What we read this week, 21 November 2014:

The concept of customer success may be strange to some of us coming from the on-premise enterprise technology side. This post from Totango, and more importantly, the Forrester report it points to do a pretty good job at explaining the concept and pointing to the leading players.

Knowledge-Centered Support – The Methodology That Really Works – (Atlassian)

How to Reduce Waste with Process Mining – I recently came across the concept of process mining, courtesy of a coursera course Process Mining: Data science in Action. From the course description: “Process mining bridges the gap between traditional model-based process analysis (e.g., simulation and other business process management techniques) and data-centric analysis techniques such as machine learning and data mining.” From what I have seen so far, it uses event data (think your CRM’s or case tracking system’s audit trail) to analyze process bottlenecks and dependencies. I will provide additional reviews of the concept and the course when it ends, but if you have some time check it out, it is free.

The 7 Laws of Customer Success – More on customer success and the mindsets driving this rapidly growing discipline (gainsight).

How Do People Get New Ideas? – written in 1959 by Isaac Asimov and only recently published, this article discusses the conditions for creativity (MIT Technology Review).

What we read this week, 14 November 2014:

How can a customer journey lead to data enlightenment? – Mapping and instrumenting the customer journey through the company’s multiple touchpoint, and some ket points to consider (mycustomer.com)

Predicting Customer Lifetime Value – decisions about customer tier and service levels are frequently driven by the ability to predict customers’ lifetime, or long term, value. Frequently this prediction is based on past experience, “our best customers will continue to be our best customers.” The article describes a statistical model used to analyze those predictions and the challenges in doing that. Key quote: “We found that best customers continued to be best customers at a much lower rate than we expected […] If a significant proportion of future best customers comes from past poor customers, you risk losing them.” (Kellogg Insight)

How to Get People to Like You: 7 Ways From an FBI Behavior Expert – several helpful tips on creating rapid rapport with strangers (Time)

Are these the five biggest hurdles to successful customer service? – a good summary of common customer service failures in execution and management (mycustomer.com)

Don’t Be a Metrics Slave – Support organizations are highly instrumented organizations and easy to measure, and frequently metrics are used poorly to the detriment of all involved. This article points to several common pitfalls I have seen a number of people fall into frequently (Enterprise Irregulars)

What we read this week, 7 November 2014:

Welding with Autodesk CEO Carl Bass – How many CEOs do you know who can do their endusers’ job? (The Financial Times, subscription required)

Tailor Your Presentation to Fit the Culture – many of us managing global organizations often face communication challenges when implementing initiatives across multiple locations. This post discusses some of the deep cultural foundations behind that. I have to admit that I was skeptical before reading the post, but found it enlightening and worth sharing (Harvard Business Review Blogs Network)

Two Worlds Colliding: How LinkedIn Could Take On Salesforce – Considering how critical CRM systems are to every customer facing organization, what impact would supporting customers with additional linkedin input have? (Techcrunch)

Firestone Did What Governments Have Not: Stopped Ebola In Its Tracks – on preparation for crisis (NPR)

10 Ways to Spot Great Teachers (and Avoid Crummy Ones) – on what makes an excellent teaching experience (The Talent Code, link via FTWORKS).

Two articles about the current and future states of enterprise technology – The Future of Enterprise IT: An interview with Geoffrey Moore (CIO) and The Harsh Reality Of The New Enterprise World by Gainsight CEO, Nick Mehta (techcrunch) – articles offer distinct perspectives on the industry and are definitely worth your time

What we read this week, Halloween 2014 Edition:

pumpkin

A new feature on the blog, where we’ll list a few interesting articles touching on enterprise technology, support and services and other interesting topics. These are listed in no particular order:

On Financial Times, Big tech start-ups bypass Silicon Valley on the technology industry’s expansion beyond Silicon Valley (subscription required)

On Nir & Far, The Link Between Habits and Customer Satisfaction on creating and reinforcing customer habits for retention and profit

On mycustomer.com, Customer Journey Mapping Series, start with Why is the customer journey so complex and what does it mean for business? and continue

On Harvard Business Review blogs, The Key to Change Is Middle Management reviewing the critical role middle management plays in any organizational transformation effort

On Bloomberg View, Bad Math That Passes for Insight – how much of that do you see regularly?

Sometimes The Extra Mile Is Free

Restaurant scene

Recently I visited a coffee shop while waiting to meet a friend. Walking in, I was impressed – the place was large, well lit and tastefully decorated. The food and pastries in the display cases seemed attractive, with quality ingredients and professional preparation. Clearly, those who designed and built this business aimed high, and their prices reflected that. As a long time observer of service operations I started wondering – could they deliver on the promise of the decor and the food?

Considering I only had tea, I couldn’t tell anything about the food except that other diners seemed to be enjoying it. However, from the service perspective I left with a few nagging points that directly apply to other service organizations:

  • The food is served in nice porcelain dishes which the crew clears once the customer has left. But, they do not wipe the tables, consequently, each table had some crumbs. Very few, but noticeable. When clearing the tables they do not use a tray, so we got a chance to see one of the crew walking slowly with a pile of dishes on her arms, trying not to drop them. Solution – get a tray, and put a little wet towel on it. Clear the dishes into the tray, wipe the table and be done.
  • When ordering a drink they take your order at the counter and bring the drink to the table. But, the crew has no clue how to walk straight while holding a cup. It was very comical watching one of them holding a saucer with both hands and walking slowly trying not to spill the coffee. Solution – rehearse, work on your muscle memory.

In both these cases, not only did the operation look unprofessional, but the employees were visibly embarrassed.

  • Last – my tea was delivered to the table in a cup. There was nowhere to dispose of the teabag nor were there sugar or stirrer on the table. I had to get up and get them myself, negating the point of table service. Solution? You guessed it. Bring a saucer, and place a few bags of sweetener on each table.

Now, there is a common thread between all these points. Fixing them will cost the business absolutely nothing, but requires an observant manager with a burning desire to keep improving the service. This begs the question, how much improvement could each of us make to our support operations at zero cost while helping our employees increase their skill and professionalism? How much better can we make them? What if we took the time to observe our organization from the side, and inspect every move and every action as they are perceived by the customer? Sadly, in many situations this seems to be everybody’s last priority.

Broader impacts of organizational maturity

Office success

In a previous post we reviewed the various maturity stages for a support organization. Having done that, the next question becomes “so what?”, or in other words, how does support’s increasing maturity impacts the value it delivers to the company?

To answer that, let’s look at the relationships with internal constituents, such as corporate management and adjacent organizations, as well as external entities like partners and customers?

Most of us can imagine how the dialog between support and engineering evolves in line with the increasing maturity. Support organizations transition from a front-end where every customer case is escalated to identifying failures, advising customers on product use and having responsibility for the customers and for the well-being of the installed base. Similarly, the interaction with sales shifts from adversarial to a partnership.

But, how does all this deliver actual value to the company?

If we think about it, there are three ways customer support can add value to the company:

  • Increased efficiency, so support costs for unit of revenue grow slower than revenue does
  • Creating additional support revenue through value added services
  • Reduced discounting during the sales process through collaboration with sales, minimized problem impact on customers
  • Accelerated and enhanced consumption of the products, driving the customer to add capacity or licenses

Additionally, a mature support organization can provide the company extensive amounts of information about the products and the customers, for example:

  • Ways in which customers use the products
  • Challenges and difficulties customers face while using the products
  • The most commonly used features, and those used rarely or never at all
  • Interaction with third party partners, their capabilities and deficiencies
  • Customer satisfaction and its drivers and inhibitors

However, the reliability of this information and the ability of the support organization to be a valued partner depends on the maturity of the support organization, but also on the maturity of the entire company.

What’s your experience in taking your organization to higher maturity levels? Where did you encounter the biggest challenges?

Enterprise Support Maturity Model:

Layered rainbow colored pyramid

Several weeks ago Harvard Business Review published a blog post by Vikram Bhaskaran, titled “Customer Support Hierarchy of Needs” which I read carefully but found unfocused and lacking in depth. Consequently I felt the challenge to develop a better, more coherent, model for enterprise technology support.

Eventually I came up with the following as a base model (see the bottom of this post for the naming choice). It is still a work in progress and as it is being refined I’ll post additional versions to the blog.

Pyramid Simple

The model attempts to capture the two most critical investment any organization makes, technology and people, and show the path they progress along in order to deliver a more comprehensive experience. It is based very loosely on the concepts introduced by the various CMM models and progresses through the various maturity stages.

We can all understand that the various maturity phases will not have clear transitions. In fact, most support organizations I have seen tend to have different segments of their operations at different maturity levels. The common theme for all, however, is the desire to make progress along the path to a more mature level of operation. Obviously, as companies expand, efforts may be directed to other pressing concerns, such as global expansion, supporting additional product lines, or adding third parties to the support chain. However, the need for continuous progress up the maturity levels is shared by most support executives I have met.

As a a first step I’d like to define a few of the essential characteristics for each phase identified earlier:

  • Chaos – Usually exist only very early in a company’s life, ad-hoc process, lack of infrastructure, metrics or dedicated staff. Support is provided by engineering teams and frequently personal heroics are key to resolving problems with any significant urgency or complexity. Third party partners may provide some support to specific customers, but the interaction is mostly at the technical level.
  • Managed – Basic processes exist, along with entry level staff to manage non-technical customer communication (e.g., case opening, status requests). Technical interactions still managed by engineering. Basic case tracking and multi-channel capabilities exist. Metrics are used but will usually focus on cost and volume. Basic offering parameters defined and communicated to customers.
  • Professional – Processes more elaborate than those at the Managed level, and may include various escalation guidelines. Technical staff is added to the support organization, and the infrastructure becomes more sophisticated in capabilities as well as utilization. Customer Satisfaction will be added to metrics. Sporadic collaboration with engineering. Additional services may be offered, such as Support Account Managers or extended coverage (e.g., around-the-clock or weekend) to supplement standard offering.
  • Proactive / Predictive – Knowledge management is used, customers are alerted to potential problems through proactive notification. Close collaboration with engineering teams to ensure high impact or widely encountered problems are addressed rapidly. Vendors can predict which customers or hardware components will encounter certain failures and act accordingly.
  • Invisible – Support integrated into other organizations in the company, resting a continuous customer experience through all points of interaction. Multi-channel interaction through forums and other social and traditional channels channels managed consistently. Customer intimacy used to eliminate failures, reduce their impact on customers and increase value derived from products.

How does this model fit with your experiences? Surely everybody who has been in this business for some time has a similar concept about the progress support organizations make over time. It does open the door to understanding the different perspectives to this progress, which we’ll discuss in future posts

The main reason I chose to call the model Enterprise Support Maturity Model as opposed to the frequently used Hierarchy of Needs is that while an individual’s needs have hierarchy, an enterprise customer has choices. We can imagine a hungry person might give up dreams of self-actualization while searching for food. A customer, on the other hand, will go looking for other, more competent vendors.

Product Design and Customer Satisfaction

Rugby Player scoring a Try!

The usually excellent Barry Ritholtz posted a massive rant against the location of the power switch on his MacBook Air laptop, ending with the promise to reconsider the purchase of additional computers, with this flaw being the driver.

In support we come across similar situations regularly, especially with smaller implementations, where customers find that certain product features diminish the value they can derive to the point of eliminating all value generated by the product. Those customers also make it a point to tell us about it. This raises several questions. First concerns the mechanism for disseminating customer feedback into the decision making ranks. The second looks for a way to evaluate the scope of the problem (or in other words, what part of the customer base is affected by the problem and to what degree?)

Now, how would you address the challenge from Mr. Ritholtz? Obviously there is a range of options to handle the individual customer, but I am curious about methods to convert feedback into actionable information. Any ideas?

Is It An Airline? Is It A Hotel? No! It Is Enterprise Technology Support!

Drawing of scale on blackboard

The blog’s previous post discussed confirmation bias and the way it impacts our actions. Frequently we encounter a different type of bias, usually coming from individuals not very familiar with enterprise technology support, and holding their support teams against standards that may not necessarily apply. Support managers find themselves having to explain the difference between supporting enterprise technology and a number of popular consumer businesses, including an airline, an on-line apparel vendor, a hotel chain and a department store.

There is no doubt that each of these companies have mastered customer service for their customer base, and surely there is a great deal we can learn from them, but in order to do that we need to understand the differences and ensure we adopt those those behaviors that can help our customers and us.

In the past I wrote about the differences between high volume / low complexity operations and low volume / high complexity ones. There are additional models that can help us complete the picture.

Stacey‘s Matrix attempts to illustrate the complexity in decision making in relation to two dimension:

Stacie Matrix

The first, on the horizontal axis is the degree of certainty in which the problem addressed is understood, including the cause and effect chains. The vertical axis then illustrates the degree of agreement between the various participants on the way to resolve the problem.

If we use an airline as an example, the vast majority of cases handled by its service employees is relatively straight forward and falls within the “rational decision making” zone of the chart – both the customer and the airline are clear on the objective and the way to accomplish it. Even when recovering from a service failure (e.g., lost luggage or a cancelled flight) there usually is clarity concerning the cause and effect as well as agreement on the best way to resolve the problem.

Now let’s look at enterprise technology customer support situation. Most company have done a good job at documenting their knowledge and making it accessible to customers, partners and other users. This causes the support organization’s workload to contain an increasing proportion of cases where either the cause and effect chain are not clear, or there is little agreement on how to achieve the end result, or both. These disagreements take us away from the relative comfort of the rational decision making zone, and increasingly into judgmental or political decision making, or even into the complex decision making zone.

Now, in order to resolve the customer’s case, customer support must ultimately reduce the uncertainty and clarify the cause and effect chain for the problem. Once that has been accomplished the case can be resolved and the problem eliminated. Consumer brands rarely have to address the need for reducing complexity and gaining agreement on the problem definition or the cause and effect chain leading to it.

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