We all are aware of the three fundamental principles of People, Process and Technology. Every organization focuses on these three elements. The people factor in all the above has been limited to reskilling in new technologies and training on methodologies and frameworks besides training on soft skills like communication. In an ever-changing digitalization era, how people behave and engage in the workplace is also important. This is beyond behavioural skills like inter personal skills, communication skills and so on.
In this paper, we will explore the following:
- Behaviour of people in digital workplace
- Patterns of behaviour of people
- How patterns of behaviour affect Service Delivery?
- How ITSM tool sets can be used to identify such behaviour patterns?
- Building data on Patterns of Behaviour
- By extrapolating the identified patterns, how can we predict performance?
- How can we arrive at an effective team composition by using those predicted performances?
Behaviour of people in digital workplace
Prior to the digital era, people behaviour was mostly characterized by individuals gathering knowledge and putting in long hours of work. Behaviour was controlled by authority and supervision. Traditional structures had:
- less complex work
- reliance on formal reporting relationships
- less need for change
- less need for continuous competency development
- less demands on time
- independent business units conducting its own activities, such as sales, marketing and training,
- mostly co-located,
- working in proximity
- And most importantly, predominantly manual work with less tracking trails. Enforcing compliance to processes and policies was manual and employees leveraged their discretion to bypass manual processes.
Digital transformation is the alignment of technology, employees and processes to improve operational efficiency. Agility is synonymous with digital transformation and a digital employee experience covers how employees work, what tools they use, and the culture they exist within.
In the current digital era populated by millennials, authority and supervision has only a limited effect in controlling behaviour. Boredom with repetitive processes, frustration with detailed processes, impatience in dealing with the customer and other aspects all reflect a behaviour that cannot be controlled by tight workflows, supervision and authority alone.
2 Patterns of behaviour - People in the Workplace
The following are a few kinds of behavioural patterns exhibited by people in the current digital area.
- Impatience in dealing with the customer, resulting in a pattern of rude behaviour by service desk staff
- Frustration with detailed, tool driven processes / workflows leading to shortcuts and process deviations
- Boredom with repetitive processes leading to errors due to lack of focus
- Silo behaviours leading to collaboration issues (Collaborative Agile work demands team work).
The above patterns of behaviour result in poor service delivery as reflected in the following parameters:
- Consistently low user satisfaction rating of less than 3/5, for causes like impolite wording, being non-responsive, etc.
- Delay in release to production due to multiple ‘rejection of change’ requests by CAB due to causes like insufficient / incomplete details in change requests
- Higher mean time to resolve and SLA misses due to avoidable escalation of Known Errors to L2 and L3 team
- Higher mean time to resolve and SLA misses due to incorrect categorization of tickets.
Before we move forward, let us look at how a team or team composition can affect service delivery.
When a contract is signed for a new project the immediate next step is to form a team. Resources are onboarded from the available pool or hired from external sources. The hiring / onboarding is based on the experience and skillset of the resources, as evaluated during the interview sessions. There is no denying the fact that if a person has good experience, he or she will perform well in service delivery. However, we do see that in reality, despite sufficient experience / skills, service delivery issues still arise.
The patterns of behaviour consistently displayed by few of the resources in the team, over time, affects the quality of service delivery. Hence it is important to gather, analyze & monitor these patterns of behaviour to eliminate one of the main causes of service delivery issues.
4 Identifying Patterns of Behaviour using ITSM Toolsets in digital era
The table below provides examples of how we can use ITSM tool sets to identify these Patterns of Behaviour
Service Delivery Issues and related Behaviour Patterns
||Pattern of Behaviour
||Service Delivery Impact
||Tracking behaviour pattern in ITSM Toolset (some pointersP
||Identify instances where ticket hops have circled back to previous recipients / assignment group
A resource, ‘X’ does not put in enough effort to understand the issue but simply assigns the ticket to another team or team member, ‘Y’. ‘Y’ then finds that the ticket has to be handled by some other team or team member, ’A’. Here, ‘X’ who first handled the ticket should have understood the issue and assigned to ‘A’ for resolution but just to avoid the effort, ‘X’ passes to ‘Y’.
|This behaviour impacts turnaround time for ticket resolution (including team average time)
It may also lead to possible SLA breaches
|Can be tracked using User Persona –
1. Analysis of 6 -12 months of data at an individual level or at an assignment group level will help you understand if multiple ticket hops are typical of this resource or assignment group
2. Data should be included from the individual’s previous projects
3. Eliminate justified cases
4. Set general baselines on ticket hops and see if the individual’s average exceeds the baseline for that level of skill / experience
||User Satisfaction survey scores
A service desk agent consistently gets user rating of less than 3/5 for causes like impolite wording or language, being non responsive etc.
|This will impact the overall satisfaction of the end users with IT Service Desk even
if tickets are closed within SLA
|1.Individual persona reports can be used to identify individuals who consistently get a feedback rating of 3 or less on a scale of 5
2.When a new team with 5 members is formed and 3 of them have Individual persona report cards of 3/5, we can safely assume that the overall satisfaction level for the newly formed Service Desk team will be negatively impacted. Ultimately, it is the individual member’s scores that impact team scores.
||Avoidable ticket escalations to L2 / L3A
A resource, ‘X’ from L1 team makes only a single cursory search in the KEDB and when he does not find an exact match he immediately escalates to L2 team. L2 team, in turn, resolves the ticket by referring to KEDB. Here ‘X’ should have tried different related key words to see if a solution is available in the KEDB but does not put in the required effort to check the KEDB thoroughly.
|This behaviour impacts the turnaround time for ticket resolution (including team average time).
It may also lead to possible SLA breaches. It increases the average incident resolution cost/time.
|Can be tracked using User Personas –
1. Analysis of 6 months of data at an individual level, across projects, will help you understand if there is a consistent pattern to this
behaviour (tickets assigned to next level without proper reference of KEDB and L2 fixing it immediately).
2. Eliminate justified cases
3. The individual’s pattern of behaviour can be tracked and benchmarked against other members of equivalent skill / experience in the organization or team.
4.If the team composition has more than 50% of such members, we can extrapolate and predict that the TAT (turnaround time) of the team is going to be more than organizational benchmark.
||Multiple review of RFC by CAB
Change Manager not showing due diligence in ensuring that all required details are gathered / filled in the RFC and forwarding partially filled RFC to CAB.
|Assigning critical changes to such Change Managers may result in delay in approval of RFCs and consequently missing the change window.
||Can be tracked using User Personas –
1. Analysis may reveal a consistent behaviour pattern with respect to few change managers / individuals whose RFCs get regularly sent back by CAB requesting additional information
2. Apart from skill issues, this could also be attributed to carelessness in getting all the details updated.
3. If we have one or more change managers, the TAT for changes will naturally increase. The process will also be less efficient as it unnecessarily consumes the time and effort of CAB members in reviewing the same RFC repeatedly.
||Service Request classified as Incidents
In spite of repeated training, an L0/L1 team member repeatedly raises a service request as an incident ticket. This may be due to: carelessness, an attempt to please the end user or a skill-related issue.
|This will impact the turnaround time of genuine incident tickets as those will only be examined after analyzing the service request tickets that have been wrongly classified as incidents.
||Can be tracked using User Personas –
1. Analysis may reveal a consistent behavioural pattern relating to the few individuals who repeatedly classify tickets incorrectly.
2. Like in prior cases, if a team has more than 50% of team members displaying this pattern of behaviour, we can extrapolate and predict the team performance levels.
||Higher mean time to resolve incident tickets
A team member, ‘X’ closes tickets just a few minutes before target time even though he has resolved the ticket much earlier.
Alternatively, due to superior skills, ‘X’ closes the ticket ahead of time but does not pick up a new ticket and prefers to maintain his average ticket closure per day at same levels.
|This will impact the ability to correctly forecast team productivity. This will also impact the ability of the Service Delivery Manager to confidently commit to year on year service level improvement
||Can be tracked using User Personas -
1. Analysis of 3-6 months of data at an individual level, across projects, will help you understand if an individual’s mean time to resolve over a given time period is consistently close to the service level target
2. If the time lag between ticket closures and owning a new ticket is high, then this may also be indicative of a similar pattern of behaviour.
3. A team, in all cases, comprises strong performers and average performers in order to balance the average TAT. If strong performers slow their TAT, the overall TAT comes down thus impacting team performance.
||Delay in completing onboarding tasks
Lower priority given to requests related to new hires.
Less attention given to the quality of products and services provided to new hires.
|This will impact the ability of new hires to be productive quickly, as well as create an unfavourable environment for the assimilation of the new hire into the organization
||Can be tracked using User Personas or Assignment groups
1 Analyze the tickets related to on boarding issues raised by new hires
2 Analysis can help identify which request fulfillment, group or individual is taking the maximum time and preventing a smooth and hassle free onboarding experience
3 Analysis can also help identify if the delay is due to genuine dependency or due to delayed initiation of tasks by the concerned individual
4 Further analysis can reveal the readiness of the fulfillment groups or individuals managing fulfillment groups to ensure timely onboarding of new hires.
||Incorrect Risk Severity Analysis
Service Delivery Manager or person responsible for assigning severity not making adequate consideration assigning the correct severity or probability to impacts and risks.
This will also result in being over prepared or under prepared for a risk or impact
|This will impact the ability of the teams to channel resources towards mitigating the most severe risks
||This can be analyzed at the Service Delivery Manager or Risk Owner level.
1 It can be analyzed if a pattern of occurrence of risks or the assignment of risk impact is more or less in alignment with the risk analysis – High probability risks not occurring and low probability risks consistently occurring is a pattern that can create insight into the ability of the team to predict and handle risks effectively.
2 A team or group within a team, if unable to handle risks effectively, will lead to service delivery issues which can be predicted based on a pattern of behaviour in the past
||Proactive contribution to Service Improvement Problem Management
||This will positively impact the ability of the support teams to continually improve their service quality and achieve service delivery excellence.
||1. Analyze which team member or groups within Service Delivery is / are raising above average number of proactive problem tickets
2. Similarly, analysis can also be performed with respect to individuals providing the maximum number of suggestions for automation at the time of closing a ticket
3. From a team perspective, the presence of such members can be extrapolated to predict improvement in the quality of a service.
5 Building data on Patterns of behaviour
As we can see from the above, gathering data on individual patterns of behaviour will help us obtain much needed insights into an individual’s behaviour at work. The data collected should only relate to few specific issues, like history of user satisfaction response received, number of times insufficient details filled, etc.
6 Extrapolating Patterns of behaviour - How we can predict performances
As we can see from the above examples, the patterns of behaviour of team members does impact overall team performances. In most organizations, we are governed by a Statement of Work and agreed performance levels are specified in those SOW/Contracts. These SLAs and performance levels are generally negotiated and agreed but many times we struggle to meet those performance levels. Onereason being we do not have individual performance levels based on past behaviour patterns of the individuals who become part of delivery teams.
7 Arriving at an effective team composition by using predicted performances
The critical analysis that must be conducted immediately after we agree and sign on performance levels with customers and before forming delivery teams should include:
- What is the planned team composition?
- What are the patterns of behaviour of the individual members of the team?
- Using those patterns of behaviour, can we have some baselined numbers for performance of employees with similar experience / skill set? (E.g. Average time taken to close a ticket by an agent)
- Extrapolating these baselined individual performances, we can know to a greater extent the possibility of meeting those agreed levels of performance.
Even assuming we cannot predict accurately, we can at least know what the pitfalls there are and how to proactively manage them.
While automated workflow helps in the stream lined execution of work and productivity of teams, certain set patterns of behaviour in individuals and teams can prevent service delivery from achieving an optimal level of service delivery excellence.
The Performance Analytics, User Personas, and Reporting capabilities of ITSM tools can be fine-tuned to help analyze the various patterns of behaviour of individual team members and by extrapolating from them, team or individual performance. Service Delivery Managers can then have necessary oversight to ensure that these behaviour patterns do not affect the overall project performance and take proactive steps to address the behavioural issues.
Identifying patterns of behaviour, forecasting performances using ITSM tool capabilities and applying gamification to change those behaviours, will result in game changing trends in Enterprise Service Delivery capabilities and lead to higher levels of Service Delivery Excellence. This may not have been possible a few years ago, but now with analytics and enhanced reporting features at the user level, these are very much possible with very little extra effort. This is one of the areas where AI also could be extensively used in ITSM to provide more insights on patterns of behaviour and how it impacts Service Delivery.
While we look for use cases and application of AI in ITSM, patterns of behaviour is certainly an area for AI application in ITSM.
About the Author
Pudupet Gurunathan Bhoopal
Director, Delivery – CIS – ESM – COGNIZANT
Lead Auditor for ISO 20k and ISO 27K, DSQM, ITILv3 Foundation Certified
Bhoopal is an industry recognized senior ITSM professional with more than 22 years of IT industry experience. Bhoopal has been serving Cognizant since Jan 2017 and he was instrumental in building the DIAL (SIAM) framework. Currently he is leading the Enterprise Service Management Process Consulting team.
Prior to Cognizant, Bhoopal was leading the HP Service Manager / ITSM Practice in HP Professional Services for more than 6 years. Apart from leading HP Service Manager practice, he was also leading the IT4IT /SIAM / Devops / Agile capability build & consulting. Bhoopal has been in various consulting roles in HP since 2004.
Bhoopal can be reached at [email protected].
About the Co Author
Senior Manager, Service Delivery – CIS – ESM – COGNIZANT
MBA (IT& Systems), PMP, CSM, ITIL V3, DevOps Foundation Certified
Sriram is a senior ITSM professional with more than 18 years of IT industry experience. He is a long timer in Cognizant and was also member of the core group that established the ITSM aligned support framework in COGNIZANT. Prior to COGNIZANT, Sriram was part of the delivery excellence teams in Standard Chartered Scope International and ICICI Infotech.
Sriram can be reached at [email protected]