Powering Customer Experience with Predictive Analytics – Planning

The customer experience arguably the most important part of your business. Without customers, you aren’t able to meet any of your business goals. Without a solid customer experience, you will have a hard time attracting and keeping new business.

How do we even start to tackle the customer experience and understand what our audience wants? One piece at a time. During this series, we’ll explore each step of the customer journey and how you can use predictive analytics to create more effective marketing plans for your customer experience.

Read the previous post here: 

Let’s walk through the steps to follow to put a predictive forecast together effectively.

  • Project – Set the strategy, goal, and desired outcome
  • Pull – Extract the data from where it lives
  • Prepare – Clean, refine, and prepare the data set
  • Pick – Identify which variable to predict
  • Predict – Create the prediction
  • Plan – Build a plan of action from the forecast

predictive analytics process


You could just start running the predictive algorithm on a data set to see what happens, but it’s more efficient to have a plan and know what you’re after. To begin, start with building out the project. This includes setting your goals and understanding your desired outcome. What stage of the customer experience are you trying to get more insight into? If you don’t know that information, start more simply with “what’s the problem I’m trying to solve” or “what’s the question I’m trying to answer?” – once you have that information you can start to build out your requirements. This includes making sure you have the right predictive algorithms and you have access to the right data sets. Your project plan will also help you build out your timeline from start to finish. When is your analysis due (if to a client or stakeholder)? Working backward, do you have enough time for insights and analysis, enough time to clean the dataset and run the algorithm? You should be able to answer all of these questions before getting starting on the data set.


The next step is to pull the data you’ll use for your analysis once you’ve got a good sense of the overall project. There are a lot of good options when it comes to running a predictive forecast. CRM, Marketing Automation, Email Marketing, and social data are all good places to start. You can also use your revenue or accounting data. If you don’t have access to any of that data you can go with data that is publicly available. Some good quality options include, but are not limited to, Google Trends,, and Statista.


Once you have your data set, you need to clean and prepare it. You’ll want to be aware of any missing data, any anomalies, or anything that could make the algorithm get stuck. You also want to make sure it’s in the correct format. For instance, if the date column came out as a long string, you’ll want to convert that to something more standard like YYYY-MM-DD, Make sure the format matches what lives within the algorithm script. If not, you could spend a lot of time redoing the analysis. You might also find that special characters or emojis (depending on the source of the data) got into the file. You’ll want to make sure that kind of data gets cleaned up and cleared out.


When you have a clean dataset you need to determine which is the most important variable to predict on. This is where having a handle on the different methods of predictive is helpful. First, you can run a driver analysis to determine the variables that are the most important to your audience. Once you know what is the most important, you can run the time-series analysis to determine the likelihood of an event happening. Watch this video to learn more about the predictive models:


Now it’s time to run your algorithm and get your prediction. There are a couple of options for running a predictive algorithm. You can purchase something off the shelf. At this time, there aren’t a lot of great off the shelf options unless you’re an enterprise company. An off the shelf predictive model can cost you upwards of six figures. You can build the algorithm yourself. There are free coding tools, such as Python or R-Studio, that can accomplish this. The investment would be the time to learn how to code in those languages. The third option is to hire a consultancy, like Trust Insights, to run the model for you.


You have your results – you’re ready to dive into the insights and build a plan to execute and measure. Are the results what you expected? Are there any surprises? The plan that you build will align with the overall goals you outlined originally in the project. The part of the customer experience that you’re focused on will dictate how you build out and execute your plan.

In the next post, we’ll cover using predictive modeling at the awareness stage of the customer experience and the different datasets that can enhance your planning.

In the meantime – if you have questions about the customer experience or predictive analytics let us know!

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