In times of constant change – at a higher speed than ever – how can you stay competitive, exceed your customers’ expectations, and keep your employees motivated and engaged?
This is the daily challenge for companies today, and the latest trend is to find the answers in data. The data considered by most companies for this purpose can be classified as:
- Operational data – stored mainly in ERP, SCM, CRM, and HR applications
- Experience data – stored in experience management or social media platforms
- Benchmarking data, relevant for the industry on the country, region, and/or global level – accessed through or made available by consulting firms or public institutions
The overwhelming amount of information available – combined with the pressure of media and experts who stress that “data is the new oil” – increase the intensity of the daily struggle of finding relevant and meaningful data.
What are the main benefits of an empathy-based approach to data?
Data indeed has huge potential, but only when you succeed in extracting from it the relevant insight that will allow you to:
- Boost your bottom line
- Improve and create products and services that matter
- Increase your employees’ motivation and consequently retention rate
- Grow your brand awareness and reputation
By adopting an empathy-based approach, you can identify the relevant data that allows you to deeply understand your customers and stakeholders (external and internal), and their behaviors, motivations, and aspirations.
To do this, you should consider design thinking, probably the most well-known empathy-based methodology, as it has proven tangible benefits. There are compelling statistics and studies that support the adoption of an empathy-based approach:
- According to the Design Management Institute, between 2005-2015, companies that embraced design thinking showed a 211% return over the S&P 500.
- According to a Forbes article published in 2018, 92% of employees would be more likely to stay if their company empathized with their needs. Further, 78% of employees would leave an employer for equal pay if the other company was empathetic. Rae Shanahan, chief strategy officer at Businessolver, estimates that the war for skilled and engaged talent can impact businesses’ bottom lines by more than $600 billion in lost productivity per year.
- The 2019 Deloitte study, “Exploring the value of emotion-driven engagement,” concluded that “beyond marketing, emotional and contextual data can foster deeper emotional connections across all key moments with customers to increase their lifetime value while also decreasing their likelihood of switching brands.” And 75% of customers expect a brand to know why they purchased the product.
If this seems too abstract, consider the example of an airline that saw its revenues and customer satisfaction decreasing at a specific airport. There could be countless causations, but an empathic analysis of customer behavior according to the data the company gathered proved that the dissatisfaction was related to the lack of cold and hot beverages. As the flight schedule changed and departed early in the morning, the airport shops were closed.
Based on this, the company found a simple solution: Offer water and warm drinks (coffee and tea) to travelers. This apparently simple measure in this specific situation was enough to increase sales and customer satisfaction.
How to implement an empathy-based approach towards your data
There are three main steps you should consider when implementing an empathic strategy towards data, in line with the three phases of design thinking:
In this phase:
- Formulate the achievable result and goal
- Identify the main stakeholders involved
- Understand their main motivations, behaviors, and pain points, using interviews, direct observation, simulation, research, and/or brainstorming.
Once you have successfully completed the previous phase, you will have all the information that allows a deep understanding of the stakeholders. This is the time to change your perspective from people-centric to data-centric, while defining your prototype. Approach the collection of data available and:
- Cluster the data based on business-specific criteria identified in the previous phase: demographics, age, motivations, behaviors, etc.
- Separate the abnormal behavior, that is, data outside the “normal” thresholds.
- Find the correlation in the data to understand in what measure acting on one will influence the others.
These steps are important prerequisites when you want to design algorithms, statistical models, or understand how to implement machine learning for your specific case. A prototype should be created with the identified use case and consequently validated with a subset of relevant stakeholders before the implementation. The incremental approach, based on prototyping and validation before investing significant resources in the main phases of development of the solution, will create real value and bring the business closer to its main stakeholders (internal and external).
The last phase consists of fine-tuning and final testing of your prototype, preparing everything to be deployed and released.
For more on this topic, read “This Holiday, Be Thankful For A Great Customer Experience.”