As software capabilities evolve from data collection through data analysis to the latest developments in Artificial Intelligence (AI), it’s generating exciting possibilities for the Contact Center reporting tools as well.
Not only for processing, interpreting and extrapolating the rich seams of customer data generated on contacts (calls, chats, email) but also as an effective means of optimizing agent performance, training, and recruitment.
But before acquiring the latest technological solutions as they emerge, the contact center leader must first derive and implement a clear strategy, which begins by identifying and prioritizing the specific problems to be solved on the company’s customer service performance goals, and how the new methods will complement existing resources.
Inherent to the strategy must be the ability to measure its effectiveness.
If the company still measures the success of its call center in terms of time and cost, it will require substantial change. Change will mean evolving from good old efficiency outcomes like Average Speed to Answer ASA, Average Hold Time AHT, the number of abandoned calls, and Service Level to customer perception, attitude, and behavior.
Big Data – and what to do with it
Each call handled by the contact center contributes to the valuable feedback that allows a company to gauge customer response, identify areas which must be improved, and adjust accordingly.
Advanced contact center reporting tools (often referred to as Analytics) are descriptive in nature and provide companies with the ability to collect and categorize these calls and retrieve customer data and history as and when required.
An individual call may not reveal much in isolation, but harvesting the collective power of these massive data sets identifies patterns and trends that can be translated into actionable information, and can quickly flag problem areas as they occur.
Advances in computer science, such as machine learning and Artificial Intelligence, go further than just identifying trends and employ algorithms and complex models to assemble computer-generated predictions based on the historical lessons learned from the information collected.
Collectively known as Predictive Analytics, they can be used to interpret and extrapolate data to identify weaknesses and anticipate trends and problems.
The conclusions can be further refined through simulation or optimization to give deeper insights than traditional reporting or to improve specific decisions.
By using a model to predict the outcome of a decision and compare its anticipated outcome against those of the alternatives, prescriptive analytics can go further by recommending a course of action.
Ability to accurately recommend “next best action” to the contact center agents is the holy grail of contact center efficiency.
The importance of having a strategy
Contact Center stakeholders face the challenge of selecting a software with the best return on in investment. But many are not sure how to get started with contact center analytics and feel they must make a signiﬁcant investment in new tools and skills.
The desirable outcome will determine the mechanism for a proof of concept but addressing a business problem that can provide quick and quantifiable win is highly recommended.
Analytics should be driven by the business problem you want to solve
Whether it is improving customer experience, increasing operational efﬁciency, managing risk, and compliance, or ﬁnding new business opportunities, it is important to identify and prioritize the problems and areas of improvement you wish to address before deciding upon a clear course of action.
The strategy must also take into consideration the resources already available and how they can best be leveraged and optimized by the addition of advanced software capabilities.
Measuring and Improving Performance
To be able to ascertain whether the strategy is successful, objectives such as ‘improved Customer Satisfaction’ will have to be clearly defined and measured.
Customer service is the responsibility of an entire organization, and it’s important to get all the stakeholders on board by convincing them of the need for and the benefits of changing course.
It will be an iterative process of trial, measurement, adjustment, then a trial, analysis, adjustment again, ad infinitum.
Build or Buy?
After you’ve solved a high-impact business challenge and gained buy-in from decision makers you will need to determine if you should purchase or develop a solution. Many of the large enterprises with experienced Data Scientists tend to develop their package. These firms primarily take on this complex task because analytics is a critical differentiator for them.
While others purchase Contact Center analytic packages that can be relatively quickly deployed and consumed by knowledgeable users. Often these packages are offered by the same vendor that powers Contact Center’s queuing platform.
Open source tools such as the R programming language, Python, and Spark are also available. However, domain-specific languages like R, in general, do not suit Contact Center’s low-latency production environments.