Connecting Data to Revenue

A long-time client recently told me that, for their data and metadata management efforts to be viewed as successful by Senior Leadership, improvements in these disciplines need to be directly associated with increases in revenue. This is a new demand, and this demand needs to be satisfied quickly. My client must make the connection or risk loss of funding for data and metadata management initiatives. Thus, a new and unexpected opportunity arose.  It’s all in the data.

Immediately embracing the challenge, I set out to research, define and recommend a series of steps that can be used to associate data activities to increases in revenue. Since there was no silver bullet available on the internet, I quickly jotted down the following handful of steps and prepared to discuss them with my client:

Recognize Where Revenue Comes From

We all know that revenue comes from sales. The simple equation of “revenue equals price times units sold” focuses on income from sales of goods or services. For most organizations this definition holds true but only shows a piece of the picture.

Revenue can also come from secondary sources and take on different meanings depending on the context. Revenue can be projected as expected lifetime value from a customer. Revenue can be generated through partnerships and relationships. For non-profits, revenues are determined through gross receipts. Revenue directly impacts an organization’s income statement. Looking at your income statement can quickly answer the question of where revenue comes from.

Identify Factors Impacting Revenue

Factors that impact revenue are often specific to where the revenue comes from. Directing the right questions at the right people – or using the data to analyze cause and effect, is focused on determining what influences revenue fluctuations. Factors are often data-related…or can be found by analyzing the data itself.

Choosing the right market for revenue growth is important. Choosing the market is influenced by the data that you have on that market, the timeliness and quality of that data and the confidence people have in using the data to make important decisions.

Removing friction from the sales process is a factor that impacts revenue. Aligning your sales and marketing functions also impacts revenue. These factors are data-focused as friction through poor information or misaligned Sales and Marketing often lead to decreases in sales. Efficient sales processes and aligned business functions are often data-related and directly impact revenue.

Determine Impact Data Has on Revenue

If your organization has not yet linked data and revenue, the act of determining impact requires the ability to project into the future. You can do your best to associate past data and information capability enhancements to revenue changes, but the data is not often available to make that connection. If you look to the future, you can benchmark your present state and report your results along the way.

For example … What will be the results of salespeople becoming better equipped with customer data and information? Will it lead to stronger customer relationships leading to expanded portfolios and new revenues? Is there a way to demonstrate the effect that more information has on sales results?

What will be the impact on direct customer revenue when your customers are provided self-service access to product and service data and efficient purchasing capabilities? Data and information are precious resources that impact every line of your income statement.

Articulate Connection Between Data and Revenue

Since sales is often considered similar to revenue, it is important to look for a direct connection between data and sales. There are several ways to connect increased customer value and sales to the information you have about your customers.

In one example, Amazon not only does a good job tracking what you buy and when you buy it, but they track and report to you (suggest) what other customers have purchased related to your purchases. The data has clearly demonstrated to Amazon that these connection points often lead to additional sales.

A supermarket customer loyalty program is promoted to decrease prices for regular customer, while the true value comes in the data that the supermarket records. The stores know what you buy, how often you buy it, when you buy it and they  keep track of items that are bought together.

In these cases, the organizations can articulate that improvement in revenue can be attributed to improvements in data and analytics. This connection is not always obvious, and you will need to have evidence (in terms of cause and effect) to prove out the relationship.

Direct Data Actions at Revenue

Once you have identified and documented business factors that influence revenue, and you have recognized the impact the data and information have on these business factors, it is important to direct the actions you take at improving the management of that data.

These data actions may include the implementation of formal data governance and stewardship practices to assure accountability for the definition, production and use of the data. These data actions may include the management of metadata associated with building confidence in data that impacts revenue generating factors. These data actions may include the development and delivery of strategic analytical platforms that permit data scientists to predict trends and study the cause and effect of revenue changes.

The most important consideration for directing data actions at revenue is to assure that you can connect the cause (the data actions you take) to the effect (changes to revenue). This relationship is not always easy to quantify yet, as demonstrated in the request I mentioned at the start of this column, this type of request is real.

Measure Changes in Revenue

The last step of this process is to measure the impact the data actions mentioned above have on reported revenue. To accurately measure the impact of the data actions requires that you take a benchmark measure associated with each stream of revenue and observe changes to the revenue against the timing of the specific actions you take.

Conclusion

The cause and effect of data actions and changes to revenue play a large role in being able to relate data management to financial improvements in the business. Organizations tend to focus on efficiency gains, reduction of costs and reduction of risk as ways to relate the data actions they are taking to the organization’s bottom line.