Making a Sales Legend with Data Driven Selling

September 15, 2020 | 4 minute read
Mary Correnti
Product Manager, Oracle Sales
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Being a sales rep is hard. Don’t let anyone tell you differently. But when you're good, you're not just good - you're a sales legend who keeps the entire business running.

There’s both an art and a science to establishing yourself as a trusted advisor to your customer, from positioning your product as the best fit over competitors, to negotiating terms and conditions that work both for your customer’s organization and your own, to keeping a deal alive when the budget is lost or a decision maker leaves the company.

Plus, there’s the challenge of hitting a quota that can feel extremely unrealistic, even when these obstacles aren't popping up throughout a deal cycle. Then there’s the most difficult set of challenges that sellers face today: data challenges. 

There's a popular sales saying, ‘Time kills all deals.’  Well, there should be an additional saying, ‘Bad data kills all opportunity.’ Once a seller realizes they’ve been working off of bad CRM data, irrevocable damage has been done. 

Bad CRM data is a fast route to rejection

Basing customer outreach on bad CRM data—whether duplicate, inaccurate, or incomplete data—sets sellers down the quickest and surest path to rejection. I faced a fair share of this rejection during my first stint in sales.

Once, I called a customer to pursue what I thought was a good up-sell opportunity. But because their install base information was inaccurate in the CRM, my customer asked me, “Shouldn’t you know that we already own this product?”

Another time, I sent the Director of IT at my top account a well-crafted persona-based email. But because my CRM wasn’t up to date on his not-so-recent promotion, I was embarrassed when his email reply read “Not interested” with “Chief Information Officer” in the signature. I lost my one chance to get to the decision maker. 

I once received a call back from a customer I was very excited to finally connect with. The excitement was short lived when she told me she was already in a deal cycle with my colleague. “Do people at your company even talk to each other?” she asked. I knew for a fact I had checked her record in the CRM and there were no recent interactions with her. Must be another duplicate contact, I thought.

From then on, I was forced to incorporate some very inefficient habits into my workday. After all, I wanted to at least have a chance to try and sell something. I began to always double check customer contracts and spend data in systems outside of the CRM before reaching out. 

I made sure to cross reference CRM job title data with LinkedIn before prospecting. I triple checked every contact for duplicate records to make sure no other reps at my company were selling into the same prospect as me. After too many email bounces and unsuccessful dials to contacts with missing phone extensions, I stopped relying on the CRM for customer information altogether. It became just a place I was required to log my activity.

All the time I spent validating CRM data left me with little time to personalize my outreach. I began sending generic, cookie cutter emails, and resorting to questions like “What’s keeping you up at night?” when I finally got a prospect on the phone.

Back then, I wondered why our company didn’t just buy us a new CRM already! Knowing what I know now, I understand that wouldn’t have fixed the issue. Instead, what my company needed was a new customer intelligence strategy. We definitely could’ve benefited from the four-step approach Oracle has designed for its customers.

Oracle’s outcome-focused approach to customer intelligence for sales:

1. Data quality

The first step to true customer intelligence is setting a solid data foundation. Oracle CX customers leverage built-in capabilities to clean and de-dupe customer data and create a single Golden Customer Master Record for their front and back office systems to work from. The record continuously incorporates the best attributes from the strongest sources.

2. Data enrichment

Once the data is consolidated and clean, it’s enhanced with continuously refreshed second-party and third-party data at both the account and contact levels, as well as validated postal addresses. 

3. Data analysis 

What sets Oracle’s AI strategy apart is that it’s fueled by clean, complete, and accurate data. The result is intelligent customer data analysis and segmentation.

4. Data action 

Finally, the business can now act on this high-quality data, delivering contextually relevant information and prescriptive actions to sellers.

The result of Oracle’s customer intelligence for sales strategy is an experience far different from the one that I had as a seller. Sales reps no longer need to deal with duplicate accounts and contacts in the system. They have access to all of the strongest attributes collected by marketing, service, commerce, and finance right inside of their CRM. Profile elements like job titles, phone numbers, addresses, and emails are constantly refreshed so reps never have to spend time validating this data in other internal or external systems.    

Because this clean, complete, and accurate data is fueling Oracle’s AI, sales reps can be confident that the prescriptive recommendations they’re given in the CRM will set them down the right path. And real-time company news signals and smart talking points enable sellers to keep their outreach as personalized and relevant as ever, never being forced to ask their prospects “What’s keeping you up at night?” again.

Learn more about Oracle's customer intelligence solution for sales, and schedule time with your account team to discuss how our approach can benefit your business.


Mary Correnti

Product Manager, Oracle Sales

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