Speech analytics refers to the process of using speech recognition software to obtain information on a service rendered in order to better understand how it can be improved or simply perform a routine quality check or employee training. At present, the use of this technology is most popular with call centers for monitoring and instructional purposes with previously-recorded, or even live voice calls.
Having been around for more than 15 years now, speech analytics is considered a mature technology. More recent progress in its application includes the ability to identify sales opportunities, check compliance with the latest regulation as well as detect customer sentiment. While there is plenty of potential linked to its use, the sector is still facing some challenges with regards to its uptake.
Here are five of the most important constraints contributing to the predicted gradual growth patterns in the coming years:
1) Speech analytics hasn’t gone mainstream yet
As it stands, the technology is not yet considered a highly sought after application for businesses not related to the call center sector. Hence, despite it being around for some time, it is only just moving forward from the pilot and early-adopters stages. This is a relatively long lag with regards to its uptake with a slower predicted annual growth of 10% for the next three years as compared with 12% in previous years.
2) Analytics-enabled quality assurance (AQA) is still underused
Analytics-enabled quality assurance takes employee-managed QA to a level efficiency not otherwise possible through manpower alone. Making sure internal protocol is being adhered to is paramount to ensuring good service standards are upheld. Speech analytics software flags and ranks calls or employees that require management attention automated. AQA simultaneously provides valuable insight into detected customer needs and opportunities, including the analysis of emotional cues in both text and voice interactions. Despite the promise this powerful tool holds, speech analytics is still underutilized due to a slow-growing following.
3) Real-time speech analytics has a limited number of use cases
As a technology that has been on the market for over a decade, there are constant advancements in the field of speech analytics. A more recent example of this is the real-time analytics feature that notifies employees of upselling and cross-selling opportunities, detects any fraudulent activity or makes customized suggestions on how to best deal with customer complaints. Even with this level of development, there are only a limited number of use cases so far. Hence, the collective experience from using these advanced analytics is still lacking for it to develop industry standards that can really gauge the interest from other industries and improve growth based on a solid reputation of demonstrated value.
4) Speech is on the rise, despite contrary predictions
With the evolution of technology, the use of voice calls to contact businesses was repeatedly forecast to reduce given the rise of other text-centered communication channels. However, the growing importance of speech and its role in voice-controlled devices has been consistently overlooked. The lagging significance destined to this thriving market trend highlights another constraint to the uptake of speech analytics technology in the realm of workforce optimization.
5) Integrating speech analytic tools with predictive analytics is expensive
Currently, speech analytics is most commonly being applied in the more basic form of simple speech and phrase recognition software. While this is a more efficient and cost-effective tool than the human-powered alternative, there is more potential and value for a business to integrate with predictive analytics. However, the more complex nature of incorporating this type of intelligence, which also requires an analyst to interpret the data, is costly.
Overall, while there is great potential in the speech analytics industry, integrating the existing system with the right approach is critical to growth in this market. The present challenges the promising sector faces are primarily linked to its young track-record in practice, an underestimation of the rising importance of speech data and the higher costs associated with predictive analytics.
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