ABOUT THE AUTHOR. Stephanie Macedo Field Marketing Manager. Follow Me on

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2 ABOUT THE AUTHOR Stephanie Macedo Field Marketing Manager Follow Me on Stephanie is the Field Marketing Manager at AgilOne. She helps to develop and market regional marketing events across the US and Canada. Stephanie also works closely with the content team to develop relevant and interesting ebooks, customer stories and s. She is passionate about predictive marketing technology and helping retailer create the ultimate customer experience. 2

3 TABLE OF CONTENTS INTRODUCTION 4 DIRTY DATA 5 POORLY MAINTAINED MODELS 8 IN-ACTIONABLE INSIGHTS 11 CONCLUSION 13 MARKETERS CHECKLIST AND RESOURCES 14 3

4 INTRODUCTION Data-driven marketing can increase a company s revenue and improve the customer experience. However, there are a few common mistakes that are made when implementing this type of marketing strategy. This ebook will address how to avoid these data-driven marketing pitfalls: Dirty Data Poorly Maintained Models In-actionable Insights 4

5 DIRTY DATA When it comes to your customer database, the old saying holds true: garbage in, garbage out. If you base your customer segmentation or predictive models on bad or incomplete customer profiles, you will get the wrong recommendations for your customers. Acting upon those recommendations inevitably leads to even bigger problems. Sending customers the wrong recommendation is much worse than not sending any recommendations at all. 5

6 Data scientists will tell you that data preparation before analysis can make up 95% of all the work. This groundwork is invaluable when the results render high quality and accurate data. Working with dirty data can cause marketers to: Mix up a person s gender Misspell a person s name Include obscenities in a name or address (yes, people can get creative when filling out your forms) Send undeliverable or direct mail Recommend the wrong store location Recommend irrelevant products or services Confuse household members Send multiple mailings to the same household Offer incentives or discounts on a product that has already been purchased Incomplete information about a customer can lead to the wrong conclusions about behavior and profitability. Another common error is drawing conclusions based on only one aspect of a customer. 6

7 For example, perhaps a customer browses your website frequently but always ends up buying in the store. If you don t reconcile your online and offline data sources you may mistake the website customer as low value, when he or she could be very profitable. In a different scenario a customer spends a large amount and buys often, but returns items just as frequently or makes frequent calls into your call center. This behavior also influences profitability. At a minimum you should link customer data from the following sources: behavior Web behavior Online transactions In-store transaction Loyalty program interaction Call center interaction Returns and complaints The data may be controlled by different parts of the organization and physically reside in different databases on different servers. This can cause the data to be difficult to organize and integrate. But looking at interactions across channels is necessary to keep accurate customer profiles and determine customer s true value. notes 7

8 POORLY MAINTAINED MODELS After collecting a complete and accurate profile of every customer, it s time to apply customer analytics and predictive models. A predictive model is an equation that explains the likelihood of a certain action. This could be how much money a person will spend, how likely someone will respond to an offer or stop using a service. After collecting accurate data, it is important to choose the correct model, or equation, to fit the data. 8

9 There are multiple types of predictive models, but three main models that marketers should be familiar with are clustering models (segments), propensity models (predictions) and collaborative filtering (recommendations). Different predictive models are appropriate for different types of data. Plan for ongoing maintenance of predictive models or they will not produce accurate recommendations. Many marketers make the mistake of hiring consultants to build a one-time model, but don t plan for ongoing revision of these models. If you think about the model as an equation, it easy to see why one model, or one equation, will not be right for all data sets. For example, if you wanted to calculate the radius of a circle, you are not going to use the same equation to measure the height of a square. To be most effective, predictive analytics models for marketing (and any other predictive models) require constant tuning. The models will not continue to produce accurate recommendations unless you revise them over time. This is where machine learning can be extremely valuable. The term machine learning can sound daunting, but it is simply the ability for a software program to continually improve its algorithms or calculations. The best predictive analytics solutions will automatically modify their algorithms over time as they receive new data points and learn from the accuracy of their earlier predictions. 9

10 Check with your analytics vendor to see how their predictive algorithms are maintained. Part of their services should include refreshing both the customer data and models on a daily basis to ensure ongoing accuracy. If you build models yourself, make sure to plan for the resources that will maintain the models over time. notes 10

11 IN-ACTIONABLE INSIGHTS Perhaps the worst infringement is to collect customer data and apply predictive analytics, but not take any action. Data insights are great, but unless you re applying customer data and customer predictions to your daily marketing campaigns, you won t see any returns. Unless you apply your customer analytics insights in your day-to-day marketing programs you will see zero return. 11

12 Too often customer analytics or business intelligence programs don t integrate with service providers or other marketing systems. This leads to situations where a marketer may learn very interesting insights, but have no way to act on them. Once you have access to newly defined customer segments, or learn customer behaviors or preferences, make sure that you can convert these findings into targeted marketing campaigns. To be able to harness the power of your data, you ll need system integration and be able to connect your customer database and predictions database directly with your marketing software such as your service provider, direct mail house and website. If you use modern applications, they should have open APIs and allow for connectivity, but the process of building connectors can be tedious. So make sure to ask your analytics vendor about their support for your marketing apps. notes 12

13 CONCLUSION Data-driven marketing and predictive models have huge potential benefits, but you have to be aware of the potential problems before pursuing a new data-driven strategy. Before making a major investment, think through how your data and analytics will be maintained over time and how they will ultimately impact your marketing programs. Use our checklist to get started or to evaluate your current efforts. 13

14 THREE COMMON DATA-DRIVEN MARKETING MISTAKES CHECKLIST To Avoid Dirty Data, Correctly Record and Integrate: Gender Correctly Spelled Names Eliminate Typos in addresses Eliminate Wrong Mailing Address Update Addresses If A Person Moved Correct Store Recommendations Correct Household Information Remove Duplicate Mailing Addresses Behavior Web Behavior Online Transactions In-store Transactions Loyalty Program Interactions Call Center Interactions Returns and Complaints 14

15 What additional categories are relevant for your business? Poorly Maintained Models: Check with your analytics provider to see if they run routine or daily updates to your customer data and predictive models. Arrange to have resources in place to routinely update custom built predictive models. Inactionable Insights: Ensure that your customer analytics system can integrate with your marketing software: Your ESP Your direct mail system Your website Other? Other? 15

16 Create a plan that makes sense for your team. Ensure that all data can be utilized and there is a point person to oversee data management. Connect with your analytics provider to ensure their support is available each step of the way. notes 16

17 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! 17

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