by David Kirkpatrick
, Senior Reporter
Obtaining accurate and complete form fields for prospects at lead capture starts the beginning stage of the complex sale off on the right foot. Taking charge of that data and making use of it throughout the sales pipeline can improve the entire Marketing-Sales process.
This case study takes a look at how Blue Coat, a corporate online security and network optimization company, utilizes technology to achieve a large amount of data captured at lead generation, and then how the company makes use of that data throughout the Marketing and Sales process.
This overall data-driven strategy led to an increased Marketing-Sales alignment, and increased its contact database by 11%, its marketing-qualified leads by 25% and reduced its lead rejection rate by 20%.
"We do a lot of lead generation through the Blue Coat website, and a lot of other microsites that we have," said Manu Kaushik, Director of Marketing Operations, Blue Coat. "But, I think the challenge that we have had is pretty common across all companies. We essentially wanted to capture account-level details from the end user."
He added, "We wanted firmographic information because that's really important for our lead-to-sales process."
Step #1. Utilize technology at the point of lead capture
Kaushik described three methods of obtaining the firmographics that Blue Coat desired at lead capture:
- Include all firmographic information — such as number of employees, company revenue, etc. — on lead capture forms. The downside is a long form which can negatively impact conversion.
- Use progressive profiling. Begin with a short form and continue to add more firmographic data points as the lead engages with the company in the future. The downside is that information is never gathered if the lead does not return to the website or otherwise engage with the company.
- Utilize external sources by collecting basic information and then take advantage of technology in the form of external data sources to fill in additional firmographic details from the basic form fields.
He explained Blue Coat chose the third option, but one problem was the external data source only processed new leads in batches.
Kaushik explained a disadvantage to the batch processing: "When it comes to a lead, you have to be really quick."
After looking into other external source solutions, Blue Coat settled on a vendor that seamlessly integrated with its marketing automation and CRM software solutions, and also provided the additional firmographic data on a case-by-case basis. This removed the delay issue of processing the external data requests in batches.
With this process in place, Blue Coat was able to directly capture leads with relatively short online forms
and also essentially immediately receive the additional firmographic data from the external source and begin the process of profiling the new lead.
The online form was still not as simple as "name" and "email" address. It included 10 (11 for the “contact us” form) required fields:
- Email address
- First name
- Last name
- ZIP Code
- Product category of interest
- "Message" box for comments about the prospect’s inquiry (only included in the "contact us" form)
But, through the external data source, the form allowed Blue Coat access to additional firmographic data points — such as size and revenue of the prospect's company — that allowed the marketing team to immediately begin higher-level segmentation and scoring to move those leads through the Marketing-Sales pipeline.
Kaushik said people filling out the online forms typically provided the correct company name, and if a field was inaccurate, most often it would be the telephone number. He believed this was because the person was simply seeking information — such as for the asset download — and wasn't ready for Sales.
He added the automated firmographic information Blue Coat received was 80% to 85% accurate.
Step #2. Place lead capture forms in multiple locations
With the process in place that included an online form balancing the number of fields against information Blue Coat required to pull the additional firmographic information from the external data source, the team made sure the form could be found across Blue Coat's website.
"We have forms on different sections of the website," Kaushik said. "First, you will go to product solutions where we have white papers and a few assets that you can download. If you want to download that, there is a registration form because those assets are gated."
Another example was trial offers on the website.
The site also included "contact us" forms that Blue Coat treats as a priority form. Anyone filling out the "contact us" form was sent to Sales as a priority lead because that person raised their hand and asked to be contacted by Sales.
The "contact us" form differed from the standard online form by one element — it included a "message" field for the person include additional information.
Step #3. Utilize business intelligence for data standardization and hygiene
On top of its customer relationship management solutions, Blue Coat had a SQL-based business intelligence (BI) system that served as the database clearinghouse for its marketing automation and CRM software.
The BI system aggregated all of Blue Coat's prospect and customer data at the account level and performed standardization and hygiene on the data, and included business rules.
After data was processed by the BI system, it was returned to the software system — marketing automation, CRM, etc. — that was currently using that data.
Kaushik added the BI system was also used for prospect segmentation.
"If we have a campaign coming up, and we want to create two segments," he said, "we leverage that database, and the information is standardized."
Step #4. Include customer prospect and channel partner prospect in lead segmentation
"About one-and-a-half years back we didn't have good, standardized data," Kaushik said. "We didn't have proper automation inside our tools to tag the lead as a customer lead or channel partner lead, which I think is super important."
He explained if a new lead was from a customer account, Blue Coat wanted to engage with that lead in a different way, and prioritize that engagement.
Blue Coat's database includes segments for customer and channel partner leads, and each segment goes into different lead nurturing tracks.
Kaushik added certain companies are targeted in specific fiscal years, or even business quarters. Leads from these "prospect companies" were also given priority in the BI system's business rules.
Step #5. Engage in lead scoring and take advantage of regular performance reports
One advantage of having a standardized and clean database is the lead scoring and qualification process, and the ability to create useful, accurate performance reports.
Blue Coat utilized the SiriusDecisions' demand waterfall model of marketing qualified lead, sales accepted lead, sales qualified lead, etc. It also used its database to score leads into the different categories, and to produce standardized reports on the Marketing-Sales pipeline.
Kaushik explained the benefits of Blue Coat's robust database: "If we have standardized and clean data, then we can basically have better numbers reflecting on those kinds of reports."
Having clean data with a large number of fields populated in each record helps in both lead qualification, and also for Sales in following up with those leads effectively.
The lead scoring process at Blue Coat was a collaborative effort between Marketing and Sales.
Marketing operations performed predictive analysis on the data that was already in the BI system to find:
- What profiles were best for Blue Coat
- What profiles don't matter to Blue Coat
To do this, the group looked at all opportunities from the previous four quarters to uncover information such as profiles connected with opportunities lost, campaign activities that led to converting a lead to an opportunity and activities that led to converting a lead into a sales-qualified lead.
After the analysis, Marketing and Sales sat down together to determine the best profiles. From there, an algorithm was created based on the business logic of the idea profile and simulations were run on a subset of prospects in the database to see if the logic worked.
Kaushik added because Blue Coat is global, each region's profile was customized to reflect specific country-level requirements for the most effective lead scoring.
Leads were scored based on database fields such as job title and business function — for example a "director of IT" would have the title, "director," and the function, "IT" — and also behaviors such as website visits or event participation. Leads were also scored based on internal attributes such as targeted company or strategic account.
Lead scoring also included suppression rules to score down, or disqualify, competitors and to dive more deeply into customer company leads to determine if there was cross-sell or up-sell opportunities for Sales.
Marketing at Blue Coat tracks the conversion of leads through the pipeline — lead to marketing qualified to sales qualified — then regional conversion rates, and finally down into return-on-investment.
"We basically have top campaigns listed on a spreadsheet every month that we review, and we look into the pipeline that those campaigns have generated to understand what's working and what’s not from top activities in each region," Kaushik said.
He continued, "We then have this Marketing scorecard
which gives us visibility in terms of regional and geo-targets, and we have created this amazingly powerful marketing pipeline measurement report which gives us a holistic view of the overall impact of field marketing."
Kaushik added the big three KPI pipeline tracking metrics "buckets" were:
All of these metrics are automatically produced within the BI system database and are used to fine tune marketing activities.
Step #6. Perform data analytics
Kaushik said when Blue Coat rolled out lead scoring, the idea was for it to become an iterative process where each quarter, marketing would look at the results and see what leads qualified for each stage in the pipeline and then find out if those leads met the criteria and expectations of Sales.
From these quarterly reviews, if the team determined that the logic needed tweaking, a full data analysis would be run. If the simulation found an area for improvement, the process would be modified.
"We basically do [modification] only when we feel it will help," Kaushik explained. "Until we are sure, based on the data and then the simulation that we perform, we don’t roll out any modifications."
He added that regional requirements might entail modifying the lead scoring process for that specific geographic area.
Step #7. Create alignment between Marketing and Sales
Harmony between Marketing and Sales can be a challenging goal, and for Blue Coat this was almost more of a result than a planned for step in this entire data campaign. Kaushik described the whole process and helping Marketing and Sales alignment "quite a bit."
He said, "The quality of leads have improved significantly. We had a high rate of rejection, and we received regular feedback from the sales team. They basically said that quality is not right, the profile is not correct. So we captured all that feedback into the lead scoring process."
Kaushik continued, "This improved the overall efficiency, and also helped in better aligning the sales and marketing teams because they were receiving good, quality leads, and the overall process was much smoother that what it was before."
He said the entire data-driven effort generated confidence within Sales.
Some key metrics from the campaign include:
- Contact database grew 11% in 10 months
- Lead rejection rate decreased 20%
- Volume of marketing-qualified leads increased 25%
Kaushik also said Blue Coat’s CEO set a goal of increasing the pipeline from field marketing 1,900%. The marketing team went in, and working backwards through the sales pipeline, determined the number of raw leads each region needed to generate in order to hit that target.
He said the monthly reports contributed to tracking that ambitious goal, and that pressure has led to really getting into the analytics of each campaign to emphasize what works and cut non-producing efforts from the overall strategy.
Examples of cut programs included industry event campaigns that weren't productive in generating marketing-qualified leads and pipeline, and cold calling campaigns.
Kaushik offered several takeaways from this campaign:
- "Data quality is extremely important for success."
- "Nothing would work if the marketing and sales teams are not aligned."
- "No matter how good the campaign is, if the follow-up process is not optimized it just won’t work."
- "Every report depends on good quality data at the back end. If data is not standardized, the reports you are generating are not good enough."
- "Lead scoring doesn’t mean anything unless, and until, the sales team is involved."
On the entire campaign, he added, "It has allowed us to create a holistic view of the marketing pipeline, which gives us terrific insight on the overall impact of field marketing."
- Online lead capture form
- Marketing lead performance scorecard
— Blue Coat's database marketing vendor
Related ResourcesLead Generation Optimization: 7 'must-haves' to improve your campaign planning processMarketing Data: Using predictive analytics to make sense of big dataMarketing Analytics: 3 steps to help Sales and Marketing improve productivityDigital Marketing: Be relevant, data-driven and preciseDownload Your Free Excerpt from the MarketingSherpa 2013 Marketing Analytics Benchmark Report