When designing customer journeys to meet diverse customer expectations, what would you rather base your design on: Hypothetical guess-work about the types of customers you have, or actual insight from behavioral data?
There is little new about using personas to tailor marketing efforts and customer experience. However, traditional personas have relied on demographics and other personal descriptive data on customers in terms of “who they are”. This can be an effective way of humanizing the audience, but in today’s diverse environment these personas seldom capture the customers’ actual relationship with the product or service. Most would agree that a good customer journey is about tailoring the experience to the customers’ actual needs and expectations and not simply their age, gender or zip code. This is where behavioral personas come in handy, and in our view clearly outperform traditional approaches.
Example segments (illustrative)
In 2019, BearingPoint collaborated with Plan Norge in redesigning the customer journey for their donors. The aim was to further improve their popular child sponsorship program (known as “Planfadder”) to drive increased loyalty and revenue.
In order to improve the customer journey, the idea of tailoring it to different types of donors quickly became the starting point. BearingPoint introduced the concept of behavioral personas which, in essence, are stereotypical descriptions of what donors actually do, as a basis to understand their needs and preferences. The underlying rational for this was that by understanding the different types of behavior we can see in interaction data between donors and Plan Norway and the sponsored child, we would be better able to understand the “what” and “why”, and thereby design a journey that would meet the donors expectations more effectively. This could be done by designing differentiated customer journeys, each well-tailored to observed patterns in actual customer behavior, and then map each donor to the best suited journey based on recent behavior.
The work was done by iteratively combining quantitative and qualitative methods. We used workshops to understand how todays customer journey could be improved and to identify relevant data to understand customer behavior. Machine learning algorithms on behavioral data were used to discover naturally occurring behavioral patterns in the customer base, which was a solid starting point for truly understanding the different types of donors. Interviews with actual donors enabled us to answer the “why” for each of the identified behavioral segments and enrich our understanding. In short, the results were highly convincing and can be summarized in two key take-aways:
Behavioral personas, in combination with qualitative research, provide highly useful insights that answer the “what” and “why” rather than just “who”. These insights are valuable on a strategic level because they enable us to fundamentally improve and redesign a product or service. An illustration of this is how two of the validated groups of donors differed. Both where very concerned with giving money to charity, but where one group did this because of the relationship with the sponsored child, the other did not want to get emotionally involved with the sponsored child. This insight clearly demonstrated how important it is to understand the “why” if the goal is to improve the customer journey.
Interestingly, when analyzing how identified behavioral personas were distributed across different demographic variables such as age and postal codes, we found no clear patterns. The general conclusion was therefore that donor behavior had little to do with demographics, and consequently that donor behavior and expectations cannot be understood or predicted based on demographics.
By neatly combing advanced analytics, domain expertise and qualitative research, we where able to gain substantial insight even by using “rather simple machine learning models”, as one of our AI-experts put it. The hardest part of the project was probably to decide which behavioral attributes to use for segmentation, which is all about understanding which differences in customer behavior are relevant for which types of customer journey differentiation, rather than just using a lot of data. By having a clear understanding of what we wanted to do with the insight, we were able to develop very specific and actionable donor segments within a few short iterations. When conducting interviews with donors we were surprised of how well the actual donors matched with their segment and how useful these multidimensional behavioral stereotypes were in sorting out the “why” among all of Plan’s donors.
For the client, the project provided an eye-opener: A structured combination of a precise goal and problem definition, advanced analytics and qualitative research can be a both simple and very powerful approach to truly understanding your customers. Having this understanding in place really transformed the way Plan redesigned their customer journeys: Now the journeys are differentiated based on true insight into the needs and expectations of different types of customers, rather than on the often hypothetical personas we still see a lot of elsewhere.
Karan Kathuria is a Senior Manager at BearingPoint in Oslo specializing in Data & Analytics. His expertise lies in helping businesses make better decision by leveraging analytics and establishing a data driven way of working. He has hands-on experience from several industries and two AI startups. Karan obtained his MSc. in Industrial Economics and Technology Management from the Norwegian University of Science and Technology in 2011. Email address: email@example.com.
Johannes Karlsen is a Senior Consultant at BearingPoint in Oslo. Johannes is specialized in strategic use of Data & Analytics and technology investments to improve customer value creation. Before joining BearingPoint, Johannes obtained his MSc. in Finance from the Norwegian School of Economics in 2017. Email address: firstname.lastname@example.org.
Vebjørn Axelsen is a Partner working at BearingPoint in Oslo and head of the Advanced Analytics area. He has significant experience as strategic advisor, architect and developer across data science and data engineering and has led a multitude of data science efforts across industries. Vebjørn obtained his MSc. in Computer Science specialized in AI from the Norwegian University of Science and Technology in 2007. Email address: email@example.com.