Could you prove the return on investment (ROI) of paying your monthly electricity bill? If asked, the quick answer might be: “Isn’t it obvious? The household can’t function without it.” We need the utility to support systems we depend on, from heat and water to Internet access. Trying to define a specific ROI might be tricky, however, as it’s difficult to put a dollar value on access to functional systems. Such is the challenge of proving the ROI of investing in data. Most direct marketers instinctively understand that good data is essential to effective marketing, but it’s not always easy to build the financial case to support it.
That’s not to say it’s impossible. The key is breaking the case down into benefits of making the financial investment compared to the risk of not taking action in each area. The formula is this: Define which improvements need to be made based on current challenges, then illustrate how they’ll impact either growth or cost savings. The following are five areas where impact is most common:
1. Too many third-party vendors. The goal here is never to pay for the same thing twice. Look at consolidating vendors used for contact and account data acquisition and appending. An audit often uncovers duplicate spend, especially if individual marketers buy data and don’t access a central marketing database that would allow them to see if what they need is already available. In other cases, marketers may work with vendors who use the same sources of contact and account information. Another area to consider is for companies who outsource data management. How much is spent on outside vendors and how many of them are there? Multiple vendors and siloed data may be expensive compared to the cost of bringing data in-house or outsourcing with fewer vendors. The business case should reflect one-time and ongoing cost savings.
2. Too many bad records in the database. SiriusDecisions estimates that 2 to 3% of a marketing database goes bad each month. To find savings, define how many bad or duplicate records are held in databases of applications that base pricing on number of records. Bad records are those that can’t be used due to incorrect or incomplete data.
Getting rid of bad or duplicate records reduces storage costs. Another savings opportunity is updating processes to reject or clean bad records before they enter the database. SiriusDecisions estimates that it costs about $1 to clean a record before it enters the database, but if it’s allowed in and used over the course of a year, the cost to fix it can cost as much as $100. By that time, money has been spent on marketing and selling to an inaccurate record, and this results in wasted effort and depressed response rates. Multiply cost-per-contact by the number of communications sent or telemarketing attempts made to arrive at savings potential. Also estimate performance improvement from selling time gained by not wasting time on bad records.
3. Not enough of the right contacts in the database. The flip side of too many bad records is not enough good ones. Very often companies tell us they have lots of names but not the right people in the right roles in the right accounts at the volume needed to reach goals. If your database doesn’t have enough of the right data, estimate what it will take to fix it. Then, look at the potential pipeline and revenue outcome if the data was available given current conversion rates. How many more inquiries and leads could be delivered? Remember too that reducing dependence on sourcing contact data from third parties by improving the volume of inbound contacts can lower overall costs.
4. Not enough information in account and contact records. Marketing has to segment contacts and align them to accounts to gain the promised impact of marketing automation investments. Productivity suffers when the data to do this is inconsistent or nonexistent.
Small percentage point improvements in conversion rates can have a big revenue impact over time. The data business case can point to improvements in response rates and the impact on conversion rates and average deal size to estimate upside from improved segmentation. It should also show the impact in terms of lead waste recaptured based on leads sent to the wrong reps or partners and not rerouted effectively.
5. No linkage to corporate master data management initiatives. Marketing often maintains siloed data, tools, and vendors and may not be connected to broader corporate master data management. This results in additional costs from not leveraging existing in-house resources. A side benefit is that by making marketing a customer of the broader corporate data management, they gain a seat at the table to set requirements if they weren’t invited before.
Building benefits
The case for investing in better data isn’t just for marketing to fight. The other function that stands to benefit most from better data is also the one that finance is most likely to listen to: sales. Like marketing, the sales team needs correct and complete account and contact information to do their job well. Marketing has two choices: Enlist sales in the effort to win over management; or, if sales has already made investments in better data, work with the sales and IT teams to take advantage of existing resources.
