One of the most powerful tools you can offer your clients is data modeling. Analyzing customer data answers questions, provides clarity, and allows your clients to make informed decisions about how they want to grow their business. However, it’s easy to get lost in the analysis and present findings that have little to no value to the client. Avoid delivering irrelevant information by asking these 10 questions to establish what the client already knows, to identify questions and obstacles, and to discover what they hope to achieve.
Step One: What Do They Know?
Clients are looking to you to add value and further insight into what they know about their customers. If you don’t establish what that information is, you can waste time going over insights they already possess. Understanding the client’s knowledge base will not only guide your analysis but will also let you know what information you can confirm for them, what will be contradictory to their findings, and what will be new revelations for them.
1. Who are your customers?
With this question, you want to understand who they believe their target audience is and how much information they have about their customers. This includes demographics, geographical, lifestyle, and behavioral data. As you talk through this, you can look for any gaps in their understanding. How can you use data modeling to give them a more complete and accurate picture of who their customers are?
2. What is working?
Where have they seen the most success? Are there certain segments of their customers that outperform others? What seems to be the entry product/service customers buy? Ask about their marketing methods. Which ones yield the highest response rates, greatest return on investment, and largest increases in sales. How can you help them grow this success and apply it to other aspects of their business?
3. What is not working?
Do they see problems in their current marketing strategy? Do they know why it’s not working? Analyzing customer data can determine whether or not they are going after the best prospects. If they are not, they can adjust their audience. If they are, then it’s time to look at the strategy. Should it be tweaked or replaced?
4. What are they struggling with?
What obstacles are they facing? Is there information they are unable to find? What are their pain points? Pay close attention to these answers because this is why they are coming to you. This is what they need help solving.
Step Two: What Do They Want to Accomplish?
Goals create clear directions and help define the scope of a project. Laying out the questions the client wants answered will help keep everyone on track as well as establish expectations. If a client has unrealistic expectations, this is where you want to identify them and let your client know what is and is not possible, so they won’t be disappointed with the results. These questions will help you create a strategy and a plan for how to move forward with the analysis.
5. Who do they want to target?
Does your client want to expand their target audience to reach new demographics or go after more people who look like their current customer base? Data modeling analyzes the customer data and identifies who is most likely to make a purchase, so your client can focus their marketing efforts on top prospects.
6. How would they like to grow?
Does the company want to grow through new product offerings, expand their territory, or reach new prospects? Analyzing customer data can identify which products new customers are buying, find new markets with high potential for growth, and locate new prospects who look just like your top customers.
7. What does success look like?
Is success finding out information about their customers? Is it a certain response rate to the next marketing campaign or an increase in sales? Define what success looks like before you get started. This makes sure everyone knows what the goals and expectations are for the project.
Step Three: Practical Questions
Once you know where you are starting from and where you are heading, it is time to ask the practical questions.
8. What data is available?
Do they have good customer data? How many records do they have? Do they have transactional data? Does the data need to be cleaned up and enhanced before you can model it? The answers to these questions will help you establish how much data preparation you will need to do and how long the process will take.
9. Have there been any major changes in the company?
Ask if there have been any acquisitions, mergers, or new locations opened. If so, identify those locations prior to analysis. If a bank has several new acquisitions, those branches may have very different customer bases than the rest of the branches. Knowing this up front will help you understand any anomalies that come up in the analysis. You might even want to analyze those locations separately.
Another item to consider before analysis is brand changes. If a company has made major changes in their target audience, take some time to understand what those changes were and why they made them. When did the shift occur? How does their current target audience differ from their previous demographic?
10. What marketing channels are being used?
Do they use direct mail, digital marketing, or omni-channel marketing? Will you need to analyze the differences in direct mail only customers versus those that receive digital only or those that receive both? How do they currently segment their customers for each marketing tier?
Wrap Up
Since data models are custom built, these questions will help you identify what your client needs and how you can help them. Not every question needs to be asked or will be relevant for every client, so use your discretion. The goal of asking these questions is to understand what your client needs, identifying how you can help, and setting clear expectations for what will be done through the data modeling process.
Creating clear goals and using proactive communication will enhance your client’s experience as you work together. If your client is ready to grow their business, we can partner with you by analyzing customer data and walking with you through the process. Find out how.