Intelligent CIO North America Issue 25 | Page 42



Overall , if you can get more efficiency with less investment , and if customers get the experience they ’ re looking for , that ’ s a good thing . That ’ s why personalization , when you get into these more advanced data use cases , can be so powerful .
How can prediction models help businesses make informed decisions to achieve their future goals ?
Predictive models can often get complicated , with data becoming hard to understand or maintain . For us , the first step is making data very easy to understand . Once it is made descriptive , as opposed to cryptic column names , you can then start to build machines that learn algorithms to predict things like churn or propensity to buy .
You can start to integrate your acquisition platforms ahead of that information coming along with records of past leads . It helps you be more efficient in knowing who you should engage with first and what messages engage people , but also gives you flexibility to start custom building your own systems .
We ’ re now seeing the desire not only for simple predictions like churn , but also other events that you can engage customers with , and look at behaviors to predict and inform other decisions .
We built Snowplow with the mission of not only supporting third-party AI / ML platforms , but also providing increasingly sophisticated teams with the ability to use their own Machine Learning technologies . asking a question , it makes the brand more relevant and the perception of their service increases .
How can organizations begin to utilize their data creation more effectively ?
Many businesses invest in a tool that isn ’ t quite right for them and work backwards , attempting to shoehorn their needs into it . Snowplow has designed a data creation platform to customize the entire approach and build a data structure that ’ s right for you and explains what your customers are doing .
Each business ’ customer journey is unique and you want to be able to bring it together in a way that ’ s descriptive of that journey . The first action should be to map that out and understand your model , customers and the needs for your platform .
Prediction is estimating the probability of an event happening based on all the features and behavioral data points you have created throughout a customer ’ s journey . You begin to delight customers by predicting their intention , as well as their outcome . If businesses can be exactly where the customers are while they ’ re
The next consideration is where you are collecting that data from and what policies you want to enforce . You might be obtaining data from an IoT device , for example , so you ’ d need to think about your creation point and then apply policy by considering data sovereignty and GDPR .
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