Usamos cookies. Tienes opciones. Las cookies nos ayudan a mantener el sitio funcionando sin problemas e informar sobre nuestra publicidad, pero si deseas realizar ajustes, puedes visitar nuestro Aviso de cookies para más información.
Utilizamos cookies propias y de terceros para analizar su actividad en el sitio web con el objetivo de enviarle publicidad personalizada y mejorar el funcionamiento de la web. Puedes aceptar todas las cookies pulsando el botón “ACEPTAR” o seleccionarlas en función de su funcionalidad pulsando el botón “AJUSTES”.


Ask a marketer what the word targeting means to them, and you are likely to get as many different responses as the number of people you ask. It is a process, it is an objective, it can include multiple marketing elements or just a single one. It is a science, an art, or both. Companies have whole departments devoted to it. There are privacy concerns, regulatory considerations and cannibalization implications. Memories of success and memories of failure. The one thing every marketer has in common, though, is that they think about targeting – a lot. And so they should, because successful targeting means everything to successful marketing!  

Knowing who, what, when, where, how to spend money to elicit a desired reaction. It is measured by favorable performance in sales, retention, profit and/or cost figures. Get it wrong and you will lose much. Get it right and the sky’s the limit. Thankfully, Merkle has developed a science over the years to compliment the equally important element of art. Why the need for both? Because people’s wants, needs and interests can sometimes be more easily identified by patterns in data and other times by a marketer who understands, has been there, seen that, empathizes – gets it. Recognizing and leveraging the value of both the art and science of marketing is a competitive advantage.

Could you be doing a better job of targeting? A much better job? Below is a short list of things to consider as you weigh this question.

  • Do you have access to quality information on your customers and prospects? Do you fully use it?
  • Are you familiar and capable with optimization versus insight methods?
  • Have you performed any type of customer or prospect segmentation?
  • Are experimental design considerations included in your test plans? (cell size, significance, etc.)
  • Do you focus more testing on creative and messaging than audience and offer?
  • How well do you understand and use your real versus perceived value proposition?

At Merkle, we have the quantitative and qualitative experience, knowledge, capabilities, and access to data necessary to deliver targeting improvements for our clients. We have the combined expertise in data, analytics, and marketing strategy to accelerate our clients’ targeting improvement efforts. We follow a disciplined approach, first assessing the current situation and then mapping out an improvement plan that achieves immediate success where feasible, building as quickly as possible over time.

We believe that all marketing should be targeted, not just in “targeted” vehicles. This includes the usual precision channels such as email, direct mail, mobile SMS, and telemarketing as well as mass (television, radio, and print) and digital media –inclusive of online advertising, social, mobile apps, and search. Targeting is informed by insights gained from segmentation and other means, and further driven by several areas of advanced analytics including predictive modeling, machine learning, customer value identification, and forecasting.

Predictive Modeling

Merkle builds more than 1,000 models annually. Each model is a unique solution specifically tailored to the business problem our client is trying to solve. We do not start with “canned” models or use off-the-shelf tools when creating targeting solutions. Our statisticians partner with marketing analysts to ensure that there is a strong understanding of the business, ultimate goal, and planned use of the model. All these factors have a distinct influence on the type of model built, the dependent variable, the data transformations, technical methodology, and the final mode. We then work with our clients to implement the models in a way that enables optimal results.

Increasingly, with the rapid expansion of anonymous and known customer targeting within digital media and channels, our models are being scored on big data and in real time.  Traditional predictive analytic approaches often give way to fast scoring and adaptive machine learning approaches.  Our teams have the cross-over big data technology and analytics capabilities to build, implement and put into production these solutions.

Additionally, we leverage our experience in predictive modeling both within and across industries, reviewing models by a modeling review board which ensures that every model is subject to our best thinking and strict standards across the organization. Merkle has strong experience modeling both behaviors (response, conversion, net conversion), life changes (likelihood to be in market), risk, lifetime value, and a variety of other metrics.

En nuestra compañía