How to take advantage of AI: 4 signs you’re ready to make the jump

Predictive marketing campaigns are the next frontier for marketers seeking to acquire new customers and maximize the lifetime value of their existing customers. Why? AI driven campaigns identify the customers that are most likely to buy and buy again, then recommend targeted promotions and products. It’s technology you encounter every day, whether shopping on Amazon or skimming through Netflix. All of those personally tailored recommendations you couldn’t live without are possible because of the machine learning technologies that support those platforms. And now, thanks to cloud computing, predictive marketing is on its way to becoming truly democratized. What was once an expensive proposition accessible only to large enterprises is now affordable and mainstream. In many ways, predictive marketing has moved from a luxury to a necessity, one every organization needs to survive and remain competitive.

Thankfully, barriers to entry for these advanced technologies are lower than ever before  every organization should be able to take advantage of predictive analytics technology. Still, some are better suited than others.

How do you know if you’re ready to take advantage of predictive marketing technology? Here are four indicators it’s time to take the leap:

1.      Data Readiness: Your data is relatively clean; Data dictionary is available; Data ownership is defined clearly within the organization; 

Predictions are based on machine learning. A machine is computer software that can process large volumes of data, discover hidden patterns and identify success stories. In order for those outputs to be accurate, the inputs must be clean. If you already have a data warehouse or some sort of data repository containing clean (standardized) data, you’re off to a great start.

It is also important to clearly define data ownership within your teams. Inevitably, there will be some data related cleanup to do. Data owners are responsible for making data-related decisions, as well as working continually to ensure data remains clean and accessible.

2.      Existing Baselines: You measure performance of your campaigns and the return on investment; You segment (or plan to segment) your customer base

The ability to measure the effectiveness of predictions produced by machine learning is critical. How do you know if you’re better off using predictions than your current method? If you’re already measuring the performance of your campaigns, great. Otherwise, you should work to establish ways to measure the success of your existing methodology. This will help you adjust your campaigns and marketing methods to ensure maximum ROI.

3.      Customer Journey: You capture data from most of the touch points along your customers’ journey

Machine learning is about processing data to understand cause and effect. The more data points you can feed to the machine, the better it gets at understanding what caused a customer to purchase or abandon the cart. You should look to capture all customer touchpoints and interactions, from website visits to call center interactions, outbound marketing campaigns, sales history, returns, surveys, etc. Collecting this information will help you identify critical gaps in the customer journey, allowing you to revise your campaigns and marketing practices accordingly.

4.      Data Driven Culture: Organizational culture encourages data-driven decision making

Even the most accurate predictions are meaningless until embraced by organizational decision makers. At times, prediction recommendations can be counterintuitive. But you can always understand why the machine recommends a specific course of action. Having a data-driven culture that encourages data-driven decisions is an absolute must if you plan to implement machine learning, not to mention manage a successful predictive marketing campaign.

Does your organization meet the criteria above? If the answer is yes, it’s time to boost your marketing efforts with an AI-driven approach.

Thank you for reading the post.

Questions? We’re happy to help. Get in touch at info@vectorscient.com

Author’s bio: Suresh Chaganti, Co-Founder & Strategy advisor at VectorScient. Suresh specializes in Big Data and applying it to solve real world business problems. He brings in 2 decades of experience in architecting B2B and B2C applications across a variety of Industry verticals. Connect with suresh on linkedin 

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