Analytics quick wins: Five New Year resolutions to thrive in a climate of economic uncertainty
How can advanced analytics help you through the COVID-19 storm? We reveal some ways to strengthen your business and prepare you for long-term success.
The global spread of COVID-19 has created a financial and healthcare crisis no one seemed fully prepared to meet. As stimulus programs wind down, the extent of the consequences will become clearer.
On the economic front, most businesses have reviewed their costs and made changes to protect their long-term future, with the burden often falling on employees in the form of job losses and reduced hours.
In March, US weekly jobless claims reached a peak of nearly seven million. Before COVID-19, that figure had not exceeded 700,000 since the 1960s.
But job counts tell only part of the story. According to the World Economic Forum, 11.7% of working hours have been lost in the first three quarters of 2020, with the impact felt disproportionately by low-income earners.
Broaden your thinking
As business leaders struggle in this tough economic climate, reducing their cost base is an understandable response to reduced demand and increased uncertainty, but it’s not the only way to survive. Consider, for example, the opportunities advanced analytics present in this new environment. COVID-19 has turbocharged digital transformation and changed the way consumers think about product/service delivery. A year ago, no one could have predicted the rapid rise in telehealth, for example.
As we leave 2020 behind, now is a perfect time to consider making some New Year analytics resolutions.
Resolution 1: I’m going to boost my organisation’s analytics capability
The ability to optimise business settings typically reflects the maturity of an organisation’s analytics capability. Most attempt to gain an edge through the proliferation of their customer data. However, the degree of sophistication to harness the potential of those enormous datasets varies significantly. Are your analytical outputs still all focused on retrospective analysis of the past? Or have you moved into future orientated predictive and prescriptive analysis as represented in the chart below?
Quick wins stem from thinking about actions that move you towards the next level of maturity.
Focusing your BI reporting in the right places
Most organisations will employ business intelligence (BI) reporting to help management understand what is happening in the business. An increasing number of entities now employ interactive BI reporting and visualisation tools to allow users to delve into the BI data and try to understand the reasons behind what is happening.
When developing your BI reporting process, it’s important to think about the measures you track and report, and how they relate to one another. This will increase the focus of your reporting as well as the value of your data, and ensure you’re not swamping users with too much information. It might help to consider BI reporting as a hierarchy of measures categorised as:
- End outcomes that determine business fundamentals – for example, product sales and revenue
- Lead indicators that give early clues as to how the end outcomes will perform in the future – think about your end-to-end product sales and customer lifecycles to derive these. For example, home-loan approvals as a lead indicator of home-loan drawdowns.
- Behavioural indicators – tracking how your customers interact (or don’t interact) with your business and the activity of your sales staff will help inform future operations that ultimately determine end outcomes. For example, the number of customers using online sales channels.
No time like the present to develop your predictive analytics
Increasingly, public and private sectors are using predictive analytics to guide decision-making. If your organisation has yet to make that step, now might be the time to start. It doesn’t have to be hard or complex. You might consider one business problem, such as customer retention, and see how a machine learning solution can improve your customer engagement strategies, for example, by identifying customers that are most likely to take their business to a competitor in the near-term.
If you don’t have in-house capability, external advice doesn’t have to be an elaborate process to add significant value.
Regardless of where you are on the maturity scale, the next incremental step might be quick and rewarding.
Resolution 2: I’m going to shift my thinking on my customers from short-term to long-term
Unlocking long-term customer value is one of the greatest analytical opportunities for many businesses.
This is particularly true for industries where people have a long-term ongoing need for the product or service, and the opportunity to differentiate from your competitors is relatively limited.
Telcos, utilities, banking, insurers and other financial service providers are good examples.
All businesses will have a focus on their near-term financials. The current-year profit-and-loss account and balance sheet will always retain its allure as they headline the next set of financial statements market participants see.
However, near-term financial incentives are often inconsistent with realising long-term customer value. For example, young banking customers (teenagers and students) tend to generate relatively little revenue for banks. With a near-term focus, a bank might invest relatively little in these customers. However, later in their lives they will need more valuable banking products such as a home loan. A long-term focus would see greater investment in these customers in recognition of their long-term customer value.
Strategies with lasting appeal
To achieve a pricing and loyalty strategy that maximises long-term shareholder value, it’s essential to understand which individuals will deliver high or low long-term value.
When you achieve this, you can target your product pricing so you don’t miss potential revenue opportunities or discount in way that cannibalises existing revenue more than it encourages new sales.
Additionally, paying attention to existing customers can help avoid the predictable customer attrition that occurs when the business focuses mostly on potential new customers.
Getting the balance right with your pricing and loyalty strategy is a fine art – not just a matter of luck – requiring well-directed customer analysis that could significantly improve your strategy. It could also have major positive ramifications for your customer and shareholder value.
Resolution 3: I’m going to tailor my analytics solution to the problem at hand
Off-the-shelf advanced analytical products are typically slick in their presentation and the way they can be implemented in your IT infrastructure. They are built with this in mind to appeal to as broad an audience as possible.
There are plenty of use cases where these can be of great benefit, but sometimes, a generic solution won’t give you the answers to a complex business problem. For example, a retention prediction model might highlight customers who are most likely to take their business to a competitor but offer no insight on what to do about it. In these cases, you may want to consider a tailored solution where you have a need for greater insight.
A tailored solution can explicitly incorporate your specific product, delivery channel, customer and/or market location factors.
An advantage in doing things differently
No two businesses are the same and their analytical solutions shouldn’t be either. Sometimes competitive advantage can stem from doing things differently with your analytics approach. For example, if your advanced analytics approach allowed you to understand the long-term value of your customers better than your competitors, your business decisions informed by this might yield greater long-term shareholder value.
Resolution 4: I’m going to gain deeper insight through personalisation
More and more businesses are attempting to personalise their pricing, marketing and services. If done well, engaging customers with a personalised approach and remaining relevant to their needs is a great strategy.
Some guiding principles to focus your strategy
To gain the most out of personalisation, keep two key questions in mind:
- Is my personalisation approach deep enough to make it genuinely personal?
- Have I considered whether my approach will generate overwhelming negative sentiment about the business?
Personalised marketing is a good example. We’ve all received marketing emails from businesses that attempt to talk to us as if they know us and who we are. Most of the time these miss the mark. Broadly speaking, businesses accept this. The product sales from the minority that do engage with the marketing is deemed to be worth it.
Does the upside outweigh the downside?
Do you understand the power customers may have to affect your business when they don’t engage? The best case is that they are ambivalent to the engagement. But some will view it negatively and attach negative sentiment to the experience and your business.
Have you tried to establish whether the upside from customers who engage with a personalised approach outweighs the downside from customers who view the engagement negatively?
A quick review of your personalisation approach may be very enlightening and change the way you think about engaging with your customers.
Resolution 5: I’m going to make sure our analytics governance structure is rigorous
Many businesses use machine learning and other models to support decision-making, often in real time. In the haste to deliver, it can be easy to overlook putting adequate model governance in place, which can expose your organisation to significant risks. Take, for example, Amazon’s AI recruitment tool from 2014/15, which was biased against women. The tool was built using data from CVs over the prior 10-year period and so reflected male dominance in the tech industry.
With some thoughtful preparation, you can set up a rigorous governance structure based on these essential components:
- Risk management framework – Including risk appetite, risk monitoring and controls
- Governance guidelines – For system design, privacy and fairness guidelines, for example
- Tracking – Of modelling system performance, changes, accountability and incidents
- Audit and review – To ensure ongoing fitness for purpose and value realisation.
Any business can be vulnerable to governance weak spots, especially during times of economic uncertainty. The following considerations highlight common areas of concern that you can address to help strengthen your governance structure:
- Design – Have you correctly formulated the model to solve the intended business problem?
- Operations – How is the model performing in its regular course of operation? This includes people processes, such as oversight, authorisation, and processes to maintain their performance, as well as systems that support the infrastructure.
- External issues – Are there any changes outside of the bounds of the model? This includes changes to upstream and downstream processes that cause the model to behave differently from its original design and intention.
- Community and regulatory expectations – Does your model meet expected standards? This includes privacy, fairness, and regulatory conditions, such as those governing the sale of financial products.
Simple steps towards a robust framework
Putting in place a good governance structure doesn’t have to be hard. A quick review of your current structure will help ensure you are maximising the value from your modelling infrastructure. It will also help ensure you are minimising the risk of experiencing a damaging governance failure at a time when your business may be less resilient than normal to shocks.
Analytics quick wins – where do you start?
By having a conversation. Get your stakeholders in a room and think about your analytics strategy and the potential opportunities raised in this article.
Many businesses have invested heavily in their analytics capability over the past few years. Arguably, economic uncertainty is when this should pay most dividends, as you look to secure your short-term future and position well for the recovery.
Other articles by
Other articles by Daniel StonerMore articles
Lessons learned one year on – Algorithm charter for Aotearoa New Zealand
Take a look at the lessons learned from Taylor Fry's newly released review of the Algorithm charter for Aotearoa New ZealandRead Article
Algorithm Charter for Aotearoa: Six things to be doing now
What should New Zealand government agencies consider when implementing the Algorithm Charter for Aotearoa New ZealandRead Article
Related articlesMore articles
The road not taken – exploring ‘what if?’ with causal analytics
How do you evaluate the impact of high-stakes high-cost decisions? The latest advances in causal analytics are showing the wayRead Article
Where are the businesses most reliant on JobKeeper (January)?
Our interactive map shows the proportion of businesses receiving JobKeeper by local government area from April 2020 to January 2021Read Article