7 Tips For Using Data Analytics To Inform Revenue Operations - SmartData Collective

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By analyzing the revenue streams of a particular business, you can gain deep insights about the processes that the company uses to make money. In some cases, this can tell you where funds and time are being wasted, while others might learn that they’re missing out on opportunities to increase efficiency, or even uncover some segment of the market they should be emphasizing further with their sales efforts.

Organizations that use data analysis to improve their profitability can use the following techniques to streamline their operations and reorient their business workflows.

1. Cut Down on Your Churn Rate

No matter how good a given service is, some customers will stop using it over time. This is known as the attrition or churn rate of the business.

Retaining existing customers can be even better than attracting new ones, especially for a growing business. Customer lifetime value and acquisition cost figures can tell your business a great deal about why they lose customers and what they need to do in order to hold onto them.

2. Learn How People Interact with Your Digital Assets

Collecting information about customers’ online user experience is an excellent way to learn how they actually interact with your site and other sales touchpoints. Analyze the relevant logs to see if there are any areas that seem to be trouble spots or cause frequent support requests.

While you don’t want to make changes unnecessarily, numerical data that seems to suggest there’s some issue can make a strong case for improving your product by developing new features or code refactoring. Spending a little money on improvement now can translate into increased revenue later, considering that some studies have suggested that even user experience optimizations offer a 9,900% return on investment.

3. Optimize Recurring Sources of Income

Firms that make use of subscription plans or intermittent ordering workflows can rely on insights derived from their revenue analytics to calculate the average term length their customers buy into. This data is also a perfect way to figure out whether a particular type of customer would respond to being upsold on any additional products or services.

Taking a closer look at the average value of goods ordered is a good way to find situations where people might be open to buying a more expensive package.

4. Eliminate Logistics Hiccups

Collecting information about your organization’s current order fulfillment workflows can help you locate potential problems before they become big issues. When customers receive everything they order on time, there’s a much greater chance of them ordering more expensive products or services in the future.

Those who only deliver virtual goods, such as apps or digital music, can use similar types of data signals to identify potential problems related to their own distribution platforms.

5. Don’t Be Afraid to Change Database Platforms

Picking out the right analytical database can go a long way toward making sense of all the data your organization is collecting. Companies that have revenue information stored in a conventional flat spreadsheet might do well to opt for a relational database like MySQL or Postgres. Those who have massive notes or snippets files would probably like something non-relational such as a Hadoop-based solution.

Imperative and declarative software packages, such as classic xBase-style solutions, may work in certain edge cases as well.

6. Clean Up the Data Once It’s Extracted

It’s usually best not to use data straight from an API, regardless of how comprehensive that information might be. Standardizing and validating your sales data is an important process, because otherwise you won’t know whether you can actually trust any inferences drawn from it.

By taking the time to sort the data into some kind of logical array, you can also ensure that the analysis process takes much less time – and with more accurate revenue projections – than it would have if the number crunching had to happen all at once.

7. Read Up on Machine Learning Before Deploying It

Artificial intelligence-based revenue analysis technology can provide deep insights into how different revenue streams could be improved. The problem is that many people who use them don’t know quite how the underlying processes work. Learning more about the way that your chosen analytics package comes to its conclusions will help you identify outliers and spot any inconsistencies that the software doesn’t.

Learning more about any part of the business analysis process can go a long way toward making smarter data-driven decisions, so it pays to invest a bit of time in mastering your chosen data analysis package. Dedicating a little extra effort now can pay huge dividends when it comes time to make big changes to your revenue stream in the future.