Thursday, 7 September 2017

The MQL Trap and What Marketers Should Measure

The effectiveness of marketing in a B2B provider firm can be increased by focussing on metrics that reflects entire sales funnel. A marketing qualified lead (MQL) implies a lead that is ready to hand-off to a sales person has been viewed in recent time as definitive way to measure marketing’s effectiveness. However, it wouldn’t be intelligent to focus all of marketing’s time and budget in top-of-the-funnel activities and limit the department’s capability to impact to help impact in later in the sales funnel. This is what Gartner calls ‘MQL Trap’. Marketers are required to broaden their scope from generating leads to contributing in actual revenue generation. 

Moving Beyond MQLs — Other Ways to Measure Marketing Performance 

MQL Conversion Rate: A typical inside-out sales funnel encompasses qualified leads (MQLs, SALs), prospects, opportunities, and concluding at closed deals. If the conversion at each step is tracked and looked over time, it will give a fair idea of the bottleneck and businesses accordingly can model campaigns to improve their performance. 

Competitive Win Rate: How is the performance against competitor on a quarter-over-quarter basis gives an important metric to track for sale success. However, it's important not to measure only the overall win/loss rate. A more holistic measurement includes: 
1) Win rates against the most common and direct competitors 
2) Win rates against competitors in other parts of the Gartner Magic Quadrant (As a Leader, it is important to track against Visionaries and Challengers as you would ideally like to prevent them from moving into the Leaders quadrant). 

A majority of revenue comes from renewals (especially with cloud solutions) and upsells/cross-sells, so retaining customers (with their recurring revenue) and growing account revenue are vital. More than 70% of respondents in a Gartner survey rated interaction with marketing as extremely important in their willingness to remain customers and increase their spending with the provider. Hence, it is wise to include several other metrics that measures the role of marketing in this front. 

Customer Retention Rate
Upsell and Cross-sell Rate
Average Revenue Increase/Customer
Reference/Case Study Growth

A Big Data Approach for Predictive Lead Scoring 

Case#1 DocuSign, a provider of digital transaction management solutions, was faced with a challenge of having four times as many qualified leads as it could handle with its existing sales team. So, instead of hiring more salespeople, DocuSign implemented a predictive lead scoring application that helped classify leads based upon propensity to close. As an example, the model determined that companies from two specific industries were far more likely to buy than were companies that used a specific lead management system, while venture capital-backed companies were less likely to buy. Since the implementation, DocuSign has seen a 38% improvement in leads that converted to closed deals. 

Case#2 SolarWinds spends a lot of money with external lead generation vendors, and it was critical to optimize that marketing spend so the right types of leads got into the top of the funnel. Now, when it gets a batch of leads from, for example, a white-paper campaign, it runs them through the models created by Mintigo to see how they score. For example, the model found that a company was more likely to buy if it had to deal with Health Insurance Portability and Accountability Act (HIPAA) compliance or had a call center, and SolarWinds could look for leads that scored well in those categories. 

Case#3 Citrix, a provider of virtualization, mobility management, networking and cloud services, built its own regression models using existing data, and then utilized Demandbase to clean up and append that data. While its internal models helped improve conversions for individuals, the emergence of buying teams meant that it needed to look at company level data, which it didn't have internally. It worked with Lattice to get proprietary and social media data about attributes, including a company's job postings on LinkedIn, regulatory actions, credit information and Web traffic, and built those into the predictive models. The leads were given a score from 1 to 5; leads of 4 or 5 were indicative of a high propensity to buy. In fact, those with a score of 5 wound up converting into opportunities at a 70% higher rate compared with all leads, and the value of the deal in the pipeline was nearly double.

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