The terms “self-service analytics” (SSA) and “machine learning” (ML) are frequently used interchangeably, but the concepts behind these terms are a world apart. In self-service analytics, specific tools are designed to aid the user in inputting data or interpreting results (output). On the other hand, a machine learning algorithm is a special software that has the capability to learn from data. An ML algorithm contains rules, which can be applied to a data set.
Can Organizations Succeed with Self-Service Analytics?
The primary objectives of an SSA platform are to offer rich insights by associating the most appropriate tools for the available data and collect accurate information from the right personnel. As an example of this, when customer-service staff takes a customer call, the information is saved in different locations such as phone logs, call-center log, or social-media channel. In self-service analytics, these data channels can be combined together.
The emergence of SSA platforms is a major turning point in data analytics — promoting citizen data scientists without technical knowhow to the levels of data professionals. SSA systems promote open analytics — where the average business user has access to tools for advanced data analytics.
Now SSA platforms have empowered all business employees, regardless of their job function or designation, to make good decisions on the fly. Improved decision-making helps the business grow and enhances the return on investment (ROI) from technology investments.
Because SSA engines can easily create reports or build custom dashboards, the customer-service staff are free to pursue other important tasks like providing in-person response to customer complaints or troubleshooting problems.
In the future, most employees will work with self-service analytics engines, thus leaving routine automated tasks to machines and concentrating more on important work. Additionally, when an SSA platform is powered with ML or automated machine learning (AML) tools, the net effect can be formidable.
SSA and ML can intersect in very meaningful ways to transform the way business analytics is done in many businesses across the globe. A DATAVERSITY® article titled Investing in Analytics for the Decade Ahead explains how ML technologies help self-service analytics stay compliant.
SSA empowers the business staff to:
- Prepare and use data for quick insights or competitive intelligence
- Work across teams and departments to create superior products
- Access data and analytics tools without technical knowledge, and gain access to better data insights or key metrics
- Get quick answers to support daily decisions
- Deploy data pipelines in production
The 21 Best Self-Service Analytics Tools and Software for 2022 offers additional information.
Powering SSA Platforms with ML Tools
In the SSA world, users input data, and machines or tools perform the data analysis. ML algorithms also analyze data and produce insights. The only difference is that in the first case, human users are the source of data, whereas in the second case, data can come from a wide variety of sources, mostly machine data.
SSA platforms usually include self-service reporting systems. This can be especially helpful in a system that collects customer reviews to improve products or services. In another scenario, a company may be trying to analyze the source of their website visitors to better optimize the site. In such a case, trained ML algorithms may be used to predict user behavior.
Machine Learning Improves the SSA Experience
The top benefits of using machine learning tools in a standard SSA platform are:
- Helping uncover inaccurate and inconsistent data
- Helping uncover hidden patterns and new information in datasets
- Immediately detecting when there is some change in data
If the supervisor in a manufacturing plant is using an SSA platform powered with ML tools, they can easily detect when problems surface on the production line or when equipment breaks. The detection and diagnosis is almost instantaneous. A self-service analytics system without ML tools would require thousands of hours to manually go through every line of code to detect changes or problems.
Automated machine learning offers even faster and cheaper anomaly-detection processes, which saves the manufacturing plants money and hours of human labor. Many manufacturing units have deployed SSA and AML together to get the maximum cost and labor benefits.
SSA and ML Together Can Boost Contact Centers
According to a McKinsey article, most businesses are now heavily investing in technologies to gain deeper understanding of their customers and reap the benefits of high-quality customer experience (CX).
Businesses are realizing that customer surveys and questionnaires form the bulk of their CX strategy, but these outdated methods no longer fulfill the objectives of customer experience in a digital business world.
McKinsey conducted an online survey, in collaboration with Alpha Sights and Gerson Lehrman Group, to gauge the responses of 260 CX leaders across industry verticals. Although 93% of respondents reported using Customer Satisfaction Score or Customer Effort Score for measuring CX performance, only a dismal 15% of respondents stated that they were satisfied with their current CX evaluation method, and a tiny 6% “expressed confidence” in such a measurement system. They pointed out data lags and low response rates, among other drawbacks in their CX systems.
The good news is that now businesses of all shapes and sizes can lawfully collect data through technology-enabled data channels (smart phone, social media, websites) deployed across their business units. Companies are heavily investing in data and analytics platforms, and to connect and interact with their customers, they study customer behaviors and preferences to predict future customer trends.
These businesses also have a unique opportunity to integrate data from a wide variety of sources across the customer journey, which includes chats, emails, social, apps, and IoT devices. Best of all, these advanced data and analytics platforms enjoy full compliance to data privacy and security regulations.
The future of CX lies in superior, data-powered, and predictive and prescriptive systems, which not only predict future buying trends but can also offer timely recommendations to customers as a value-added service.
Though most Contact Centers today have the basic data and analytics infrastructure, they haven’t yet taken full advantage of advanced data technologies in ways that truly put the customer first. Today’s actionable insights not only predict what is about to happen, but can also recommend corrective steps to mitigate business risks.
The net gain? Reduced operating costs, increased profits, and higher customer-satisfaction scores.
A common call center use case is improving FCR. Lots and lots of data must be collected, through agent notes, voice-of-the-customer, routing data, and automatic call distributor data — though this data may not be perfect.
Organizations have to proactively collect and manage this data and, over time, move toward 100% accuracy. With the ultimate goal of using technologies to improve FCR performance, any organization can conduct and scale up pilot tests throughout their call centers for maximum benefit.
SSA and ML Intersect in More Use Cases
Some examples of ML-powered, self service analytics scenarios to streamline workflows:
- Analyzing Workloads: An SSA tool can help the user track time spent on each task in a workflow, while ML algorithms can help the user discover trends in the workload, based on past task-performance metrics.
- Workflow Visualization: An SSA tool may help build visual dashboards of performance-metrics in the workflow; while ML algorithms may help provide insights about process performance under varying conditions.
- Embedded Analytics: Think of a situation where the typically overworked business user does not have to leave a running application to get quick insights or competitive intelligence about some product, or some emergent technology. Embedded analytics promises continuous flow of insights and intelligence to automate workflows, improve productivity, and trigger critical actions. Highly advanced embedded analytics systems combine AI, ML, and predictive analytics to boost business functions.
- Self-Service BI: Advanced AI and ML tools embedded in augmented self-service platforms make gaining insights easy for business users. A 45-minute webinar shows how natural language query (NLQ) is used to help users explore data instantly and discover insights with embedded ML. Augmented Analytics Use Cases explains how advanced analytics platforms perform with AI, Ml, and NLP technologies.
Conclusion
Self-service analytics and machine learning work together to solve business problems by providing insight into what people do in real time without requiring them to think about abstract models and analysis. Finally, according to Moving towards Self-Service Analytics,
SSA and ML together are contributing to data democratization and data culture.
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