It is no coincidence that the majority of business analytical activity centers around the customer. This is where most opportunity and risk is to be found, and so it makes sense to optimize these activities using analytical techniques and technologies.
Applications of customer focused analytical methods abound. On the opportunity side of the equation we want to offer customers the most relevant products at the most appropriate time. To this end analytical methods are used in marketing to focus campaigns on customers and prospects that are most likely to respond positively. It’s an all-round win situation, with campaign costs reduced (because fewer customers are targeted), reduction in the nuisance factor (communication is more relevant), and an increased lift, which in turn converts to greater profitability. Retailers particularly are using analytical methods to target customers more effectively. Location analytics has recently been added to the arsenal, where promotions are delivered to mobile devices when a customer is in range of a store. Offers can be made during the check-out process, and the positioning of goods in a store is very often driven by market basket analysis. Financial services also uses analytical techniques to optimize customer interactions, and because of the regulatory constraints, these same companies have to tie risk analysis in with their customer interface. And so loans are targeted at individuals whose risk profile is acceptable, and extensive use of fraud analytics means it is much harder for fraudulent intent to be successful. The rapidly developing Internet of Things IoT means devices and sensors give much more information about customer need and intention. The automobile industry is already placing sensors in vehicles which in turn can advise owners of an upcoming service, and this even extends to manufacturers of large construction machinery, where usage is monitored and advisories sent to users. Microsoft recently entered into an agreement with Miele, the white good manufacturer, so that streaming data from appliances can be processed, and various services offered to customers.
It should be clear that interaction with customers is only effective if it is targeted and timely. This implies two things – analytical models need to be be developed for better targeting, and real-time infrastructure needs to be in place for timely customer interactions. However in many organizations these two needs are disjoint. The development and deployment of analytical models can be a lengthy process with various discontinuities slowing things down. One of the more serious is the discontinuity between model development and model deployment – a process that can be measured in weeks or months in many cases. Analysts and data scientists develop models, but it is often developers who code them and deploy them. Anything that can reduce this latency should be valued highly – although there are more than just technical and methodological issues at stake here.
Big data technologies mean we can analyze customers in many more dimensions. Graph analytics reveal relationships, social media analytics betray sentiment, streaming data can show real-time customer status, location analytics can deliver real-time opportunity – and so on.
Speed of model development and deployment needs to operate at a latency that is compatible with meaningful customer interactions. And there is a macro factor in all of this. Just as global communications have increased economic and business volatility, so the ability to interact with customers in a real-time framework will also increase volatility. This race will be won by the speediest and most responsive organizations, and anything less than a fully integrated analytical environment will mean customers receive messages that are untimely and largely irrelevant.