for any business. Text analysis allows organisations to delve deep into customer reviews, feedback forms, social media mentions, and customer support interactions. By analysing this unstructured text data, businesses can gain valuable insights into what their customers like, and dislike, and what problems they face. This information can inform product development, marketing strategies, and customer experience improvements, ultimately leading to higher customer satisfaction and loyalty (and spend) while reducing churn.
Example: A call centre is an important component in enhancing customer experience and decreasing customer churn. Conventional models for predicting customer churn primarily rely on customer data gathered from past transactions and demographic information. However, this approach overlooks the inclusion of important context provided directly from customers, including their needs, desires, wishes, and emotions. Text Analytics is capable of generating categories like billing problems and product issues, which can then be incorporated into predictive models. This valuable information provides insights into the purpose behind each customer interaction and customers' motivations, resulting in a 2 times faster reduction in customer churns on average. (Source: Lexalytics)
Competitive Intelligence with Text Mining and Text Analytics
Staying ahead of the competition is a constant challenge in today's fast-paced business environment, particularly as businesses fight for share of a decreasing wallet. Text analysis can help businesses monitor their competitors' activities by analysing public sources like competitor reviews, news articles, and social media discussions. By tracking and analysing competitor sentiment and customer feedback, organisations can identify gaps in the market, capitalise on their competitors' weaknesses, and refine their own strategies to gain a competitive advantage.
Driving Operational Efficiency and Risk Management with Text Analysis Techniques
Text analysis can be applied internally to improve operational efficiency and risk management. By analysing employee feedback, internal communications, and performance reviews, businesses can identify issues within the organisation, such as low morale, communication breakdowns, or operational bottlenecks. This insight can be used to streamline processes, enhance employee satisfaction, and reduce operational risks.
Additionally, text analysis can also be used for monitoring external risks. By analysing news articles, social media, and industry reports, businesses can stay vigilant to emerging threats, such as public relations crises or market disruptions, allowing them to take proactive measures to mitigate these risks.
Example: As a food and beverage research company, Technomic’s data analysts had the challenge for categorising ingredients provided by a data vendor. Using a trained machine learning model, they were able to automatically put ingredients in the appropriate sub-categories at over 98% accuracy, saving Technomic up to 40 people-hours of labour per category. (source: Lexalytics)
Incorporating Text Analysis Outputs into Machine Learning Models
Text analysis can be a valuable component in building predictive models, especially when dealing with unstructured text data. For example, when predicting a customer's propensity to repurchase, a business could utilise unstructured feedback from multiple channels (email, call centre, and surveys) to identify key themes and sentiment indicators that can be incorporated into a predictive model. Here are some other areas where Text Analysis can be incorporated into Machine Learning models:
Feature Engineering: Text analysis helps convert unstructured text into structured features that can be used in predictive models. This involves techniques like sentiment analysis, entity recognition, and topic modelling. These extracted features can be combined with other structured data to improve the predictive power of models.
Sentiment Analysis for Customer Churn Prediction: Suppose a telecom company wants to predict customer churn. By analysing customer comments and reviews, sentiment analysis can be applied to gauge customer sentiment towards the company. Positive or negative sentiment scores can be used as features in predictive models to forecast the likelihood of a customer leaving the service.
Text Classification for Spam Detection: In email or message filtering systems, text classification can be employed to distinguish between spam and legitimate messages. By analysing the content of emails and messages, the model can predict whether an incoming message is likely to be spam or not, enabling automatic filtering.
Topic Modeling for Content Recommendation: Streaming platforms like Netflix use topic modelling to recommend content to users. By analysing the text descriptions, reviews, and user feedback for movies and TV shows, they can create predictive models that suggest content based on a user's viewing history and preferences.
Customer Support Ticket Resolution Time Prediction: Customer support teams can use text analysis on support tickets and queries. By examining the text for keywords, sentiments, and complexity, predictive models can estimate the time required, as well as the best team to resolve a customer issue, enabling better resource allocation and improved service levels.
Market Sentiment Analysis for Stock Price Prediction: Financial institutions can use text analysis to analyse news articles, social media posts, and press releases related to publicly traded companies. Sentiment scores extracted from the text can be integrated into predictive models to forecast stock price movements.
Review Ratings for Product Sales Prediction: E-commerce platforms can utilise sentiment analysis on product reviews to predict future sales trends. If a product consistently receives positive reviews, the predictive model can forecast higher sales for that product in the coming months.
Bringing it all Together
It is important to create and understand the relationships between the insights extracted from Text Analysis. For example, it is not enough for a retailer to understand that their customers are talking negatively (sentiment) about shoes (topic). The retailer would also need to understand other themes that are related to or occurring at the same time as these, such as sizing or availability, in order to provide richer context. Similarly, when analysing competitor reviews online it would be more useful to understand all the relevant context together. This may also require the integration of other operational data that could impact decision-making related to the Text Analysis.
Getting Started
Text Analytics is an exciting application of machine learning and NLP, that is also mature from a technology perspective. Even more exciting is combining the results with other traditional machine learning models and predictive analytics. While the technology exists, it is important to ensure that it is implemented appropriately.
Leverage our team's experience with machine learning and text analytics; get in touch with us to book a free consultation to understand the opportunities you have to implement unstructured text analytics and drive better decision-making in your business.
Cybersecurity: What is a cyber-aware culture, why it’s important, and how to create one.
Using cloud software like Xero, MYOB, or Salesforce? Our free whitepaper provides a foundation of cloud cybersecurity considerations to make sure your data is secure.
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Employees can be a business’s greatest cybersecurity asset or its biggest vulnerability. As modern businesses place greater emphasis on technical cybersecurity defences and controls, cyber attackers are increasingly focusing on the ‘human factor’ by targeting employees.
It’s often said that a business is only as secure as its least informed employee. No matter how much a business has invested in cybersecurity, the unfortunate truth is that it can all be undone in seconds by a single employee who lacks a basic understanding of cybersecurity. This is why building a “securityaware culture” within an organisation is not just a high return on investment, but a cybersecurity necessity.
So, what is a Security-Aware Culture? Read the whitepaper to get a grasp on this important element in protecting your data, improving compliance and safeguarding intellectual property.
To discuss how to protect your data and IP, leave your details below, and an expert will contact you.
Or call us on +61 8 8238 6500
5 Real World Examples Using Predictive Analytics to Reduce Customer Churn
It's a well-known marketing adage that it's more expensive to acquire new customers compared to retaining existing customers. However, customers have become more discerning and have many mechanisms to shop around for alternatives. At the same time, there has never been a more important time in business to reduce costs and improve the return on sales & marketing efforts. One way to do so is to reduce customer churn. The challenge is that it's not always apparent which levers will be best to reduce churn, as well as how those levers can be integrated into everyday business processes in an automated manner. This is where Predictive Analytics can play a crucial role, firstly by identifying the levers and secondly by mitigating the risk of human bias in our decision-making. The good news is that while machine learning and predictive analytics used to be the realm of data scientists and engineers, modern tools are providing the means for business to test and implement these models into their processes.
Below, we have summarised 5 key considerations for using Predictive Analytics to reduce customer churn. The proof is always in the pudding though - if you would like to book a free Machine Learning workshop with us, contact us today.
1. Use Predictive Analytics to Understand Customer Behavior
Predictive analytics can be applied to customer data to gain insights into their behaviour and preferences. By analysing historical data, businesses can identify patterns and trends in customer behaviour that are markers for a desired customer outcome (e.g. repurchasing). This analysis can help businesses understand why customers churn and what factors contribute to their behaviour. With this understanding, businesses can take proactive measures to address those issues and prevent customer churn.
Example: Harley Davidson harnesses the power of predictive analytics to identify potential buyers, attract leads, and successfully seal the deal. Harley Davidson relies on their AI program to identify individuals who have the highest propensity to make a high-value purchase. From there, a sales representative takes charge, reaching out to these potential buyers and guiding them through the purchasing journey until they find their dream motorcycle. By directly targeting customers, they can ensure a highly customised experience which ultimately results in greater satisfaction. Predictive analytics helps provide a personalised service to customers when they're ready to buy, while allowing the business to concentrate their efforts on serious buyers. (Source: Forbes)
2. Leverage Predictive Analytics to Identify Churn Indicators
Predictive analytics can also be used to identify churn indicators. By analyzing various data points such as customer demographics, purchase history, and engagement metrics, businesses can build predictive models that can identify customers who are at a higher risk of churning. These models can help businesses take targeted actions to retain those customers, such as offering personalized discounts or reaching out with proactive customer support.
1. Focus on attributes that the business can change: It is important to identify and analyze factors that the business can actually control and influence. This will allow for effective interventions to be implemented in order to reduce customer churn.
2. Choose only a handful of indicators to focus on: Instead of analyzing a large number of attributes, it is recommended to identify a few key indicators that have the highest impact on customer churn. This will help in simplifying the model and making it easier to interpret and act upon. It is crucial to prioritize the attributes that are most relevant and influential in determining customer churn.
3. Experiment with results so that you can measure impact: After identifying the key attributes, it is essential to conduct experiments and tests to measure the impact of each attribute on customer churn. This can involve running A/B tests or implementing targeted interventions to gauge the effectiveness of changes made to these attributes. By continuously experimenting, businesses can refine their predictive models and improve their ability to accurately predict and prevent customer churn.
Example: Hydrant, a Wellness brand based in the US, has successfully leveraged Predictive Analytics to identify churn indicators and predict churn propensity with 83% accuracy, while increasing conversion rates and average customer spend by 2.7x and 3.1x respectively, when compared to control groups. The predictive model creates detailed forecasts for each customer's possibility of churn. With these accurate individual forecasts, Hydrant dynamically segments customers to receive tailored marketing messages and discounts that match their future buying power. (Source: Pecan.ai)
3. Use Predictive Analytics to Personalize Customer Experiences
Customer Experience is where the brand promise is delivered, and where expectations are either met, exceeded, or missed. Often, the challenge is understanding where the customer is in their customer journey and what initiatives would nudge them to a business goal based on their own individual context. Integrating predictive analytics models into the Customer Experience enables businesses to tailor their interactions and offerings to customers as a segment. By understanding customer preferences and behaviour, businesses can provide personalized recommendations and offers that are more likely to resonate with customers. This personalized approach can enhance the customer experience and increase customer loyalty, reducing the likelihood of churn.
Predictive analytics can be used to personalize customer experiences by analyzing customer data and making predictions about each individual customer's preferences, behaviors, and needs. Here are some steps to use predictive analytics for personalization:
Collect and integrate customer data: Gather data from various sources such as customer profiles, purchase history, website interactions, social media activities, and customer feedback. Ensure that the data is accurate, up-to-date, and properly integrated.
Clean and preprocess the data: Cleanse the data to fix any errors, remove duplicates, and handle missing values. Preprocess the data to transform it into a suitable format for analysis.
Define customer segments: Use clustering techniques or customer segmentation algorithms to group customers into different segments based on their similar characteristics, preferences, and behaviors. This helps in understanding different types of customers and tailoring experiences accordingly. Pro-tip: Leverage Text Analysis to identify key themes in unstructured customer feedback in order to build richer customer segments.
Analyze and model customer behavior: Use predictive modeling techniques like regression, classification, or recommendation algorithms to understand customer behavior patterns. This can help predict future actions, preferences, and likelihood of certain events, such as purchases or churn.
Develop personalized recommendations: Based on the predictive models, make personalized recommendations to customers. For example, suggest relevant products, promotions, or content based on their past behaviors or similar users' actions. This can be done through targeted advertising, on-site recommendations, or personalized emails.
Real-time personalization: Implement systems that use real-time analytics to personalize the customer experience in the moment. For example, show personalized product recommendations as soon as a customer visits a website, or tailor the website content based on the customer's browsing behaviour.
Measure and optimize: Continuously monitor customer engagement, conversion rates, and customer satisfaction to assess the effectiveness of personalized experiences. Use A/B testing to compare different personalization strategies and fine-tune the models and recommendations based on the results.
Example: Having a truck breakdown is not only a bad experience for the driver and business operations, it also costs the business money. Using connected devices and machine learning models, Volvo is able to predict when a truck is likely to breakdown, before the event has occurred. The essence of connected services and proactive maintenance lies in the fact that, thanks to wireless technology and sensors, Volvo can gather copious amounts of real-time data from a vehicle. By analysing this data and identifying patterns, they can effectively forecast and preempt any potential malfunctions. This allows customer to plan a workshop visit at their convenience, and promptly address the issue before it results in an unforeseen breakdown. (Source: Volvo Trucks)
4. Measure the ROI of Predictive Analytics
Measuring the return on investment (ROI) of predictive analytics is crucial to assess its effectiveness and justify its implementation. Businesses can track key metrics such as customer retention rates, revenue generated from retained customers, and cost savings from reducing churn.
By comparing these metrics with the costs associated with implementing predictive analytics, businesses can determine the ROI and make informed decisions about the use of predictive analytics for customer churn reduction. Using A/B testing is a critical component of measuring the success of Predictive Analytics programs, and can be especially effective if tangible metrics such as churn rates and revenue are attributed in the model.
What are predictive models?
Predictive models are algorithms that use predictive analytics to forecast future outcomes based on historical data. These models use statistical techniques, such as regression analysis and time series models, to identify patterns and relationships within the data. By analyzing factors that have influenced customer churn in the past, predictive models can predict future churn and help businesses take proactive actions to retain their customers.
Reducing customer churn is a crucial objective for businesses. Machine learning and predictive analytics can play a significant role in achieving this objective. By using predictive analytics to understand customer behavior, identify churn indicators, personalize customer experiences, engage in proactive customer retention measures, and measure the ROI, businesses can effectively reduce customer churn and improve their overall profitability and growth.
At Advance Business Consulting, we're passionate about helping our customers get the best value out of their data and technology. Predictive Analytics and machine modelling are some of the techniques that our data geeks love to work with our customers on. If you're interested in exploring how these can enhance your business, book a free workshop.
Cloud Security - A Shared Responsibility | Understand the basics
Using cloud software like Xero, MYOB, or Salesforce? Our free whitepaper provides a foundation of cloud cybersecurity considerations to make sure your data is secure.
Download your free whitepaper below
Using cloud software like Xero, MYOB, or Salesforce? Our free whitepaper provides a foundation of cloud cybersecurity considerations to ensure your data is secure.
While it would be nice to say that security in the cloud is purely the responsibility of the cloud service provider, the reality is that such an important responsibility must be shared between the cloud provider and the consumer.
However, ‘cloud’ is a broad term that describes several different technical frameworks, and the security responsibilities are shared differently in each.
This paper will cover the three most common cloud frameworks and how security responsibilities are split between the provider and the consumer.
To discuss how to protect your data and IP, leave your details below, and an expert will contact you.
Or call us on +61 8 8238 6500
Top Data & Business Intelligence Trends 2023 Event - Recap
Top Data & BI Trends 2023 Report
Top Data & BI Trends 2023 Event - Recap
Jeremy Sim from Qlik delivered an exciting talk about trends impacting data and business intelligence from a global perspective, and our Q&A session provided an opportunity to dive a little deeper and discuss artificial intelligence and the impact this is already having on our roles.
You can access the report here.
We’ve provided a link to our other BI whitepapers here.
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Essential Eight Best Practices Guide: Multifactor Authentication
Excel and BI tools are great tools for your business, but when is each more suitable?
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Have you ever been annoyed by those multi-factor authentication (MFA) codes that are sent to you via SMS or email when you try to log in to your online accounts? While it's true that these codes can be a bit of a hassle, they serve an incredibly important purpose - protecting your accounts from being stolen. Also, we can actually make those codes a much more seamless experience with lower friction and fewer steps to gain access to your systems.
In today's digital age, over 5 billion people are connected to the internet, and any of them are potential cybercriminals who may try to guess your username and password to gain access to your accounts.
While it's essential to use strong, unique passwords for all of your online accounts, it's not always enough to protect you from account takeovers. That's where MFA comes in. This security feature requires you to provide an additional form of verification, such as a code that is sent to your phone or email or available in an app, which is needed to log in to your account. And it's incredibly effective - MFA has been shown to block 99.99% of account takeover attempts.
Check out the whitepaper which covers MFA in more detail above.
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Or call us on +618 8238 6500
WHITEPAPER | Excel or a Business Intelligence solution? When is it just better to stick with Excel.
Excel and BI tools are great tools for your business, but when is each more suitable?
Microsoft Excel has been a pillar of business since the late 1980s, and since then, it’s only grown in its usefulness and relevance to modern business. However, as businesses’ requirements for analysis and reporting grew, along with increases in the volume and sources of data, Excel began to struggle.
In the past few years, we’ve seen an evolution of the tooling into Business Analytics (BI) platforms. These platforms use modern programming languages and data storage techniques to speed up and automate repeated tasks, removing human involvement and reducing overhead on a business.
Despite what evangelists from both sides preach, both Excel and BI tools have their place in modern business. Knowing when to leverage each toolset can save time, effort, and cost –leading ultimately to better, faster, and more accurate decision-making.
In this paper, we look at both sides of the story and highlight when switching to BI tools can improve business outcomes and when it’s best to stick with Excel.
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Or call us on +618 8238 6500
CYBERSECURITY VS INFORMATION SECURITY – WHAT’S THE DIFFERENCE?
Managing risk to avoid unnecessary cost and disruption is a key focus for any maturing organisation. Information Security is a broad area, of which cybersecurity is only one element.
Managing risk to avoid unnecessary cost and disruption is a key focus for any maturing organisation. As organisations grow and mature, they inevitably begin reviewing risks to their information and digital systems. In this space, cybersecurity and Information Security are two terms that are often used interchangeably, and while there is overlap, understanding the differing scopes and methods of each can equip an organisation with more tools to treat risk and ultimately help reduce their overall risk profile.
Download this free whitepaper and level up your data security and cybersecurity posture.
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10 benefits of intelligent information management
Information management can unlock many benefits and make your team more efficient.
The infographic linked below shows 10 of the most common benefits of an intelligent information management solution.
No more information silos
Free up time to work on more valuable tasks
Collaborate with colleagues and partners outside your company
Only one version of any document
Security and compliance for your business
Click here to see the complete list of benefits and view the infographic.
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WHITEPAPER | HOW TO MANAGE TECHNOLOGY RISK IN YOUR BUSINESS
Risk management is the foundation of protecting your investment.
HOW TO MANAGE TECHNOLOGY RISK IN A SMALL OR MEDIUM-SIZED BUSINESS.
Everything you’ve heard about cybersecurity comes down to managing RISK. Even the best system isn’t 100% foolproof and there is always some risk.
Our whitepaper below focuses on managing technology and cybersecurity risks and the principles can be applied across all risk types within a business.
Put simply, managing risk is at the foundation of protecting your business, and your data and avoiding disruption.
To discuss cybersecurity protection strategies for your business, leave your details below and an expert will get back to you.
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