AI-Powered SaaS Lead Acquisition Solution for the B2B Marketing Startup

We delivered a full-scale, AI-based solution to automate lead acquisition service for the client, a B2B marketing startup. This solution empowered the client to provide their customers with advanced lead outreach, qualification, and classification capabilities.

As an aspiring startup providing digital marketing solutions, the client sought to improve the accuracy and reliability of lead classification procedures for its B2B customers.

Each business had to manually classify inbound leads into ‘hot’, ‘warm’, and ‘cold’ leads, which took a tremendous amount of time and effort. Depending on how much data one needs to process to perform an accurate lead classification, it might take several hours to classify a single lead.

Additionally, the more data outreach specialists had to process, the higher the risk of human error, which led to inaccuracy and unreliability in the classification procedure, in general.

The long-term consequences of the wrong lead classification included:

Missed lead generation opportunities: If the lead was incorrectly classified, the wrong communication strategy could be chosen. For example, if the lead was ‘hot’, according to the company’s classification criteria, but marked as ‘cold’, the outreach specialist might assign the wrong priority to that lead. In this case, the lead might be contacted via email rather than messenger, leading to a slower response rate.
Profit loss: Wrongly classified leads might not be reached in time or not contacted at all, leading to revenue loss.
Reputational damages: By failing to provide efficient lead classification service to their customers, the client risked a poor reputation, which might cause customer churn and lost profits.

Challenge

During the discovery phase, we defined the following challenges the client was experiencing:

Slow and inefficient lead processing

A typical lead generation process for the client’s customers consists of the following steps:

Lead acquisition: They manually collected data on leads from external sources (such as CRMs and social media) and marketing campaigns they performed with the existing client base by sending them commercial proposals via email, SMS, or WhatsApp.
Lead qualification: After a potential lead responded to the mail sent, the assigned outreach specialist (operator) qualified them as leads and added them to the CRM system.
Lead classification: The outreach specialist started chatting with the lead. During the conversation (which might take hours or even days, depending on how quickly the lead replied to messages), they defined which group to attribute the lead to.

At that point, the main challenge for the client was the general length and inefficiency of lead processing. Each step of the procedure was handled manually. As their customers worked with leads across multiple channels, including Facebook, Instagram, WhatsApp, and email services, they had to manually classify leads acquired from each channel.

The client needed to streamline the entire process, not only its last step — lead classification — so that operators could reach out to potential leads at the right time, through the right channel, and with the right message to convert them into leads and then accurately classify them based on the pre-defined classification criteria. They needed process automation functionality to accomplish that goal.

Performance limitations

The client’s solution couldn’t handle large data volumes, which led to performance slowdowns and could cause disruptions as the number of leads grew. They needed to ensure the solution’s stable performance under high loads.

Lack of a pricing strategy to stimulate recurring profits

The client needed a consistent pricing strategy to guarantee stable, predictable profits. Initially, they distributed their solution only through the ‘pay-as-you-go’ pricing model, where the customers paid only for the number of lead acquisition campaigns they launched.

On the one hand, this model proved itself cost-efficient for the customers who didn’t intend to use the solution regularly. However, the ones who needed to use it repeatedly lacked a more flexible pricing option that would allow them to plan their budget for the solution.

The client’s previous solution had limited performance capabilities, so modernizing it to reach all their goals was impossible from a functionality standpoint. Instead, we needed to build the new one from scratch.

We started with defining the goals for the client’s project:

Streamline lead processing to maximize lead acquisition rates.
Automate lead classification processes for the client based on pre-defined criteria to make classification accurate and, hence, reliable.
Ensure the platform’s scalability to operate under increasing loads, effectively processing multiple users simultaneously. To do this, we needed to define the approximate load capacity at a given time, determine the scalability requirements, and choose a suitable performance solution.
Provide a recurring subscription payment plan for the solution. Here, the goal was to define the client’s business model with the new solution or suggest one for them. Then, we could decide whether to leave the already existing ‘pay-as-you-go’ model and add another one (or more) or replace the existing payment model with the new one(s).

Minimum Viable Product (MVP)

At the MVP stage, we decided to take the client’s previous solution and conduct so-called ‘broadcast’ campaigns to reach potential leads by sending mailings across multiple communication channels.

For the MVP, we set the following goals:

Test out the mailing feature
Validate the efficiency of different outreach channels
Validate whether the solution for automated lead classification was really needed

At that point, we would add the dedicated mailing functionality to the existing solution, allowing us to start testing as quickly as possible without spending much time and money on development.

We chose email, SMS, and WhatsApp as communication channels, which allowed us to avoid cost-intensive integrations to validate our hypotheses. First, we developed the functionality and integrated it into the solution. Then, we uploaded a file with the leads’ contact information. The solution automatically composed a contact base by parsing that data from the uploaded file. Next, we began sending emails with different promotional offers to each client in the contact database.

The response rate across channels was enormous. Therefore, the data volumes we processed when qualifying inbound leads were too large to handle quickly (remember, we had to manually classify leads). We were satisfied with the MVP’s results, which proved the main hypothesis: to conduct lead acquisition campaigns across multiple channels efficiently, one needs a solution that automates lead classification procedures.

Scrum/Agile approach

The client needed to launch the solution to the market as quickly as possible, so our goal was to arrange a quick and efficient development process within a tight schedule. We chose a flexible, agile methodology for the project, which enabled us to provide frequent updates through sprints that typically lasted two weeks to a month.

By closely collaborating with the client and synchronizing progress throughout the development process, we delivered a market-ready lead qualification solution within the agreed-upon schedule.

Performance optimization

To achieve stable performance under high-volume loads, we comprehensively tested each major release and then tested the whole system in action. At that point, the priority was given to load tests, where we checked the system’s performance with current data volumes and simulated conditions after three months, six months, and twelve months after the solution’s launch (assuming that the number of leads will grow continuously).

With such a comprehensive performance picture, we could minimize potential slowdowns or disruptions when handling data on multiple leads and across multiple channels simultaneously.

Infrastructure optimization

To achieve the solution's scalability, we deployed it to AWS-powered cloud hosting. By the time we started working on the project, AWS had already proven itself efficient in building highly-scalable solutions. We have successfully implemented it on multiple projects where performance and scalability were among the key priorities, so the choice of AWS was obvious.

Solution

We empowered the client to run efficient lead acquisition campaigns by making the entire lead acquisition process — through potential leads’ outreach, qualification, and classification — automated, error-free, and hence efficient and reliable. Our team achieved this with an intelligent lead acquisition platform that introduced the following features and solutions:

Integrating the solution with Meta’s Facebook and Instagram enabled automated data collection about users who expressed their interest in the client customers’ products advertised on those social platforms (in the form of likes, shares, or comments).

The ML-based service analyzed past lead interaction patterns (types of messages, response rates, communication channels, classification criteria, etc.) and automatically classified inbound leads based on that data.

The client’s customers could run targeted email campaigns across qualified leads via Gmail and/or Outlook.

The Zapier app enabled automated lead workflows, including notifications on new leads, CRM updates, assigning a new task to an outreach specialist, and other notification types based on the pre-set trigger events. Customers could configure triggers that should follow those events in the app. Together with the client, we determined that the solution should process around 2,000 lead workflows per business account daily, and Zapier could handle that data volume.

The integration allowed sending automated notifications to the selected leads via SMS and WhatsApp.

Besides social media, email, SMS, and other external communication channels, clients could reach out to prospects immediately via the embedded chat that was seamlessly integrated with Facebook, Instagram, and WhatsApp.

To ensure sustainable profit generation for the client, we introduced a monthly payment model, in addition to the existing ‘pay-as-you-go’ model. According to the new model, the platform’s users were charged a fixed monthly rate upon reaching the pre-defined limit of mailings sent to prospects (i.e., the number of lead acquisition campaigns). If the limit was reached before the end of the month, they were automatically subscribed to the new monthly plan.

Technology Stack

Frontend

React

Backend

Python (Django admin)

Database

PostgreSQL

Infrastructure

AWS

GitLab

With the delivered solution, the client managed to achieve the following results:

Enhanced productivity of lead acquisition campaigns: We automated lead processing end-to-end so that the client’s customers could retrieve meaningful data on prospects from the relevant outreach channels, accurately classify them based on the initially established classification criteria, and conduct targeted promotions among them, which increased sales rates.

Sustainable business model: With the two major payment models—‘pay as you go’ and the monthly subscription—the client could provide customers with the flexibility to choose the model that best fits their budget and software usage scenario. That is, the client managed to broaden the customer base and receive stable profits.

Brand recognition: By launching the innovative lead acquisition solution to the market, the client introduced a new way of processing leads for businesses worldwide, gaining massive positive feedback.

More Case Studies

AI-Powered Medical Surgery Recording App

Advanced AI-powered iOS Application Integrated with Innovative Health Tech Software Platform

Mobile development UI/UX Data Science

Machine Learning

Healthcare AI ML

Emerline Helped an AI Company Optimize Store Layouts and Improve Sales Predictions for Large Retailers

Our AI and Data Science professionals developed tailored machine learning models contributing to the improvement of sales forecasting and store layout optimization for major retail leaders in the United States.

Data Science Data analytics Retail AI ML

AI-powered Document Search Service for B2B Customers

Emerline has developed an innovative AI-powered chatbot document search service tailored for B2B customers, designed to significantly enhance document management and retrieval processes in a business environment.

Data Science

Machine Learning

Custom development Software & Hi-tech AI ML Chatbots

all case studies

AI-Powered SaaS Lead Acquisition Solution for the B2B Marketing Startup

Background