Hub You
#1 in Business Subscribe Email Print

You are here: Home > Business > Marketing Direct > Database Marketing - in Search of Statistical Significance

Tags

  • monitored
  • approach
  • smart
  • customer behavioral
  • outperform customer
  • their responses

  • Links

  • Reciprocal Linking
  • The Greatest Key to Success in Life is to Visualize Your Dream Goals Today!
  • Why Predict the Future?
  • Hub You - Database Marketing - in Search of Statistical Significance

    Tip For Successful Freelance Designing
    Spend a little money on your clothes and briefcase or portfolio-type bag to create a good impression. People may deny it but they will always think: expensive clothes, lots of money, doing well, good designer. Dress smart, but not trendy - no one likes trendy designers.When you meet the client, I would always advise you to smile at the first moment and look them directly in the eye. Of course, some
    s, which have been monitored vis-?-vis their responses to a specific offer. The Customer set is divided into two subsets of equal (or comparable) size & similar types of Customers (in respect with profile & behavior): a test set (or model train set) and a validation set. The model shall be developed against the test set and
    Getting Internet Traffic For Your Website
    Traffic is what everyone is talking about it, but how do you get it. One of the best ways of getting traffic is using articles will increase traffic to your web site. There are many of article directories on the internet; with the right article submitter software you can automate the entire article submission process. The way to do this is by writing interesting, informative and useful articles relevant to
    The goal of database marketing is to increase marketing efficiency & Customer lifetime value, with the smart use of Customer data. In example, use Customer data to identify Customer groups, which would yield high response to offers, in order to address them directly.

    Database marketing is based on Customer information related to:
    • Customer behavior
    • Customer profile & demographics

    Based exclusively on behavioral information, one can classify customers into RFM (recency - frequency - monetary) or RF cells. The goal is to identify Customer groups with high expected response rates. Different RFM cells are expected to provide significantly different expected response rate (especially the ones linked to the most recent Customers). The more significant the statistically expected difference is, the higher potential business value this grouping yields. In order to apply RFM, one does not need statistics skills. Therefore this approach is less costly, since it is simpler and requires only customer behavioral information.

    Predictive models based on both behavioral & demographic data, can outperform Customer groupings based solely on behavioral data (like RFM).

    In order to develop such a model, one needs to use behavioral & demographic data of a set of Customers, which have been monitored vis-?-vis their responses to a specific offer. The Customer set is divided into two subsets of equal (or comparable) size & similar types of Customers (in respect with profile & behavior): a test set (or model train set) and a validation set. The model shall be developed against the test set and

    5 Keys to Leadership in Business... More Than Just Managing
    Leading vs ManagingWhether you are the owner of your own business, the chief executive of a corporation, or a manager rising through the ranks, it is critical to develop your leadership skills. Great leadership brings great results. A great manager can get great results but the results reflect on a project or goal, not on the long term process of leading people. A manager can bring a project in on t
    elated to:
    • Customer behavior
    • Customer profile & demographics

    Based exclusively on behavioral information, one can classify customers into RFM (recency - frequency - monetary) or RF cells. The goal is to identify Customer groups with high expected response rates. Different RFM cells are expected to provide significantly different expected response rate (especially the ones linked to the most recent Customers). The more significant the statistically expected difference is, the higher potential business value this grouping yields. In order to apply RFM, one does not need statistics skills. Therefore this approach is less costly, since it is simpler and requires only customer behavioral information.

    Predictive models based on both behavioral & demographic data, can outperform Customer groupings based solely on behavioral data (like RFM).

    In order to develop such a model, one needs to use behavioral & demographic data of a set of Customers, which have been monitored vis-?-vis their responses to a specific offer. The Customer set is divided into two subsets of equal (or comparable) size & similar types of Customers (in respect with profile & behavior): a test set (or model train set) and a validation set. The model shall be developed against the test set and

    Change
    PEOPLE - The most obvious reason we see a faster rate of change is because we are producing a lot more people and people cause change. People make things - they come up with new ideas - they compete for scarce resources. Whatever sorts of things people do, we'll see it happening more and faster.TECHNOLOGY - Since technology is a product of the human race, we can expect the rate of t
    significantly different expected response rate (especially the ones linked to the most recent Customers). The more significant the statistically expected difference is, the higher potential business value this grouping yields. In order to apply RFM, one does not need statistics skills. Therefore this approach is less costly, since it is simpler and requires only customer behavioral information.

    Predictive models based on both behavioral & demographic data, can outperform Customer groupings based solely on behavioral data (like RFM).

    In order to develop such a model, one needs to use behavioral & demographic data of a set of Customers, which have been monitored vis-?-vis their responses to a specific offer. The Customer set is divided into two subsets of equal (or comparable) size & similar types of Customers (in respect with profile & behavior): a test set (or model train set) and a validation set. The model shall be developed against the test set and

    Sun Zi Art of War - Managing a Large Force Like a Small Force
    Sun Zi said: Managing a large force can be similar to managing a small force. It is a matter of organization and structure. To direct and control a large force can be similar to directing and controlling a small force. It is a matter of communications and formations. - Chapter Five, Sun Zi Art of War In these lines, Sun Zi talk about how to use a large force like a small force. The fo
    , since it is simpler and requires only customer behavioral information.

    Predictive models based on both behavioral & demographic data, can outperform Customer groupings based solely on behavioral data (like RFM).

    In order to develop such a model, one needs to use behavioral & demographic data of a set of Customers, which have been monitored vis-?-vis their responses to a specific offer. The Customer set is divided into two subsets of equal (or comparable) size & similar types of Customers (in respect with profile & behavior): a test set (or model train set) and a validation set. The model shall be developed against the test set and

    Presenteeism: The Hidden Costs of Business
    (prez.un.TEE.iz.um) nPresenteeism, a relatively unknown concept, is the complement of Absenteeism. It is defined as the measure of lost productivity cost due to employees actually showing up for work, but not being fully engaged and productive mainly because of personal health and life issue distractions. Currently, Presenteeism is estimated to be up to 7 ? times more costly to employers than absent
    s, which have been monitored vis-?-vis their responses to a specific offer. The Customer set is divided into two subsets of equal (or comparable) size & similar types of Customers (in respect with profile & behavior): a test set (or model train set) and a validation set. The model shall be developed against the test set and validated against the validation set. A modeling algorithm can be applied (e.g. logistic regression analysis), against the test set data, in order to identify the variables, that influence significantly ‘the probability to respond to an offer’ (which is the dependent variable). Validation of the model follows. It involves identifying most of the actual responders in the validation set, given that these Customers are known. After being validated, the model can be used in a test campaign.

    Various obstacles may appear during this modelling process:
    • There may be no capture of customer reactions to previous offers, therefore no data to model on.
    • If the model does not validate sufficiently against the validation group, then the model may be a failure. This may mean that factors affecting significantly the customer behavior, are not captured among the data available or are not used in the model.
    • Many customer databases hold Customer behavior info, but limited demographics on the Customers. Lists with consumer demographics (offered by many in the USA), can be used to enrich Customer data with demographics.

    A validated model can be applied on the whole Customer database, to identify a group of Customers with high propensity to respond positively to a similar offe

    HTTP = HTML link (for blogs, profiles,phorums):
    <a href="http://www.iadvice.info/article/30744/iadvice-Database-Marketing--in-Search-of-Statistical-Significance.html">Database Marketing - in Search of Statistical Significance</a>

    BB link (for phorums):
    [url=http://www.iadvice.info/article/30744/iadvice-Database-Marketing--in-Search-of-Statistical-Significance.html]Database Marketing - in Search of Statistical Significance[/url]

    Related Articles:

    How Switchplates Can Turn a Room from Dreary to Dazzling in Seconds

    Change Management Disruptions of Your Competitors

    An Introduction to Affiliate Marketing MLM Network

    Bookmark it: del.icio.us digg.com reddit.com netvouz.com google.com yahoo.com technorati.com furl.net bloglines.com socialdust.com ma.gnolia.com newsvine.com slashdot.org simpy.com shadows.com blinklist.com