Wonderflow – which has recently raised $20 million in funding – offers a similar service, which it describes as ‘voice of the customer analysis’. Using artificial intelligence, Wonderflow analyses customer sentiment, and uses its data lake to determine which product features should be improved (to enhance overall customer satisfaction). According to Venture Beat, Wonderflow helped a leading electronics company increase its vacuum’s star rating by 0.7, after determining that customers found it difficult to clean the filters of a previous product version.
Rather than acting on historical customer feedback (from surveys and other qualitative data), many CX analytics platforms enable companies to pre-empt and predict customer satisfaction.
Indeed, McKinsey states that this is a key part of a data-led approach to CX, with algorithms generating predictive scores for each customer based on journey features. “These scores allow the company to predict individual customer satisfaction and value outcomes such as revenue, loyalty, and cost to serve,” states McKinsey. “More broadly, they allow CX leaders to assess the ROI for particular CX investments and directly tie CX initiatives to business outcomes.”
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Additionally, at a more granular level, predictive analytics platforms (and their real-time nature) can also enable companies to deliver daily customer engagement – and prevent problems from occurring. McKinsey cites one example of an airline which was able to identify customers at risk of churn due to delays or cancellations, and to offer them personalised compensation in order to maintain a positive relationship.
As well as retaining customers, predictive analytics platforms (or personalisation engines as they’re also known) are an effective tool in building relationships and generating loyalty from consumers. Measuring customer lifetime value (CLTV) is one key factor, for example, which can be used to identify the most profitable customers and to deliver the right promotions or communication at the right time.