ABOUT THE AUTHOR. Dominique Levin VP of Marketing, AgilOne. Follow Me on

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2 ABOUT THE AUTHOR Dominique Levin VP of Marketing, AgilOne Follow Me on Dominique is the VP of marketing at AgilOne. She joined from Totango, a leader in customer success management software and was previously CMO at Fundly, helping non-profits with social media fundraising, and CEO and CMO at LogLogic a big data analytics company which was sold to TIBCO. She has lived and worked on four continents and when not at work plays ice-hockey or hits the road sightseeing with her husband and three kids. 2

3 TABLE OF CONTENTS INTRODUCTION 4 CLUSTERING MODELS 5 PROPENSITY MODELS 9 COLLABORATIVE FILTERING MODELS 12 CONCLUSION 15 RESOURCES AND NOTES 16 3

4 INTRODUCTION Predictive Marketing Analytics can be a mysterious realm for most marketers and we understand why. With all that customer data, it can be hard figuring out the most effective way to make your data work for you. This ebook will walk you through 10 predictive analytics models in 3 categories: Clustering, Propensity, and Collaborative Filtering. Each predictive model can help increase revenue and delight your customers through highly targeted and segmented marketing. We break down what each model does and how to most effectively leverage that model to cash in on your customer data. 4

5 CLUSTERING Clustering is another term for customer segmentation. Algorithms are able to segment customers based on many more variables than a single person ever could. It s not unusual for two clusters to be different on 30 customer dimensions or more. We call these dimensions the cluster DNA. 5

6 The example below shows some of the factors that could make up a cluster s DNA. The most common clustering algorithms are behavior-based clustering, product-based clustering (also called category-based clustering) and brand-based clustering. 1. Behavior-based Clusters The behavior-based clustering model groups customers based on how they behave while purchasing: do they use the website or the call center? Are they discount addicts? How frequently do they buy? How much do they spend? How much time will pass before they purchase again? This algorithm helps set the right tone while contacting the customer. For instance, customers who only buy with heavy discounts may be great targets for inventory-clearing sales, whereas customers who typically pay full price would be better targets for a sneak-peek promotion for a new product line. 6

7 2. Product-based Clusters Product-based clustering models group customers based on what types of products they tend to buy. See the example below of a product-based segment or cluster. You can see people in one customer segment tend to ONLY buy sweaters, whereas those in another customer segment buy different types of active wear products, such as outerwear, sportswear, swimwear and watches but never kids clothes, intimates or jewelry. This is useful information when deciding which product offers or content to send to each of these types of customers. 7

8 3. Brand-based Clusters Brand-based clusters, quite simply, tell you what brands people are most likely to buy. When a specific brand releases new products, you will know who is likely to be interested. But these models can also offer broader insights into related brands that may be of interest to a customer. The following graphic shows how brand-based clusters can work. The results of the model displayed here illustrate that customers who like Tahari also tend to like Calvin Klein and Nine West, but would not be interested at all in Desigual or notes 8

9 PROPENSITY Propensity models (also called likelihood models) are what most people think of when they hear the term predictive analytics. Propensity models make predictions about a customer s future behavior. 9

10 4. Predicted Lifetime Value Algorithms can predict how much a customer will spend with your company over time. At the moment a customer makes his or her first purchase, you may already have a lot more information than just the initial transaction record. For example, you may have and web engagement data as well as demographic and geographic information. By comparing this new customer to many others who came before him or her, you can predict future lifetime value with a high degree of accuracy. This information is extremely valuable as it allows you to make value-based marketing decisions and to allocate your marketing spend appropriately. Once you have access to reliable predictive models, it makes sense to invest more in those acquisition channels and campaigns that produce customers with the highest predicted lifetime value. 5. Likelihood to Engage A likelihood to engage model predicts how likely it is that a customer will click on your links. No marketer wants to annoy their customers or encourage them to unsubscribe. So by using a likelihood to engage model, you can potentially send fewer s to each customer, drastically lower unsubscribe rates and achieve higher customer engagement. 10

11 6. Likelihood to Buy - First Purchase The likelihood to buy model for first purchases evaluates nontransaction customer data, such as clicks and website interactions to predict which customers are ready to make a first purchase. Once you re armed with this data, you can modify any discounts or offers to maximize profitability. People who are likely to buy won t need deep discounts while customers who are not likely to buy may need a more aggressive offer to entice them to make a purchase. 7. Likelihood to Buy - Repeat Purchase Once a customer makes a first purchase, the next goal is to maximize revenue and profitability from future purchases. The likelihood to buy model for repeat purchases evaluates earlier transactions and other interactions to determine which customers are likely to buy, and which customers may need more encouragement in the form of a gift or discount. This model helps to maximize both revenue and profitability from each member of your customer base. 11

12 COLLABORATIVE FILTERING The common marketing term for collaborative filtering models is recommendations. These recommendation models were made famous by Amazon with their if you like this product, yuo might also like suggestions. There are three basic types of recommendations - upsell recommendations, cross-sell recommendations, and next-sell recommendations. 12

13 8. Upsell Recommendations Upsell recommendations are typically made to customers at the time of purchase, such as at the time of online, phone or in-store purchase. Super-sizing McDonalds meals would be a classic example, but examples can be found in all industries. You could suggest a higher-end version or a multi-pack of the same product at a better price. For examle, upsell recommendations are typically tied to a specific SKU where each product has suggested products that can be used as upsells. 9. Cross-Sell Recommendations Cross-sell recommendations are also made at the time of purchase. Rather than recommending buying in bulk or buying a version of a specific product, cross-sell recommendations are made to suggest other products that are typically bought in conjunction with a specific item. The recommendation could read: customers who bought this item also tend to buy and you could offer a modest discount if the customer decides to follow your cross-sell bundle. Like upsell recommendations, cross-sell recommendations also tend to be tied to a specific SKU. Every product has suggested products that can be used as cross-sells. 13

14 10. Next-Sell Recommendations Next-sell recommendations are typically made after a customer has already purchased a product. This type of recommendation could be included in a thank you page or in the purchase confirmation . The best next-sell recommendations are specific to each customer and take into account more customer data than just the most recent transaction. An example of a next-sell use case is documented in the book Data Driven Marketing by Mark Jeffery. The book describes how a large home improvement store used data to discover that customers who build decks tend to be in the market for a grill shortly thereafter. The company then launched a clever next-sell campaign recommending grills to these customers. notes 14

15 CONCLUSION Getting the most out of your predictive models Predictive analytics models have the potential to be very beneficial, but they aren t effective unless you can tie them to your day-to-day marketing campaigns. So make sure that your predictive analytics platform is directly integrated with your marketing execution systems such as your service provider, website, call center and/or POS system. It is better to start with just one integrated and actionable model that can drive effective marketing campaigns, rather than instantly using all 10 models without a way to take marketing action on the predictive insights. 15

16 REQUEST A DEMO Increase your revenue and delight your customers with indepth customer profiling, predictive marketing intelligence, and targeted campaigns. Get a free demo of AgilOne s Predictive Marketing Platform today! 16

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