J. Morrison + T. Dallaire + M. Vinski

You IMAGINE THIS SCENARIO: are designing a new customer experience to drive a shift in customer behaviour. You have reviewed the reports and dashboards describing current behaviour. You have asked customers how they felt and incorporated their feedback in hopes of crafting an experience that meets their needs. But even after ticking these boxes, your customers are still behaving the same way as they did before.

You and your colleagues are stumped. What is it about your new experience that is falling flat? You’ve provided exactly what customers told you they wanted — so why hasn’t it resulted in the desired behaviour change?

This is an ideal question for a behavioural scientist — why someone’s behaviour deviates from their stated intentions and motivations. As customer experience becomes an increasingly crucial part of business strategy, an understanding of behavioural science will be an increasingly valuable asset. A competitive advantage can be built by understanding both the spoken — and more importantly, the unspoken — needs of your customers and using that information to craft experiences that truly drive behaviour change.

The behavioural science approach seeks to drive behaviour change by combining insights from academic theory, customer research and data, and industry expertise to build models for decision making. These models isolate the cognitive, emotional, and social barriers people are facing, creating a foundation for designing solutions to help customers overcome these barriers.

The fact is, modern organizations require customers to make lots of complex decisions — but they aren’t always great at creating a context where people can make the ‘right’ choice. For example, the process of selecting the right mix of financial products is daunting for any consumer — even those who have studied finance. Often unintentionally, banks confuse customers with jargon, complex product attributes, and an overwhelming array of offerings. With such a high level of complexity involved, many customers can feel overwhelmed and fall through the cracks — missing out on valuable opportunities to improve their financial well-being.

There are many tools in the behavioural toolkit that can help people navigate complex decisions. To help customers make better financial decisions, you could provide aids that simplify calculations; translate jargon into everyday language; or highlight the behaviour of peers in order to build confidence that a decision is the right one. The biggest challenge we face when crafting solutions is often not in identifying a possible tool (there are many), but in determining which tool is the right one for the particular individuals we’re looking to influence.

When developing solutions, academic research is a great starting point, but the results tend to be generalizable to the entire population. When working with specific groups, solutions with broad applicability may not be as effective because specific groups have unique characteristics.

For instance, a highly effective solution for office employees may not work as well with hospital nurses. Their training is different, as is the context in which they make decisions. When presented with the same decision about, say, a financial product, these two groups may reach their decision in profoundly different ways.

It makes sense that academics stay broad with their research: If they began to subdivide the participants in their studies, why stop with office workers and nurses? Why not separate out airline pilots and hotel concierges, too? What about office workers from banking versus transportation? What about two banking organizations with completely different corporate cultures? The number of subdivisions could be infinite.

As a result, what we end up with is a set of empirically tested models for decision making that assume a homogeneous population (what our marketing colleagues might call ‘Gen Pop’); another set of heterogeneous stakeholders in a business setting; and the behavioural scientists in the middle, trying to match different decision-making models with the stakeholder groups for which they are the most applicable. This is where behavioural scientists have found their niche in the corporate world: as translators of the theoretical knowledge of academia into the applied domain of business.

Navigating the gap from theoretical to applied can be challenging, but it can be overcome much more effectively when we combine forces with our colleagues in data analytics. The data scientists of the business world create the foundational capability to understand precisely who your customers are and how they behave. Behavioural scientists can then use this information to better explain why customers are behaving in certain ways, what is motivating them, and ultimately, how best to incentivize desired new behaviours.

In this way, the skills of both teams complement each other. Data scientists connect behavioural scientists with data-driven insights required to scrutinize and validate assumptions about human behaviour, and behavioural scientists can use these insights to determine the best tools to drive behaviour change. The result is a set of solutions that can drive sustainable behaviour change, because they have been crafted to target specific groups.

A practical example of this approach is work we recently completed with a subscription-based service company looking to reverse declining revenues. The company approached our analytics team with a common situation: They had a problem, a lot of data, and a limited strategy for leveraging it. Their main question was how their customer base would respond to changing subscription rates and how modifying prices would impact their bottom line.

With rising costs and increased competitive pressure in the market, the client wanted to determine if a change in prices would sacrifice too much market share and decrease revenue. When considering the impact of a rate change on customer perception and subscription behaviour, an important consideration is how to tell customers about a price increase in order to minimize the likelihood that they will churn.

The process for answering these questions began with digging into the data to understand what information the company had been collecting and how it tied back to business decisions. The team found that price elasticity varied significantly across customers. Some would cancel after a price increase and some wouldn’t, and this appeared to vary based on customer background and preferences. This variability made it challenging to forecast revenue because there was no data to benchmark price elasticity — meaning they couldn’t forecast how many customers would be at risk for cancelling if prices rose.

We dug into the economic literature to identify price elasticity models similar to the client’s market and built a model using the client’s sales data to forecast revenue at different price points. This model had its own set of assumptions that were validated with other client data to identify key factors that drive revenue. We could demonstrate, for example, that a certain assumption was dependent on customers completing a key step in the sign-up

Competitive advantage can be built by understanding the unspoken needs of your customers.

process. If they did so, they were less likely to cancel their subscription after the first six months — showing our client how subtle changes in the experience can result in a longterm impact on behaviour.

To answer the second question about how to communicate rate changes, we leveraged funnel analytics and customer insights to build customer segments based on experience and purchase decision factors. Understanding the factors that drive the decision to subscribe for the main customer groups, we were able to leverage the behavioural toolkit and work with the communications team to build a set of micro-targeted marketing messages for different segments.

With insights like these, we were able to incorporate our solutions into a roadmap that forecasted a range of scenarios, showing what the upper and lower bounds of the revenue increase may be, depending on the effectiveness of different changes to the process.

This behavioural insights + data analytics approach generated a truly holistic solution: the business learned whether it should raise or lower rates to generate more revenue, but also, if it should change rates, what steps of the customer journey would be most crucial to realize gains in revenue.

In closing

We hope it’s easy to see how the disciplines of behavioural and data science can work together, and how each complements the other’s ability to disentangle the mysteries of human behaviour. Going back to your attempt to craft a new experience for your customers from the beginning of this article, how might things have turned out if you had taken the approach described above? What if you had recruited the skills of some data scientists to assess current customer behaviour instead of just relying on what your reports told you? What if you had implemented findings from behavioural science to identify and understand peoples’ unconscious biases and motivations instead of asking customers directly and expecting them to answer honestly?

Better yet, what if you had done both? There is no guarantee that you would have succeeded in changing the behaviour you were seeking to change. But even if you didn’t, you would likely have a much clearer understanding of where and why things fell flat, giving you a better shot at success in the future.

James Morrison is a Manager in the Behavioural Insights Practice at PWC Canada. Thomas Dallaire is a Senior Manager in the Data Analytics Practice at PWC Canada. Dr. Melaina Vinski is the Behavioural Science Lead and Senior Director of the Behavioural Insights Practice at PWC Canada.

Most organizations struggle to create a context where people can make the ‘right’ choice.