Analytics' Role in Value-Added Support
Leveraging Technology to Unlock
Business Intelligence
Author: John Ragsdale, Vice President of Research, SSPA

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Analytics enable value-added support in three primary ways

In the February issue of the SSPA News, Trisha Bright, SSPA’s VP of Member Programs, discussed what companies needs to know about Value–Added Support, with references to reports and presentations to help raise visibility for this new take on the strategic potential of support organizations.  What role do analytics and business intelligence play in moving toward Value-Added Support (VAS)?  SSPA Research thinks they play a huge role, and in fact, some aspects of VAS are only possible because of the power of analytics to derive business intelligence from existing warehouses of customer and product data.  Three primary areas in which analytics enable customer service organizations to transition to VAS are:

• Leveraging customer interactions to deliver cross-enterprise insight.  Companies have spent a decade creating a “360º view” in CRM systems, but aren’t doing much with all the data.  There is a goldmine of business intelligence hidden in data warehouses of recorded agent conversations, case notes, resolution information, etc., and analytics can unlock the value of this captured information.
  
  
• Successfully incorporating offer management into inbound support.  Companies wanting to create new revenue opportunities by introducing upsell and cross-sell into inbound support centers can leverage analytics to gain real-time insight into what is the best offer to extend to a customer for a specific interaction.  Making offers contextual to the issue is a great way to increase campaign success.
  
  
• Delivering exceptional, highly accurate Web self-service.  Increasing the success of every support channel is a great way to provide additional value to customers, and when it comes to Web self-service, analytics can vastly improve self-service success by analyzing customer preferences, technical environment, products and version in place, service history, etc., and factoring these into any self-service search or diagnostic process to delivery highly accurate results.

Creating Customer Business Intelligence for the Rest of the Enterprise

Support management often complains that they are not getting the respect they deserve within companies.  There are 2 sure fire ways to address this.  One is generating additional revenue, which will be discussed in the next section.  Another method is to provide critical business intelligence to other departments so they understand how support’s intimacy with the customer generates incredible insight. As shown in Figure 1, support management should leverage customer intelligence to bring needed insight to these departments:

• Product Marketing/Development.  This is one area tech support teams already leverage reporting to provide input to development on needed features and bug fixes, but by adding analytics to the mix some additional intelligence can be gained, or at least make it much easier to perform more root cause analysis on which products/versions are impacting which types of customers in what way.  Being able to deliver timely reports to product marketing and development based on customer conversations can give support more clout in helping prioritize bugs and features, identify areas within the applications needing redesign, etc., without having to pay for costly usability labs or other controlled test programs.
  
  
• Corporate Marketing.  Most marketing departments have a large budget for customer focus groups and surveys to determine what the reaction is to brands, new product advertising, company announcements or promotions, etc.  While on the phone with support, customers frequently voice their opinions on all of these things, yet the information is not captured.  By using analytics on recorded customer conversations, insight into everything from marketing campaigns to earning announcements to board of director appointments can be identified and passed along to corporate marketing.¹
  
  
• Sales. How do your products compare to your competitors (price, quality, design, features) in the eyes of customers and prospects?  What made a customer decide to select, or not select, your products?  What are weaknesses of a competitor’s products?  Sales organizations have competitive intelligence experts on staff to identify this information, yet customers freely offer up many data points in support conversations.  Again, this information is likely not captured today, but by analyzing recorded conversations and/or case notes and custom fields, support interactions can deliver critical data to the sales organization.

By becoming the “go to” folks for development, marketing and sales on customer trend information, service and support will not only have increased visibility within the company, but new found respect as well.

Leveraging Analytics for Successful Offer Management

Extending upsell and cross-sell offers to customers has become a common practice in the consumer world, and is gaining adoption within B2B environments as well.  Obviously, a great way for a support organization to gain additional visibility and clout within a company is to deliver additional revenue, and it turns out that selling in inbound contact and tech support centers works…if you use analytics.
Analytics addresses one of the biggest barriers to successful selling:  the customers feel blindsided by a sales pitch during a service call.² To overcome this, analytics can:

• Present an offer in context of the interaction.  We all have had the experience of ordering something from a catalog, and having the agent taking the order offer specially priced merchandize that made no sense.  “Thank you for ordering that lawn mower; let me tell you about the tea cozies we have on sale today!”  If the offer is not in context of the interaction it is unlikely to be successful.  Offer management software analyzes the customer history, which products are in place, the technical environment, any known preferences, as well as the reason for today’s call, and prompts the agent with an offer to extend involving a companion product, a newer version of an existing product, or an add-on accessory that makes sense to the customer.
  
  
• Make the offer extension a natural part of service.  Offering a customer an upgrade to a newer product or version which addresses the current problem is an ideal way to transition from service to sales. Analytics will track all existing products, compare those to the products currently owned by the customers, and identify automatically where an additional purchase may solve a support problem.  Thought agents likely know this information for critical product issues, keeping track of every variation in environments where many products or product versions are supported is difficult.
  
  
• Leverage offers to do more than sell.  Extended offers are not all upsell or cross-sell.  For unsatisfied customers, or for those who a real-time analytic has determined at high risk for churn, extending an offer to join a beta program, receive a discount for an upcoming training class, wave the fee for an upcoming conference, or just send out a t-shirt or coffee mug can help appease the customer and build loyalty.

Dazzling Customers with Exceptional Web Self-Service

Delivering increased value to customers means verifying every support channel is as effective as possible. An area where analytics can make a huge difference in keeping customers happy is fine tuning Web self-service.  Analyzing search strings, knowledgebase article click throughs, customer ratings for content, and other click stream data can increase Web self-service accuracy by identifying:

• Content gaps. Intelligent search vendors, such as InQuira and KNOVA, offer content gap analysis reports, which look at all customer Web self-service search strings that produced a null result in order to identify topics of interest to customers for which no content exists today.  By understanding the intent of customer questions, the analysis engines can pinpoint missing concepts from a knowledgebase taxonomy and prioritize areas for knowledge authors to concentrate on first for the biggest impact to self-service success.
  
  
• Poor content.  Companies often rely on customer ratings to identify incorrect or poorly written knowledgebase articles.  But with response rates to useful/not useful prompts of online content so low, relying solely on customer feedback means bad content sticks around much longer than it should.  Analytics can identify suspect content by a variety of clues, such as tracking the amount of time an article is viewed by a customer, if a customer continues to search after viewing a piece of content, and depending on your implementation, detect content that goes unused because of poor categorization.
  
  
• More accurate search results.  The same technology Amazon uses to determine which books you will want to buy can be used to identify what content is most likely to help a customer, but in the past companies rarely invested in analytics to help customers—only to sell to them.  This has changed with analytic-based microsites, which can dynamically create customer self-service sites depending on customer profiles, demographics, service levels, purchase history, etc.  And, by pre-filtering all knowledgebase searches for only products, operating systems and geographies appropriate for the customer, more accurate search results are delivered every time.³

The SSPA Recommends

As we’ve seen with Oracle’s recent purchase of yet another analytics/business intelligence vendor, one size doesn’t fit all when it comes to analytic tools.  And, with vendors calling basic reporting “analytics” these days to capitalize on the buzz around BI tools, it is very hard to even identify sources for good analytic technology. If you are launching a project to bring more analytic capabilities into your service and support technology stack, here are good places to look for the right technology:

• Your marketing organization.  Particularly for >$1B companies, your marketing department probably has some strong analytics already, used to drive successful marketing campaigns, manage eCommerce ads, mine customer databases for demographic trends, etc.  While these tools are marketing centric, larger systems, like SAS and Teradata, are very flexible and can be used within support with some customization.
  
  
• Analytics built into CRM/ERP suites.  The larger enterprise application vendors, Amdocs, Infor, M2M, Oracle and SAP, have acquired and/or built data warehouses, analytics and business intelligence tools.  Unlike core reporting, analytics may be an add-on module.  If you are interested in additional analytics, start with the main application vendors you work with, find out what options they have, and if their analytic tools can be used for data outside of their applications.  For example, your CRM analytic tools must also be able to analyze CTI stats and usage metrics from an eService knowledgebase.
  
  
• Partners to CRM and customer service vendors.  Though some eService vendors have a strong analytics core, most partner for advanced analytics.  Check the partner website for the vendors whose technology you are using within the service organization and see who the vendors recommend for analytics.  These partners should have predefined reports based on the vendor’s schema, getting you up and running with the most common analysis quickly.
  
  
• Analytics specialists.  There used to be quite a list of niche analytics vendors, but as consolidation continues within this industry, there are fewer and fewer left.  Here are some strong specialists to consider, though note that most are heavily focused on marketing analytics: [x+1], Intelligent Results, Kefta, and Touch Clarity.

ENDNOTES

If you have any questions about this article, please contact Shawn Santos at ssantos@thesspa.com or 858.674.5491.