Lead generation

Help customers generate more leads
Attached benefits:

  • More Deals created

  • Faster Time to Value

  • Better Deals/Win ratio

  • Ultimately, win more deals and grow their business

The idea is to suggest new leads to the customer right after the sign-up flow so the user can start filling their pipeline with leads without delay. We suggest leads based on the data they provide us while signing up for our CRM.

Note: You might find some incorrect copy or wrong spacing as this is still WIP and I haven’t made it public for everyone. Thanks for your time :)

Team

We started working with a small team and we had:
- Design (Me)
-PM
- 2 Developer (full-stack)

Duration: Overall time spent on prep and delivery of 2 experiments
Experiment 1: 30 days
Experiment 2: 15 days

Collaboration:

  • Regular stand-ups

  • Design reviews with the team

  • Customer interviews

  • Weekly ideation session

Pic credit: Midjourney :)

Process

Our primary goal centers on delivering exceptional value to our end users, achieved through swift iterative cycles. The diagram clearly illustrates our process, beginning with validating the problem statement and drafting the wireframe, followed promptly by engaging with customers. Simultaneously, our team embarked on crafting the solution, paving the way for various experiments to emerge. Our main objectives focused on augmenting the quantity of leads generated by customers, directly aligning with our North Star metrics.

Discovery

Customer problem statement & defining the success

Together, we engaged in a brainstorming session to accurately identify the core issue, which led to the formulation of our Leap of Faith Assumptions (LOFAs) and the outlining of essential elements for our idea's success. Among these LOFAs, a key focus was on validating whether our approach aids customers in converting more leads. We also established critical metrics, notably aiming to "shorten the duration from trial to paid customer conversion." I took on the role of facilitating these sessions alongside the Product Manager, incorporating input from stakeholders across various departments.

Target personas

Our main objective is to make the lives of salespeople easier and provide them with lead suggestions at their fingertips.

Direct quotes from our users:

“Your CRM should provide us lead ideas as other competitors are doing in the market and help us stay focused on the actual work. - Sales representative”
”We have to struggle a lot to find leads suitable for our business needs and it would be really helpful if you could build some feature like LinkedIn sales navigator and provide us more leads.
- Sales representative”

Discovery

Early prototypes

I jumped on defining the flows and building some early prototypes that we could use for the brainstorming session and during the customer interviews.
Process: Created the quick flows on Lucid/miro > Validated it with the team > Quick wireframes on Figma > Design review with design org > Iteration to hi-fi > Hand over to devs.

Discovery

Validating prototypes (qualitative)

We started capturing real-time customer feedback alongside live running experiments using the Figma prototypes.

We did some interviews with the trialists (for the first time) and got some amazing insights. It was a bit difficult to remind them about the feature as the Proactive leads functionality is in the sign-up flow and not a lot of users remembered it.
All the interviews helped us to back-up the data that we captured on amplitude and move ahead for the experiment 2.

Delivery

Setting up experiments

It was quick as engineers were working alongside on the development and iterated the solutions on the go. I had to hand over the designs 1 week in advance. (sprint mode)

We set the experiment for 45 days to understand the following metrics:
- Company becomes paying
- Deal/lead created from the proactive leads modal
- Leads inbox usage increased
- 2 days stickiness

What now? Let’s check the process. Ah! It says learning and iteration….

Zoom-out and learn from the data.

We had the qualitative and quantitative data with us for the analysis and define the next steps. The overall impression was great from the experiment and we decided to experiment the same with a different set of users. This time we focused on existing users (the most sensitive user base) and showed them the new lead suggestions at different touch points. Later we got the green signal to deliver it for all the new customers as it showed a nice increase in trial-to-pay metrics of the new users and carried out the experimentation for a different user segment.

Iteration and deliver

Brainstorm and planning

I took the lead and planned a few sessions with the whole team, devs, and just designers to map out the next experiment, define the success, and plan the customer interviews. This time we focused a lot on the customer experience, awareness, and quality of the lead's suggestions

Learnings

Feedback from customers

We decided to run a new experiment for a shorter time as the user base was bigger and we could get the inputs shortly.
We used the feedback form to get the qualitative data right away from the users. Straightaway we asked questions like: Do you like the feature?, Would you like to receive lead suggestions in the future? and a space to talk out loud :)
It helped us to go back to our iteration and research phase and define the next steps..

Long story short

We heard the customer's problem defined the target user > and went straight into discovery and ideation as this was proved that this is a problem and customers are struggling with leads generation > set up the experiment > and validated the solution on the go with both qualitative and quantitative approaches > defined the next steps and iterations and repeat.
It allowed us to rapidly validate the idea and build things on top of it. I loved the building > understanding > iterating phase as it got me closer to the customer problem and helped me get into their shoe with the relevant data. It became so clear for us to laser focus on the quality of the leads for existing customers as we heard it loud and clearly from the feedback. We have a complete solution and now we have to improve the system for generating good quality data.