Successful Self-Service Strategy

When it comes to customer service, simplicity is critical. Companies can improve customer experiences primarily by limiting the amount of effort it takes for customers to find answers to their questions and accomplish their tasks. Here lies the appeal of Web self-service, which for many consumers has become the preferred communication channel.

Instantly available, 24/7 online customer self-service portals are gaining ground over conventional agent-assisted support, marking a significant shift in consumer attitudes toward the technology. And, contrary to popular belief, interest in Web self-service technologies is not just coming from younger consumers. The technology is changing the behavior of consumers of all generations. In fact, a recent study by Forrester Research found that 72% of consumers, regardless of age, prefer self-service to picking up the phone or sending an email when it comes to resolving support issues. This certainly is welcome news for organizations looking to cut customer service costs and maximize revenue.

There are several elements to consider for successful self-service strategy.

The success of Web self-service depends on the quality and quantity of the information available and the ease with which it can be accessed. Online customers are extremely impatient and information-hungry, so the material available to customers through self-service needs to be succinct and direct, even in response to queries that are not.

The self-service option has to be easy to find on the Web site. To call more attention to the portal, organizations can prominently place a link to the self-service portal on the homepage and other common support pages that feature company, product, and services information. And, since a self-service portal is an extension of a company's Web site, it should have the same look and feel as the rest of the site.

Once on the portal, 80/20 rule applies which means that you assume that 80% of site visitors are looking for about 20% of the content, so that 20% should be easy to find.

As for the content itself, it should be clear, to the point, and easy to understand. This can be achieved by including graphic elements, such as diagrams, charts, and bullet points. When doing so, make sure the graphics are optimized for the Web. If they're not, the Web site could take too long to load, which might cause some customers to abandon it for a more costly agent-assisted channel. Consider keeping content to an eighth-grade reading level, so the average 13- or 14-year-old can make sense of it.

Ensuring accessibility also means that the site should support a variety of Internet browsers, operating systems, assistive technologies for the blind, and, of course, mobile platforms. The latter is becoming more important, especially when one considers that almost a third of all Web traffic today comes from mobile devices.

To make a self-service section even more effective, it can be combined with an automated guidance system that enables site visitors to enter questions and then takes them to specific responses without forcing them to scan an entire database for the answer they need.

One such system is marketed by WalkMe, a San Francisco start-up that enables Web site owners to enhance their online self-service options with interactive on-screen step-by-step instructions displayed as pop-up balloons. The balloons can be programmed to appear automatically when the site visitor rolls his cursor over certain items or when he clicks on a help button.

Customers who can't find answers on their own in a self-help knowledge base might be inclined to call a customer service line, but they are more likely to type their question into a Google search bar, and companies have no control over the results that the Google search returns. This presents a number of problems for a company. Not only has the visitor left your site, but he can find information that you may not want him to see.

Virtual agents are another option companies can use to help customers find what they're looking for. IntelliResponse's Virtual Agent technology simplifies its Web self-service options. The software helps site visitors to find the single right answer to their questions. To keep information current and relevant, it strips outdated FAQ entries, learns over time how to group and respond to questions, and captures data about customer service queries to find precisely what customers need so your organization can fine-tune how it presents information on its Web site.

Companies can also use Web chat to help customers through the self-service maze. It's a tool that's already widely accepted by consumers and businesses alike. LiveWebAssist chat enables agents to push prepared content such as photos, graphics, or Web link, to customers on the site with a single click.

Along with chat and virtual agents, companies can use assisted browsing, or cobrowsing, to move self-service interactions along. This functionality lets the agent—or possibly the virtual agent—temporarily take control of a customer's computer screen. Not only does this improve the self-service experience, but, when interactions move to the contact center through either phone or chat, co-browsing can reduce the average handling time.

It is important to measure response time. Perhaps the most effective measure is the number of customer questions that are submitted and get a response. This can apply to those questions where the customer finds the answer on her own as well as those that are answered through a social community or by a representative of the company. Consider these elements:

• the number of issues resolved per month through social communities. This includes the number of new questions posed to and answered by the community, the percentage of issues resolved by members of the community rather than company employees, and the number of "this article helped me" votes received.
• the number of issues resolved every month through FAQs and company knowledge bases. This includes the number of page views that both receive per month.
• the average cost to resolve issues through channels that involve a company employee. These include phone, email, and chat.

And then, as with any customer service channel, it's important to collect user feedback about the self-help experience. As with any other customer service channel, this can be done through customer surveys, Web analytics and search logs, customer interviews and focus groups, usability testing, and collaborative design processes.

For self-service to be done right, it should be in the interest of the customer. You do not want customers to use self-service because they are forced to. You want them to use it because it serves their needs.

Galaxy Consulting has 16 years experience in optimizing self-service on companies web sites. We can do the same for you. Contact us today for a free consultation!

The Power of Knowledge

Your contact center agents must be available and equipped with the knowledge they need to handle customer issues quickly and efficiently.

However, with the explosion of new channels such as Internet, social media, and mobile computing, many companies lack the tools and processes required to empower their employees to deliver great customer experience.

Organizations struggle with static, siloed knowledge systems that not only provide redundant, often inaccurate information, but are costly to maintain.

Companies that have invested in creating a Powerful State of Knowledge are delivering great customer experiences, which translate into sustainable growth and profitability.

To achieve powerful state of knowledge, companies must be able to:

1. Establish a single knowledge base. Consolidate your knowledge into one single source of truth and make it available to agents and customers across your web site, mobile, and social channels. Tie knowledge to analytics and key performance indicators (KPIs) to present valuable content and address information gaps. This new level of visibility makes it easy for agents to:

• Update knowledge
• Identify potential customer issues
• Provide fast, accurate resolution

If you become driven by market demand for enhanced self-help services and internal demand for efficient productivity improvements, you can transform your customer and employee support systems, taking your existing separate knowledge repositories and establishing one central cross-channel knowledge base. This solution will help to raise efficiency and reduce the cost-per-call of your agents, and it will also improve the quality of the customer support you provide to your customers.

2. Social media has evolved knowledge management from static data residing in a structured database to dynamic, unstructured data created in every social interaction. As a result, you must monitor customers’ social conversations on Facebook, Twitter, and other sites to analyze sentiment and prioritize and respond to service issues.

3. Not many organizations are using traditional knowledge base technology. Instead, many are attempting to embrace the chaos that Big Data, social media, and the move to the cloud create, yet they still face challenges bringing it all together to make the most out of the information.

Unified indexing and insight technology enables just that - tapping into full knowledge ecosystems and providing support agents, employees and customers with contextually relevant information. This unprecedented access to actionable insight has helped companies achieve dramatic results, such as a 30%+ reduction in case resolution time, 10%+ increase in customer self-service satisfaction and more.

The need to make the most of organizational knowledge, to get as much value from it as possible is greater now than ever before. Organizations of all sizes are finding themselves with overwhelming amounts of information, often locked away in silos--different systems, different departments, different geographies and different data types, making it impossible to connect the dots and make sense of critical business information.

Traditional KM initiatives have considered knowledge a transferable commodity that can be stored in a system of record and used mechanically. Yet, in reality, knowledge goes beyond data and information, and is personal and contextual.

Data is factual information measurements, statistics, or facts. In and of itself, data provides limited value. It must be organized into information before it can be interpreted. Information is data in context organized, categorized or condensed. Knowledge is a human capability to process information to make decisions and take action.

The building blocks of knowledge are everywhere, fragmented, complex, unstructured, and often outside the systems of record (in the cloud, in social media, etc.). The key is to bring it all together, and presenting it in context to users.

Unified indexing and insight technology is the way that forward thinking companies access knowledge and experts. The technology brings content into context--assembling fragments of structured and unstructured information on demand and presenting it, in context, to users.

Designed for the enterprise, unified indexing and insight technology is built to bring together data from heterogeneous systems (e.g. email, databases, CRM, ERP, social media, etc.), locations (cloud and on-premise), and varied data formats of business today, It securely crawls those sources, unifies the information in a central index, normalizes information and performs mash-ups on demand.

The technology can be context-aware, relying on the situation of the user to anticipate and proactively offer enriched, usable content directly related to the situation at hand such as solutions, articles, experts, etc. from across the vast and growing ecosystem.

Best Practices for a Higher Return on Knowledge

Bringing relevant content to your agents and customers will increase productivity, create happier employees and drive higher customer satisfaction. Follow these best practices to achieve a higher return on knowledge:

1. Consolidate the knowledge ecosystem. Bring together information from enterprise systems, data sources, employee and customer social networks, social media, etc. Connect overwhelming amount of enterprise and social information.

2. Connect people to knowledge in context. Connect users to the information they need, no matter where it resides, within their context and in real-time.

3. Connect people to experts in context. Connect the people associated with the contextually relevant content to assist in solving a case, answer a key challenge or provide additional insight to a particular situation.

4. Personalize information access. Present employees and customers with information and people connections that are relevant, no matter where they are, and no matter what they are working on.

Investing in the creation of a powerful state of knowledge builds a defensible advantage in delivering great customer experiences. Those experiences lead to sustainable growth and profitability by driving customer acquisition, customer retention, and operational efficiency.

Service and support agents can solve cases faster. No longer do agents need to search across multiple systems or waste time trying to find the right answer or someone who knows the answer. They will have relevant information about the customer or case at hand right at their fingertips: suggested solutions, recommended knowledge base articles, similar cases, experts who can help, virtual communication timelines, etc.

Customers can solve complex challenges on their own. Logging in to customer self-service, customers will see a personalized and relevant view of information form the entire knowledge ecosystem (from inside or outside your company) intuitively presented so that they can solve their own challenges.

Employees can stop reinventing the wheel. When every employee can access relevant information, locate experts across the enterprise, and know what does and does not exist, they can finally stop reinventing the wheel.

Galaxy Consulting has 16 years experience in this area. We have done this for few companies and we can do the same for you.

Personalization in Content Management

Content personalization in content management makes your users' experience more rewarding. Content personalization targets specific content to specific people. One simple example is showing code samples to developers and whitepapers to business users.

Segment Your Users

The first step to delivering a personalized customer experience is to segment your visitors so you can present them with what’s most relevant to them.

Any good personalization strategy starts with a fundamental understanding of your customer’s behavior, needs and goals. Upfront research goes a long way to building out the personas and having the insight from which to develop an approach to personalization. This may already be gathered through ongoing customer insight or voice of the customer programs, or be more ad hoc and project based. Regardless of the approach, be sure that any approach to personalization is grounded in a solid understanding of your users.

The next step in the process is to define the audience goals and objectives so you can know if the personalization efforts are successful. These may include top-line key performance indicators such as conversion rate or online sales, or be more specific to the personalization scenarios (i.e. landing page bounce rate). Try to be specific as possible and ensure that your measures of success directly relate to the areas of focus for your personalization efforts impact.

Personalize Your Content

In order to provide personalized content, it is necessary to determine which content is most effective for each audience segment. This content mapping process can be done alongside the audience segmentation model to ensure you have the right content for the right user at the right stage. If we use the business users and developers example from above, we can personalize the home page for the developers segment to talk about things related to the technology and how it can be extended while we serve business users with information related to how they can achieve their goals using this solution.

The biggest mistake organizations make with personalization is thinking too big and getting overwhelmed before they even start. It is exhausting to even start thinking about how to deliver the right message to the right person at every single interaction. Starting with a few specific personalization scenarios can help you more rapidly adopt the processes and technology and see what works on a small scale before expanding.

Here are a few example rules-based scenarios for an insurance company:

• If a user in a specific region of the United States visits the site, show them regionally specific rates and agent information.
• If a user has shown a specific interest in a vehicle, show images and offers that include that vehicle.
• If a user is an existing customer (as identified through specific site actions or e-mail campaigns) feature tools and content that help them maintain their relationship with you.
• If a user has already subscribed to the newsletter, replace the subscribe to newsletter call-out with a different offer or high value piece of content.

As you begin to think about the overall customer journey and digital experience, this list of scenarios is going to be far more detailed. However, it should not be more complicated than is necessary to accomplish the organizational goal of making it easier for audience segments to achieve their objectives while having the best possible user experience.

The process of content mapping and scenario planning will inevitably surface holes in the inventory of your existing content. Obviously, they will need to be filled. This will require some combination of recreating existing content for different audiences in addition to generating some which is completely new. Not to mention the ongoing process of updating and managing these content variations based on what’s working and what’s not.

Personalization in CMS

It would help to develop a content model and taxonomy for your CMS that is aligned to your audience segmentation approach. By tagging content appropriately you can often automate many areas of personalization. For example, display all white papers from a specific vertical industry.

Regardless of what tool is used to manage all of this complexity, it will require custom configuration. Some systems are naturally more user friendly than others but none of them come out of the box knowing your audience segments, content mapping, and scenarios. All of this information, once determined and defined, will need to be entered to the system.

Rules-based configuration is the most common type of work you’ll do with a CMS which is literally going through a series of "If, Then" statements to tell the CMS what content to show to what users. It’s important to have someone inside your organization or agency partner that owns the product strategy for personalization and can ensure it is consistently applied and within the best practices for that specific platform.

Sitefinity content management system has a simple interface for defining segments through various criteria such as where the visitor came from, what they searched for, their location, duration of their visit, etc. You can define custom criteria and have any combination of AND/OR criteria to define your segments.

Testing Your Personalization

Once your audience and content plans are sorted out and the technology is configured, it is time to test the experience from the perspective of each segment and scenarios within segments. You should test each variation on multiple browsers and mobile devices.

Some CMS allow to impersonate to test your results. For example, Sitefinity allows you to impersonate any segment and preview the customer experience on any device with the help of the mobile device emulators. This way you can be sure how your website looks like for every audience on any device.

Measure the Results

After you’ve segmented your audiences, personalized their experience and checked how your website/portal/CMS is presented for different audiences on different devices you should see the results of your work. They can be measured by the conversions and other website KPIs for the different segments compared to the default presentation for non-segmented visitors or to the KPIs prior to the personalization. Measuring will help you iterate and improve the results further.

Going forward it will be possible to revise previous assumptions with new information which is substantially more valid. Using the built-in analytics within your CMS or third party analytics, you’ll be able to watch how each segment interacts with the personalized content and if it was effective.

Galaxy Consulting successfully implemented content personalization for few clients. We can do the same for you. Contact us today for a free consultation.

Is Your Web Site Optimized for Mobile Devices?

Many people are highly dependent of their mobile devices for every day interactions, including mobile commerce. Our society is becoming highly mobile and connected. In the latest Shop.org and Forrester Research Mobile Commerce Survey, it's estimated that U.S. smartphone commerce will grow to $31 billion by 2016.

Those organizations that can best serve mobile customers will have an advantage in the competition. With a surge in mobile traffic comes the added potential to connect with and sell to customers through mobile commerce. Having a concrete mobile infrastructure plan and strategy is no longer an option, as it had been in recent years, but rather a must to compete in any customer-facing situation.

But despite this upward trajectory, retailers and other consumer-oriented companies still express some hesitancy about investing in multi-device environments. There is still some apprehension by companies, when it comes to moving forward with mobile planning. Companies still struggle to maintain uniformity across multiple device experiences when there are various screen sizes, operating systems, hardware specifications, and loading speeds to consider. One fear is that of the unknown, but security, data management, and simply proving a use case and subsequent return on investment are concerns as well.

The key issue in smartphone shopping continues to be the form factor, which can make navigation more difficult for customers. In addition to slower page load times on smartphones, some customers are concerned about the security of the transaction or simply complain that the experience just is not the same.

A successful mobile experience, like many other customer experiences, is about fulfilling customers' needs. First-time users of a mobile site or app tend to be less satisfied with their mobile experiences than frequent users because of their lack of familiarity with layouts, navigation, and functionality according to the survey of the mobile users. Knowing the different kinds of mobile devices customers use is critical. It is pertinent to develop a strategy that encompasses all types of customer scenarios.

Before embarking on any one mobile strategy, it is important to learn how your company's customers most likely would use their mobile devices. In addition to enabling customers to interact how they wish, any company looking to optimize its mobile presence must naturally consider the effects on the business as well, and how mobile usage will impact other lines of business and cross-channel marketing efforts.

In addition to justifying a use case and ROI for mobile, companies that wish to get into the mobile side of business must be aware of its limitations. Under ideal circumstances, companies want to engage with their customers and cultivate a one-to-one relationship while taking into consideration CANSPAM and privacy regulations. It is very important to adjust taxonomy and information architecture for the mobile experience. A lot of searches are made using mobile devices, so search also has to be optimized.

Optimizing your mobile site or developing a native application is no simple task. There are security considerations, as well as device-specific functions, to consider. Don't take a cookie-cutter approach. Some companies make the mistake of simply cloning online information without considering that consumer behavior on the mobile phone is dramatically different. Justify mobile ROI with consumer insight.

Consider security. Create a military-grade security infrastructure, while maintaining user-friendly design. Hire the best user interaction designer to design the security setup interaction.

Utilize mobile wisely. Once someone has discovered your brand through search, referral, or a marketing message, and they download the app, this may indicate a loyal customer. The app can be a great way to maximize and monetize that loyal relationship because it's in a controlled environment.

Galaxy Consulting has experience optimizing information architecture and search for mobile devices. Contact us today for a free consultation.

Data Lake

A data lake is a large storage repository and processing engine. Data lakes focus on storing disparate data and ignore how or why data is used, governed, defined and secured.

Benefits

The data lake concept hopes to solve information silos. Rather than having dozens of independently managed collections of data, you can combine these sources in the unmanaged data lake. The consolidation theoretically results in increased information use and sharing, while cutting costs through server and license reduction.

Data lakes can help resolve the nagging problem of accessibility and data integration. Using big data infrastructures, enterprises are starting to pull together increasing data volumes for analytics or simply to store for undetermined future use. Enterprises that must use enormous volumes and myriad varieties of data to respond to regulatory and competitive pressures are adopting data lakes. Data lakes are an emerging and powerful approach to the challenges of data integration as enterprises increase their exposure to mobile and cloud-based applications, the sensor-driven Internet of Things, and other aspects.

Currently the only viable example of a data lake is Apache Hadoop. Many companies also use cloud storage services such as Amazon S3 along with other open source tools such as Docker as a data lake. There is a gradual academic interest in the concept of data lakes.

Previous approaches to broad-based data integration have forced all users into a common predetermined schema, or data model. Unlike this monolithic view of a single enterprise-wide data model, the data lake relaxes standardization and defers modeling, resulting in a nearly unlimited potential for operational insight and data discovery. As data volumes, data variety, and metadata richness grow, so does the benefit.

Data lake is helping companies to collaboratively create models or views of the data and then manage incremental improvements to the metadata. Data scientists and business analysts using the newest lineage tracking tools such as Revelytix Loom or Apache Falcon to follow each other’s purpose-built data schemas. The lineage tracking metadata also is placed in the Hadoop Distributed File System (HDFS) which stores pieces of files across a distributed cluster of servers in the cloud where the metadata is accessible and can be collaboratively refined. Analytics drawn from the data lake become increasingly valuable as the metadata describing different views of the data accumulates.

Every industry has a potential data lake use case. A data lake can be a way to gain more visibility or to put an end to data silos. Many companies see data lakes as an opportunity to capture a 360-degree view of their customers or to analyze social media trends.

Some companies have built big data sandboxes for analysis by data scientists. Such sandboxes are somewhat similar to data lakes, albeit narrower in scope and purpose.

Relational data warehouses and their big price tags have long dominated complex analytics, reporting, and operations. However, their slow-changing data models and rigid field-to-field integration mappings are too brittle to support big data volume and variety. The vast majority of these systems also leave business users dependent on IT for even the smallest enhancements, due mostly to inelastic design, unmanageable system complexity, and low system tolerance for human error. The data lake approach helps to solve these problems.

Approach

Step number one in a data lake project is to pull all data together into one repository while giving minimal attention to creating schemas that define integration points between disparate data sets. This approach facilitates access, but the work required to turn that data into actionable insights is a substantial challenge. While integrating the data takes place at the Hadoop layer, contextualizing the metadata takes place at schema creation time.

Integrating data involves fewer steps because data lakes don’t enforce a rigid metadata schema as do relational data warehouses. Instead, data lakes support a concept known as late binding, or schema on read, in which users build custom schema into their queries. Data is bound to a dynamic schema created upon query execution. The late-binding principle shifts the data modeling from centralized data warehousing teams and database administrators, who are often remote from data sources, to localized teams of business analysts and data scientists, who can help create flexible, domain-specific context. For those accustomed to SQL, this shift opens a whole new world.

In this approach, the more is known about the metadata, the easier it is to query. Pre-tagged data, such as Extensible Markup Language (XML), JavaScript Object Notation (JSON), or Resource Description Framework (RDF), offers a starting point and is highly useful in implementations with limited data variety. In most cases, however, pre-tagged data is a small portion of incoming data formats.

Lessons Learned

Some data lake initiatives have not succeeded, producing instead more silos or empty sandboxes. Given the risk, everyone is proceeding cautiously. There are companies who create big data graveyards, dumping everything into them and hoping to do something with it down the road.

Companies would avoid creating big data graveyards by developing and executing a solid strategic plan that applies the right technology and methods to the problem. Hadoop and the NoSQL (Not only SQL) category of databases have potential, especially when they can enable a single enterprise-wide repository and provide access to data previously trapped in silos. The main challenge is not creating a data lake, but taking advantage of the opportunities it presents. A means of creating, enriching, and managing semantic metadata incrementally is essential.

Data Flow in the Data Lake

The data lake loads extracts, irrespective of its format, into a big data store. Metadata is decoupled from its underlying data and stored independently. This enables flexibility for multiple end-user perspectives and maturing semantics.

How a Data Lake Matures

Sourcing new data into the lake can occur gradually and will not impact existing models. The lake starts with raw data, and it matures as more data flows in, as users and machines build up metadata, and as user adoption broadens. Ambiguous and competing terms eventually converge into a shared understanding (that is, semantics) within and across business domains. Data maturity results as a natural outgrowth of the ongoing user interaction and feedback at the metadata management layer, interaction that continually refines the lake and enhances discovery.

With the data lake, users can take what is relevant and leave the rest. Individual business domains can mature independently and gradually. Perfect data classification is not required. Users throughout the enterprise can see across all disciplines, not limited by organizational silos or rigid schema.

Data Lake Maturity

The data lake foundation includes a big data repository, metadata management, and an application framework to capture and contextualize end-user feedback. The increasing value of analytics is then directly correlated in increase in user adoption across the enterprise.

Risks

Data lakes therefore carry risks. The most important is the inability to determine data quality or the lineage of findings by other analysts or users that have found value, previously, in using the same data in the lake. By its definition, a data lake accepts any data, without oversight or governance. Without descriptive metadata and a mechanism to maintain it, the data lake risks turning into a data swamp. And without metadata, every subsequent use of data means analysts start from scratch.

Another risk is security and access control. Data can be placed into the data lake with no oversight of the contents. Many data lakes are being used for data whose privacy and regulatory requirements are likely to represent risk exposure. The security capabilities of central data lake technologies are still in the beginning stage.

Finally, performance aspects should not be overlooked. Tools and data interfaces simply cannot perform at the same level against a general-purpose store as they can against optimized and purpose-built infrastructure.

Careful planning and organization of data lake strategy is required to make this project a success.