How MySQL solved their Sales & Marketing challenges




An interview with Lesley Young, who was the VP of worldwide Sales for MySQL, and then head of sales for the MySQL division within Oracle. Few products are as well known as the ubiquitous MySQL database. The company behind the database was also one of the great success stories in the Open Source world, and ended being acquired by Sun (now Oracle) for approx. $1 billion. Making money in Open Source businesses is a lot harder than it may appear on the outside. Lesley tells the story of how she and Zack Urlocker, running marketing partnered to solve the sales and marketing challenges that the company faced when trying to monetize MySQL.

Tell us a bit about the MySQL business model

MySQL had worldwide awareness, and broad adoption across SMB and Large enterprises that drove a high volume of website visitors and downloads. However, while we had a big, broad market to focus on, Open source is a commoditization play relying on low average selling prices (ASP). To build a profitable business we couldn’t have a field sales organization selling a $50K or less average sale value. The math did not work. It became clear that combining an automated inbound Demand-to-Close process with high volume over the phone selling was a much more profitable model. Once the model was agreed upon, my focus, with significant support and partnership from marketing, was to build Integrated Marketing and inside sales machine to accomplish the following:

  • Build a cost effective, high margin software sales channel
  • Leverage online marketing technologies to drive demand
  • Remove noise from the process
  • Automate measurable demand to close (D2C) process
  • Route sales leads to live resource at appropriate point in process
  • Execute a high volume of monthly transactional sales

A smaller traditional Enterprise Sales team in North America and Europe addressed major accounts and the Telco vertical.

What were the sales and marketing challenges when you first arrived at the company?

  • Worldwide awareness drove a high volume of website visitors and downloads (50,000 a day) but identifying potential buyers was like trying to find a needle in a haystack.
  • There was a big chasm between sales and marketing about the definition of a lead
  • All downloads and visitors were created equal and there was no methodology to filter forever free users from potential buyers
  • My SQL lacked the sales processes, sales tools, metrics, and alignment with marketing required to improve lead conversion, pipeline development and measurement of marketing ROI.

How did the sales funnel look?

  • The top of the Sales pipeline was brimming with raw leads unfortunately it took herculean efforts to uncover the potential buyers. Less than 1% of the raw leads were converting to customers
  • The Lead to Opportunity conversion rate of 1 opportunity to 571 leads
  • There were not enough forecasted opportunities
  • There were no clear sales indicators, and no sales route to move a free user to a paying customer

What were the first steps you took to tackle these?

  • We partnered with marketing to build a closed-loop marketing and sales process called “Demand-to-Close (D2C)”. The D2C process is used to describe and track the activity from outbound campaign to the tracking of visitors and return visitors through landing pages on the website, scoring those appropriately, and then feeding that information to the sales development team or inside sales organization for closure
  • Implemented outbound call-to-action Marketing programs to all downloads so we could start to capture potential buyers and segment forever free into separate nurture paths based on response behavior.
  • Implemented lead scoring to understand what actions and campaign responses likely buyers completed. This gave us measurable clues as to which leads and lead sources the sales team converted to closes.
  • Used measurable lead scoring “clues” to build multi-touch nurturing programs for leads not “sales ready”.
  • Improved inquiry inbound routing and segmentation based on capture of inquiry demographics and activity and dispositioned inquiries to the appropriate next follow up activity.

What did you learn from these first efforts?

  • Architect the website to help capture and segment interested audiences; free user, trial user, or potential buyer.
  • It is critical to build an integrated sales and marketing D2C machine to deliver “sales ready” leads using lead routing, scoring and conversion analytics methodology
  • Continually iterate the D2C machine plumbing to measure conversion rates across the process. It was tedious and time-consuming, but the knowledge gained enabled us identify gaps in the quality of the D2C funnel and was the most significant contributor to improving conversion rates
  • Participation in the process by both sales and marketing questioning / understanding the trends enables us to jointly come up with solutions and plans.

How did you improve?

  • Decreased sales response times to assigned leads coming from marketing
  • Increased Lead to Opportunity conversion rates from 6:1 to 3:1
  • Nurturing leads lead to an average deal size increase of 18%.

What did you find were the key metrics to help you manage the funnel?

  • Lead VolumeTrack and measure by campaign, by salesperson, by geography


  • Lead Conversion –Track and measure funnel stage-to-stage, so you can do trending and comparisons of conversion rates from raw lead to inquiry, inquiry to contact, contact to opportunity, opportunity to close/buy — by geography, lead source or other criteria.


  • D2C ContributionTrack and measure bookings/revenue contribution from marketing programs by source, geo, sales rep.
    • Contribution 25%
    • $ Per lead = $115

What lessons do you have from this experience that you feel would be useful to pass along to other Open Source companies, or to other SaaS or subscription revenue companies?

  • Build a cross functional team with constituents from sales and marketing to gain alignment on what and how marketing activities support sales execution. Understand which visitors, downloads, trials, and leads the sales team touches. “A tightly integrated marketing and sales around the shared task of demand creation close between two and seven times the number of deals of those that do not”-Sirius Decisions
  • Implement an automated process to capture key visitor, download, trial, and lead data and continue to augment the data each time an activity takes place. Use the data to segment, route, and disposition leads to the appropriate marketing or sales follow up action. Disposition based on activity ensures focus and reduces cost of sale because sales resources are used on only the most qualified sales opportunities, buyers who are ready to buy.
  • Establish a set of Demand to Close (D2C) conversion rate metrics that drive the company to profitability for your business. Track success of marketing activities and sales against those conversion rates quarterly. Agree which metrics need to be managed and measured by marketing, which metrics need to be managed and measured by sales, and where there is overlap, jointly measure both the marketing team and sales team to make sure the interlock is there, and everybody is working in concert toward shared goals.
  • Continually test, and determine what works, what doesn’t work. Encourage feedback from the marketing and sales teams in the trenches. Make changes, track, and do it again.
  • Present metrics in Monthly joint Sales & Marketing meeting. Debriefing on volume and conversion metrics helps identify gaps and trends in the business and gives a view into future months bookings health so that sales and marketing can modify /adjust plans.
About the Author

David Skok

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  • David,

    Interesting interview. Curious about the segmenting — did they segment the website visitors into (free forever, trial, probable buyer) via different landing pages and capturing URL params, or by offering opt-in incentives targeted to each segment and tracking from there?

    Also, do you know which systems they implemented — off the shelf, homegrown?

    Thanks for another solid post.

  • From Dries Buytaert: Some additional interesting metrics on MySQL in his blog post: The history of MySQL AB:

  • Hi Andrew,
    Yes we did segment the website visitors. We tried to drive them to “Free user” landing pages and Opt-ins target to potential commercial buyers.
    We implemented Eloqua with MySQL homegrown scoring database,, and a homegrown data warehouse for Demand to Close (D2C) analytics tracking

  • Gotcha, thanks Lesley!

  • For Lesley, what was the differentiation that identified a potential purchaser? What things indicated to your team that they should pay more attention to a given lead than another?

  • Leads with the following activities: multiple activities in 30 days, use of the ROI calculator, Trial activation, Contact me…
    Free users download a lot of technical white papers

  • Great post! We at Zend have had very similar challenges and took a similar approach to addressing them.

    Lesley: I’m assuming you too had a vast contact database and spent time/money on nurturing it and moving potential customers through the sales process. What metrics did you use to measure campaign influence?

  • We built a vast database. At the beginning all we captured was email address. You are correct we spent time and money nurturing in order to gain demographic and level of interest information. As we gathered this information we scored the leads to determine those who were likely to be potential buyers.
    Campaign influence was in three ways – number of sales leads, # of transactions from the campaign, and bookings from the campaign. We also looked at the performance of the campaign over time (3 M, 6M, 1 Year).

  • Bryan Cheung

    Thanks for a great post!

    Lesley, I’m curious—what was the process for diverting certain leads over to field/enterprise sales vs. the phone-based inside sales team?

  • L Lloydyoung

    Hello Bryan,
    The routing to Enterprise Sales was based on Named Key Accounts. These accounts had high level of MySQL usage across multiple departments.

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