Implementing Predictive Intelligence Part 2: Cisco's Path to Auto-Qualified Leads

 In Predictive Intelligence

A few weeks ago, in a webinar featuring Cisco, SiriusDecisions and 6sense, we presented the best practices and common pitfalls of rolling out predictive intelligence.

In this second installment of the Implementing Predictive Intelligence series (read the first installment here), we look at Cisco’s path to auto-qualifying leads in its global demand center. Marketing manager Sean Beierly shares his 7-step roadmap for a successful predictive intelligence rollout. This post will be the first of two covering Cisco’s and Sean’s learnings.

Cisco’s Auto-Qualified Leads

For Cisco, predictive intelligence was rolled out as part of a big data initiative called Auto-Qualified Leads. The project focused on installing a predictive intelligence platform that featured advanced analytics above and beyond the basic activity scoring and tracking Cisco was already doing. The goal was to optimize Cisco’s lead-to-revenue processes and help achieve their ambitious opportunity pipeline goals. Cisco learned a lot through the process, and below are the first three steps they’ve identified as vital to seeing success with predictive intelligence.

  1. Understand Your Lead Flow

Predictive intelligence is meant to weave data-driven insights into your existing lead flow process. To do this effectively, you’ll want a detailed understanding of how leads enter your system and move all the way to closed business. Understand important processes, handoffs and flows by answering questions such as:

  • How are leads generated in your system?
  • Do you purchase leads?
  • How are leads qualified?
  • Do you have existing models that generate leads?
  • What is the data flow through the system?
  • How are leads routed to sales, channel partners and resellers?
  • Who are the teams and individuals that receive the leads for follow-up?
  • How will predictive leads integrate into this process?
  • What kinds of enhancements do you need to make to integrate of predictive leads into your existing process?

Outlining the lead flow process will give you a grasp of the inflection points where predictive intelligence and data-driven decision-making can make an impact on revenue and conversion. The Cisco team focused on points in their lead flow where predictive could help them uncover leads showing patterns of activity that were statistically significant of purchase intent and conversion to a sales-qualified lead. The goal was to move from interruption marketing to on-time marketing — getting to prospects at exactly the moment they were ready to connect with Cisco.

For your organization, the focus might be on driving net-new leads, honing marketing segmentation, aligning messaging to  prospect behavior or identifying the accounts most likely to convert. Regardless of what you want to accomplish, knowing your lead flow process is the place you’ll need to start.

  1. Engage Your Sales Leaders

Cisco found that engaging their sales and marketing leaders early was crucial to the success of their predictive intelligence rollout. In each region, marketing leads briefed sales leaders and their teams, explaining the concepts and goals of the initiative. Presenting the teams with a comparison of baseline and predictive intelligence pilot results was an important part of building buy-in and excitement.

The sales teams were integral to the success of the initiative as they provided the human resources and effort that ultimately tested and delivered the value of predictive intelligence. To ensure their support and engagement the global demand generation team conveyed the value proposition of predictive intelligence as follows:

  • Sales would have more relevant conversations with customers who were were highly likely to buy
  • Sales would get more qualified leads and close more business from predictive leads
  • Insights and data about accounts and contacts would be made available

For Sean, a hugely valuable part of the process was having a program manager from the predictive project who could translate the data science concepts to the sales teams. In rolling out predictive, organizations can’t forget that effective communication and engagement of their sales team is integral to the program’s success.

  1. What to Expect from Predictive Intelligence?

By its very nature, predictive intelligence is built on model refinement and machine learning. This is an iterative process during which results and metrics improve over time as you collect more data, identify predictive activities and hone the models that surface in-market accounts and prospects. Setting the right internal expectations about this process is key to a successful and sustained predictive effort.

To keep the momentum and support around their predictive intelligence initiative, Cisco focused on incremental wins that could deliver short-term, proof-of-concept results to their marketing and sales teams. Sean and his team focused on improving conversion rates with a quality-over-quantity approach. This gave them room to focus on improving models without the immediate pressure of having to drive large amounts of net-new leads.

Finally, and perhaps most crucially, predictive leads will need to work within the existing flows and operating procedures of your marketing and sales teams. Cisco worked to ensure that predictive leads met a standard that ensured they could flow through the system and be presented to sales within their CRM interface. Cisco enhanced this process by working with 6sense to include a “reason to call” and important insights about why the sales person should contact the prospective customer.

What’s Next?

In the second part of our Cisco predictive rollout timeline we will review:

  • Where does predictive fit into your marketing and sales process?
  • Understanding your capacity for change
  • Data hygiene and predictive intelligence
  • The importance of a phased rollout

In the meantime, watch the conversation that inspired this series: Predictive Intelligence: From Rollout to Revenue.

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