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#gilbanesf Web Engagement: Personas and Molding the Customer Experience

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Ryan Bennett avatar

One of the final sessions from last week's Gilbane conference (see our coverage here) in San Francisco focused onPersonas, Market Segmentation and User Experience design. Strong assertions were made (e.g., "data without segmentation is crap") and key lessons shared. Here are the highlights and take-aways.

Melissa Casburn of ISITE Design and Randy Woods from non-linear creations were the speakers for the session, which was moderated by the Gilbane Group's Ian Truscott.

Personas: The Basics

Aquick overview of the key aspects of personas was delivered. They are:

  • Named user types with personal characteristics
  • Based on research
  • Tied to an organizations' marketsegmentation strategy

You can have as many persona's asmakes sense, but to start put energy into the ones that most directlymap to business goals (lead generation, sales, etc.)

Robustnessis Key

According to Casburn and Woods the more robust theresearch backing personas and market segmentation the more likely youare to reach your business goals using them. Not all data is createdequal.

There are 3 sources of persona data:

  1. FormalInquiry
    This includes market research, focus groups, 1-on-1interviews, surveys, and user testing. It is considered the most robustsource of persona data.
  2. Internal Company Knowledge
    Customer service interactions, sales interactions, web analytics, searchlogs. This data isn't as strong as formal inquiry.
  3. AmbientData
    This includes social media information, user generated content, etc. Thisis the least robust and reliable of the data sources.

DevelopingUseful Personas

Casburn and Woods then described some ideas andmethods behind creating effective personas. It's not just enough to havepersonas defined. A badly thought-out persona can be a negative inreaching business goals:

  • Think about what your users wantfrom the site, and how they would go about doing it.
  • Determinewhat the user's motivations and pain points are.
  • Recommendcontent and site features to address those issues.

MostOrganizations Want Something from the Visitor

Woods then jumpedinto a topic that isn't always discussed directly. It's not enough tojust look at what the visitor may want, most organizations also wantsomething from the visitor. The site may want to:

  • Sell aproduct
  • Convert a visitor into a sales lead
  • Convince avisitor to make a donation
  • Convert them to a newsletter subscriber
  • Have them engage by rating or commenting

Organizationsneed to define their preferred business outcomes and determine whatmetrics they can collect to track them. The website should beseen as a negotiating field between the visitor and the organization.Effective personas supported by research and metrics will help guidethat visitor to the desired business outcomes.

Levels ofUser-facing Segmentation

Woods kicked this section off with a quotefrom a blogger:

Data without Segmentation is Crap

Casburn proceed tolay out some ways for sites to get started with user segmentation.

TheBasics: Segmenting by Core Target Audience

If you don't have a lotof research or data to analyze you can start with segmentation bypicking your top target audiences and then providing paths through thesite that answer their questions and lead them towards the businessgoals specific to them. Casburn then provided an example of a universitysite where the the homepage provided different tracks for visitors inthe following way (a subset):

  • Students - Providinginformation on campus life, classes and majors, sports.
  • Parents -Cost details, financial assistance data
  • Alumni - Meetinginformation for alumni groups, Expansion plans, Ways to donate

Thesetarget audiences are looking for very different things from thewebsite, so just getting started with the basics of segmentation holds alot of value for the organization.

Digging a Little Deeper:Segmenting by Task

If the organization has a deeper level ofknowledge about their visitors they might start segmenting based on thespecific tasks the visitors are trying to accomplish. An exampleprovided was from a travel recommendation site where the primarysegmentation was:

  • Traveling with Pets
  • Traveling with Kids
  • Traveling with Friends

Deeper Still:Relationship Segmentation

In this case, the organization knowssomething about the visitor, perhaps via a cookie allowing theorganization to connect with what the visitor has done in the past, orif they are a registered user. Here, the site would detect thisrelationship when the user arrives and tailor the message specificallyto them.

The example used here was the Zipcar local car sharing service. If youare a first-time visitor the site promotes information on ways to signup, cars available in the city the visitor is coming from, etc. If theuser is a returning user the site provides content specific to theirpast usage (new cars in locations they have picked up), links to theiraccount, links to book a reservation, etc. The information given to the useron the homepage was very different, but presented within the samestructural framework.

Categorization in Real Time

Casburn and Woods went into some technical detail relating tomethods of providing real-time categorization and personalization. Theybegan with noting that not all Web CMS products easily allow for real-timepersonalization.

Their examples in this segment were based on theirprior work with Sitecore (news, site) which provides both a Rules Engine andassociated Online Marketing Suite -- which allows marketers to both trackhow visitors are using the site as well as build rules to allow the siteto respond automatically to specific types of usage.

They were carefulto note that other WCM systems -- such as CoreMedia, Day Software, Ektron,  SDL Tridion, Vignette/OpenText and others -- support similar processes, and that many other Web CMS products do not have this capability, at least not without a lot ofcustomization.

Scoring Pages Based on Business Goals

Usingthe Sitecore tool set Casburn and Woods showed how you define specificgoals and then score specific pages within the site as to how theyadvance that specific goal or show the user to be interested in onething vs. another.

Learning Opportunities

Probable Conversion Scoring

For example, in a lead generation/purchasing track avisit to a product page might be scored a 5, but a visit to the pricingpage for the product might be scored an 8, to represent that the pricingpage view is indicative of a stronger desire to make a near termpurchase.

As the visitor navigates the website their score is updated andmonitored and based on the score relating to specific goals the site canthen react in real time to provide more relevant information. So, auser who has a high score as a near-term purchaser could be offered aform to contact sales, or a callback from the sales team.

Other Forms of Scoring

Scoringcan also be used to remove visitors from contention or detect action ofcompetitors. Scoring might be set to zero for known search bots or forcompetitor visits so that the site's analytic information doesn't getmuddied with "false positives".

In a more malicious example, in onesite, the system was trained to detect visits from specific competitorsand when those competitors went to download white papers from the siteit would allow them to download, but would give them old, outdatedversions of the documents. This was meant more as a subtle tweak to competitors rather than a core business strategy, but it shows the powerof these tools to react to specific business circumstances inreal-time.

A key point that Casburn and Woods made is that, eventhough there's a lot of technology making this happen, the control ofwhat gets tracked and how the system responds needs to be in the handsof the marketers and folks leading the business.

The site needs to reactquickly based on business requirements, and this is not an IT task.

ASmall Case Study

Woods and Casburn then walked through a quick casestudy example:

  • Mike, an "urban hippie" living inPortland want to goes skiing at Mt. Hood, which is nearby
  • Mikedoesn't have a car, so he goes to the Mt. Hood website to look attransportation options
  • He sees Zipcar listed on the Mt. Hoodtransportation page and thinks that's a good idea, so he clicks the link
  • Hearrives at the Zipcar page and looks at the cars listed but he doesn'tsee any information on whether those cars can hold his skis or gear, orif they have ski racks
  • He leaves the Zipcar site in frustrationwithout signing up for a membership

Zipcar, however, happens to becollecting metrics on user visits and one of the marketing staff islooking through the data to try to determine why people left the site.This person sees Mike's visit and sees that he was looking for availablecars before he left, and it's also shown that Mike came from the Mt.Hood website.

This gives enough information to come up with a simple theory:visitors who are coming from skiing-related sites might be interestedin knowing if the cars can handle skiing gear.

This information isn'trelevant for everyone, as not all users are using Zipcar in areas wherethere is local access to skiing, but using real-time personalization arule could be quickly put into place to show skiing information to userscoming from skiing sites or who are in markets with local access toskiing.

Zipcar could quickly implement the new rule then check back later to see if there was a positive impact. If the theory was correct then the rule would stay and perhaps expanded on, if it didn't work out then it's quickly removed.

The Wrap -- Simple Testing is Critical

It's key to be able to effect change and quickly test the results. This allowsmarketers to show quick wins which might enable them to go even deeperwith their market segmentation and categorization projects. More importantly, it allows them to run lots of small experiments.

All of this optimization involves a certain amount of guesswork. Only through continued testing and refinements do we make progress. Look for this when evaluating your WCM and WEM technology investments.

About the author

Ryan Bennett

Ryan Bennett is a web content management and web engagement solutions architect. He is the co-founder of San Francisco-based consulting firm Cylogy, Inc.