Virtual Floorspace » web design

In-Depth Customer Interviews: Task Analysis & Mode of Interaction

Paul Bryan, Usography Corporation (www.usography.com) — Tue, 10 Nov 2009 13:51:05 +0000

Customers access business web sites to achieve a goal or specific purpose. They are rarely there just to look around, unless they are looking for a job and want to understand the company better. Customer goals can often be grouped into distinct modes, such as reading content, finding products or documents, purchasing products or services, learning new processes or procedures, etc. Understanding these modes is important when designing the interactive space, to enable customers to easily enter a mode that clearly facilitates the activity and presents options related to that mode in a consistent way.

For example, in a resort web site or kiosk, guests may be looking for leisure activities, finding products they forgot, reading about the history of the area, etc. Each of these modes should be simple and straightforward to find in terms of access points, make it easy to achieve the most common and most valuable goals, and represent the task using design components that appear especially suited to the activity (i.e. high affordance). The modes should not be cluttered with lots of options that are unrelated to the mode customers have indicated they want to work in. Marketers often want to surround and interject this experience with lots of selling options, but many times this is interpreted by users as visual noise that damages the perception of the experience and isn’t effective. Looking at the analytics for such off-task design elements, I’ve nearly always found clicktrhroughs to be near zero. If they have to be there, make sure they are not obstructing progress in the primary activity. Billboards are okay, but on the side of the road, not in the middle of the road. And spaced apart so that the visual signal to noise ratio is at a reasonable level.

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In-Depth Interviews: Participant Characterization pt. 2

Paul Bryan, Usography Corporation (www.usography.com) — Sat, 10 Oct 2009 12:08:39 +0000

Participant characteristics are captured during the course of an interview, and are further developed and refined when the data is analyzed and coded. During the interview, one way to capture participant characteristics is to ask questions that are specifically formulated to reveal those characteristics. For example, the research may ask, “How many television sets do you own?” The answer then places the participant into a category based on the number of television sets.

Scales are data collection instruments that assign a numeric value to individual characteristics. There are a number of types of scales that are useful in design research. Some of the most common types are:

Rating scales – A rating scale asks participants to evaluate aspects of a product or service and to assign a predefined value to it, ranging from highest or best to lowest or worst.
Likert scales – A Likert scale (pronounced lie’ kurt or lick’ ert) has an initial statement, and then a 5 point scale that ranges from strongly agree on one end to strongly disagree on the other end.
Semantic differential scales – A semantic differential scale presents personal attributes as a scale of polar opposites. Participants assign themselves, or researchers assign participants to a position on the scale that indicates the degree to which they match one end or the other of the scale, or where they fall in the middle.

Scales isolate personal characteristics that are key factors in online behavior, and designate degrees on the scale where each participant would either place themselves, or where the researcher would place them based on responses to questions and exercises. The individual dimensions represented on the scale are usually derived from an initial set of user data. In a pinch the research team can brainstorm a relevant set of dimensions based on direct or indirect experience with use behavior.

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In-Depth Interview: Participant Characterization pt. 1

Paul Bryan, Usography Corporation (www.usography.com) — Wed, 07 Oct 2009 23:30:46 +0000

The Participant Characterization module captures a very detailed description of each participant, in terms of attributes that are most likely to impact online behavior. The research team needs to fully understand these characteristics in order to fully understand and interpret the responses of participants to various questions, and to group participants who have certain key characteristics in common into behavioral segments or archetypes.

The objectives of the Participant Characterization module are to:

Capture personal characteristics of participant
Define significant participant attributes using questions, scales, and exercises
Measure dimensions that are most likely to differentiate online behavioral segments

When researchers conduct a survey, they attempt to eliminate participant bias by selecting a random population that represents the population of interest. When interviewing customers, most researchers do not have the resources to interview a statistically valid sample of participants, and therefore participant bias is always present. The characteristics of the individual participant put a “spin” on all of the data that is captured. However, instead of eliminating bias, the researcher attempts to gain a deep understanding of each participant, so that unique characteristics can be distinguished from characteristics that can be generalized to a portion of the population.

An important goal of in-depth customer interviews is to discover common characteristics that cluster participants into groups that behave in similar ways on web sites. From those clusters, researchers can extrapolate a behavioral segmentation of the customer population, and build an understanding of that segment in order to formulate mental models or interaction patterns that they have in common. These results are then the basis for a design strategy that will meet the specific needs and wants of that segment of your customer population. Customer characteristics, when combined into a profile, can be measured quantitatively to determine the prevalence or financial importance of a given customer segment.

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Using Personas to Guide Web Design Strategy

Paul Bryan, Usography Corporation (www.usography.com) — Tue, 08 Sep 2009 21:43:17 +0000

Personas are fictitious users of a web site who are representative of a segment of real users. Presumably, personas are created from customer or market data, thus enabling designers to consider the needs of their most frequent and/or most important customers during the concept and design phase of large, costly projects.

Below is an example persona that was created to represent a segment of mobile phone customers. (FYI: This persona is partially based on Gizmodo’s iPhone demographics article, and was uploaded to the ixda discussion group in answer to a post).

Name: Christine Martinez

Age: 31

HHI: $82,000

Location: NYC

Education: Bachelor’s degree, Communications

Job title: Human resources generalist

Marital status: Single, involved in medium term relationship

Internet IQ: High

Shopping IQ: High

Adoption segment: early majority, fashion forward

Primary goals related to cellphone purchase: Email access, web browsing, car safety

Most relevant features: Touchscreen, Pandora access, voice activated dialing, social media app integration

Drivers: Convenience, customer service, cost of phone+data plans, in-sync with trends

Loyalty: High, despite frustration, does not want hassle to switch.

Favorite web sites: Zappo’s, Amazon.com, Hotels.com, woot.com

Internet profile: 2 hours per day, non-email internet usage

Technology profile: Dell laptop (provided by job); iMac at home; iPod touch; iPhone 3G

Social media profile: Averages 45 minutes per day on Facebook, Twitter, LinkedIn, delicious

Favorite TV shows: Dancing with the Stars; Grey’s Anatomy; Men in Trees

Reading now: Ad Age, Ad Week, BrandWeek, Outliers, The Time Traveler’s Wife

Characteristics that impact purchases: Tracks deal web sites like woot.com and , always searches for coupons, willing to spend to not appear out of touch, does not monitor bills

Business value: Periodic high impulse spend despite cautious purchase patterns; refreshes technology every 2 years; open to fashion add-ons

Purchase barriers: What she reads has a big impact on purchase decisions. Negative remarks in Twitter or other social media interpreted as fact. Depends heavily on smart search feature to find products. Wants to see how she or home will look with product, so she often won’t buy until her friends have it.

Purchase Tunnel Dropoff: Difficulty viewing total price before purchase, lack of clear arrival date, lack of clear return policy, better deal on similar item that has same appearance value

Requested content/features: High-level comparison that includes discounts, feature demos, toll-free customer service that is in USA with phone number on web site home page

Switch behavior: Low-switch behavior. Unlikely to switch complex services unless she feels customer service has cheated her. For non-complex switch situations, e.g. cable TV, will switch when she sees an ad with clearly superior pricing and equivalent feature set. Unlikely to switch for features.

Quotes:

- I am on a mission. I go to the Internet with a specific purpose in mind. I don’t browse around for no practical purpose (except Zimbio and YouTube)

- I want to see what other people say about it before I make a decision. If a product is good, it will be popular.

- I don’t trust those companies you never heard of before.

- I don’t want to start from scratch every time I go back to a web site. I like stores and web sites to remember me, the ones I trust, that is.

- I bought it at Best Buy because I had a 10% off coupon

- I don’t like a lot of marketing noise. I don’t trust it and when I see a lot of mixed marketing messages it makes me think that they are desperate and confused about what they are selling.

Method for tracking this customer type with web analytics:

- Entry through marketing campaign on affiliate site that has content targeted to 30 yr. old single female

- Purchases fashion accessory that has higher than average price

- Views many photo pages, does not view many detailed specs pages

- Responds to clickthrough articles and ads with fashion and appearance as main topics

- Search terms: most popular, best deal

Typical purchase scenario:

- Sees ads on TV and billboards

- Sees friend with product

- Google search

- CNET review

- Discussion forums (professional, technical)

- View in store

- View cost breakdown

- Search for coupons, deals

- With a discount, on occasion when she feels prosperous, takes the plunge, buys 2 or 3 accessories to make product look better

Experience gaps:

- Product links from Facebook pages of friends to catalog

- Realistic visual cost meter on services

- Customer service guarantees

- In-store video product details mixed with humor, popularity, and deals; accessible on web site for replay

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Written by Paul Bryan, Usography Corporation (www.usography.com)

Linked In: http://www.linkedin.com/in/uxexperts