Written by: Kayla Matthews journalist & writer
Companies have access to more information about their customers than ever. Online storefronts, social media and email marketing all provide major opportunities for collecting data on what their customers prefer and how they behave when they shop.
Many businesses, however, don't take full advantage of this data. With the right approach, it is possible to use the data that these companies have access to build better processes that improve customer satisfaction.
Below, we'll cover the best practices for analyzing data to improve customer experience.
1. Start With a Plan
To keep your analysis and data collection scheme focused, you should always start with a plan of how you'll use analytics to improve customer experience. This will usually start with identifying a problem that a business has.
These may be metrics that need to be improved — low customer satisfaction rates, high lead times or poor first contact resolution figures. More general problems can also help structure your plan, but a specific goal works best. Ideally, you'll have some KPI that you can track as you analyze data and tweak business processes. This will let you see if your use of big data is making a difference.
2. Know How Businesses Use Big Data
Even if you've worked with big data before, it's important that you know how businesses and retailers are using big data right now to improve the customer experience. An idea of common industry uses of big data can help structure your approach.
Many businesses, for example, use data to improve their marketing operations, aiming to create more accurate and relevant promotions for their customers.
It's also not uncommon for logistics companies to use big data to predict shifts in demand, helping them prevent and shocks to the supply chain. Anyone working with big data can use that information in roughly the same way, using historical and real-time sales data — combined with other relevant information, if possible — to more accurately forecast demand fluctuations.
3. Keep the Data Accurate and Relevant
Big data is only an effective tool when the data being used is accurate, up-to-date and highly relevant to the question you're trying to answer.
When working with big data, it's important that you keep your datasets regularly cleaned and updated. Otherwise, you risk working with outdated or incorrect information that can lead you to the wrong assumptions about a particular customer base.
Ideally, the data is also very comprehensive. When working with customer preferences, and sales data especially, the things that are true at one point in the year or month may not be true at any other time. Some products may be very seasonal. A customer base may also tend to only purchase items at a specific time of month or part of the year. Unless you have enough data to capture these variations, it's possible that you'll miss out on them and be more likely to make incorrect assumptions.
4. Take Advantage of Advanced Analytics Technology
Cutting-edge analytics technology can often make a significant difference when handling big data.
Data scientists use artificial intelligence, for example, more and more often to find subtle patterns and correlations in large datasets. Many modern business intelligence platforms incorporate AI in one way or another, meaning that it's also not too difficult to begin using AI to uncover new relationships in big data.
By leveraging this kind of technology, you can sharpen your analysis and potentially improve the accuracy of your forecasts.
How Any Business Can Improve Customer Experience With Big Data
Greater access to data collection means that almost any company with the resources can leverage big data analysis. As more data is becoming available all the time, big data is also likely to become even more useful in the future.
However, a strong collection and analysis plan will be necessary if a company wants the maximum value of the data they collect. Planning to improve a specific metric or area of operation — like overall customer satisfaction or the accuracy of sales forecasting — will help structure a big data analysis project.
Companies can also take advantage of some of the latest tech — like AI — to further improve their analysis.