IDENTIDAD-CLIENTE

Artificial Intelligence for Customer’s identity

When customers know what they want, companies must figure out what it is. However, according to Gartner, CMOs only invested 29% of their budgets in new technologies to meet their needs.

Probably marketing specialists do not have the appropriate technology or the technology they have isn´t enough. However, often marketing departments do not have full knowledge of the capabilities of the technologies they have paid for and consequently, do not take advantage of them.

To guarantee a high return of the investment for the business (ROI) the marketing specialists should start by auditing their technological ecosystem in their organizations to finally set the best ROI strategy.

However, the assumption that having a specific technology will guarantee to make money, is a common mistake. According to a survey conducted by the consultant company McKinsey, the technological gap between leading companies and others staying behind is growing. This means companies that make data-driven decisions will make the difference as compared to the rest of companies that are still struggling for basic data analysis and technology.

For both, innovating companies and the ones lagging behind, data analytics emerges as an opportunity to offer better insights, in other words, having a healthy data culture. Some will implement it and others will not. Therefore, some will obtain a higher ROI at the expense of companies that will lag behind as they will not implement a data-driven culture.

Once a healthy data-driven culture and appropriate technology are implemented, the time to listen to customers has come. As said before, the more consumer’s attention is captured, the more customer’s personalization will grow. The marketing departments invest around 14% of their budget to achieve the so long waited for personalization, very little in return for what it offers.

AI-based Customer identity

The need to know customers opens the adoption and use of AI not only at marketing scale but also at a broader and general level in the organization. The survey conducted by McKinsey Global Institute containing more than 3000 companies called “Artificial Intelligence: The next digital frontier?” shows that the first AI users are closer to the digital frontier. It´s precisely at this frontier where the leading companies in each sector are found. AI is present in all the groups, at the core business of the value chain, it is used to boost revenues, reduce costs, and has total executive management support. The companies that have not adopted AI technology at a certain scale or at the core business, are not sure of the revenues they could expect with such an investment.

Innovative companies know that existing and future customer personalization is achieved by customer’s knowledge. Here is where AI is introduced completely, and marketing specialists are trained to identify each one of them and personalize their actions.

One of the key elements of the customer’s identity techniques is that it allows us to characterize actionable concepts. These actionable concepts are the result of incorporating the business expert knowledge to the AI, also called Intelligent Observation Systems. The result is a set of concepts providing a broader customers perspective.

Therefore, customer’s identity techniques enable companies to obtain, for instance, better insights into consumer’s habits. Once they are identified, the different areas of the organization have enhanced the ability to make personalized offers leading to a greater benefit while delivering greater levels of customer satisfaction. For example, churn may be avoided once the customer’s satisfaction is analyzed. Additionally, price predictive models for certain products can be set.

Innovative companies are the ones able to respond to the classic questions: How do my customers behave? What value of information can I obtain from their habits? How can I improve the companies´ revenues? Partly, it´s due to their capacity to be always at the state of the art.

 

IDENTIDAD-CLIENTE

Inteligencia Artificial para conocer la identidad del cliente

Cuando el cliente sabe lo que quiere, las empresas están obligadas a conocer ese querer. Sin embargo, según Gartner, los CMOs solo invirtieron el 29% de sus presupuestos en nuevas tecnologías que les permitieran satisfacer sus necesidades.

Es posible que los especialistas en marketing no tengan la tecnología adecuada ni sea la que tienen la suficiente. Por otro lado, también es común que los departamentos de Marketing tengan una visión incompleta de las capacidades tecnológicas que poseen y, por lo tanto, no estén usando todo por lo que han pagado.

Para garantizar un alto retorno de la inversión (ROI) para el negocio, los especialistas en marketing deberían comenzar con una auditoría del ecosistema tecnológico que está siendo usado dentro de la organización para, finalmente, poder establecer la mejor estrategia que derive en un alto ROI.

Sin embargo, creer que por tener una determinada tecnología ya se está destinado a generar dinero es un error común hoy en día. Según un estudio de la consultora McKinsey, la brecha tecnológica entre aquellas compañías líderes y las más rezagadas, está creciendo. Esto significa que aquellas que usan los datos para tomar decisiones están marcando las diferencias con el resto, que aún están luchando por lo básico en cuanto a análisis de datos y a tecnología se refiere.

Para las empresas innovadoras y las rezagadas la aparición del análisis de datos como una realidad para ofrecer mejores insights significa estar en posesión de una cultura del dato saludable. Unas lo implementan y otras no y por tanto unos obtendrán un mayor ROI, en detrimento de las empresas rezagadas que no han implementado esa cultura del dato.

Una vez tenemos una cultura del dato sana y la tecnología necesaria, llega el momento de escuchar al cliente. Como decíamos al inicio de este trabajo, mientras la captura de la atención del consumidor se incrementa, aumentan las necesidades de personalizar a los clientes. Los departamentos de Marketing invierten de media, un 14% para lograr la tan ansiada personalización, poco para los resultados que ofrece.

IA para la identidad del cliente

La necesidad de conocer al cliente, facilita la adopción y uso de la Inteligencia Artificial, no solo en el campo del Marketing sino a escala y en toda la organización.  Aquí nos encontramos con la encuesta a más de 3000 empresas realizada por McKinsey Global Institute,  llamado “Inteligencia artificial: ¿La próxima frontera digital?”  y descubrimos que los primeros usuarios de IA tienden a estar más cerca de la frontera digital. Es en esta frontera donde se sitúan las empresas más grandes dentro de cada uno de los sectores. Despliegan IA en todos los grupos, usan la IA en la parte más central de la cadena de valor, adoptan la IA para aumentar los ingresos y reducir los costos, y tienen el apoyo total del liderazgo ejecutivo. Las empresas que aún no han adoptado la tecnología de inteligencia artificial a escala o en una parte central de su negocio, no están seguras de los rendimientos que pueden esperar de una inversión así.

Son este grupo de empresas, las innovadoras, las que saben que la manera de personalizar las acciones a los clientes, presentes y futuros, es conociéndole. Es aquí donde la IA se introduce de lleno y capacita a los especialistas en Marketing a que puedan identificar a cada uno de ellos y personalizar sus acciones.

Uno de los elementos clave de las técnicas de identidad de cliente, es que permiten caracterizar a los clientes a partir de conceptos accionables. Estos conceptos accionables son los resultantes de incorporar el conocimiento experto de negocio a la IA, también llamados SIOs o Sistemas Inteligentes de Observación. El resultado es que el conjunto de estos conceptos permite obtener una visión amplia del cliente

Por tanto, las técnicas de identidad del cliente sirven a las compañías para obtener, por ejemplo, un mayor conocimiento de los hábitos de sus consumidores. Una vez se han identificado, las distintas áreas corporativas están capacitadas para realizar ofertas mejor personalizadas, que les permitan un mayor beneficio al mismo tiempo que satisfacen a los clientes. Como ejemplo, sirve para evitar el abandono una vez hemos analizado qué les produce insatisfacción. Pero también puede ayudar a establecer modelos predictivos de precios para determinados productos.

Las empresas innovadoras son las que están pudiendo responder a las clásicas preguntas ¿cómo se comporta mi cliente?, ¿qué información de valor puedo obtener de sus hábitos?, ¿cómo puedo mejorar los beneficios de la compañía? En parte, por su capacidad de estar siempre a la vanguardia.

fraude

Supervised and unsupervised learning techniques for fraud detection.

Artificial intelligence is redefining fraud prevention techniques as it allows to obtain information based on experience. This information consists of transaction activities, behavior and trends. Before the use of artificial intelligence, the applied methods were based on rules that helped to analyze historical fraud patterns but could not prevent them. Although these models could identify fraud attempts, they did not provide information of the future.

Nevertheless, the technological sophistication of fraud crimes is higher and more precise and efficient attacks have grown during the last few years

As a result, leading companies dealing with potential fraud crimes, mainly in banking and insurance, must increase monitoring accuracy and acuity of customers potential risk for the institution. Decision-making on accepting or rejecting payments, limiting charges refund and reducing operational and reputational risks is much easier now.

Fraud prevention in the future will depend on a combination of supervised and unsupervised automatic learning techniques. Supervised automatic learning finds patters based on historical events, factors, trends, etc., and unsupervised automatic learning looks for relationships and variable links, a combination of both methodologies would help to prevent fraud in the following:

Detection in real time. The use of artificial intelligence enables the detection of attacks in real time, instead of weeks that usually takes to start receiving reverse requests of charges.

Thwarting the most sophisticated attempts of fraud. Fraud techniques get more and more sophisticated. Artificial intelligence would help to prevent and reduce these attacks.

Scoring in real time. Provide analysts a scoring for a better perspective to set the limits to maximize sales and minimize losses in real time.

Immediate transactions. Fraud prevention systems based on AI enable immediate transaction´s approval provided it is within the reverse charges threshold of the main debit and credit cards.

Reduction of false positives. False positives are reduced thanks to supervised and unsupervised automatic learning, whereas current techniques cannot efficiently detect them. Frequently, when a customer pays an unusual amount of money or from a new location, the card is blocked by the system as it interprets it wrongly as a suspicious activity. With artificial intelligence, it is possible to identify more precisely any change in customers expenditure habits.

Profitability in low margin products. AI has allowed insurance companies to continue their profitable business and attract new customers whose historical purchases are not part of the historical supervised learning of fraud systems

Supervised and unsupervised learning should be complemented with experts´ knowledge aiming at a mixed approach to focus their attention in more suspicious cases detected by AI.