What every lost lead can teach you

Leads leakage is natural, just like the desire to improve sales conversion - according to Econsultancy, 78% of businesses were dissatisfied with their conversion rates in 2016. Whether a conversion rate drops or simply falls short of a sales manager’s goals, what should be the next step? Can sales managers handle the win/loss ratio with something more than team motivation and crossed fingers, probably?

A reasonable way out is to systematically analyze why leads fail to convert. Gartner’s estimation of the possible 50% improvement in the sales win rate as a result of a decent win-loss analysis can entice many sales managers to kick off their lead conversion research. Drawing on our CRM consulting experience, we suggest marrying the Deming PDCA (plan-do-check-act) model and a data-rich CRM system to succeed in the task. In brief, this is how the tandem should work:

Plan: Win/loss analysis based on CRM data enables a sales manager to learn the reasons for leads falling through ‘pipeline cracks’ (e.g., is it the sales team’s performance or inadequate marketing messages?). Those reasons help to define improvement goals (e.g. to decrease the number of leads lost from the healthcare industry) and outline a plan to attain them.

Do: The solution is quickly put into practice via the CRM (for example, best practices from top performers are delivered to weaker sales agents).

Check: Before-and-after comparison of win/loss reports delivered by the CRM shows the efficiency of the applied solution.

Act: The CRM documents the changes and their results, supports further improvements based on the solution (from Do), as well as keeps recommendations for the next PDCA cycle.

Evident as it is, a lot in the improvement cycle depends on the quality of conversion analysis (the Plan stage). That’s where the wit of CRM consultants comes in to show how to use the CRM data and what tweaks may be needed to make win/loss reports actionable.

Dynamic conversion analysis

The thing is, sales managers are drowning in data but starve for the information on why leads are draining. At the same time, ample CRM records form a solid basis for conversion analysis that can be managed either directly in the CRM system or in any other preferred tool. By dynamic conversion analysis, we mean that sales managers start the PDCA cycle by looking at a bigger picture (one-factor analysis of lead losses by company size, industry, or channel, etc.) and then study data correlations using multiple factors so as to get insights into leads leakage.

To illustrate, we picked a list of 6 factors that is by no means exhaustive:

• Company size. Analyzing conversion rates across size groups allows sales managers to indicate problems with approaching certain types of customers.
• Industry will help to detect how well the sales team performs and spot trends in the targeted domains.
• Location. Zeroing in on customer geography hints at problems with understanding local market conditions or cultural aspects and allows to early notice alarming signs (for example, local competitor campaigns).
• Channel. Analyzing the leads data channel-wise helps reveal the least profitable and effective channels.
• Sales agent completes the picture with the image of sales team performance. For example, who loses more leads in numbers and projected revenue?
• Sales cycle stage. Detected patterns (most leads fail to convert after a proposal was sent) lets the company notice when leads’ interest starts going downhill and adjust the sales process to avoid negative activities in the future.

Though the results of one-factor analysis can be enough for some companies, in other cases multiple-factor analysis is needed to identify exactly why the conversion rate is low. Here are the examples based on the chosen 6 factors:

• Company size with Sales agent/Location/Industry. The results show that conversion rate in the group of 100-200 employee leads is 4%. Drilling down, a sales manager sees that the majority of lost leads was nurtured by a certain sales person (a performance management problem?), or came from a certain region (a market change?), or industry (a missed industry trend?).

• Location with Channel. Conversion rates are low in two regions - Georgia and Florida. A deeper dive into data shows that the ads channel in Georgia performs twice worse than the average level.
• Channel with Reason correlation can allow indication of problems with the quality of messages. For example, the conversion rate for leads from website is low. Drawing on the reason factor as well, the manager sees that ‘poor value proposition’ is the most common one for those leads. So, maybe the website messages are unclear?
• Sales agent with Sales cycle stage/Industry/Company size. This combination allows to identify problematic stages for particular agents. As the industry or size group tell what types of customers this sales agent fails to approach effectively, such evident performance issues let a sales manager timely organize sales coaching and shift resources (reassign leads from tough regions to more experienced performers).

Advanced analysis, extra data and extra diligence

To make the analysis of lead losses more insightful, a company can also look into more aspects such as loss reasons, nurturing activities and process deviation.

Reasons for losing leads can be either recorded by sales managers in the CRM at the end of activities or go as an option list tailored to a company's business. While the former requires no software tuning, the inconsistency of data complicates its analysis. The second option saves sales managers’ time and enables automated analysis by loss reasons, yet the list should be elaborated wisely and allow explanations. A typical one may include:

• Weak value proposition
• High price
• Insufficient technical expertise
• No resources
• No budget
• Competition (lost to a local/incumbent vendor), etc.

Either way, to minimize sales people’s bias, it makes sense to let other parties (sales managers or customers via a quick post-decision survey) verify the indicated reason. Though the majority of customers are likely to skip such kinds of surveys, those who answer will give a company an extra point for consideration.

Further review of the nurturing activities history allows a sales manager to learn what exactly went wrong on the sales rep’s side, which is especially important for high-revenue losses. While some of the records can appear in the CRM automatically (emails, and phone calls), others will require sales people to sacrifice 2-5 minutes for logging communication details. Here, the sales team’s diligence impacts directly the quality of conversion analysis.

If the company has a standard lead nurturing procedure or documented standards, deviations can be also monitored in the CRM. Some of them will be evident after reviewing the activities (like, miscommunication), others can be tracked even automatically (like, using timers to detect delays). 

PDCA plus CRM equals a higher conversion rate

As a starting point for improving the lead conversion rate, ScienceSoft’s CRM consultants suggest applying the PDCA model and using CRM records to dig into the reasons for lead leakages and find out how to fix them. Perhaps, the biggest challenge on the way is not tweaking the software, but making continuous improvement a part of the corporate culture. In case of success, this will eliminate the need to advocate logging required details (while watching over their quality) and other changes required to manage the sales conversion rate.