Assessment and market development is mapping and identifying every single one of your company’s traits and attributes so that your management team has detailed knowledge on where you’re company are better, where you’re company are average and where you’re company are worse of than best practice, peers, competitors, and market leaders. Enabling the management team to develop traits and attributes they need to gain a competitive advantage.
It is also about identifying which attributes, traits want, or needs that made today’s customers and clients trigger their purchase in the first place. This provides your management team with knowledge on which traits and attributes they need to reinforce to strengthen the company’s customer and stakeholder relations. The service assessment and market development can also identify and map traits, attributes, needs, and wants in blank segments. Blank segments are prospects and stakeholders that, for some reason, are out of your company’s reach today. This way, your management team knows which traits and attributes the company must be developed to reach and grow into those blank segments.
When traits, attributes, needs, and wants for existing clients and blank segments are defined and fed into our matrix. Our algorithm goes to work, creating mirror audiences for each segment in existing and new markets. Mirror audiences are prospects who have identical digital profiles to your existing clients, stakeholders, and blank segments. That means quadrupling the numbers of potential core customers. It is also having detailed knowledge on which traits and attributes that need developing to conquer a quadrupling of the blank segment.
There is no international standard such as ISO, veritas, or any other, that we can apply for or comply with regarding our matrices, algorithms, or models. So to ensure trustworthiness and credibility to our processes and solutions, we have decided that everything we do or develop has to originate in A level research. The same goes for our data gathering. Any data used or utilized in developing our services, matrices, algorithms, or models must be gathered in accordance with established scientific methods.
Consumer economics and behavior in the B2C and B2B markets are constantly evolving, so any assessment has a limited lifespan. We recommend all our clients to redo the service assessment and market development every 18 to 24 months unless there is a major change in the industry who triggers the need for an immediate response.
Any company associated with LFCG will have access to any updates in our models, metrics, algorithms, and research. We will upgrade all our services twice each year, thus providing our associates with detailed information on what has been done, anticipated effect, recommendations on any new technology to implement, and educational videos and instruction if needed on what and how.
This Stage Includes
Quantitative Research & Design
Quantitative research is the systematic empirical investigation of observable phenomena via statistical, mathematical, or computational techniques. The objective of quantitative research is to develop and employ mathematical models, theories, and hypotheses pertaining to phenomena. The process of measurement is central to quantitative research because it provides the fundamental connection between empirical observation and mathematical expression of quantitative relationships.
Qualitative Research & Design
Qualitative research relies on unstructured and non-numerical data. The data include field notes written by the researcher during the course of his or her observation, interviews and questionnaires, focus groups, participant-observation, audio or video recordings carried out by the researcher in natural settings, documents of various kinds (publicly available or personal, paper-based or electronic records that are already available or elicited by the researcher), and even material artifacts. The use of these data is informed by various methodological or philosophical assumptions, as part of various methods, such as ethnography (of various kinds), discourse analysis (of various kinds), interpretative phenomenological analysis, and other phenomenological methods.
In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships between a dependent variable (often called the ‘outcome variable’) and one or more independent variables (often called ‘predictors’, ‘covariates’, or ‘features’).
The most common form of regression analysis is linear regression, in which a researcher finds the line (or a more complex linear combination) that most closely fits the data according to a specific mathematical criterion. For example, the method of ordinary least squares computes the unique line (or hyperplane) that minimizes the sum of squared distances between the true data and that line (or hyperplane). For specific mathematical reasons (see linear regression), this allows the researcher to estimate the conditional expectation (or population average value) of the dependent variable when the independent variables take on a given set of values.
Less common forms of regression use slightly different procedures to estimate alternative location parameters (e.g., quantile regression or Necessary Condition Analysis) or estimate the conditional expectation across a broader collection of non-linear models (e.g., nonparametric regression).
Regression analysis is primarily used for two conceptually distinct purposes. First, regression analysis is widely used for prediction and forecasting, where its use has substantial overlap with the field of machine learning. Second, in some situations, regression analysis can be used to infer causal relationships between the independent and dependent variables. Importantly, regressions by themselves only reveal relationships between a dependent variable and a collection of independent variables in a fixed dataset.
To use regressions for prediction or to infer causal relationships, respectively, a researcher must carefully justify why existing relationships have predictive power for a new context or why a relationship between two variables has a causal interpretation. The latter is especially important when researchers hope to estimate causal relationships using observational data
Statistics & Data Science
Statistics is the discipline that concerns the collection, organization, analysis, interpretation, and presentation of data. In applying statistics to a scientific, industrial, or social problem, it is conventional, to begin with a statistical population or a statistical model to be studied. Populations can be diverse groups of people or objects such as “all people living in a country” or “every atom composing a crystal”. Statistics deals with every aspect of data, including the planning of data collection in terms of the design of surveys and experiments.
Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, machine learning, and big data.
Data science is a “concept to unify statistics, data analysis, and their related methods” in order to “understand and analyze actual phenomena” with data. It uses techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, domain knowledge, and information science. Turing award winner Jim Gray imagined data science as a “fourth paradigm” of science (empirical, theoretical, computational, and now data-driven) and asserted that “everything about science is changing because of the impact of information technology” and the data deluge.