As customer centricity is becoming the core of most businesses, a lot of industry giants are opting out for ways that can lead them in the direction of customer journey mapping and empathy-driven prototyping. It has become a popular belief that it is no longer the product that leads but customer and user preferences that’s leading the game.
This is where design thinking steps in, where businesses use their sensibility and methods to match customer’s need in a way that is technologically feasible and viable enough to generate customer value and tap market opportunity. Simply put, it is about creating solutions that are realistic and executable.
Brought in vogue by the popular design firm IDEO, design thinking when combined with decision science results in an infusion of empathy with engineering. This not only ensures a practical and creative resolution of problems but puts customer at the centre of applications and at the starting point to develop new products and solutions. However, that can be a challenge as the first instinct of pursuing analytics driven solutions could be using statistical techniques and solutions. But ensuring design in analytics projects from the initial stages can lead to the right blend of sensibility, technical feasibility, business viability and consumer needs.
Design thinking isn’t exactly new and has been implemented for many use cases apart
from product development—especially in areas like data analytics and decision sciences.
The idea is, design thinking puts user and their needs as the starting point of developing new product and solution and ask questions like for whom are we designing, what is the problem customer is experiencing, how to improve the performance and achieve scalability, among others. Data scientist and analyst can make up for great professionals in design thinking, given the right set of tools that is at their disposal.
Why blend design thinking into analytics:
Now we know that design thinking enables leading brand to continually engage with customers in an emotional way and that combining analytics with it can prove to be exceptionally revolutionary, embedding the two can unlock new opportunities for organisations and let their customers have exceptional experiences. Additionally, it can:
- Creating a vital human centred design process.
- With the high quality data, design artifacts can be created, addressing the needs of real user.
- When mixed together, it can reap business benefits and
- And most importantly ensures customer centricity.
As MuSigma in one of their blogpost writes “Following traditional problem-solving approaches such as the Situation-Complication-Question-Answer (SCQA) model may cover the business problem at hand, but does not ensure that the consumer will be able to (or want to) consume the solution. This is where empathy comes in, i.e., putting yourself in the customer’s shoes and asking the right questions. This introspection often leads to a redefinition of the problem itself.” This is how important it is to ask questions and lead to consumer empathy.
Design thinking and analytics use cases:
A wide spectrum of industries ranging from manufacturing, financial services, telecom to retail are beginning to realise the importance of design thinking combined with big data analytics to ensure customer centricity. With an idea of simplifying the complex digital customer experiences, IBM has been working with organisation across industries by deploying deploying IBM Design Studio combined with the IBM big data platform enabled by Apache Spark. “The primary objective is to lead a revolution for creating a human-centric design focused on big data applications for customers”, says Karan Sachdeva, Sales Leader Big Data Analytics APAC, IBM in the company’s blogpost.
IBM has also developed a framework for innovation called IBM Design Thinking, that places end users at the centre of innovation when tackling problems and developing solutions. This means that clients themselves end up being a vital part of the innovation process. Right from re-envisioning the customer experience to planning a product release, IBM’s designing thinking solutions can help convert ideas to outcomes.
The best example of design thinking is Apple, where they made use of customer centricity by focusing on connecting people with each other, instead of focusing on building best mobile with a combination of best equipment and features, as most other companies would do. By adopting design thinking, they were able to increase customer’s self confidence through a stylish device that extends them.
Companies like Google, LinkedIn also make use of design thinking to deliver superior customer experience. In one of our earlier articles, we had mentioned about how LinkedIn has combined behavioural engineering, design thinking and big data technology to create this addictive behaviour in its users.
With the customer data that enterprises are generating they are at the luxury of creating a unique experience at individual level. Analytics plays a critical role here by ensuring the most effective catch for the customers. It can utilise available customer data to create personalised offers based on their past patterns.
Design thinking has also been implemented extensively in the area of HR analytics by the likes of Microsoft and ISS, that is helping them to attract, develop and retain talent.
On a concluding note:
Having understood the idea of design thinking and analytics, it wouldn’t be unfair to say that it is mostly focused on solutions and action oriented processes. It can be easily applied by those who aren’t necessarily designers and over a broader context across businesses. However, it is important to have the right design framework in place so as to fetch just the results that you are looking for.
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