Predictive analytics enables you to develop mathematical models to help you better understand the variables driving success. Predictive analytics relies on formulas that compare past successes and failures, and then uses those formulas to predict future outcomes.
Predictive analytics, pattern recognition, and classification problems are not new. Long used in the financial services and insurance industries, predictive analytics is about using statistics, data mining, and game theory to analyze current and historical facts in order to make predictions about future events.
The value of predictive analytics is obvious. The more you understand customer behavior and motivations, the more effective your marketing will be. The more you understand why some customers are loyal and how to attract and retain different customer segments, the more you can develop relevant, compelling messages and offers.
Predicting customer buying and product preferences and habits requires an analytical framework that enables you to discover meaningful patterns and relationships within customer data in order to achieve better message targeting and drive customer value and loyalty.
Predictive models have been used in business to assess the risk or potential associated with a particular set of conditions as a way to guide decision making. Predictive models improve marketing effectiveness by analyzing past performance to assess how likely a customer is to exhibit a specific behavior in the future.
Marketing and sales professionals are beginning to capture and analyze many different types of customer data—attitudinal, behavioral, and transactional—related to purchasing and product preferences to make predictions about future buying behavior.
Today's challenging environment is forcing more organizations to explore predictive analytics. Commonly used by market researchers when analyzing survey data, predictive analytics can also be applied in real-time scenarios, such as personalizing offers to customers or improving an online customer experience.
There are various approaches to predictive analytics, and most depend on clean databases and the ability to mine data to look for patterns or to create classifications. It is important to understand the various approaches so you know when to use which one.
This article provides a quick explanation of the nine most common data-mining techniques used in predictive analytics. Becoming familiar with them will go a long way toward enabling you to recognize patterns in customer preferences and buying behavior.
1. Regression analysis. Regression models are the mainstay of predictive analytics. The linear regression model analyzes the relationship between the response or dependent variable and a set of independent or predictor variables. That relationship is expressed as an equation that predicts the response variable as a linear function of the parameters.
2. Choice modeling. Choice modeling is an accurate and general-purpose tool for making probabilistic predictions about decision-making behavior. It behooves every organization to target its marketing efforts at customers who have the highest probabilities of purchase.
Choice models are used to identify the most important factors driving customer choices. Typically, the choice model enables a firm to compute an individual's likelihood of purchase, or other behavioral response, based on variables that the firm has in its database, such as geo-demographics, past purchase behavior for similar products, attitudes, or psychographics.
3. Rule induction. Rule induction involves developing formal rules that are extracted from a set of observations. The rules extracted may represent a scientific model of the data or local patterns in the data.
One major rule-induction paradigm is the association rule. Association rules are about discovering interesting relationships between variables in large databases. It is a technique applied in data mining and uses rules to discover regularities between products.
For example, if someone buys peanut butter and jelly, he or she is likely to buy bread. The idea behind association rules is to understand when a customer does X, he or she will most likely do Y. Understanding those kinds of relationships can help with forecasting sales, promotional pricing, or product placements.
4. Network/Link Analysis. This is another technique for associating like records. Link analysis is a subset of network analysis. It explores relationships and associations among many objects of different types that are not apparent from isolated pieces of information.
It is commonly used for fraud detection and by law enforcement. You may be familiar with link analysis, since several Web-search ranking algorithms use the technique.
5. Clustering/Ensembles. Cluster analysis, or clustering, is a way to categorize a collection of "objects," such as survey respondents, into groups or clusters to look for patterns.
Ensemble analysis is a newer approach that leverages multiple cluster solutions (an ensemble of potential solutions). There are various ways to cluster or create ensembles. Regardless of the method, the purpose is generally the same—to use cluster analysis to partition into a group of segments and target markets to better understand and predict the behaviors and preferences of the segments.
Clustering is a valuable predictive-analytics approach when it comes to product positioning, new-product development, usage habits, product requirements, and selecting test markets.
6. Neural networks. Neural networks were designed to mimic how the brain learns and analyzes information. Organizations develop and apply artificial neural networks to predictive analytics in order to create a single framework.
The idea is that a neural network is much more efficient and accurate in circumstances where complex predictive analytics is required, because neural networks comprise a series of interconnected calculating nodes that are designed to map a set of inputs into one or more output signals.
Neural networks are ideal for deriving meaning from complicated or imprecise data and can be used to extract patterns and detect trends that are too complex to be noticed by humans or other computer techniques. Marketing organizations find neural networks useful for predicting customer demand and customer segmentation.
7. Memory-based reasoning (MBR)/Case-based reasoning. This technique has results similar to a neural network's but goes about it differently. MBR looks for "neighbor" kind of data rather than patterns. It solves new problems based on the solutions of similar past problems. MBR is an empirical classification method and operates by comparing new unclassified records with known examples and patterns.
8. Decision trees. Decision trees use real data-mining algorithms to help with classification. A decision-tree process will generate the rules followed in a process.
Decision trees are useful for helping you choose among several courses of action and enable you to explore the possible outcomes for various options in order to assess the risk and rewards for each potential course of action.
Such an analysis is useful when you need to choose among different strategies or investment opportunities, and especially when you have limited resources.
9. Uplift modeling, aka net-response modeling or incremental-response modeling. This technique directly models the incremental impact of targeting marketing activities. The uplift of a marketing campaign is usually defined as the difference in response rates between a treated group and a randomized control group.
Uplift modeling uses a randomized scientific control to measure the effectiveness of a marketing action and to build a model that predicts the incremental response to the marketing action.
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The above approaches and the use of predictive analytics take you beyond the traditional slicing and dicing of your data so you can be smarter and more agile when marketing.
With predictive analytics, you can gain faster insights and optimize programs by simultaneously testing copy, offers, and creative rather than deploying the more traditional A/B-testing methodology, which may take longer and so delay course adjustments.