Whilst the world of Data and AI offers significant opportunity to drive value, knowledge of their potential and mechanics are mostly confined to data practitioners.
As a result, when business users look for solutions to their challenges, they are typically unaware of this potential. Instead, they may ask technical teams to deliver a software solution to their problem, outlining a capability via a set of features.
However, when making this leap we risk missing out on the opportunity to build more effective systems using data and analytics, creating “part solutions” to our challenge.
Let’s use a real-life example to illustrate this …
Case Study: Customer Engagement
One of our customers is a major financial services firm, which has a number of touch points with their B2B customers. This can include a variety of interactions including customer support calls, service contract renewals and even customer complaints. These interactions are driven by their large, globally dispersed customer team.
The goal of the customer team is to increase retention of their high-value customers and, where possible, to upsell them to more expensive service offerings. As such, they see that every interaction is an opportunity to build better relationships with customers, and to suggest compelling offers for new products.
Let’s imagine the head of this customer team looks for support to better achieve their aims …
A classic approach would be for the customer team to turn to the world of software for support.
Knowing the possibilities that a modern software system can bring which puts all the information about a customer in front of the customer team member during interactions (akin to a “Single Customer View”). This information could include:
- General customer details (e.g. sector, size)
- Purchasing history (e.g. services they current subscribe to, volumes)
- Usage (e.g. how often they use a particular product or service)
- Recent interactions (e.g. what happened during the last interaction)
- Offers (e.g. what did we last offer them and how did they react)
This could create an invaluable asset for the customer team – by having all of the relevant information at hand they can have more informed discussions.
However, the customer team still needs to fill the “gap” between being presented information and achieving their goal of customer retention and product upsell. They do this using standard scripts, or by interpreting the information presented to consider appropriate discussion points.
So while the software system supports their aims, the human brain is left to do most of the work.
In the above example, the head of the customer team didn’t request a software system – instead, she turned to an internal data professional for advice. After some conversations, the data professional identified the potential for analytics to support the customer team.
They engaged us with the concept of building a “next best action” engine that could support more intelligent customer conversations. Working with the customer team and the internal data professional, we developed a system that presented the relevant information (as above), but crucially added:
- Enriched data outputs (e.g. expected customer lifetime value)
- Predicted outcomes (e.g. likelihood that the customer will churn in next 3 months)
- Suggested “next best actions” (e.g. best offer to present to the customer which maximised the chance of conversion, best action to reduce churn risk)
These capabilities spoke more directly to the customer team aims, and demonstrated a significant uplift in retention and upsell. The system has since been rolled out to the global teams, and is considered to be one of a few “core applications” for the organisation – a real success story.
Software vs Data Projects
It is important to note here the similarities in the delivery of the system between these 2 approaches: fundamentally, the majority of the work involved in both approaches would be considered software development. After all, developing clever algorithms only gets you so far – to realise value we need to implement software systems to deliver wisdom to end users, and to support resulting actions by integration with internal systems.
However, the key difference in mindset that leads to the approaches described are driven by 2 characteristics:
- Knowledge of the Data Opportunity – a key factor in the above example was the presence of a data professional who could empathise with the head of the customer team, and identify the potential for analytics. Having this viewpoint available ensured that the broader capabilities of software AND data were available when considering a possible solution to the challenge presented. Without access to this knowledge, this would likely have turned into a “single customer view” software project.
- An Openness to Design Thinking – in the world of software design best practices, there are 2 (often conflated) concepts: “design thinking” (empathise and ideate to develop effective solutions) and “user-centred design” (put the user first when designing user experiences). In software-first projects, the focus is often on the delivery of a solution that has been pre-determined, leading to a user-centred design process. When we consider the world of data, the lack of understanding of the potential solutions in this space can lead more naturally to a “design thinking” process, where we focus more on “how can we solve this challenge” as opposed to “how do I build this software system really well”.
Adding Data Thinking to “Software-First” Projects
So how do we ensure we consider the broader opportunity, and potential that data and analytics provides, when presented with a software development project? We can accomplish this with 3 steps:
- Enable a Design Thinking Approach
Design thinking allows us to empathise with a challenge and ideate to find solutions, as opposed to focusing on the delivery of a pre-determined solution. Within this context, we can focus on the broader aspirations, constraints and consequences so that a solution can be considered which connects more closely to the business outcomes.
- Include Data Knowledge
During this design thinking activity, it is essential that we have representatives who understand the potential that data and analytics represents. In this way, the team is able to consider the broader set of capabilities when designing possible solutions.
- Design the Data flow
Data is always a consideration in software design. However, the potential of analytics requires us to think differently around the flow of data through a system with a view to delivering value-add capabilities. This takes us beyond thinking about how we store and manage data, and towards a situation where we consider new data sources, data access, and the lifecycle of model-driven data outputs (such as predictions or actions). This is particularly important where the “data” opportunity may be added to a system at a later date, once core “nuts and bolts” functionality has been delivered.
Data + Software + Design Thinking
The approach described here enables us to leverage the opportunity that resides on the bounds of data and software, and fundamentally deliver more value to users by delivering richer capabilities more aligned to business outcomes.
Moreover, we’ve seen that effective application of design thinking, combined with deep knowledge of data, analytic and software, has enabled us to deliver significant value for customers that goes way beyond solutions that may have been originally imagined.
Author: Rich Pugh, Chief Data Scientist