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Predicitive AnalyticsPredicitive Analytics can predict customer behavior anytime, anywhere. Paul Gaynor, partner at the management consultancy PwC, represents this thesis. Gaynor draws a graphic comparison: instead of looking at customer behavior in the rearview mirror as before, providers can now look ahead through a telescope. Our sister publication cio.com asked around among users and consultants and came up with seven pieces of advice for a better customer experience:
Everything to Predictive Analytics on CIO.de
1. Hyperpersonalisiertes Marketing: For Bindu Thota, Vice President Technology at the online clothing and accessories retailer Zulily, the following factors are essential to addressing customers precisely: the right message at the right time on the right channel. The customer wants to be presented with the right product categories, the right selection, the right price level, the right delivery times and more. Data-driven technology enables Zulily to create a personalized collection of thousands of products for customers every day, says Thota.
2. Virtual Concierges: Consumers are now used to being offered an immediate customer experience without media disruption, says Ravi Narayanan, Global Head of Insights and Analytics at the consulting firm Nisum. This claim actually extends to everything. According to Narayanan, Spotify and Netflix implement this in an exemplary manner: the streaming services always adapt their suggestions to the content that the user is currently consuming, like a virtual supervisor sitting next to them.
3. Prediction of customer needs: Lance Gruner, Executive Vice President Customer Experience at Mastercard, wants to use predictive analytics to predict not only the amount of customer complaints, but also their level of difficulty. The aim is to resolve every complaint as quickly as possible.
AT & T’s efforts go beyond that. In its own words, the company has a customer experience Machine LearningMachine Learning System implemented that analyzes petabytes of data. AT&T wants the entire lifecycle of the Customer contactCustomer contact analyze and be informed by the system in good time if the likelihood of customers drops and a consumer develops from a proponent of a brand to a rejectionist.
Everything to CRM on CIO.de
Everything to Machine Learning on CIO.de
4. Avoid migration: Dealers have calculated that it is cheaper to keep customers than to acquire new ones. So you need to prevent customers from churning. According to Seongjoon Koo, chief data officer at marketing consultancy JD Power, predictive analytics can identify customers who are on the move. In these cases, by the way, the simple method of offering customers a price reduction or installment payments often promises success, Koo continues.
5. Resource management: PwC manager Paul Gaynor supports companies in combining data from the branches, logistics and customer behavior in order to use resources as efficiently as possible. Dennis Amorosano, Senior Vice President at Canon Information and Imaging Solutions, works with remote monitoring, predictive analytics and predictive maintenance in order to make real-time diagnoses and detect weak points in the equipment in advance.
6. Support for back office: Internally, customer service receives feedback by phone and email, via social media, from colleagues in the escalation team and from other channels. This information can hardly be managed without predictive analytics. Mastercard manager Gruner therefore calls predictive analytics a “key tool”.
7. Optimize deliveries: The goods should be there today or tomorrow at the latest – more and more consumers expect that. Dealers and their partners from logistics and delivery must work together to ensure reliable and fast deliveries. Predictive analytics helps to include factors such as damage to the fleet, optimized transport routes, delivery quantities and more.
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