Taking a multi-technology text analytics approach to processing customer feedback can optimize program cost and benefit over time and improve ROI compared to single-technology platforms.
With growing numbers of players investing in rules-based text analytics and combining them with survey technologies to create “platform solutions” it is important to understand the potential downfall of locking in to a singular text analytics technology, requiring the company to conform to it, rather than applying the right point solution at the right time customized to the company’s situation and data.
Software for text analytics has evolved, mostly based on an underlying set of techniques termed natural language processing (NLP). The predominance of this method in commercial software risks overshadowing two complementary text processing methods, machine learning and semi-automated analytics, which are equally relevant and in some instances are more appropriate in handling very large volumes of feedback data.
Set against the backdrop of different – and often disjointed – customer insight initiatives and customer feed-back channels, companies can now start to build highly effective technological solutions to integrate feedback, provided they fully evaluate their internal needs and do not settle for suboptimal or limiting technological solutions. With an integrated point-solution approach, the results can be very valuable and a full payback should be expected within a single fiscal cycle, not to mention the long-term upside.
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