Post sales service delivered 45% of companies’ gross profits on only 24% of their total revenues (Source: HBR) by the end of the first decade of this century, and since then subscription revenues have consistently grown nearly five times faster than S&P 500 Industry benchmarks over the last decade. The distinction between product and post-sales service sales is slowly blurring. To maximize revenue and profit, companies must focus on the customer experience, on how they can use and get value, and the experience they get from the customer support and success teams.
Reacting to service requests is a huge expense for organizations. Service centers spend significant time just categorizing the reported symptom into a problem, and schedule more than 70% of the support engineers reactively because of a reported problem. Because of the stringent SLA, organizations are forced to find sub-optimal solutions for immediate recovery and are forced to come back again at least 30% of the time to fix the same problem.
If the company has hard assets, the service organization stocks aftermarket parts inventory at multiple echelons just to support the response commitments to their end customers, but still, ends up facing stockouts and costly, last-minute shipments more than 10% of the time. These challenges have gone up even more with the recent supply chain constraints. Furthermore, if field service is involved, technician dispatch is based on a hierarchical model and unfortunately results in either deploying expensive resources for simpler problems or implementing sub-optimal fixes to the equipment.
How Ascendo Helps?
Ascendo provides a predictive Artificial Intelligence-based customer support solution to elevate the experience of customers and agents in every interaction. Ascendo applies advanced modeling to incoming customer questions and detects the context and intent behind the questions. Through that, it uses semantic inference to find the symptom, root cause, problem, and solution from across multiple solution articles. Ascendo also predicts customer sentiment (with sentimental analysis) and predicts customer-specific and broader problem trends to avoid future problems.
Ascendo connects with the company’s service CRM, installed base, and internal knowledge base to provide prescriptive recommendations within the existing service workflows. For companies that make hard assets, Ascendo has additional models to help with the logistics and field service teams. By looking at the problem and failure trend, Ascendo also helps provide predictive part demand recommendations for the logistics team. For companies that include hard assets in the field, Ascendo provides the game plan in terms of fixed procedures and part recommendations before a technician visits the customer site.
Support teams want quick mechanisms to have their customers get self-help first. With improved technology bandwidth, a good number of customers first perform either a search or ask a chatbot to get the answers. Ascendo through its contextualization and correlation engines, combined with its self-learning capabilities, immediately identifies the context and the intent behind the search queries and chatbot questions and finds solutions from the in-depth knowledge it has garnered from within the support organization. Because of this, the “self-help” customers inherently feel better about appropriate or relevant solutions being suggested. The solutions become more meaningful.
Support teams want to seamlessly get agents to help their customers when self-service may not be preferred or sufficient. Ascendo has the knack to identify if end customers require actual live help with the Agents. Ascendo can automatically offer and transition the customer to get live help from one of the Agents.
Support teams want to make sure Agents are getting the needed help to solve the issues. Ascendo helps the agents so they exactly know if the previously tried solutions did (or did not) work. Ascendo combines the “context” behind the customer questions and the tried solutions to further recommend better solutions to the Agent. In Agent Assist mode, Ascendo looks into prior cases of similar nature and additional knowledge sources that are more reserved for internal audiences within the company and provides recommendations to the Agents.
Before the “remote” workforce model, many agents were collocated and had the luxury of “swinging” their chairs and talking to their colleagues to get help. There is an inherent challenge in that Agent is “supposed to know who to go to for help”. With an increased remote workforce that has become a bit harder. Also, knowing the expert has become a unique trait of Agents rather than corporate knowledge.
Even in a colocation scenario, Agents tend to ask their neighbor “hey, do you know the answer for this?” and not necessarily the true expert that might know the answer in the back of their hand. This is where Ascendo comes into help. Ascendo has a way to automatically identify the “experts” based on how prior similar issues are solved, how many, and how effective they were. Ascendo uses its unique modeling technique to derive the expert by first understanding the context in which the question is being asked, knowing the intent, quickly mapping the issues that were similar to being asked, and detecting the experts based on how well they solved the issues. Ascendo knows the difference between quantity and quality. The modeling technique uses customer satisfaction on the prior solutions to improve the ability to determine the expert.
If you ask Support Leaders what keeps them up at night, it would be delivering a fantastic support experience with the best customer services at an optimized cost structure. Depending on the type of company, onboarding a support engineer can take anywhere from 6 weeks to as much as 12 months. This does not even include initial shadow/review time spent on the new support engineers. Support Leaders not only about the cost of onboarding but also the opportunity cost of the time. Ascendo is proven to help in reducing the onboarding time due to its inherent nature of being an “experts in back pocket” model. Engineers can easily learn the solutions by querying within Ascendo and that reduces the time and the areas they need to look to provide solutions to customers.
Another issue that Support Leaders grapple with is customer escalations. Once a customer escalates, they do all they can including involving the best resources, being available for the customers, potentially offering new concessions, and other benefits to keep the customers happy. They are being forced to become reactive and counterintuitive to them wanting to be in charge and lead. They can rely on Ascendo on two fronts. First: Ascendo predicts potential escalations before they occur and second: Ascendo provides proactive support by predicting the sentiment of the customer using hidden signals and communications from the customer users. Ascendo does this in real-time and alerts the support agents so they can pay even more close attention to the customer's problems. This type of model allows the customer leaders to switch their engagement with their customers from being reactive to proactive and they can regain their advantageous position of being in the drive to realize the outcome – delightful customers.
Support leaders are seeking to understand hidden trending issues before they become serious. Given the nature of the Ascendo modeling techniques, it can group or cluster the incoming issues based on the context, intent, and symptoms. No manual effort is needed from the support team to involve multiple team members to comb through the tickets to identify the top issues.
Ascendo knows the top issues could change over time and constantly looks at them and provides the support leaders early guidance on the top issues. Knowing top issues also allows the Support Leaders to develop appropriate solutions – do they need to make fundamental changes in the product? do they need to train their engineers more? or do they need to enrich their knowledge? The early alerts on top issues also allow the Support Leaders to focus their resources on important things. They can assign fewer resources to focus on issues that matter most to their customers.
Support Leaders can also empower their support delivery teams to increase self-service and auto-resolution of many routines and/or high-volume customer questions. Instead of support agents needing to look at every issue, they can review Ascendo recommendations and use that to address their backlogs more effectively including auto-answering with possible solutions. The advantage is twofold: their customers get solutions quickly and their agents' productivity increases.
Field engineers/Field Techs come into play when the company makes hardware/electromechanical type of products or makes complex products that require a field person or a professional engineer to work with them. Companies are always looking to reduce both the time and the number of trips the engineers spend to solve issues in the field. Customers also want a quick turnaround – they want the tech to come and complete the work quickly instead of needing to come multiple times.
Ascendo can provide a game plan using its arsenal of solutions - it provides the optimum predictions on the root cause, the area of the fault, and recommendations on parts if such is needed. Field engineers can use this information and are much better prepared before they even visit the customer. They have a fair knowledge of what the problem might be and also can carry the parts. The diagnostics become simpler and no more need to come back again for a part. The entire resolution could be done in one visit and in less time. Such a saving in labor and travel is significant. Their customers are also much happier as they get their systems back up and running optimally in less time. This win/win is possible because of Ascendo’s exhaustive self-learning capabilities.
Support Operation teams are a glue to connect people, processes, and technology to the needs of the various support delivery teams. They are incentivized to think about the future and create practical paths to connect the dots. Given the complex nature of the global service teams, Support Operations is looking to use AI/ML-based solutions that work with their existing deployed systems and artifacts and elevate them to the next level.
Support Operations are leveraging solutions like Ascendo because Ascendo automatically learns them from their existing data. Ascendo has the needed connectors to work with their CRM systems and other knowledge/help articles and further enriches them in a meaningful way. The Setup process within Ascendo being straightforward helps with the quick implementation.
The Support Operations team is also looking to deploy value-based solutions, and they are wanting to move away from user-based models to value-driven models. Given the way, Ascendo can be easily consumed and deployed and the pricing model based on value realization, it makes it easier for the Support Operations team to try out and deploy Ascendo solutions.
Support teams that need to have spare parts face the unique challenge of needing to stock spare parts in one or more locations (depots) to satisfy support contract commitments they have with their customers. Depending on the mission-critical nature of their products, they may be offering various service levels such as 4-hour replacement, 8-hour replacement, or Next Business Day (NBD). Given their customers are geographically dispersed, support teams may have to stock spare parts in more than one distribution center and depots.
Global companies may also take into consideration specific country custom rules when deciding the location and quantity of their spare parts. Ascendo can use various information such as the field failure characteristics, customer install base growth trend, specific consumption pattern at the depot-part level, and type of support contracts, and recommend the spare parts stocking requirements.
Using Ascendo, the logistics team can optimize their spare plan in a much more effective way. Ascendo works in conjunction with the support teams’ ERP system and the planners can use Ascendo's recommendation to determine their reorder point (ROP) at the spare part-location level. Ascendo uses the prediction logic and provides customer SLA risk and the logistics team leaders can use that to take proactive actions. Furthermore, Ascendo can detect the change in install base movement and the change in the company's third-party logistics team locations and provide proactive alerts for the logistics team to better plan their spare parts move as well as procurement.
Ascendo is razor-sharp and focused on the customer support experience. Using the fundamental semantic inference models combined with self-learning models, Ascendo addresses every step in the customer support lifecycle journey.
Ascendo is providing multiple returns on investment for the customers and a few customers are now beginning to embed Ascendo in their new revenue models. The pact areas can be summarized into the following:
Improve customer experience and Churn (Faster fixes, Less downtime, Higher Satisfaction)
Service efficiency (Increase workforce productivity, Increase in incidents handled by a support member)
Cost reduction (Fewer resources/Reduce multiple visits/Fewer dispatches/Less inventory)
Improve Product Feedback
Ascendo fundamentally turns companies to become proactive and predictive in the way they offer support services to their customers.