Idea to Design: 'Web to Store' Case Study by Iris Yuster
What is unique about Iris is her ability to combine social understanding of user behaviour with interaction design (UX) skills.
One of the most important keys to the success of an App is the understanding of user behavior. A company can have the most advanced technology and a great idea but if the front-end interface implementation is too complicated, annoying or poor from the perspective of the end user, then user satisfaction is low and the company’s expectations for revenues can’t be realized.
Planning an App involves analyses of: business goals, technological capabilities, and potential user behavior. The right combination of these three components creates the formula for success.
This field has been researched years before the App world was born. The ability to translate behavior theories into a successful App interface is the challenge. In this post I will give some insights into this analytical process with a specific example.
The Idea: Mobile Shopping App
Expanding The Web-To-Store Strategy
Although most purchases are still made in bricks-and-mortar stores the shopping experience starts before this. This is the strategy named Web-To-Store: users browse the web to prepare for their visit by checking store location, product details, promotions and more. Mobile phones have greatly expanded that phenomena from being implemented “before” to “during and after” an in-store visit, since users have the ability to browse the web anytime, anywhere. The leading retail companies have realized that in order to create a new revenue stream and increase customers’ loyalty they must have a Mobile Shopping App of their own.
Today there are several Mobile Shopping Apps in the retail market that enable direct access to related shopping information (for example, “users reviews”) and actions (for example, “Add to shopping list”).
When entering store, Macy iBeacon
sends reminder to open the Shopping App
The main challenges of these Apps are:
- Relevancy – send real-time personalized offers;
- Quality – of shopping services and product information;
- User experience – easy-to-use and intuitive flows that support “Buying Decision”.
Technology Uniqueness Aspect
The Mobile Shopping App in this case study has a personalization engine, which enables the detection of specific user buying behavior and with that, sends relevant offers, special cross sale and up sale, a personalized shopping list, quick checkout without waiting in a queue and more.
Another important component is the analysis of the users of this App and how they can benefit from it.
The “Shoppers” Analysis
The shoppers’ characteristics can be analyzed at different levels: Demographic – such as age, gender, socioeconomic status, geographic location, family status etc. This level of analysis is important although it’s limited as the users’ groups are treated as segments so it does not necessarily reflect their shopping behavior.
Daily Routine – Users have different journeys, the goal is to choose the most attractive users targeted for the retailer and to illustrate App usage patterns while they are shopping.
We need to understand what happens to the user from the moment he opens the App until the time he decides to tap “Buy”, “Download”, or any other revenue- related action.
Shopping behavior – Shoppers have different types of behavior while shopping such as:
- Goal Oriented – know exactly what they are looking for
- Browsers – check for different options
- Socially Oriented – follow friends and other people’s shopping activities
- Explorers – like to see what’s new and to learn more about each product
These different types of behavior are influential factors in App usage as each type has special needs and expectations.
Cognitive Process during Shopping
A user’s cognitive process for “Buying Decision” includes components such as motivation, evaluation and selection. The Shopping App should support this cognitive process and should be planned to minimize the effort and time to make a “Buying Decision”. The longer the process takes, the higher the probability the user will abandon the App.
How to translate it into a successful App?
Taking everything into account we have to deal with the challenge to translate analysis into practice. We know that we have different types of user behavior, we understand their expectations and their special needs, and now we must find a way to fulfill all that with a single App. Is it possible? Although it’s challenging I know it is.
- “Socially Oriented” users make their “Buying Decision” based on their friends’ shopping; therefore they need clear access to their friends’ shopping recommendations;
- “Goal Oriented” users who are focused on their predefined shopping list would benefit from recommendations related to products on this list;
- “Browsers” can get lost without access to relevant personal recommendations, based on their purchase history and like-minded users. Maximizing the usage is possible by planning an App with the right balance and layout of these options, and to allow each user to find his personal path in the App.
Therefore it is very powerful to use recommendation engine in the right places in the App, and for the right users.
- For example, adding social recommendations for a product in the “Goal oriented” path could have miscorrelation with the main need of quick checkout, and might prevent purchase;
- While having the same recommendation in the “Social Oriented” path will lead to an impulse purchase.
Maximizing the usage is possible by planning an App with the right balance and layout of these options, and to allow each user to find his personal path in the App.
To sum up, the Mobile Shopping App case study demonstrates the various aspects that need to be analyzed and integrated in a single App in order to be the leader in a strongly competitive market. The understanding of user behavior and the ability to translate it into a simple adaptive interface is the key to creating the App that users would want and use.
image credits: apprine.com; 3degreesagency.com, canadiangrocer.com
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Nicolas is a senior VP at Orange Innovation Group. Serial innovator, he set-up creative BU with an international challenge, and a focus on new TV experiences. Forward thinker, he completed a thesis on “Rapid Innovation”, implemented successfully at Orange, and further developed at nbry.wordpress.com. He tweets @nicobry
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