The customer experience is 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 posts here:

Congratulations  – you now have a paying customer! Once someone is a customer they have a different set of needs and you have a different set of responsibilities, especially if you want to keep them long term. Let’s dig into the second half of the customer experience, the owner’s journey.

customer experience

 

E-Commerce data

With customers comes revenue. Revenue powers your business. You want to be able to predict seasonality with your revenue so you can do your financial planning, tune-up spending or pull it back. Using the same predictive algorithm, you can run a forecast against your e-commerce data, getting a better sense of when you can expect the highs and lows of incoming money. You can think about comparing this analysis to your leads forecast from the evaluation stage. This will help you understand the lag between leads and customers, and if the peaks and valleys trend the same way. Having this data will help you stabilize your incoming revenue and make it more predictable for your financial planning.

e-commerce data

Customer profile data

Converting one customer is great. Converting more customers is even better. Once you have a database of customers and their profile you can use predictive analytics to figure out how to win more customers. You should be capturing data that tells you when you’ve won or lost customers, including the “why”, “what” and “how”. If you pull all of your customer data from your marketing tech stack into one massive spreadsheet it will be nearly impossible to analyze using manual methods. Instead, you can use the driver-analysis algorithm to determine the factors that comprise the profile of a customer that you are more likely to win. This analysis will help you understand the things that are the most important that you need to be doing and offering up. Once you have the variables that outline the most important things to your target audience you can run a time-series analysis to figure out when to have that information in front of other potential customers – increasing your likelihood of conversions.

As we move through the owner’s journey, the next phase we’ll explore is ownership and dig into your customer support data.

 


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