How can Cognitive Computing improve your Marketing Efficiency?

How can Cognitive Computing improve your Marketing Efficiency?
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by Sanjeev Kapoor 28 Jan 2020

Nowadays, Artificial Intelligence (AI) is presented as a threat to most employees. It is projected that AI systems will make redundant millions of jobs in different sectors, including not only labor-intensive positions but knowledge workers as well. The marketing sector seems to be no exception to this rule. AI tools are expected to increase marketing automation and to obviate the need for human-mediated processes when executing activities like marketing campaigns. However, for the near future, such job losses are not foreseen, as we are still not in the era where the entire marketing process can be automated. On the contrary, modern AI tools should be considered as productivity and convenience vehicles. Thanks to AI marketers are provided with unprecedented opportunities for doing more in less time e.g. generating more leads with less effort and at a lower cost. Therefore, marketing managers have better think about how to best leverage AI capabilities, rather than worrying about losing their jobs.

Cognitive computing is AI’s segment that is best suited for improving sales and marketing operations. Cognitive computing is based on AI and signal processing. It offers a wide range of technology capabilities including machine learning and reasoning, but also a range of perceptive technologies like Natural Language Processing (NLP), Speech Recognition, Visual Scene Analysis, and Dialog generation.

Read More: How AI and Machine Learning can make Enterprises More Competitive

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Cognitive Computing in Marketing: The Drivers

The use of cognitive computing in marketing operations is grounded in the proliferation of customer data and the availability of high-end computing infrastructures, which are both prerequisites for cognitive computing. Specifically, cognitive computing in marketing is driven by the following factors:

  • Explosion of the Customer Data Volumes: Modern enterprises have access to exponentially growing volumes of customer data, which are larger than ever before. These data stem from a variety of different sources, including the customer’s internet browsing behavior, the customers’ interactions with helpdesks, as well as information about the customers’ online transactions. Likewise, the advent of social media has enabled enterprises to enhance their customer databases with additional information. Furthermore, it’s nowadays possible to store and manage very large volumes of customer data in cost-effective ways, as a result of the rapidly falling storage costs. This growing volume of customer data provides a sound basis for implementing cognitive machine learning systems that enable customer-centric operations.
  • Availability of High-End Computational Infrastructures: With a very large amount of customer data at hand, enterprises can test and validate various hypotheses regarding customers’ behavior (e.g., which customers are most likely to respond to an offer). Nevertheless, this requires computationally demanding operations, especially when data-intensive data mining techniques (e.g., deep learning) are used. Thanks to Moore’s law and to the advent of cloud computing, enterprises are nowadays provided with cost-effective access to large amounts of computing resources as required for mining very large datasets and enabling cognitive marketing operations.
  • Need for Marketeer-Friendly Operations: Marketing and CRM (Customer Relationship Management) are destined to support marketers in their day-to-day tasks, including operations like database segmentation and loyalty program development. Marketers need to access these systems based on user-friendly interfaces like NLP interfaces for defining segmentation rules and voice-enabled interfaces for executing marketing campaigns. The latter are intelligent functionalities that are supported by the cognitive computing paradigm.
  • Potential for Discovering Hidden Patterns of Marketing Operations: The use of Machine learning algorithms can be used to unveil hidden patterns of customer behavior. Such patterns can accordingly lead to the development of novel marketing strategies that reinforce known techniques such as RFM (recency, frequency, monetary) analysis for marketing segmentation.

 

Tip for Successful Cognitive Computing Deployments in Marketing

Overall, companies cannot afford to ignore the potential of cognitive computing and AI in marketing. Rather, they must take advantage of cognitive capabilities in order to improve the competitiveness of their marketing departments. Here are some tips in this direction:

  • Use Cognitive Computing to extend existing CRM and Marketing Automation Suites: Cognitive computing is kind of new for marketing departments, but IT is not. Most marketers are nowadays using some sort of Marketing Automation and CRM (Customer Relationship Management) systems that save them time in marketing processes like running campaigns, segmenting customer databases, designing loyalty programs and setting up customer service processes. In several cases, existing tools leverage cognitive functionalities like NLP in the definition of customer segments. State of the art cognitive computing capabilities must, therefore, be deployed as an add-on to these systems towards optimizing all stages of customer experience. Likewise, AI functionalities should be typically deployed on top of legacy CRM and Market Automation tools, leveraging on the large volumes of customer data that reside in their customer databases.
  • Business Objectives and Business Processes First: Cognitive Computing and AI are currently overhyped, many companies are seeking to enhance their current processes with some form of cognitive functionalities in order to keep up with the trend. However, this is not the right approach. It’s important to start from the business objectives and their Key Performance Indicators (KPI). For example, companies are likely to target improvements in the efficiency of their loyalty programs towards reducing churn and increasing the average customer lifetime value. AI should be a means to achieving these objectives, rather than a target per se. Similarly, AI tools shall be blended into effective marketing processes. In most cases of AI deployments, there might be a need for reengineering marketing processes as a means of improving their efficiency.
  • Adopt a people-centric approach: It’s important for employees of the marketing department to perceive AI as a valuable digital assistant, rather than as a threat that will eventually take their job. To this end, companies should adopt a people-centric approach in the way they introduce, deploy and leverage AI tools in modern marketing. CMOs (Chief MarketingOfficers) must work closely with the HR (Human Resources) departments in order to convey the right message to marketing officers. Likewise, they will have to explain how cognitive computing tools augment current capabilities and improve employees’ productivity. Moreover, a people-centric approach should be reflected in marketeer friendly interfaces, leveraging on the NLP, speech recognition and dialog interaction capabilities of cognitive computing.

 

By and large, most companies lack the resources and expertise needed to leverage the huge amount of customer data that they collect and generate every day. This is a lost opportunity for the marketing departments of modern enterprises. Cognitive computing and AI tools come to the rescue, helping companies to spot interesting patterns of customer behavior, but also to use such patterns for generating actionable insights. That’s how cognitive computing can make marketeers better. Our tips can be a small boost to start a cognitive computing adventure on the right foot.

 

Read More: Past, Present and Future of Artificial Intelligence

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