Conversation Ready Leads

Over the past few months, I have done numerous successful implementations of our customer journey frameworks across many different customers. We are enabling sales and marketing teams to do sorts of “MRI of each deal” on the fly. During one of the implementations, I stumbled upon a question related to lead scoring algorithm performance particularly in terms of driving sales efficiency. It prompted me to start thinking about levering the insights from customer journey mapping for optimizing lead scoring algorithms. The main question that we wanted to tackle was:

Even though a lead has been qualified by marketing (MQL) via lead scoring algorithm, how and when marketing can call that lead as being “Conversation Ready” for sales teams?

As soon as I heard this question, I realized that it was about time to take our marketing analytics and customer journey mapping frameworks to the next level in terms of leveraging them for helping the sales teams with more effective conversations and follow-ups. With our frameworks, we have been enabling marketing teams to get visibility into general trends (clusters) of marketing tactics that work for each sales segment, the industry of customers, new business vs cross/up-sell etc. along with the insights into what decision makers vs practitioners engage with and in what phase of the deal cycle. The most important value add of customer journey mapping for our customers has been the ability to leverage the insights from their own data to put together marketing playbooks for different sales segments and industries across different persona. This is of utmost importance for ABM campaigns.

Coming back to the question that was at hand. In B2B sales, as the number of touches and the share of digital channels during the sales cycle continue to grow, it is becoming increasingly important to have an efficient follow-up strategy across marketing, SDRs/ADRs and sales teams. Over the years, the marketing mix has become increasingly diversified (from emails, contents, field events, social, chat bots, 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 sales. These trends are putting tremendous pressure on sales efficiency. Even though the number of tools across sales and marketing tech stack are growing, it is taking a lot longer and many more engagements to close a deal irrespective of the size, industry, etc.

Sales leaders across the board are asking for help uncover and prioritize “Conversation Ready” leads from the MQLs.

Going by the trends discussed above, few key things will continue to happen:

1. With ever-growing sophisticated and automated marketing channels and tools (marketing automation, sales sequences), the number of sales and marketing touches will continue to grow during the sales cycle
2. More and more people will get involved in buying decisions as tools as cross-department collaboration on new platforms continues to grow

These trends will continue to put pressure on sales efficiency. Sales and marketing leaders are working on finding ways to counter this trend to make sure that they are able to close the deals faster amidst. One of the key levers to help with this goal is to further optimize lead scoring by levering insights from customer journey mappings.

We wanted to take this opportunity and help sales teams get the maximum value out of this customer journey mapping insights so as to improve sales efficiency and further optimize lead scoring. With the end goal being that the conversation ready leads get in front of the reps at the right time. I mean “the right time”, which does not necessarily mean “right now”. Given the extent of data points that can be acquired and tracked in modern marketing automation and CRM platforms, lead scoring algorithms have become increasingly sophisticated but there are still some generalizations and assumptions (for the right reasons) that are part of many lead scoring algorithms.

For example: a common path in almost all scoring algorithms is for the auto qualification of trial leads to MQL status and passing it over to SDR/sales teams. Even if a person has started a trial and her persona aligns with the buyer person still that does not mean that lead is “Conversation Ready” right at the moment when she started a trial as she might not have been primed for sales conversations right away. Many times an immediate push from sales teams results in deals being lost as the “trial leads” have not had enough time to evaluate the product at their convenience with the information already shared. Campaign execution and content marketing strategy plays a critical role here by the way.

Instead of applying these so-called “generalizations” for qualified scoring it is time to leverage the engagement data stored in marketing automation and CRM platforms via marketing analytics frameworks and apply a data-driven approach for identifying “Conversation Ready” leads and this is where our customer journey mappings and the associated playbooks have been helping with optimizing lead scoring algorithms.

In this particular project, I looked at 2 years of sales and marketing engagement data to understand the touch points from the very first to the series of touches that lead to fist sales conversation and opportunity qualification. Some of the insights that we discovered were:

1. How many marketing touches and engagements it takes for a practitioner vs a decision maker for each segment (SMB, Mid-Market, Enterprise) before a sales conversation?
2. What is the sequence of these touches?
3. In what percent of cases starting a trial leads to wins- the answer would surprise many of the sales and marketing leaders who think that starting a trial means a deal is closed?

We leveraged these insights to put together dedicated journey flows for leads from different sources (especially the best-converting ones) and it resulted in improvement in MQL to Opportunity creation conversion rates by up to 12% within a quarter. These are phenomenal results and all it took was a structured analysis of the existing data without the need for additional fancy but mostly useless tools in the tech stack.

In this project also, we started with the key (and extremely relevant) business question from sales and marketing leaders. We then reviewed what we could answer with the capabilities we had put in place based on earlier asks (customer journey frameworks in this case), had follow-up discussions to understand further what was being asked for, how it would be used and then we went about building the solution with the set of tools we already had in the organization.

We are always on the lookout for inputs and examples from the marketing and sales communities to keep adding value for our customers. We would welcome the inputs from other leaders and practitioners around what kind of questions are being asked by sales and marketing teams at your organizations and how you go about answering them.