The role of customer support is constantly evolving and today is on the cusp of a remarkable transformation. Formerly, customer support centers embraced the Cloud as a way of reducing costs and increasing the profit margin of the goods that were purchased in advance. With the advent of SaaS products, customer support was required to provide 24/7 service through a multitude of channels (such as mobile, IM, and WhatsApp) leading to the rise of Conversational Support as part of the customer service process. This created an always-on presence where inquiries are responded to immediately.
The predominant SaaS business model is an annual/monthly subscription where the profits are no longer captured upfront but during the long tail of the relationship. There’s real pressure for SaaS businesses to differentiate their offerings with “real-time support” in which a customer’s issue is resolved the moment it is raised, or even more ideally, is predicted and prevented from happening at all.
SaaS products generate a tremendous amount of data that can be utilized for real-time support — such as user data, product usage data, and operational data. When this data is integrated with more traditional customer data from service tickets and CRM, companies can draw a complete picture of the situation, predict the most effective resolution, and prioritize bug fixes and new features.
The key to moving to real-time support operations is an automated data platform that can:
- Understand and access product and usage data.
- Contextualize that data with customer information.
- Tailor that data for each customer touch point within specific applications.
The Benefits of Data Automation in Support Operations
A dedicated data platform built with the specific application integration requirements and tailored analytics unique to support processes is critical for delivering real-time support. By enabling end-to-end real-time support processes, companies will bring into alignment every customer-facing team with a clear singular view of the customers’ needs.
A support operations data platform is not a data warehouse that creates a new repository for all the product, usage, and customer data. Rather, views of the data are created so that the data can be integrated and cleaned in real-time and then flow to where it’s needed. This leads to tremendous business benefits, which include:
- Insight into operations: With this platform, companies can analyze all of their operations relative to the product feature data and customer tickets. Using this analysis, businesses can then make decisions about what to do in the future, how to respond to customers, and how to improve the product based on that data.
- A comprehensive view of the customer experience: The platform offers a business-wide view that is often missed when companies only focus on the data of individual users. Every interaction a customer has with a product or support staff is tracked to create a full picture of that customer’s experience.
With product-led companies, customer lifetime value is a vital metric.
Support becomes integral to the lifetime satisfaction and brand affiliation of a customer. Support simply cannot be an afterthought any longer — it must be designed and constructed in advance because in effect, support is a key part of the product.
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A support operations data platform enables customer support to transform from a reactive process that is squeezed to reduce costs to a proactive growth-oriented posture in which support adds value to the customer. Support becomes a differentiator. Gone are the days when support agents would toggle through six-to-eight different screens on any given customer engagement. With automated support operations, problems can be addressed by support reps in real-time with the full context and understanding of the customer.
Accessing Deep Data and Increasing Predictability for Support Operations
Data can be an asset to a company when it’s accessible and easily understood. If data isn’t easy to find and can’t be used in a timely fashion, or can’t be trusted when it is found, it isn’t adding value to analyses and decision-making processes. An organization that digitizes without breaking down data silos, or making sense of machine data, won’t access the full benefits of digital transformation.
To do this successfully requires both broad data from a range of applications, as well as deep machine data. Deep data is large-scale data collection that is also high quality, relevant, and actionable. Deep data in this context is the user interactions within your product.
All of this data resides in difficult-to-reach locations as it comes from many different applications, the user interactions within the product itself, and also the DevOps tooling running beneath a digital product.
As support teams break down their data silos and begin to stitch this data together, they will realize a “network effect” of the data. More data sources, especially machine data, tend to dramatically increase the accuracy of predictive analytics. Also, as more data sources are brought together, the view of the customer becomes more comprehensive and useful. The result is that the ability to affect the quality of customer service increases in lockstep with the number of data sources.
Assisting Agents with Data Makes Them More Effective
Companies now have access to the data they need within the tools and technologies that they have invested in; unfortunately, it is often spread across support tiers, engineering, and DevOps, among other locations. The key is to align these departments and constantly learn from collaborations so that the customer support experience is transformed as reps gain access to broad sets of data and recommendations embedded in intelligent workflows. All of these disparate departments need to buy-in to the objectives of the company.
A support operations data platform offers this type of leverage, providing reps with much more data than they’ve ever had before to address customer issues. No longer do organizations need to hire data scientists or data analysts. When all the customer information is in one place made sensical with automated analysis, support reps can spend more time empathizing with the customer as opposed to logging into a variety of systems to solve problems. The rep can be more specific about the issue and explain what has gone wrong and provide more in-depth analysis, as well as resolution in the moment. Customer product data creates contextual awareness of what the customer is experiencing.
This improved understanding of customers and their challenges then allows companies to better know how to meet their needs and find potential avenues for upselling of additional products or services in the future. Ultimately, an automated support data platform upskills reps to be able to:
- Immediately identify customer issues by correlating data from many sources.
- Solve tickets quickly with contextual recommendations.
- Reduce escalations and average handle time.
- Empathize with the customer, helping them to achieve outcomes.
- Continuously improve over time as the platform learns from each resolution to better future interactions.
The Future of Support Is Predictive
Integrated data analysis within an automated support data platform allows companies to get to the root issues customers are experiencing. Companies can use the platform to understand what is happening with a product, and see the relationship between product features and support tickets. This data can be used to improve the product and fix problems.
Reps can use the platform’s built-in AI to identify where the user is facing a problem in the product and predict where additional problems might arise in the future — either for the customer specifically, or for the product more generally. That is the epitome of preventative support. Preventative support is not about resolving problems, but rather about being proactive and reaching out to the customer before the customer even recognizes there is a problem. This type of preventative support also leads to lower costs over time.
Automated support data platforms that incorporate historical data allows companies to see problem trends over time and use predictive algorithms to engage customers and improve product development.
Final Words on Data Automation
Ultimately, automated support operations transform the traditional patterns of customer support. Instead of support being a black hole of costs, it becomes an integrated view of customer experience and product performance. Support reps can solve customer issues in real-time. Product engineering and development teams can see what needs to be fixed or opportunities for future products. And management can access real-time, predictive insights and take action on them. Support now becomes a driver of revenue for the business, while also ensuring a company has a competitive advantage of greater insight and understanding of its customers, its products, and how those products are being used.
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