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Deal qualification and inspection, pipeline reviews, sales methodology standardization, multithreading.
Lack of structured sales process, unreliable forecasting, time-consuming pipeline reviews, inconsistent deal qualification.
Increased forecast accuracy, improved win rates, decreased sales cycle length, better deal execution.
Lily AI is an AI-driven organization that helps large retailers better connect their customers with the right products. Lily AI analyzes customer searches for trends, aesthetics, and occasions, and connects a retailer's shoppers with the exact products they are looking for.
Julian Dimery, Head of Sales at Lily AI, was brought on board at the beginning of 2024 to help scale Lily AI’s sales machine. The key goals were to standardize and streamline the sales process, decrease sales cycle length, increase conversion, improve sales predictability, and grow the partner ecosystem—all in an effort to hit ambitious yearly sales targets.
Prior to Dimery’s arrival, the Lily AI sales team had no formalized sales process or methodology. To get a sense of what was going on within each seller’s pipeline, Lily AI relied on pipeline reviews that consumed close to 30 hours of seller and leadership time weekly.
“Previously, we relied on the updates reps gave in pipeline meetings to understand what was happening in the field,” said Dimery. “We were so dependent on these meetings that we had them multiple times a week, taking up valuable rep and leadership time that could be spent with customers and prospects.”
Lily AI did not have a standardized framework that was used to measure the health of deals in the pipeline and help AEs understand where they were in the selling process. Opportunities often remained open for too long and became stale, or were continually pushed out. With an ever-evolving sales motion, sellers often qualified deals based on opinions, rather than data, which created a challenge for the Lily AI leadership team to make data-backed business decisions.
With limited structure and visibility into their sales process, the Lily AI team found deal cycles to be longer than desired and deal qualification lacking important context, creating a hurdle for Lily AI leadership to accurately predict revenue.
To solve these problems, Lily AI looked for a solution that could improve deal qualification and pipeline hygiene, increase forecast accuracy, and drive implementation and adherence to MEDDPICC. Dimery aimed to achieve these business outcomes using People.ai’s Opportunity Scorecards and Relationship Maps.
“We needed a tool that would help us standardize our sales process and drive adoption of MEDDPICC across our entire sales org—pretty quickly, we saw that People.ai was the obvious choice,” Dimery said.
In implementing People.ai, Lily AI wanted to ensure that every seller followed a uniform process and that each deal was validated against a consistent set of variables. To do this, Dimery worked with his People.ai CSM to create standardized Opportunity Scorecards in People.ai based on MEDDPICC. Opportunity Scorecards act as a framework to help sellers understand what key pieces of the deal are missing in order to advance towards closed-won, and give them parameters they can use to ensure deals are viable and healthy.
Lily AI also introduced stage-gating using Opportunity Scorecards, requiring deals to meet a number of necessary criteria before AEs can advance them to the next stage. When Dimery arrived, there were ten sales stages, but with the help of People.ai, he was able to streamline this down to four main sales stages. This criteria requires sellers to think about things like whether or not they’re talking to the right person, how the prospect will make a decision, how the deal is progressing overall, and what needs to happen in order for the deal to close. These scorecards have become a critical tool in 1:1s, team meetings, and pipeline reviews, helping to drive discussion around successes, next steps, and potential risks or blockers.
“We actively use Opp Scorecards in all of our pipeline meetings, which reinforces our sellers to accurately fill them out. As we’re discussing each deal, we pull up the scorecard in Salesforce and go through the MEDDPICC score in People.ai,” said Dimery.
Dimery encourages his sellers to utilize Relationship Maps in People.ai, which help to support multithreading in opportunities with large global retailers. With these maps, AEs and leaders can visualize complex buying groups and understand who else at a target organization needs to be involved in order to move deals through the pipeline. As a result, the Lily AI team has been able to be more strategic in connecting their leadership team with prospects, leveraging their founder’s network to make introductions with key decision-makers and understanding where risks and blindspots might be.
Since implementing People.ai, Lily AI has achieved significant results in just 6 months.
Improved Forecast Accuracy: By holding sellers accountable with stage-gating and standardized Opportunity Scorecards, Lily AI’s sales leadership has been able to forecast more easily and with much more accuracy. It takes them just a few hours, rather than spending 30 per week on manual pipeline reviews. Leadership uses the completeness of the Opportunity Scorecard to finetune their forecast—the higher the score on the Scorecard, the more likely the rep is to bring in the total deal value. People.ai also helped Lily AI create a report that uses the completion percentage from the scorecard to weight the ACV of each deal. This offers an additional layer of validation for the forecast by comparing deal qualification data against the forecasted numbers, allowing them to see through the fluff and get true clarity. Dimery has already seen improved forecast accuracy and is confident this trend will continue.
“People.ai is our go-to place to understand crucial pieces of each deal. Before, we were discussing next steps in our pipeline meetings and relying on seller notes, but we needed a way to easily see if forecasted deals were properly qualified—we can do that in People.ai,” Dimery said.
Shorter Sales Cycle Length: The introduction of stagegating using People.ai’s Opportunity Scorecards allows Lily AI leadership to heavily track the time spent in each sales stage and identify bottlenecks. In just one quarter using People.ai, they have seen improvements in sales cycle length, and expect to see continued improvement over time.
Higher Win Rates: With a more structured sales process and better data at their disposal, the Lily AI team has seen more deals progress steadily through the pipeline to Closed-Won, leading to an increase in win rates in their first quarter using People.ai. Sellers are able to go through a structured list to validate that their deal has what it needs to close, including key personas engaged, an identified decision-maker, and other key factors. From there, sellers can easily see what steps need to be taken to drive a deal to close.
“People.ai makes the data that we are collecting more meaningful. When you come into a new leadership role, you wonder if you can trust the data that existed before you joined. Because of People.ai, I know I can trust the data and confidently run my sales org off of it,” Dimery said.
Using Opportunity Scorecards and Relationship Maps in People.ai, Lily AI’s implementation of People.ai not only streamlined their sales processes but also resulted in tangible improvements in sales cycle length, win rates, and forecasting accuracy. By partnering with People.ai, Dimery was able to bring effective, standardized processes to the org that continue to drive better collaboration, organization, and visibility across the entire GTM org.
Learn more about People.ai or click here to get a demo and see Opportunity Scorecards and Relationship maps in action.
