There is an increasing requirement for omnichannel analytics, including email, text chat, IVR and web browsing sessions, to get the full picture of the customer’s real journey in a single interaction, in order to identify and improve any channels that failed to fulfill their requirements. Improving self-service optimization is often a quick win that can provide immediate economic benefit to businesses: in the US, a mean average of 26% of calls that go into an IVR system are ‘zeroed-out’ – rejected by the customer in favor of an operator, which is a huge opportunity missed.
Using customer interaction analytics to review these failed self-service sessions will be able to categorize many of them in order to improve the processes at a macro-level. Common findings from the analysis of these calls is that the IVR system was poorly worded, menu choices were not intuitive, or did not match current service choices. Other failures occur through mistakes in IVR routing, and there may also be problems with a lack of customer awareness that various activities can be carried out by self-service.
Integrating desktop data analytics into speech analytics allows businesses to tag valuable data automatically – such as account ID, product name and order value – from CRM, helpdesk and other servicing applications to recorded interactions. This additional desktop data can be used to enhance automated classification, which allows more targeted and efficient analysis centered on key business issues, such as customer churn, differences in call handling patterns between employees, the frequency of holds/transfers associated with order cancellations and upselling and cross-selling success rates. The use of desktop data analytics also allows the business to view the agent’s desktop activity (for example, are they spending too much time in particular applications, are they navigating the screens efficiently, etc.), and to understand how much time is being spent in each section of the call.
The next step is to get rid of the silos between channels, allowing the customer to be identified at the beginning of their ‘journey’, and for the business to be able to analyze the efficiency and effectiveness at each stage, whether a mobile app, website, self-service application or live call. The end goal is for businesses to understand where customers make their choice, where they drop out, and where the profit is within the multiple processes along the customer journey.
Longer-term, future customer contact is likely to become along polarized lines: for everyday, mundane tasks, the customer will choose the website or mobile app for self-service, leaving the contact center to deal with those interactions which are complex or emotive for the customer (as well as there being demographics for whom the contact center will continue to be primary). With the website becoming the first port-of-call for many customers, the analysis and understanding of the success (or otherwise) of pre-call web activity is a valuable source of knowledge about how effective the main portal to the business is being, as well as being able to give businesses greater insight into why people are calling.
Manually analyzing thousands of web sessions and linking them with specific customers and their phone calls is impossible, so there is a great potential for omnichannel analysis. Adding in relatively minor channels such as social media, web chat, SMS and email will make the mix more complex, and more potentially suitable for analysis. It is also certainly worth mentioning that some solutions also analyze the customer’s pre-call use of self-service via IVR, providing the agent with a background on the caller’s recent experience and offering the chance to improve self-service process failures.
Including social media, email and text chat into the analytics equation is increasingly important, and while many vendors have multichannel/omnichannel analytics within their overall customer contact analytics solution, this functionality is not yet used to anywhere near the same extent as speech analytics. This lack of uptake may have many reasons:
- the social media channel is often the responsibility of the marketing function within a business, whereas customer contact analytics – being focused on speech at the moment – is usually under the remit of the customer contact operation, meaning that harmonious, integrated analysis across channels is that much more difficult
- for most businesses, interaction volumes for email, chat, social media, and other non-voice channels are far lower than for speech, so consequently there has been less urgency in analyzing these
- there may not be a single unified view of the customers’ interactions across channels, as is the case in a siloed operation
- it can be more difficult to identify the customer in non-voice channels such as text chat or casual web browsing, so the depth of insight available may be that much
Having said that, most solution providers seem quite definite that multichannel/omnichannel analytics will grow in importance. While being able to optimize customer contact within each siloed channel, or being able to monitor the quality of an email or chat agent in the same way that businesses are now using analytics to improve the performance of a phone-based agent is useful, the real key is to include all of the stages along the customer journey. For example, understanding where potential customers drop out; the overall effort that the customer has to put in; the point at which buying decisions are made; bottlenecks in processes; the suboptimal points where customers get confused and have to place a call into the business – these are the promises that customer journey analysis makes.
There will come a time when all data generated within a business will be able to be cross-correlated to provide insights not only to the customer contact department but also to parties such as marketing, operations, and finance, so they have greater insight about issues such as price elasticity and revenue maximization. The ability to prove to senior management that the actions and insight held within the contact center have a distinct and measurable impact on the entire company – and as such is not simply a cost center – is likely to improve its visibility and credibility which should help to create a long-term holistic view and assist further investment.
The ‘tell-me-why’ and discovery modes of customer contact analytics will improve over time as better accuracy and more powerful processing provide richer and more joined-up data for analysis, and the inclusion of non-voice channels show the full picture of customer contact and its intent. There will also be major efforts to link analytics to proving profitability, including identifying “moments of truth” (points at which buying decisions are made, and long-term loyalty can be won or lost), and being able to predict and manage customer churn.
About Evolve IP’s Contact Center: Your contact center is the lifeblood of your enterprise, so anything that you can do to improve agent results and customer experience is a major win for your business. Evolve IP’s Gartner recognized omnichannel contact center provides all of the features you need to run a world-class omnichannel contact center.
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