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Every organization has a well laid support system to address customer issues promptly in a satisfying manner within the limits of well-defined SLA. The customer support software that manages the entire support infrastructure would have the basic capability to raise tickets, re-submit tickets, provide feedback, assign engineer and close tickets etc. In addition, the customer support software would also have analytics support to provide below highlighted insights that can help measure the efficacy of the support systems
- How many support cases were raised?
- How many support cases were resolved by each support engineer?
- How many support cases were resolved within the guidelines of SLA?
- What is the lean period and peak period for receiving cases?
The discussion of the current topic is to go beyond those traditional insights. I am not trying to open a debate on the nature of the support system and I am only trying to emphasize that the data gathered by support system can be effectively gleaned for more information. The information when analyzed properly can provide lot more insights which when acted upon can effectively strengthen the product.
Now let me try to comprehend what kind of insights could be obtained from support cases
- Product/ Non-product enhancements (Usability/ Features/ Documentation etc)
YouTube recently changed the VIEWS variable to 64 bit to accommodate more than 2 billion views as ‘Gangnam Style‘ video by PSY was viewed more than 2 billion times (source: https://plus.google.com/u/0/wm/4/+youtube/posts/BUXfdWqu86Q). Every product is initially created with certain scale parameters assuming it would suffice, however as time progresses and customer business grows, product might soon start hitting the limitations on certain critical scale parameters. Customer would raise a panic button immediately after hitting the limitation but support team can pro-actively raise an alarm through monitoring the critical parameters of the product. Support team will use support cases or other methodologies available to monitor and track the critical parameters of the product. When the critical scale parameters reach a threshold level, support team should immediately alert Product Managers to increase the value of the affected scale parameters.
Support team are also equipped to analyze the support cases and understand the trends to figure out the most common issues faced by the customers, such analysis can help Product Manager understand the list of needs that is not optimally solved by the product. Any improvements can lead to better customer satisfaction thereby higher retention rate leading to more up-sell or cross-sell opportunities. Increasing trend of support cases on a specific feature could also throw lot more possibilities to ponder upon
- The feature might be buggy – Wakeup call for engineering team to immediately address those issues, while Product Manager can plan for interim release to avoid further customer dissatisfaction
- The feature is not intuitive – The feature might be working properly but customers are increasingly finding it difficult to operate. Either the feature is not intuitive (usability constraints) or documentation is not clear. From the perspective of HW product, documentation often plays a key role
- The feature is incomplete – The customer needs are not fully met, wakeup call for Product Manager as the customer needs are not properly analyzed. Product Manager needs to take quick remedial action to bridge the gap between customer needs and product capabilities ASAP.
There are classic examples of customers using the product quite distinct from its intended use. Every product has few innovative customers who are always step ahead of the product team in implementing either new use cases independently through innovative changes in configuration or new solutions through successfully aligning our product with other products. Those innovative customers whom I would comfortably refer to as Innovators or Visionaries as explained by Geoffrey Moore in his book “Crossing the Chasm” do dare to exploit the complete functionalities of the product to resolve the challenges faced by them. Such customers constantly pose technical challenges and help Product Managers build better products which eventually puts us ahead of the competition. Personally it is good to have such customers and my opinion is that they are worth more than a million dollar customer.
Support engineers when consciously look out for such unique use-cases or solutions through the aid of support cases can help Product Manager identify innovative customers and capture their innovations. Product Manager can later use the data to enhance the product that can supplement those innovations or draw plans for new product offering for new ways of positioning the product (aka demand generation)
[PS: Rest of the insights is based on premise that ‘No Product is DEFECT FREE’]
Customers ask about non-existing features through support probably because of lack of understanding of the entire functionality of the product. Product Manager could use those inputs to understand new product requirements; this set of requirements will predominantly be incremental extensions of existing product capabilities. For instance, customers ask about features whose existence is generally taken for granted. In case of VoIP product, it can be as simple availability of history of call details that Product Manager would have missed to include in the product.
- Most used/ least used features
Such list can help Product Managers better prioritize features. Most used features can provide a clear view of what interests most to customer and Product Manager can target to evolve those features to create a stickiness factors. In case of least used features, Product Manager can understand the reason behind prioritizing those features and later revisit the feature prioritization strategy. Also in case of a conscious attempt to eliminate unnecessary features to make a lean product, least used features list will be handy.
Some customers do not express displeasure about the product openly and their way of showing the displeasure is to switch to a competitor product. In case of product with no recurring revenue, support cases are best alternate mechanism to figure out the active customers. How Product Managers can use the data? They can do some analysis on why the product has lost customers. Later those insights could be used to build competitive analysis and also to improve the product.
The above insights need not necessarily be exhaustive, I am only trying to emphasize that support cases when gleaned properly can provide more information and thereby I strongly assert that more additional insights could be obtained to further strengthen the product. Guess I am throwing a product idea of incorporating BIG DATA into support software to throw more meaningful insight that might rather surprise Product Managers.