Marketing Automation and Lead Management Blog

Sales Accountability to Marketing - Myth vs. Reality

One of the discussion points in the "Sales and Marketing alignment" topic is the Service Level Agreement (SLA) between Marketing and Sales for the Leads generated by Marketing. Clarity and monitoring of this SLA is critical because it drives one of the key metrics that we want to measure in Marketing - Conversion rate from Marketing Qualified Leads (MQL) to Sales Accepted Leads (SAL).

A typical SLA could be:

• Sales needs to follow up on all A Leads within one business day or 8 business hours.
• Sales needs to follow up on all B Leads within two business days or 16 business hours.
• Sales can (optionally) follow up on C Leads within a week of receiving them.

Now, let's look at how this process can be implemented and automated using Marketing Automation solutions (like Market2Lead) and Sales Force Automation solutions (like Salesforce.com). One of the key fields on the Lead object in Salesforce.com that can be used to track sales feedback on Leads is Lead Status. A sample set of values for Lead Status are:

• New: Status when a Lead is first published into the sales process.
• Attempting to Contact: Status when sales initiates follow-up on the Leads. Based on the SLA times this is one of the immediate next states that a Lead needs to be moved to.
• Invalid Information: Status when sales determines that the contact information is invalid and the Lead cannot be reached.
• Contacted: Status when a sales person successfully contacted the Individual (either by phone or by email).
• Not a Lead: After initial Contact the sales person determines that the Prospect is not looking for a solution for his business problem in the foreseeable future or if there is no fit between what the sales person is selling and what the Lead is looking for.
• Demo Request: One logical next step (in high tech especially) is either a demo request or a follow up meeting.

   

Now, from an SLA standpoint, a Marketing Automation solution needs to be able to track 2 metrics:

1. Transition of a Lead from New state to either Attempting to Contact, Invalid Information or Contacted state.
2. Time taken for the Lead Status to change from New to any of the stages mentioned in the previous point.

If these two metrics can be measured and tracked, Marketing users can automate measurement of:

1. Conversion rate from Marketing Qualified Lead (MQL) to Sales Accepted Leads (SAL). This can be done by comparing state changes from New to Attempting to Contact or Contacted.
2. SLA compliance by Sales. Marketing managers can compute the Average Time for sales follow up by Lead Score by computing the time taken for a change in status of a Lead from New to any of the states mentioned in the previous point.

I welcome comments and suggestions on this topic.

Sirius Decisions Summit 2008

I was at the Sirius Decisions Summit 2008 last week. The event was held at Henderson, Vegas. I would highly recommend this event for B2B marketers looking to learn about Marketing & Sales alignment and also best practices in this space.

Some of the topics that were discussed at the event are:

1. Marketing & Sales Integration Models
2. Marketing Measurements becomes Reality
3. Addressing the Sales Bandwidth Challenge
4. Extracting value from Social Media
5. Marketing Programs - A Global Approach
6. Reputations real link to Demand Generation
7. Sales Readiness from Concept to Critical
8. The Demand Eco-system - Progress and Problems

There were also customer case studies and presentations from:

1. Symantec
2. Cognos
3. Computer Associates
4. newScale
5. SAP AG

One of the topics that I really enjoyed was Marketing Measurements by John Neeson. This presentation was a great validation for our decision early on to invest in an integrated on-demand Business Intelligence solution to complement the execution engine of the overall solution. Some of the takeaways from the presentation are:

1. Alignment: Implementing effective Marketing Dashboards is an involved process and takes time to put in place. The exercise starts with an an intiative to bring cross functional alignment within an organization to agree on *WHAT* needs to be tracked and measured.

2. Selection: A clear definition of KPIs and Metrics that need to be tracked by an organization and also an understanding of the difference between KPIs and Metrics. John talks about how some organizations don't differentiate between KPIs and Metrics. It's been my observation too that business users tend to use these two terms interchangeably. I like the explanation of a KPI in a book called "The Big Book of Key Performance Indicators" by Eric T. Peterson. Eric describes a KPI as a metric that drives action. This is the polite way of saying, “Any KPI that, when it changes suddenly and unexpectedly does not inspire someone to send an email, pick up the phone or take a quick walk to find help, is not a KPI worth reporting.”

3. Design: A fully engaged Marketing Operations team to drive the implementation of the dashboards. One of the key challenges that a Marketing team needs to be aware of before embarking on this project is Data Quality. Dashboards are only as effective as the quality of the data that powers them. Some of the Data Quality issues that need to be looked into are  Data completeness, Data Accuracy and Data Standardization,

4. Adoption: A well thought out and thorough plan to drive adoption. One of the key drivers for adoption is relevancy. We need to make sure that the underlying technology supports setup of role based Dashboards. Role specific KPIs & Metrics, Visualization Schemes and Overall Layouts are the key ingrediants to ensure relevancy and in turn drive adoption.

I would like to hear from our readers on their success with Marketing Dashboards.

Numeric Scoring Pros and Cons

I recently read a blog post by Laura Ramos from Forrester on the subject of Lead Scoring methodologies. In the article Laura outlines the key benefits of a "Numeric Scoring Model". I agree with Laura about the need for Marketers to remove the subjectivity about the quality of a Lead. I also agree with her about some of the best practices to help Marketing and Sales bring more clarity to this topic.

1. Sales & Marketing should have quarterly meetings to review the scoring model and tune it based on results from the previous quarter.

2. Implement a scoring scheme that is easy to understand and manage. One of the methods is to use Numeric scoring. In this method a Marketer would assign points to each key Contact attribute and a weight (both self reported data and behavioral data) and compute a weighted sum of the points to arrive at  the overall Score of a Contact. This method has its pros and cons. Laura already outlined the benefits of the Numeric model that I don't want to repeat here. Here are 2 challenges with this method that I have seen:

a. Chances of sending False Positives to Sales: With a scoring model that does not support a rules engine Marketers don't have the capability to check for data completeness and authenticity for key pieces of information without which a Lead is of little value to Sales. Let's look at a sample scoring scheme (to keep this example simple I have not added any weights to each attribute):

Here is a sample Lead based on this model:

In the above example  Bob gets a score of 95, which is a very high score, and needs immediate follow up from sales. But, from the data it is fairly obvious that the phone number is bogus and we have a hotmail address. Let's take an extreme case where  Bob's  information comes into the system without an email or a phone number. In this case the Lead will still have a score of 80 points (I subtracted 5 points for email and 10 points for phone number from the original score of 95 points). In both these cases the scoring model instantly loses credibility with the sales team and we all know what happens next. 

b. How is the Lead with a score of '68' different from a Lead with a score of '77'? We all know that sales people like to spend their time selling and not learning and understanding internal processes. The last thing we want to do with a sales guy is to open a spreadsheet with 100 items and a complex formula that spits out a number. The critical thing for Marketing is to prioritize the Leads for sales and set some thresholds for follow up. For example, Leads with 80 points and more need to be followed up within 4 hours; Leads with a score between 60 and 80 points need to be followed up within 8 hours and so on. A tiered scoring method (A, B, C, D) serves the same purpose.

In my view the scoring scheme (either points based or tiered) needs to be augmented with a robust data completeness and data validation process to ensure that sales gets to work on Leads that have a high chance of conversion.  One way to augment the scoring process is to add 2 pre-processing stages:

1. Data validity check: In this step we need to check for common junk data that people enter like 'test', 'aaa', 'asdf' etc. for Name, '111-1111', '1234567890' for phone numbers, 'a@a.com' for emails etc.

2. Data Suppression: Sometimes there are legitimate Leads that are generated from Marketing campaigns that are not necessarily sales Leads. We need to suppress such Leads. For example, emails have a '.edu' extension, the Lead belongs to a partner organization or an analyst firm etc.

I would love to hear your comments.