More Lessons Learned From A Data-Driven CMO: Learning to Love Refried Beans

More Lessons Learned From A Data-Driven CMO: Learning to Love Refried Beans

Last week I kicked off a blog series featuring how I use the Digital Pi Gold Standard for Marketo to make data-driven decisions as an interim CMO at a Silicon Valley SaaS company.  This week I continue the series with my story of how refried beans – the term sales used to describe leads that re-engaged from a nurture status – created sales and marketing intrigue that will or won’t surprise you depending which side of the intrigue you’re on.

The story unfolds with lead scoring — a core capability of marketing automation that lets sales and marketing discern which leads among the masses are most worth pursuing based on their characteristics (industry, size, etc.) and behavior (opens, clicks, attends etc.). When a lead score reaches to a certain point threshold, marketing automation can declare the lead sales ready, meaning this person is ready to be pursued by sales.  The traditional marketing name for this stage in the life of a lead is MQL, or Marketing Qualified Lead.  Sirius Decisions calls these AQLs (the “A” is for Automation) which we at Digital Pi prefer because it negates the misperception that marketing people declared the lead sales ready when it’s really 100% data-driven and system-determined.  AQLs are often an ongoing subject of controversy for one or more of these reasons:

  • Goals are set, and marketers are compensated on achieving AQL goals
  • Sales team doesn’t understand them, like them, believe in them….
  • Sales and marketing automation systems don’t adequately support end-to-end AQL processes
  • SLAs stipulate how long they can remain AQL before being disposed by sales
  • Data or process errors occur that erroneously push leads to AQL that shouldn’t

 

A blue bin for leads

What happens when sales engages with a prospect that isn’t a qualified opportunity today but might be down the road?  A sales and marketing lead lifecycle process that supports this common scenario requires a lead status that sales reps use to indicate the lead isn’t ready to buy today.  The CRM lead status value for this state is typically “Nurture” or “Recycle.” At Digital Pi we prefer “Recycle” because marketing can nurture leads no matter where they are in the lead lifecycle – not just when they’re in the Recycle stage indicating they’re not qualified to buy today.

Lifecycle Path for Recycled AQLs

When a sales rep changes the value of lead status to “Recycle,” The Digital Pi Gold Standard Revenue Lifecycle Model flows the lead into the Recycle Stage and reduces the lead score below the AQL threshold to set the stage for the lead to re-engage and score back up to AQL.  That’s right, AQLs get another chance to reach sales ready again based on their behavior.   Here is a scenario to illustrate:

  1. Person visits your web site, Marketo drops a cookie; new lead clicks around accumulating a few points but not enough to reach AQL.  Person fills out a form to register for the freemium subscription, scores more points and reaches AQL.  The lead flows to the Sales Ready queue in Salesforce.com
  2. Sales rep sees the lead in the queue and changes the lead status to “Contacting.” The prospect tells her he has heard great things about their solution, and expects to have budget to re-engage next year.  The sales rep sets the lead status to “Recycle,” and creates a task due in four months to check in with the prospect.  The Revenue Cycle Model takes the score down to a level below AQL based on how high the score is above the AQL point threshold.  Salesforce.com moves the lead back to the not ready queue.
  3. One month later (three months before the task is due in the CRM to call the prospect back) the prospect visits the pricing page, downloads the ROI calculator and logs into the app.  The engagement activity pushes the lead score over AQL a second time and the lead flows through the AQL processes again.
  4. The sales rep sees the lead is back in the game and this time with some very interesting moments to talk about.

Like first time AQLs, recycled AQLs can be hit or miss – meaning their engagement may have pushed the score to AQL but they may not be ready to call.  Let’s stay grounded in reality: this is an automation system using a scoring model.  Like any model it requires monitoring so we can dial it to the desired outcome (sales ready leads).  There will always be AQLs that are not ready to call, the key is to set expectations appropriately with all the stakeholders and have a good process to continuously check AQLs and look for changes to scoring that may be necessary.

 

Beans, beans the magical fruit

By now you’re wondering what all this has to do with refried beans.   It was early in the day on site at my client when a member of the marketing team explained to me that sales had special term for recycled AQLs – yes, “refried beans.” I like refried beans on a burrito now and then but in this context, I interpreted this as a not-so-flattering way to describe recycled AQLs.   I did the only sensible thing any data-driven CMO would do:  dig into the recycled AQLs.

The Digital Pi Gold Standard Revenue Lifecycle Model stamps a custom Salesforce.com date field with the system date when a lead reaches AQL.  Then we can filter data in SFDC based on AQL Dates for custom views and reporting.  In fact we stamp first time AQLs and recycled AQLS in separate custom fields in case we want to see them separately.  In the story of refried beans, this turned out to be a very useful feature.  I created lead and contact views in SFDC that showed lead/contact AQLs with Lead Status “Open;” this shows the AQLs waiting to be pursued.  I am always careful to look at lead statuses with a jaundiced eye, meaning the status may not reflect the true disposition of the lead.  In other words, when I talk about Open AQLs I stick to phrasing like “SFDC lead statuses say these are open” instead of “sales hasn’t followed up on these AQLs.”

So what did I find?  Surprise (not): some very interesting sales ready “refried beans” made more interesting when filtered to match target accounts. Twenty showed on the list with titles that matched the ideal target profiles.  I opened five of them up to see what they did to re-engage, those I sampled were people you would expect a sales person to call based on their match to a target account and their titles – the engagement was the icing on the cake in this case.

On the one hand, sales is asking for more leads (what?????) but these re-engaged named target account leads they’ve already engaged are raising their hands and, according to the marketing automation system, should be examined and disposed by sales. I shared the list and explained all of this to sales and others with these goals in mind:

  • Implement a closed loop process with sales every week to look at a set of AQLs to learn where we can improve scoring and identify system issues
  • Show sales that AQLs are worth a look, and to dispose them to disqualified, recycle or sales accepted.

I wish I could report that the story ends there with an empty list of Open AQLs – but it doesn’t.  Open Recycled AQLs still show up in the list, good ones, some over ninety days in the queue.  What data-driven lesson can we learn from this?  Don’t expect data-driven insights to change outcomes overnight, especially where changes in behaviors are at the heart of what will lead to better outcomes.  Yes, be data-driven but also be persistent, don’t assume anything, ask questions and keep your outcomes attached to revenue front-and-center when you engage with stakeholders.  I’m optimistic that in time, sales will learn to love refried beans.

 

– Tom

tom_grubb

 

tom@digitalpi.com

Tom brings over twenty years of marketing executive leadership to Digital Pi including VP Product Marketing at Marketo. He led marketing at start-ups, mid-size and enterprise companies including Intuit, CA Technologies, ThreatMetrix, and co-founder of Bluecurve (acquired by Red Hat). Tom loves helping companies solve big marketing problems using his depth and breadth of experience, technical skills, and outsider perspective. He is an intent listener who constantly probes ideas and assumptions to drive to the best outcomes.

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