How to Improve the Reliability of Salesforce Data

Faced with thousands of duplicate records polluting their Salesforce® org, resulting in confusion and a million dollars of lost revenue per year, Docker Inc. needed a solution to their bad data problem. Sales team members had stopped relying on the CRM, marketing automation systems and campaigns were ineffective and practically nonexistent, and an accurate view of the company’s pipeline was nearly impossible to obtain.

After weighing several data cleansing options, Docker chose Cloudingo to clean their data, eliminate the abundance of existing duplicate records, and to stop the hemorrhaging of lost revenue and resources.

The infiltrator of duplicate records

One of the first tasks Melissa Warner performed after joining Docker as Director of Sales Operations was an audit and evaluation of systems and processes. What she found was the challenge of handling a multitude of duplicate records that existed in their Salesforce org, and a sales team that avoided using the Salesforce CRM Docker had invested in.

The biggest culprit causing duplicate records was Docker’s credit card transaction application. Each time a customer used a credit card, an account would automatically be created in Salesforce, no matter if the customer was a new or repeat client. “For example, we could have sixty accounts for one company because they were using their credit card once-a-month for their monthly subscription,” Melissa said. This system created thousands and thousands of duplicate records, and continued to do so. Unfortunately, with no way around the credit card processing application, the responsibility for finding a solution to the duplicate records problem fell to Melissa. “We needed a solution.”

Accurate reporting and lead tracking were non-existent

Because of the large volume of duplicate records and relevant data points scattered across multiple records, the Docker sales team couldn’t rely on the information in Salesforce. Reporting and managing lead queues were impossible. “We were tracking the same people twice because they were both leads and accounts,” Melissa said. “We couldn’t tell what stage of the sales process they were in.” Instead the sales team migrated away from the CRM and worked off of spreadsheets, using different methodologies to track their pipeline. “They each had their own method to their madness.” Ultimately, Docker was unable to reliably track their business and sales effectiveness.

Melissa’s starting point was to clean up the data they had and work from there. “My initial primary focus was to get clean data to make our sales team more efficient in following up on the right leads.”

Cloudingo to the rescue

Cloudingo’s ease of use, cost, and support team were the factors that won Melissa over. “It was so fast and so easy,” she said of her first use with Cloudingo. “I got my trial. We played around with it for about ten days and I said, ‘I need to buy this.’ We bought it and we were rolling!”

First hesitations and fears

In the beginning, virtually all Cloudingo users find themselves hesitant to make the first initial changes. Understandably so. For most organizations, data is a critical asset, the value of which is unknown until it’s lost. Not only is the data steward to blame if something goes wrong, but they’re oftentimes dealing with years of data that has cost thousands or millions of dollars to collect. The repercussions of damaging the org could be detrimental!

Despite having years of data maintenance experience, Melissa still had those first-deduping jitters. “I was nervous. I thought to myself, ‘I’m about to change everything in here!’” Melissa commented that her data was in such bad shape that even if she had messed it up, no one would notice. But she would know.

Fortunately, the Cloudingo team engendered confidence in the application and in working through the process of a data cleanup. Melissa was able to overcome her fears by engaging with Cloudingo customer service reps and utilizing training and support resources. “You guys were always available to us. Any questions we had, you were right there. We had the support we needed right from the get-go.” Melissa said.

The execution

“One of the things I recommend to people before deduping is to have a plan first,” Melissa said. “We sat down and said, ‘What is our hierarchy of cleansing?’” The data management team, working with the Cloudingo team on a plan of attack, decided to first focus on cleaning single-objects –accounts, followed by leads, then contacts. From there they moved on to deduping leads against contacts, or multi-objects.

“By having our plan, we came up with our first filters, and it was very easy for us to adjust them and have more confidence each time we used them.”

Ease of use and support

“One of the things we found with Cloudingo was that our learning curve was very short,” Melissa said in comparing Cloudingo to desktop-based dedupe and data management tools that she had used previously. In past experiences with other applications and interfaces, Melissa frequently found herself on the phone with technical support trying to solve a problem which turned out to be unsolvable due to the limitations of the product. “That hasn’t been the case with Cloudingo. Any [filters] we’ve needed to build, we’ve been able to build. It’s not something that takes us a lot of time to figure out,” she stated.

“Cloudingo has improved the reliability of our data,” Melissa said. Weekly meetings are more productive, team members are able to make sense of their data, and they’re now equipped with much better lead metrics. Docker has also been able to save money and costs and, perhaps most importantly, has gained a better profile on who their customers are.

Clean data has also enabled Docker to successfully implement a marketing automation tool, something that was not feasible before due to the low levels of data quality and high number of duplicated records. “Now there’s no question of whether someone was touched or not because we can see who Sales did or didn’t touch.”

One unexpected benefit Cloudingo and clean data gave Docker was a precise view of the state of their business. “We knew we’d have better trending info, but we had no idea how off base we were on assumptions until we began to really cleanse the data,” Melissa said.

When asked what advice she would give to another Salesforce admin who is thinking about data quality and data cleansing, Melissa said, “Make it a priority early on.”