The Consumerization of Health Insurance: Adapting to Private Exchanges

findcustomersThe new Affordable Care Act (ACA or ObamaCare) is introducing new opportunities and challenges for health insurance companies. The complex set of regulation, exchanges and integrations needed is still a political and technical mess but one thing is clear: health insurance companies will have to embrace their consumers.

It’s no secret that currently most health insurance companies’ customers who make buying decisions are not the actual consumers but employers or benefit brokers. This is about to change.

The process started even before the latest reforms and is modeled to a large part after the successful pension / retirement benefits model where companies moved from a company provided pension into a marketplace. The employer is putting in a defined contribution, and the employee is choosing investment vehicles from various providers.

The model works very similarly for health benefits with private exchanges giving employees more choices. Walgreen has recently moved its 160,000 employees to a private exchange and estimates are that by 2017, 18% of the American public will buy their insurance at private exchanges.

So what do health insurers need to do to better compete in an open marketplace? Mostly, steal the best practices established by other competitive markets such as the aforementioned retirement benefits and P&C insurance providers:

  1. Enable consumers to make an informed buying decision. While prices and coverage may be negotiated with employers, additional tools and content written for consumers is essential. For example, a “find a doctor” tool that lets you see if your physician is part of the plan, and detailed coverage comparison between plans.
  2. Give consumers full access to their information and personalize the experience. Web portals, mobile applications, email and text messages all tailored to consumer preferences and health interests. The self-service aspect will both give consumers control and save call center costs.
  3. Know your customers. Since until now most group members were not customers, the interaction was very transactional and focused on claims. A huge part of the consumerization of healthcare and of health insurance is starting to use ecommerce style tools – CRM systems that help track and manage all interactions; improving data collection, tracking and analytics to help segment and personalize user experience and wellness communications and offerings.
  4. Establish a clear measurement and analytics framework. New measures and metrics need to be put in place to judge the effects of this transition on the business and the determine best ways to react. The new measurement framework has to look at metrics such as:
    • Customer acquisition cost
    • Customer retention rates 
    • Customer profitability by source and segment
    • Customer lifetime value
    • Impact of wellness activities and user engagement with them on costs
    • Impact of self-service portal and mobile applications on call center volume and costs.
  5. Adapt and optimize marketing. The direct approach requires multichannel direct marketing.  Analytics can help guide the best mix of marketing options to achieve the different acquisition and retention costs.

A lot of people question whether going after the direct channel is even worth it. Some have had bad experiences in the past with individual members that tended to consume more healthcare since they were not always in a good enough health to hold a full time job.

The transition we are seeing to private exchanges and defined contributions seems much more substantial and can dominate the market in 5-10 years. A good toolset, marketing approach and measurement framework will be invaluable to compete for the right segment.

From Web Analytics to Customer Intelligence

CIWe recently were invited to present internally at a prominent health care payer network about the rapidly changin role and importance of web analytics. Gone are the good old days when it was enough to just run a log analyzer or put a simple tag to collect all the information needed about the interactions a customer has with you. Analysis used to be limited in scope and focus on a handful of parameters that could be optimized, such as bounce rates and conversion rates, by tweaking the checkout flows and usability improvements.

Not that conversion rate optimization is less important today but as customer interactions focus less and less on just the company website, the new critical need is to try and get a coherent picture of general customer behavior across all touch points. Instead of trying to infer customer thoughts and concerns through their clickstreams, many are now openly expressing needs and problems through social media.

This goes beyond “cross channel marketing” into the new area Forrester and others are now calling Customer Intelligence (CI). Similar to the way business data evolved from simple reporting into Business Intelligence (BI), as customer data gets more complex and varied, putting everything together and drawing conclusions and trends from it will need to employ similar methods and tools.

This is primarily a mindset change from the somewhat passive “analytics” to the broader and much more active role of managing and providing customer intelligence.

The expectations from Web Analytics professionals and systems are changing as well from the cyclical analysis and response to the providing of on demand, immediate intelligence for both individual and aggregate customer needs and problems. In some companies this evolved into a real “command center” that has 24/7 monitoring and interaction tools to listen, interact and respond to customer needs.

There are a few challenges that mark this transition:

  • Quantity: The quantity of interaction points is exploding due to social media, online videos and mobile devices.
  • Traceability: It is very hard to identify users across various media. Mapping a web user to a Facebook account or twitter feed is not always possible.
  • Immediacy: There is an overwhelming need and expectation for immediate response.

Here is a conceptual diagram of this new reality illustrating all the new interaction points being consolidated into the central Customer Intelligence and the introduction of the analytical services that can be used to optimize the user experience.

These analytical services can work on both an individual and aggregate level:

  • Individual: If we can aggregate customer data and interactions from different channels, this will dramatically improve segmentation, insight for sales and customer service professionals interacting with the customer, and services that can target offers or content in real time based on user past interest and behavior.
  • Collective intelligence: By looking at customer activity across all channels we can:
    • Optimize targeting through the different channels and our investment in them
    • Improve recommendations
    • Identify trends
    • Identify problems / issues / sentiment changes and address them quickly.

To start implementing Customer Intelligence, the process is now becoming quite similar to implementing a BI solution

  • Expand use of social listening and data capturing tools and store their data
  • Adjust data models to accommodate multiple user identifiers, channels, devices etc.
  • Redefine KPI’s
  • Define and implement analytical services
  • Adjust reporting and analytics
    • Real time
    • Dashboard level

The Web Analytics vendors are starting to step up and offer tools and support for Customer Intelligence. In upcoming posts we’ll look into WebTrends, Omniture, Google and IBM to see how their offerings stack up and the type of solutions they support.

Are eCommerce prices getting too dynamic?

This holiday season I was looking for a specific toy as a gift. I did a price comparison and found it had the lowest price at the Toys R’ Us site. When I went back to make the purchase just 2 hours later, the price has jumped up by 50%. Now I had to do my comparison all over again. That was frustrating to say the least.

This is the latest example of Dynamic Pricing. It’s been around for a while but mostly in scarcity driven industries like airlines and hospitality / entertainment. Here the rules of the game are clear, inventory is limited, it has an expiration date, securing a sale in advance has benefits and discounters can help you sell last minute excess inventory.

Now back to our dynamic pricing for $50 toys, other than a few highly desirable toys before Christmas, this is not a scarcity market. Special sale, timed sales, loyalty coupons and all these dynamic promotions are confusing enough but serve a purpose. Not being able to do a simple price comparison and place an order is annoying and will impact the buying decision. If there is always the possibility of a lower price just around the corner, then let’s wait.

Target had recently announced that it will begin price matching for all products, even against amazon but details on implementation are a bit fuzzy.

As dynamic pricing gets more widely used and noticed by consumers, how will they react?

Here are a few suggestions for retailers considering or implementing dynamic pricing strategies:

  • If the products you sell are of a limited quantity, knowing how many are there (at this price) is very helpful. What Orbitz does for example (only 3 tickets left at this price!) gives the consumer valuable information and an incentive to act fast.
  • If a price is reduced for a period of time, let the consumer know for how long it will stay at this price. Again, enables decision making.
  • Shop with confidence. While guarantees against future discounts are problematic, consider offering this to members of your loyalty club. The same way a great sales associate will tell you a sale is starting next week and he will hold the items for you so you can pick them up at the lower price, rewarding the best customers with price assurance and advance knowledge of sales will go a long way.
  • If you are putting an item below the competition, make it known. Consumers may doubt it but if they check and found it is true it will build trust.
  • Try not to put items that are dynamically priced into an email. Since you have no control over when the consumer will read the email, they may be viewing pricing that are no longer correct.
  • Feed the aggregators and comparison sites as soon as changes are made.

The key theme here is that dynamic pricing can be great if the buyers are given enough confidence and information to make decisions. Otherwise it may just make the the consumer even more hesitant to click the “Buy” button.

Understanding Multichannel Analytics

While web analytics can give you a pretty accurate picture of how well online buyers respond to online marketing activities, it fails to tell you anything about how your online marketing affects offline purchase behavior and how offline marketing affects online behavior. If you website has a 3% conversion rate, what about the remaining 97% of your visitors? If you send out 50,000 coupons and get a 2% direct response rate, what about the other 98% of those who got the coupons? Is there a way to measure what they do? Enter multichannel analytics.  Multichannel analytics is a process where all marketing channels are analyzed to develop a more complete view of visitor behavior.

The Four Marketing / Purchase Quadrants

While there are four quadrants of multichannel analytics as outlined in the figure on the right, this post will discuss the two online/offline combinations shown in red. I will briefly explain some of the issues regarding multichannel analytics, some methods of tagging offline marketing and offline purchases, and show you some of the benefits.

The biggest problem with tying in offline efforts or offline conversions is lack of a common point between the two. You have two different databases, one of online data and one of offline data. Unless you have the equivalent of a primary key, you cannot join the two data sets together. Imagine a customer walking into your store or calling your order link and giving you their unique visitor cookie. That would make it fairly easy to tie in their online behavior to their offline purchase. You would be able to track what brought them to your website and what they did before coming to your store.  Unfortunately, in the real world we cannot tie these efforts together, so we need to develop solutions. Solutions for both of the red quadrants will be discussed as they relate to the multichannel analytics integration process, as shown in the following figure:

Tracking Offline Marketing to Online Purchases

There are two solutions to tracking your offline marketing efforts. The first solution is to use vanity URLs in your offline marketing efforts. For example, if you go to DellRadio.com, you will be redirected to a dell.com URL that has some tracking code. In the URL string, you will see a parameter titled “cid”, which is used by SiteCatalyst as a campaign ID. Thus, any purchases from visits to DellRadio.com will be credited to their radio campaign.

You can do the same thing with all of your offline efforts. Put vanity URLs on your newspaper or magazine ads, in your mailers and coupons, on billboards and other forms of display advertisements. Use specific vanity URLs in your radio and TV ads, and simply have your IT department do a “301 redirect” that converts these vanity URLs into coded mainstream URLs that your analytic tool can process.

The second solution to the offline marketing effort is to promote the use of tracking codes in your offline media such as infomercials. Someone watching the infomercial can either call the phone number or order online. If they enter the promo code on the website, you will know that the order was the result of the TV ad. However, what this will not tell you is the percentage of those who came to the site from the infomercial but did NOT buy. If you simply want to allocate revenue to an offline marketing effort, a promotion code will work well with any offline media that drives traffic to your main URL. Within your analytic package, you would tag the code entry as an event, and then look at the revenue that is associated with each event (specific code for each offline activity).

Tracking Online Marketing to Offline Purchases

Now that you have a way to track how your offline efforts work to get visitors to your website, how do you measure what they do when they don’t order online?

Capture Visitor Intent

If your business is both online and retail (physical store), you can measure intent to come to the store by tracking results of your store locator and directions links. By setting these as goals, you can then see what searches were done by visitors who have expressed intent to come to your store. To help capture the buyer while he or she is in the buying mood, some stores like Barnes and Nobles offer the ability to enter a zip code to see if a book of interest is available at a local store. If so, the customer can reserve it online and go pick it up right away. If you can offer this type of service, you need to tag this event so it can capture what brought the customer to the website, and be able to tie in the physical purchase (offline) to the online marketing that resulted in the purchase.

Generate Campaign-Based Coupons for Offline Purchases

It is also possible to have your website generate a unique coupon ID that can be for the particular product that was searched.  By creating an ID that represents marketing segmentation (campaign type, campaign source, media placement, keywords, and so on), you can store this information in both your analytics package and your store database. If you use a campaign translation file for your analytics platform, you will want to include the same campaign ID as a prefix to your coupon. The same coupon concept also applies to service businesses such as insurance, reservations, home and professional service businesses, etc…, where you give the prospective customer a coupon ID that they can use to get a discount. If your business takes orders or inquiries over the phone, you could have your site coded to include the coupon code next to the phone number on all pages. By tracking the redemption of these coupons, you can compute a click-to-store conversion rate, and factor in offline revenue that was attributed to specific online marketing campaigns. This will give you a higher ROI and perhaps provide justification for more web-related investment.

Implement Phone Number- Based Tracking

Unique tracking phone numbers can also be used to measure the impact of your online marketing efforts to offline purchases. A service like Voicestar provides these tools. You can place trackable phone numbers on your site, or use services like “Click to Call” and “Form to Phone” options. Their system has an API that lets you get data right out to your analytics tool and dashboard. Tracking phone calls is very important, as it is human nature to still want to talk to someone on the phone before making a purchase decision. When using a phone tracking service, or even if you have a block of your own phone numbers to use, it is important to not have the phone numbers as a part of the static content. The phone numbers need to be integrated with an algorithm that can associate the phone number with a particular campaign.  To further tie in the visitor to the phone number, a cookie should also be set that relates to the tracking source. Thus, if the visitor leaves the site, and comes back at a later time, the initial campaign that brought him or her to the site will still receive credit for the sale.

The biggest drawback to this type of campaign tracking is that depending on what level of detail you want for your marketing segmentation, you can end up needing dozens or hundreds of phone numbers. This can possibly become expensive and difficult to manage. Instead, you can create a 3 or 4 digit “extension” that is tied to a web-related order number, and when someone calls the number, the phone operator asks for the extension. This has no incremental cost to implement.

Another phone tracking service is offered by Mongoose Metrics. Their service integrates with most web analytics tools to create an automated URL postback after each call is made.  You can perform the same type of analysis, ecommerce conversion and segmentation that you would from any other page to be analyzed. You can see instantly how well your online marketing activities are generating online revenue.

There are many ways to implement phone-based tracking, and they all require integrating your site code with your analytics platform and your backend system.

Utilize Site Surveys to Understand Buying Behavior

Another way to gauge consumer intent is to use online site exit surveys. Companies like iPerceptions, ForSee and others can provide you with surveys that your site visitors can take regarding their online experience. You can ask about the likelihood of them making a purchase offline, and how much their online experience would influence their buying decision. On your online order forms and lead forms, you can also ask the question, “How did you hear about us?” in the form of a drop-down select or radio buttons. Include your offline marketing methods as choices. If the online traffic source is “direct entry”, then you can assign credit for the sale to the way the customer said they heard about your site.

Assign Values to Online Leads

If your business model is to let visitors fill out a form to be contacted by an agent or representative, there are a couple of different ways to tie success (revenue) to a campaign. Some analytic packages let you assign a dollar value to goal conversion pages, such as filling out a request for information form, a pre-application, or other form of customer contact. This dollar value is based on two factors – the average close rate of online leads, and the average dollar value of each deal. For example, if your company closes 15% of all of its leads, and the average deal is worth $500, then the value of each lead is $75 (15% of $500). Thus, your web analytics package can compare that value to the cost associated with generating the lead, and the nature of actions that lead up to it (pages visited, items downloaded, actions taken, and so on). If your analytics tool is set up to give credit to the first campaign touch point (PPC campaign, banner ad, referral site, etc…), you can still assign credit for the lead to the original campaign, even if the visitor does not convert until a later date.

The drawback with this method is that you are dealing with averages as far as the value of a lead. With average lead values, you cannot measure if a particular campaign brings in a higher-value customer than does another campaign. You can, however, get an average picture of how effective your online campaigns are right within your web analytics tool, without having to import any external data. For many organizations, this will provide much more insight than they are already getting about their offline purchases. It does require fine tuning the value you are using as the average lead value, based on your close rates and average dollar value of a new customer.

Track Campaign IDs with Lead Form Submissions

An alternative to this is to create an offline method of tracking online campaigns when a form is submitted. Your campaign code that you use in your web analytics package can be stored in a cookie and submitted as a part of your lead form. If all these leads are entered into a database, the campaign code can also be entered, and later receive credit for an eventual sale. The exact dollar value of the deal can then also be assigned to the campaign, just like for an eCommerce site. The integration of the online and offline data would then need to be done.

Reaping the Benefits of Multichannel Integration

So far, I have touched on some of the ways to “tag” offline marketing activities so they can be read by your web analytics program, and how to tag offline behavior that is due to your online marketing efforts. However, to put it all together requires access to all the data, both online and offline, plus an integration plan that combines strategy, technology, business logic, web analytics data, BI data, implementation, analytics and other disciplines to provide the desired results. One of the benefits of a multichannel analytics integration is that you will be able to obtain actionable insights, such as these (some are industry-specific):

  • Enhanced ROI – Once you are able to assign additional offline revenue to your online marketing efforts and online revenue to your offline marketing efforts, you will see a higher ROI, enabling you to justify additional spending on both your online marketing and other web efforts, such as site testing and optimization.
  • Retail Merchandising Decisions – If your business is retail, your online data can be mined to see what items tend to be purchased together, enabling your retail operation to group these same items together for in-store customers.
  • Upsell Opportunities – If your offline customers tend to respond to particular upsell opportunities when they call in or get called back, you can use this information to target similar online customers or visitors, based on data that can be stored in tracking cookies.
  • Re-marketing Intelligence – If you know what online customers come back to your site to buy later, you can use this knowledge to market similar products or services to your in-house mailing or phone list.
  • Additional Retail Outlets – If you see a significant request for retail outlets in areas that you are not currently serving, you can have the data you need to consider expanding your physical presence.
  • New Promotional Activities – If you know that your online visitors express an interest in finding a store based on looking at particular products that they want right away or that tend to be expensive to ship,  you can create geo-targeted online campaigns that are designed to get more buyers to your store. This can also work well for seasonal or event-driven items (snowstorm, hurricanes, extended deep freeze, etc…), where the need for a product is now, not 7 to 10 days from now. By tracking these click-to-store visitors, you will be able to measure the success of these campaigns.

Hopefully, this post will give you some insight into how multichannel analytics works, some of its challenges, and how it can benefit your organization.

Enterprise e-Commerce on a Shoe String Budget?

e-commerce on a shoe string

Image courtesy of Flickr

While inexpensively built and operated mom and pop e-commerce websites are as common as snow in New England in January, is it possible to build and operate an enterprise grade e-commerce site on a shoe string budget? E-commerce at an enterprise level is not simply slapping a shopping cart to your website and calling it e-commerce enabled. The demands of an enterprise solution may require:

  • Integration with legacy systems
  • Integration with supply-chain systems
  • Support for multiple currencies and tax codes
  • Multiple store-fronts
  • Profile and history driven offer management
  • Integration with a content management system
  • Business user control over promotions and pricing
  • …and more

Challenges of integration with existing systems alone are daunting enough never mind the fancy e-commerce functionality that is often considered vital for competitive differentiation. No wonder why starting an e-commerce venture or an upgrade is considered a seven figure expense. The cost of an enterprise grade e-commerce product alone can easily account for twenty to forty percent of the budget. The other option is to go with a hosted or SaaS based approach and avoid capital expense for software and infrastructure – not a bad approach for testing the waters but in the long run, charges and fees can really add up.

A well executed e-commerce site can provide great returns on the investment by generating new revenue streams, enhancing existing ones, or reducing operational expenses – and that can’t be too bad for the budget or your career. However, in tough economic times the challenge becomes harder as getting approval for large complex projects becomes difficult and even the approved budgets can get slashed. If your budget gets cut, is there a way to still implement enterprise grade e-commerce? Can an open source e-commerce solution be the answer to the “do more with less” mantra? Is open source e-commerce ready to play with the big boys in the enterprise domain? Let’s explore these questions and the capabilities of the open source e-commerce solutions.

Let’s start with a common misconception that an open source e-commerce product requires significant customizations and the cost of customizations more than offsets any savings from not having to pay license fees. Implicit in this assumption is the notion that a commercial product requires little or no customizations. However, the real-world experience shows us that this is not the case. Even the best commercial products cannot be used out-of-the-box unless you decide to adopt their look and feel and their model of e-commerce. The cost of customizations can add up just as rapidly in a commercial product as they can in an open source one. Therefore a prudent approach would be to adhere to the industry standards and best practices and use out-of-the-box functionality in areas which are not competitive differentiators. Heavy customizations should be limited to the aspects of the website that are true differentiators and result in a unique user experience. This guiding principle applies regardless of the decision to use an open source or a commercial product.

There are a lot of inexpensive and open source e-commerce products out there; however, most of them are nothing more than a simple shopping cart. They are only suitable for the most basic needs of a simple web site. However, Apache OFBiz and Magento are two promising contenders that break from the pack and compete in the enterprise space. In this article we will primarily focus on OFBiz.

Apache OFBiz is actually an integrated suite of products that does not only include e-commerce capabilities but also provides support for accounting, order management, warehouse management, content management and more. An enterprise e-commerce implementation cannot exist as a point solution. It has to integrate and work well with other back office processes and applications. OFBiz’s integrated suite can be used to automate and integrate most back office functions. Even if you decide not to use the built-in functionality it can still be integrated with other existing systems albeit with more effort and cost. It provides enough e-commerce functionality out of the box to match most enterprise needs and the rest can be customized if needed. Here is a summary of our assessment of OFBiz:

Technical Capabilities

# Criteria Rating Comments
1. E-commerce capabilities B+ Provides Robust e-commerce capabilities OFBiz e-commerce capabilities include: catalog management, promotion & pricing management, order management, customer management, warehouse management, fulfillment, accounting, content management, and more.
2. Sign-on and Security B Granular and robust security framework The OFBiz security framework provides fine grain control of the security including multiple security roles and privileges. Roles can be used to control access to screens, business methods, web requests (URLs), and/or entire applications.
3. Technical flexibility & ease of use B Very flexible but complex  OFBiz is an application development platform that can be used to build applications and as such provides a tremendous amount of flexibility.  The use of the entire framework (which includes the database, an Object Relational Mapping (ORM) layer, business object layer, scripting support, and UI tools) is optional.
4. Integration with other apps and locations A Multiple integration methods  OFBiz business services can be exposed as services and accessed by multiple methods including Remote Method Invocation (RMI) and XML Web Services.  Integration directly with the OFBiz Relational Database is also possible.
5. Scalability A Highly Scalable  Java systems are highly scalable provided a production architecture that is designed to support heavy load.  A load balancing device and redundancy at the web, application and database servers can redundancy and scalability.
6. Relational database integration A Support for all major database platforms  The most popular OFBiz database platforms are PostgreSQL and MySQL (both of which are open source).  OFBiz has also been tested with Oracle, DB2, Sybase, and MS SQL Server.  The default installation uses an Apache Derby database which is not recommended for production use. Our research indicates some problems with MS SQL Server database – this should be investigated further prior to selecting that database platform.
7. Skill Set to support NA OFBiz framework and application are based in the following technology components:

  • XML
  • Web Development: HTML, CSS, AJAX/JavaScript, Apache
  • Java Development: Java, JSP, Freemarker, BeanShell, Tomcat application server (possibly)
  • Database Development and Administration: MS SQL Server (possibly), SQL, JDBC

Long term support of the application would require knowledge and familiarity in each of these technology sets.  While these technologies are mainstream and skills should be readily available in the future, skills and experience with the OFBiz framework that is built upon these technologies may not be.

Business Position

# Criteria Rating Comments
1. Financial stability B OFBiz is a “top level” project in the Apache Software Foundation.  The Apache Software Foundation provides support for the Apache community of open-source software projects. The Apache projects are characterized by a collaborative, consensus based development process, an open and pragmatic software license, and a desire to create high quality software that leads the way in its field.
2. Maturity of product suite B Open For Business (OFBiz) was initially launched in 2001.  In early 2006, the project went through the Apache Foundation’s “Incubation” process to review projects for quality and open source commitment.  OFBiz was promoted to a top level Apache project in December 2006.The community for OFBiz is very active.  The major web posting board receives between 20-40 postings per day relating to OFBiz.  The original contributors are very active in monitoring these sites and sharing knowledge.
3. Reference Accounts B- Total number of installations is unknown due to the nature of open source software. The OFBiz websites lists more than 70 companies that use their software. However, there are very few marquee names.

Implementing an enterprise e-commerce solution can be expensive and complex process that requires analysis and investment in people, processes, and technology. While it would be insincere to say that an enterprise e-commerce solution can be implemented on a budget in the ballpark of a mom and pop e-commerce store, the budget can be significantly reduced by:

  • Carefully crafting business requirements
  • Adapting the business model to match industry’s best practices
  • Reducing and carefully planning data migration and application integration
  • Keeping the customizations to a minimum
  • And using an open source e-commerce platform

OFBiz provides a viable open source e-commerce stack that can be used to implement enterprise grade e-commerce. When combined with good implementation practices and solid execution the combination can result in slashing costs by twenty to forty percent — which sometimes can make the difference between getting funded or getting shelved.

A new e-commerce 2.0 buying model

A just released survey of the top 40 e-commerce sites asked users to rate their satisfaction with the buying experience. Of these top 40 sites, only 2 exceeded 80% satisfaction and most are at 70% or less.

E-commerce 2.0 requires taking existing best practices to a higher level. Technical and social changes of the last 8 years have to be accounted for.

  1. Prevalence of web 2.0 attitudes I wrote about earlier
  2. Influence and communication circles are expanding. Like in the classic AIDS commercial, every customer you touch, you have the potential to touch their friends and their friends’ friends. Now at internet speed.
  3. Social Web. Using the internet is not a solitary experience anymore. People surf together, buy together, twitter all day and share everything.
  4. Web applications are expected to be faster, sleeker and with a rich user interface
  5. Available anywhere. With improved browsers in phones, the phone with its small and limited browser is fast becoming a popular and growing way to surf the web.
  6. Data, data everywhere. The proliferation of interaction channels is making it harder than ever to collect and analyze it.
  7. Service orientation: whether you call it web as a platform, software as a service, service oriented architecture or just web services, web applications are expected to be social too.

Has the cognitive buying experience changed? How should all these changes affect the forward thinking enterprise?

The classic AIUAPR model (Awareness, Interest, Understanding, Attitudes, Purchase, Repeat purchase) can be expanded upon to include the web 2.0 concepts and create a solid backbone for the e-business 2.0 infrastructure. David Mercer in his book Marketing has laid a great foundation adding a few very relevant steps into this process

 simple-process

Susceptibility addresses the set of activities that promote a brand and makes the consumer susceptible to the advertising that brings specific brand and product awareness.

Understanding is added to the Interest as research and comparison are becoming an essential step in the purchasing decision

Legitimacy is ever more important as identities of sellers have to be credible enough to result in a transaction. A strong off-line brand name, heavy advertising or good seller feedback on eBay will make it easier for customers to trust the seller.

The Repeat purchase step was divided into the components that determine if the customer will come back

Experience encompasses the shopping experience, satisfaction / experience with the product or service and even the experience with customer service. As more sites and tools allow customers to share their experiences, the impact of positive or negative experiences is magnified beyond the immediate circles and is kept for posterity.

Loyalty is the culmination of all brand efforts to make you a frequent customer who is loyal to the brand and is a brand ambassador to others.

David Mercer also suggests adding Peers and Vendor activities in parallel to the customer process and examines how they influence the decision making process

 

Peers Customer Vendor

process-10

 

 

While this model greatly expands the basic AIUAPR model it addresses the reality e-commerce 1.0

In e-commerce 2.0, a few things change:

  1. Communications are not one sided. Every communication is interactive where data and opinions get exchanged.
  2. The Peer group definition had expanded to include everyone accessible through the internet that has an experience or an attitude/opinion towards the brand, product or service.
  3. As such, it is not enough for the Vendor to try and manage customer experience as they become peers, you need to manage and have specific information and communication plans for the peers as defined at every stage.

The new model will look something like this:

It includes on top the customer, peer, and vendor relationships

anewecommer3

On the other it looks at the customer lifecycle from e-awareness to e-commerce and e-service. Each one of these buckets includes the actions and interactions in the phase.

On the bottom it has the funnel that collects all possible data from all these interactions and processes it as analytics, to be fed into the systems and decision processes that will improve the next iterations.

E-commerce 2.0 high level map

ebusinessdiagram

 

What I think differentiates this model from traditional e-commerce models:

  1. Peers. The influence of peers as part of a social network or even opinions of strangers expressed in blogs and communities or review sites had increased tenfold and has to be acknowledged and managed.
  2. E-marketing is evolving into e-awareness as marketing and PR work together to create awareness to brand and products.
  3. Analytics need to be collected at many different levels. Customer actions and interactions not just on our site but through the awareness and service channels. The interactions with the brand can provide great data as to cause and effect and ROI