Innovative retail marketers are beginning to use predictive analytics to make the most out of their email campaigns and as a result; we’re beginning to see a mass movement toward predictive marketing. Today we’ll build upon the last marketing terms glossary and get acquainted with predictive marketing terms.
Even though the definition of predictive analytics was covered in the first glossary of terms, we are going to review it as a base for the definitions to come.
Predictive Analytics – encompasses a variety of techniques from statistics, modeling, machine learning, and data mining that analyze current and historical facts about a consumer to make predictions about their future propensity to buy, churn, or become inactive. Aside from identifying prospects, predictive analytics can also help you identify the most effective combination of product versions, marketing material, communication channels and timing that should be used to target a given consumer.
With that said; we are going to drive into 10 marketing terms that directly apply to predictive analytics.
Attrition Rate - The attrition rate is the percentage of current customers who will not be buying next year. This applies to last week’s glossary term: “Churn rate” is a measure of customer attrition, and is defined as the number of customers who discontinue a during a specified time period divided by the average total number of customers over that same time period.
Big Data - is a term describing the storage and analysis of large and or complex data sets using a series of techniques such as machine learning (which we’ll define later on). Big data is about building new analytic applications based on new types of data, in order to better serve one's customers and drive a better competitive advantage against competitors.
Cluster Analysis - is a class of statistical techniques that can be applied to data that exhibit “natural” groupings. Cluster analysis sorts through the raw data and groups them into clusters. A cluster is the grouping of similar behaviors that can be observed and categorized. In the case of predictive; customers in a cluster are similar to each other in terms of buying behavior, style preferences, etc.
Data Mining - a process used by companies to turn raw data into useful information. By using software like AgilOne, to look for patterns in large batches of data, businesses can learn more about their customers and develop more effective marketing strategies as well as increase sales and decrease costs. Data mining depends on effective data collection and warehousing as well as computer processing.
Lead Conversion Rate - the lead conversion rate is the percentage of leads that subsequently became customers. You can reach this percentage by dividing total number of leads by total number of unique visitors.
Machine Learning - is an artificial intelligence discipline geared toward the technological development of human knowledge. Machine learning allows software to handle new situations via analysis, self-training, observation and experience and optimize from what it learns; facilitating the continuous advancement through exposure to new scenarios, testing and adaptation, while employing pattern and trend detection for improved decisions in subsequent situations.
Market Forecast - total level of demand for a product, across all brands, expected to result from a particular marketing effort by the competitors in the market.
Predictive Model - a predictive model is a data model, based on inferential statistics, which is used to predict the response to a direct marketing promotion.
Return on Investment (ROI) - is the financial success measurement of a direct marketing campaign. One can reach this by looking at the cost of a marketing campaign relative to the profit generated.
Targeted Optimization - can help a marketer provide experiences, content, and promotions finely tuned to the behavior and attributes of customers and prospects. Through this, marketers can discover high-value customers and other segments and be able to define comprehensive segments based on multiple sources, including historical, recent, and in-session activity. This allows for the marketer to deliver the right message and offer at the right time to the right person.
Why predictive analytics is important to you
Businesses tend to respond to customer attrition on a reactive basis, acting only after the customer has initiated the process to terminate service. At this stage, the chance of changing the customer’s decision is almost impossible.
Proper application of predictive analytics can lead to a more proactive retention strategy. By a frequent examination of a customer’s past service usage, service performance, spending and other behavior patterns, predictive models can determine the likelihood of a customer terminating service sometime soon. An intervention with lucrative offers can increase the chance of retaining the customer.
AgilOne does predictive analytics and marketing to the T. If your company offers multiple products, AgilOne can help analyze customers’ spending, usage and other behavior, leading to efficient cross sales, or selling additional products to current customers. This directly leads to higher profitability per customer and stronger customer relationships.