|
|
You know a lot about your contact center. You know how many calls are coming in. You know how your supervisors rate agents. You know your agents’ average handling time. You know that sometimes you have call spikes.
You know the “what” and the “how much.” But do you know why? Do you know why your customers are calling? Why they call the second or third time around? Do you know the reasons behind your call spikes? What drives different levels of customer satisfaction? Why certain products are selling more than others? The effectiveness of your recent marketing campaign? Which competitors are being mentioned most often by your customers, and in what context?
If you could get answers to these questions you would be able to meet your company’s most strategic and operational goals, such as improving customer satisfaction, lowering operating costs and increasing contact-center driven sales.
The good news is that you can get these answers. All the information you need is right there – in your customer calls.
The Multi-dimensional Analytics Approach
By implementing an advanced interaction analytics solution that has a multi-dimensional approach, organizations can locate and analyze the most important interactions that come into their call centers, the ones that contain strategic insights and achieve their most critical strategic and operational goals.
A multi-dimensional approach enables organizations to automatically sift through the thousands, or tens of thousands, of calls that come into the call center on a daily basis and focus only on the relevant ones. The multi-dimensional interaction analytics solution includes a range of speech analysis capabilities, such as content analysis by key words and phrases, emotion level and text mining techniques.
The first step is to perform a mass-analysis of customer interaction content on three levels: speech data (i.e., speech analytics), interaction data (i.e., CTI generated: talk pattern analysis, call duration, queue times, number of holds, number of transfers) and customer data (i.e., CRM generated: demographics, customer’s purchase history, etc.).
Next, the calls will be categorized. They can be categorized by products, technical issues, billing inquiries or analysis type – such as ‘satisfaction.’ Next, trends can be mapped graphically to reveal whether there was an increase or decrease in call type or in satisfaction levels. Finally, a root-cause analysis report will be generated to provide managers with an understanding of what went wrong, or right, and why.
Improving Operational Efficiency
Among a contact center’s top cost-intensive areas for improvement are first call resolution (FCR) rates and average handling times (AHT).
First Call Resolution Case Study: Collections Company
An international collections company wanted to measure and understand the root cause of repeat calls to its contact center. This company recognized that not being able to resolve customer issues successfully the first time around could account for 30 percent of its operational costs. The direct costs of repeat calls (telephony, agent time, overload on queue, etc.,), however, are secondary to the real cost of customer dissatisfaction, which could potentially lead to customer churn. Improving FCR became a major initiative.
This company decided to use a solution with multi-dimensional interaction analytics to help them find the relevant calls and analyze them to better understand FCR-related issues and the actions needed to be taken to correct the situation.
They recorded all the calls that came into their contact center, which is mandatory for detecting customers calling in more than once. The solution then performed a search on all the captured interactions for key phrases such as “this is second time I am calling” and “how many times do I have to call,” enabling them to automatically categorize calls with FCR issues.
The multi-dimensional approach entailed conducting searches not only key words and phrases, but also by emotion level – where heightened emotion indicates customer frustration. Furthermore, since not in all cases a customer will verbally mention the fact they are calling more than once, the solution also searched calls by customer name, ID and tracking number, or by reason for the call as noted by agents through the CRM system, with which the solution is integrated.
With these sophisticated query capabilities, the company was able to sort calls by customer ID and tracking number and generate reports that noted how many interactions did not have a first call resolution, what was the reason behind most of the repeat calls, and what percentage of interactions were not resolved during the first call.
These reports also provided a link to managers to playback the actual call, enabling them to fully and accurately understand the root cause. The company found that the three main reasons behind repeat calls were account statements that were difficult to decipher and customers who claimed to have been misinformed by agents. Accordingly, they were able to adapt back-office processes and provide the requisite coaching to agents to correct the situation and improve their FCR rates.
With the multi-dimensional approach to interaction analytics, this collections company achieved a 3 percent reduction in call volume, a 12 percent decrease in repeat callers and a 0.4 improvement in the contact center’s overall customer satisfaction score.
Average Handling Time Case Study: Leading Provider of Satellite Television Services
A leading provider of satellite television services out of the U.S. was looking to achieve several strategic goals, including: improving the customer experience, reducing churn and improving their contact centers’ operational efficiencies. To help them achieve these goals, this company implemented a multi-dimensional interaction analytics solution.
On one hand, the service provider understood that operational efficiency is one of the keys to profitability. They needed to understand how to be more effective and efficient, while ensuring they were also improving their customer satisfaction levels – a balance that is not always easily attained. They realized that one of the main performance parameters contributing to both enhanced efficiency and a better customer experience is agent average handle time (AHT).
On the customer side – they wanted to make sure customers were getting the right answers as quickly as possible, and avoiding potential frustrations. On the agent side – the more effective the agent is in handling a customer’s needs, the more time they have to serve other customers. Just two to three seconds less on each call can save a contact center millions of dollars on an annual basis (taking into account that this extra time would require hundreds of extra person hours per month, along with the average agent annual salary).
With multi-dimensional interaction analytics, they uncovered the fact that certain agents, whose AHT was aligned with the contact center’s average – sometimes had calls that were way below the average, while others were way above. The information provided by the telephony systems, i.e., minutes (or seconds) per call, provided a partial, misleading picture, with the statistical average no longer being a reliable data point.
The service provider used the multi-dimensional interaction analytics solution, which is integrated with its telephony system, to profile each agent’s AHT and get a fuller, more accurate overview of agent performance. The solution enabled them to automatically search by call category, e.g., technical issues (versus billing issues) and by sub-category, e.g., picture quality, audio quality and on-screen display.
By picking up on key words and phrases and analyzing the contents of calls with long AHTs, the company’s contact center managers were able to better understand why long calls were taking as long as they were and how they could help their agents perform better. One agent, for example, was particularly efficient with all call types except audio quality – which took 120 percent more time than the average for the call center. This agent needed coaching about helping customers solve their audio-quality issues. The interaction analytics solution, which also has incorporated an agent coaching solution, enabled them to quickly tailor coaching packages to agents and achieve greater efficiency.
In parallel, they focused on the customer, taking a look at call volumes for a given two-week period and used interaction analytics to perform a trend analysis by sub-category for each technical issue. They knew they had call spikes on certain days, but were not able to glean through each of the thousands of calls that came into their call centers during the spike period to understand which technical issue was behind these calls. Through the combination of transcription capabilities along with advanced text mining, the interaction analytics solution uncovered that the call spike was due to customers calling in about satellite signal issues. The call center transferred this insight back to the technical department which corrected the issue in record time.
Strategic Initiatives
Another critical benefit of a multi-dimensional interactions analytics is its ability to better understand which customers are at risk of defecting to the competition and be proactive in ensuring that they stay.
Case Study: Major U.S. Financial Services Company – Customer Retention
One of the U.S.’s leading financial services company, with a range of products including mutual funds, retirement planning and brokerage services, among others, had several strategic initiatives, including improving customer retention.
To improve customer retention, they first needed to better understand to which competitors their customers were turning. They had implemented a multi-dimensional interaction analytics solution to help them achieve these strategic goals.
The first step was to automatically categorize calls in which competitors’ names were mentioned, having found that their manual process was highly inaccurate. The manual process entailed agents picking a category for the call from a drop-down list of 35 category options that appeared on their screen, relating to the specific customer – and which was generated by the CRM system. The categories options included: help with Web site, inquiry about product X or Y and customer churn.
The company decided to test their manual process by switching the order by which categories were presented on the drop-down list, putting the first five categories at the bottom and the last five at the top. They discovered that regardless of which categories were listed; agents always picked one from among the first five listed.
Inaccurate categorization led to many questions that couldn’t be answered: why are we having call spikes? Which topics are driving repeat calls? And, of course, to which competitor are my customers defecting, and why?
Once the multi-dimensional analytics solution was used to automatically categorize calls, the accuracy reached was at an unprecedented 90 percent plus.
A week’s worth of customer churn related calls were analyzed, using transcription and text mining to create a report that listed all competitors with more than 10 mentions, which were listed in descending order in terms of number of mentions. Not only were the top competitors identified, but it was also revealed that while competitor X may have gotten the top mentions, competitors Y and Z seemed to be taking the bigger accounts. It was also realized that another competitor consistently had one of their staff on the line to assist in the move.
The call center could then deliver this information to the customer retention team directly from the interaction analytics solution, via e-mail alerts.
The retention team could now study the competitive offering and understand why customers were churning. This information is then delivered to the marketing team which can make the requisite changes to their product offerings and services to better ensure customer loyalty.
Interaction Analytics Best Practices
The optimal interaction analytics solution should include the following capabilities: combination of two or more speech analytics techniques (content search by key words and phrases, transcription, phonetic indexing), emotion detection, CTI analytics (speaker separation and talk pattern analysis), screen content analytics, quality evaluation scores, customer feedback, automated call categorization, threshold definition, and alarms. It should be able to capture or tag any type of interaction (voice, VoIP, chat, e-mail, co-browsing), by agent screen activity or by type of information that appears on the screen (such as customer name, segmentation, monthly bill, etc.). It should also be fully interoperable with home-grown or third-party applications, such as CRM, ERP, e-Learning, e-mail/chat, help-desk applications, and more.
Furthermore, when it combines with quality management, workforce and performance management, the contact center can achieve optimal business performance. Interaction analytics enables organizations with precision-quality monitoring, for tapping into customer interactions with strategic insights – insights that a random sampling is most likely to miss. It also enables contact centers to better link its customer interactions with its workforce planning and management processes. Workforce management combined with an interaction analytics-driven agent coaching solution can efficiently manage and optimize scheduling of coaching sessions to ensure the success of knowledge transfer.
Finally, when combined with interaction analytics, a performance management solution can generate more accurate reports that are directly linked to customer calls. These reports can be automatically delivered to key business functions in the organization to determine which actions need to be taken to improve performance (i.e., new product features, a revised marketing campaign, better scripts for agents, etc.); and enable KPI-based (key performance indicators) management tools that help set business performance goals and objectives for employees.
Organizations today are looking for quantifiable, accurate and immediate insights into critical issues such as operational efficiency, customer loyalty and retention, and marketing/sales effectiveness. With a multi-dimensional interaction analytics solution in place, the contact center today, as a hub of customer interactions, can enable organizations to leverage these interactions to meet their most critical operational and strategic goals. |
