Need for speed: Algorithmic marketing and customer data overload
Another example is a large Latin American bank which transformed itself from a little known player to an institution that, by 2010, ranked 11th worldwide in market capitalization. All offers are delivered to customers in a personalized way, based on an understanding of their preferences. In addition, information received via one channel is used to inform and update intelligence across the system in real time. For instance, if a customer rejects an offer on an ATM, the “next product to buy” (NPTB) engine is updated to ensure that the customer’s next interaction with the call-center results in a different, more suitable offer. It also used its capabilities to stay far ahead on straight-through processing (STP) across channels. ATMs are capable of 190 different transactions, which cover key sales types, such as fixed deposit creation, personal loans, credit cards transactions, loans against pensions, and simple life and accident insurance offers. Telecommunications companies that have traditionally used data mining techniques are also now pursuing the benefits of algorithmic marketing. For example, a top-three Asian telecommunications company with over 100 million subscribers established automated, realtime churn triggers that create tailored and progressively more aggressive offers in order to retain customers in an environment of rapidly diminishing loyalty. Getting into that algorithm rhythm Invest in tech: Making the shift from batch to algorithmic is like going from the age of propeller flight to jet engines. And the implications are just as momentous. To be real time, companies need very different system architectures, investment programs, programing, and security protocols. In-memory processing, an emerging technology that gives users immediate access to the right information for more informed decisions, can’t be found in off-the-shelf packages. Throw in all the security issues in protecting all those data and money transfers and you’re talking about a sophisticated and complex system. Getting these IT systems working is the greatest challenge CMOs face, according to a poll we recently ran on the topic. Algorithmic marketing requires custom programs or heavily modified packages because the nature of a company’s business, organization, and processes tend to be unique. Firms must, therefore, invest in creating integrated information systems that not only transcend organizational silos but also tie into systems operated by suppliers and partners. New organizational capabilities: Algorithmic marketing is a wasted opportunity unless companies resolve how it fits in with current organizations and processes. . For example, companies need to understand how a merchandizing manager negotiates deals with a dynamic pricing system running in parallel. Or when a mobile customer walks into a store after receiving an offer via SMS, the sales person needs that offer and customer information to understand not only how to fulfill the order but also how to cross- and upsell. Other opportunities require new teams. For example, big data and intelligent, self-learning algorithms offer the potential for companies to conduct controlled real-world testing. But how to enable, manage, and make sense of potentially thousands of these experiments, while also using time, resources, and money efficiently. Organizations need to set up and manage “test factories” with streamlined IT systems, robust underlying processes, and automated results interpretation tools, which would allow managers to test several thousand ideas every month.
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