When Your Product Design Makes Your Customers Feel Smart Create services or devices that enable users to maximize their time and money. Big Data can play a role in this fine-tuning.
By Kumar Srivastava •
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Users love products and services that make them feel smarter. The more efficiently they can spend their valuable attention, time and money, the smarter they feel. The smarter that users feel when interacting with your product, the more they love it. We call this the smart-user theorem.
Strong examples of the smart-user theorem in action abound. Facebook and Instagram save users time by enabling them to connect and share with friends and family quickly and efficiently. Similarly, apps have become popular and ubiquitous, partly because of their availability to fulfill virtually any need or task.
Related: The Secret to Successful Product Design? Simplicity.
The simplicity of the interface and the entire value chain on the iPad, the ease of planning a trip on Expedia via a mobile device or using Dropbox to store files -- these are more examples that offer powerful guiding principles for enterprises as they engage customers with their products and services.
Taking this a step further, analyzing customers' behavior can quantify the time, attention or effort required to engage with a business' products and services and bring about a new understanding of the user experience. This awareness, in turn, arms businesses with strategies to fine-tune their products and services to be more efficient, streamlined and intuitive.
If enterprises carefully evaluate and optimize their products and services to make their users "smarter," they will be rewarded with loyalty, engagement and a higher transactional value.
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User investments: attention, time and money. There are three types of "capital" that customers invest in your products and services: attention, time and money. First, users turn their attention to your messages, advertisements and product communications. They interpret and internalize your message to inform their next steps.
Consumers also spend time thinking about, searching for, discovering, deciding to access, learning about and using your products and services; it's safe to assume that they spend the same amount of time learning about your competition. Finally, there's the money part. This one's pretty obvious: Users pay you for your products and services.
The attention, time and money model provides a framework to optimize the design of the end-to-end user experience. Maximizing the value of the attention, time and money spent by users on your products and services can be achieved through a combination of baselining and experimentation.
Baselining involves breaking up the product-usage flow into logical stages and measuring the time and attempts it takes users to move through it. In addition, the consumer's reliance on certain information and features should be analyzed to understand whether they encourage a person to move to the next stage in the flow.
Experimentation is the stage whereby, through the use of data analysis or customer interviews, product problems can be identified. Hypotheses are developed and then tested through changes in the product flow until the desired goals are met.
Big data's role in smarter interactions and smarter users. Users save attention, time and money as a result of personalized and customized messages, which enable them to find the right tools to satisfy their needs quickly at the right cost. Creating these messages and products requires capabilities that the processing of Big Data can easily provide. This can involve the following types of analyses:
1. User-environment analysis, in which information is collected about the environment where users interact with the product or service.
2. User-profile analysis, whereby information is collected about consumers and their characteristics such as gender, age, likes and dislikes.
3. User-interaction analysis, in which data is collected about users' activities and behaviors as they interact with a product along the customer journey.
4. User modeling, whereby data is collected and modeled to represent the behavior of a segment or cohort of users.
The analyses and subsequent correlations are used to optimize the messages delivered to users according to their environment, profile and behavior patterns, as well as their stage in the customer journey.
As users receive personalized messages and information that enable them to be smarter by helping them complete their tasks faster, more inexpensively and with less attention, the overall value realized from the product or service increases. This leads to higher productivity for the user, higher and sustained engagement with the product or service, a customer who feels smarter and, in the end, greater value for your enterprise.
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