Use Analytics to Satisfy ConsumersIf recent action by consumers is any indication, many seem fed up with what they perceive as intrusive unsolicited marketing. The huge response to the federal do-not-call list, combined with consumer and Internet service provider use of e-mail blocking filters (despite that these activities also have unintended consequences), has to be a wakeup call for all direct marketers.
"How did it come to this?" is not easily answered. As in most matters of mind and emotion, a complex set of sociological and economic factors contributes to the seeming consumer rebellion. But, being the practical direct marketers we are, we can focus on specific actionable items.
One major factor is that we may have lost sight of the basic marketing principle: Know thy customer and give him what he wants. We are at a stage where we must avail ourselves of all the tools available to maximize our value to our customers and minimize the effect on consumers who don't find value in what we offer. New technologies to work with consumer data are making that objective much easier to attain.
Along with making the proper handling of consumer do-not-call and do-not-e-mail requests part of our standard operating procedures, we need to do better at getting to the root causes of why people are dissatisfied and improve our relationships with customers and prospects. That means starting from the consumer data and working our way back to the products and services we offer.
Customers and prospects give us information about themselves all the time. Databases are full of these useful tidbits, and call centers and other customer management systems are overflowing with details about our customers and contacts. The problem is that the information is in the form of data - tons of it. Data is good, and more data is better, but data by itself has no value if it is not turned into information.
And while we take precautions to protect the privacy of the data, we also have the chance to take advantage of the information it provides to better manage the relationship with our customers and prospects. To do so means using every tool at our disposal, and one of those tools is the technology that lets us properly manage the massive amount of data that spins off from our interactions with customers and prospects.
Turning this data into useful information is where analytical technology comes into play. A philosopher once wrote that finding the patterns in the randomness of life is the way we create beauty and make art. A similar statement could be made about analytics, which find patterns in the randomness of data so that you can discover valuable information and gain insight.
An array of analytical products is available for desktop and enterprise systems and for pros and novices alike. Generally, analytics fall into four categories:
Statistical analysis. This refers to a collection of methods used to process large amounts of data to uncover key facts, patterns and trends. Though there are numerous statistical analysis procedures, the two used most commonly by direct marketers are classification and segmentation.
Classification uses predictor fields to predict a categorical target field, such as which groups will respond to a mailing. Segmentation divides subjects, objects or variables into relatively homogeneous groups (e.g., segmenting consumers into usage pattern groups).
Popular statistical software can handle the entire analytical process: planning, data collection, data access, data management and preparation, data analysis, reporting and deployment. Use of statistical analysis to classify and segment can increase the likelihood that we communicate only with people who are more likely to be interested in our offer.
Online Analytical Processing. With OLAP, users easily and selectively extract data, then view it from different perspectives. For example, a user can request that data be analyzed to display a spreadsheet showing all of a company's widgets sold in Wyoming in August, compare revenue figures with those for the same products in October, and then see a comparison of other product sales in Wyoming in the same time period.
To facilitate this analysis, OLAP data is stored in a multidimensional database, which considers each data attribute as a separate "dimension." This management tool lets marketers quickly review history and trends to take advantage of emerging opportunities and take corrective action on developing problems.
Data mining This finds the meaningful patterns and relationships in data and provides decision-making information about the future. Data mining procedures include: association, looking for patterns where one event is connected to another; sequence or path analysis, looking for patterns where one event leads to a later event; classification, looking for new patterns; clustering, finding and visually documenting groups of facts not previously known; and forecasting, discovering patterns in data that can lead to reasonable predictions about the future.
Data mining gives a clear picture of what is going to happen in time to change it. This includes: whom the best customers might be, which customers are likely to defect or, if the right data is gathered, which carry the risk of adverse reaction to marketing offers.
Text mining. Text mining analyzes unstructured textual data by finding patterns and relationships within thousands of documents, such as e-mails, call reports and Web sites. Text mining extracts terms and phrases, then classifies the terms into related groups such as products, organizations or people using the meaning and context of the text. This distilled information can be combined with other data sources and used with traditional data mining techniques such as clustering, classification and predictive modeling.
Questions to explore include which concepts occur together? What else are they linked to? What do they predict? The answers make the marketer better able to identify and head off potential consumer dissatisfaction and maximize consumer satisfaction.
With the massive amount of consumer data generated every moment of every day, and the necessity of carefully managing the relationship with the consumer, analytics no longer are a nice thing to have, they are essential.
It comes down to building a reciprocal relationship with the consumer. If we take the time to understand consumers based on the data we already have, we'll be able to give them what they want or tell them about something of interest to them, thereby developing a relationship valued by the consumer.