Sales Follow-up of the MQLs – Overlooked Part of Marketing Analytics
MQLs are an important part of the marketing analytics frameworks across most of the modern marketing teams but the sales follow-up of the MQLs is critical. Whether it is lead scoring, database management, or reporting around the performance of the MQLs,
marketing teams spend a lot of time and energy at continuously optimizing the performance of MQLs with the sole purposes of driving higher MQL to opportunity conversion
the leading indicator for driving pipeline growth. To progress a customer from MQL to SQL stage of the funnel, there is 1 aspect of the MQL performance optimization that most of the marketing and marketing analytics teams either overlook or do not spend nearly enough time at understanding and that
set of key metrics is the sales follow-up of the MQLs that are handed over to them.
Beyond the MQLs, B2B organizations are also relying more on sales accepted and qualified leads (SALs/SQLs) now. This increased focus shows more marketing organizations are digging deeper into revenue impact, and want insights into the qualified opportunities that are influenced by the MQLs.
As the advancement in the marketing tech stacks and marketing analytics keeps accelerating at an enormous pace, we can be sure that lead scoring algorithms will keep becoming more sophisticated and smart by leveraging numerous data points with the help of AI and advanced automation across the customer journey. We will continue to discover more sales conversation ready MQLs but
unless at the same time we continuously optimize the follow-up strategy around these MQLs, we will not be able to realize the full impact of all the effort that goes into discovering and qualifying the leads that marketing teams generate.
With the growing number of touches and the share of digital channels during the sales cycle, it is becoming increasingly important to have an efficient follow-up strategy across SDRs/ADRs/BDRs and sales teams. Over the years, the marketing mix has become increasingly diversified (from emails, contents, field events, social, chatbots, etc.) and the growing number of buyers in the buying committees have resulted in lengthened sales cycles across all the segments from SMB to Enterprise. These trends are putting tremendous pressure on sales efficiency. It is taking a lot longer and many more engagements to close a deal irrespective of the deal size, industry, etc.
All these new trends around MQL generation and the sales follow-up necessitate the need to better understand the sales feedback on MQLs. Sales/SDR follow-up of MQLs is where the rubber hits the road and the insights gathered from the sales touch points are critical for MQL optimization. Across all our marketing analytics engagements, understanding SDR follow-up and SAL/SQL qualification of MQLs is the critical aspect of the over-arching lead scoring/MQL and top-of-the-funnel optimization efforts. Key metrics include:
1) Time it takes for sales to make the first attempt at the MQLs: numerous research studies have established the fact that every 1 day of delay in making the contact with the qualified leads results in the conversion rates falling by up to 30%. Ability to see in real-time the average time it takes by the SDR team in general and an SDR, in particular, to make the first attempt at connecting with the MQLs has proven to be of immense value both for the marketing and SDR/Sales teams. These insights enable marketing teams to:
– understand how to time lead creation and routing in the marketing automation platform to make sure SDRs can connect with them promptly.
– have the conversations with the SDRs around the leads that have not been engaged with and are outside the agreed-upon SLAs between the marketing and SDR teams.
– understand the correlations between MQL conversions and SDR outreach to find patterns for matching certain kinds of MQLs with the SDRs to drive the highest conversions.
These insights have proven super valuable for SDR and sales leadership.
They can see in real-time through their organization’s data how the delay in engaging with the qualified leads impacts the conversion rates. Seeing is believing, as they say! These insights also help the SDR leadership with the data-driven capacity planning for the efficient staffing of the SDR team to make sure there is enough capacity in the team to manage the in-flow of the MQLs. At the same time, SDR managers can set the goals around adhering to the follow-up SLAs and track the performance of individual SDRs against those goals.
2) Aging of the MQLs with the sales owners: while the first metric (from above) provides the insights into which MQLs have been followed up by the SDRs, there is always a good number of MQLs that are yet to be followed up. MQL aging provides insights around the MQLs that are waiting in the queue. Depending on how old the MQLs have become and their source, a decision can be made either to bump them up to the SDR queues or include them in a re-engagement campaign to increase the probability of their conversion to qualified opportunities.
3) Number of touches and time it takes to qualify the MQLs – these metrics are critical when it comes to goals planning and tracking their attainment. These insights provide valuable inputs towards building effective sequences (with appropriate offers, channels, and their timing) in the sales engagement platforms like Outreach, Salesloft. The insights from these metrics provide important inputs for building contextual customer journeys. The number of days it takes to qualify an MQL into SQL is the key metric that helps both marketing and sales teams alike as they all are charging towards attaining the common goal of pipeline generation.
Based on how many MQLs are already with the SDRs, their conversion ratio, and the time it takes for the ones that convert, teams can predict with a fair degree of confidence the attainment towards quarterly pipeline goals.
The most important part here is that instead of having to wait till the end of the month/quarter, with the goal attainment probabilities, teams have enough time to pull the relevant levers to accelerate execution and MQL generation that would help them get closer to the goals, if not beat them.
4) Cohort comparison of MQLs and SQLs in terms of campaign engagement and lead score: we all know that MQL to SQL conversion will never be 100% as there is some leakage; also stated in the law of thermodynamics (for all of us science nerds!)
From what we have been seeing, the distribution of campaign engagement (or the campaign engagement mix) and the threshold score of MQLs are many times different as compared to that of the corresponding SQLs. MQLs come from all kinds of campaigns and channels but only a part of them get qualified.
Understanding the campaign engagement mix of the qualified MQLs and their score (MQLs can keep engaging and getting higher engagement scores) provides valuable insights for the marketing teams
to not only tweak scoring but also use them for last targeted MQL engagement campaigns during the SDR follow-up (pre-SQL) phase for those MQLs who have not engaged with certain campaigns that are part of the SQL campaign engagement mix. This a very efficient way to increase the probability of conversions.
These SDR/Sales metrics focused on deriving the insights around MQL follow-up have become an integral part of our marketing analytics frameworks to help the marketing teams actively track the follow-up of MQLs and leverage these insights for building contextual campaigns and continuously optimize the lead scoring. We are always on the lookout for inputs and examples from the marketing community to keep adding value for our customers. We welcome the inputs from other marketing leaders and marketing analytics practitioners around how they are tracking the MQLs/marketing leads follow-up by the SDR/Sales teams in their organizations. Apart from the 4 key metrics that we have been tracking, are there any other metrics that would help provide valuable insights into the marketing and SDR teams?