We are developing a tool based on Artificial Intelligence to qualify all types of sales leads. There will be numerous sales leads and incoming inquiries. Attending all of them is a huge waste of time. You can’t increase the number of sales members based on the number of sales leads. You ideally want to categorize all of them so that you can focus. This will help the sales team to focus on sales leads that have a higher chance of a conversion.
This article will explain our tool under the final stages of development for improving sales leads. This is based on a machine-learning algorithm that learns on its own after an initial period of supervised learning. This would help the customer to identify quality leads using their own company descriptions.
How to decide whether a sales lead is high potential using Artificial Intelligence & Data Analytics
If you look at your current manual process, you may notice that this is already happening. This is based on the experience of the salesperson. This is mostly an intuitive process and not scalable. Instead, we can automate the whole process using Artificial Intelligence and Data analytics.
A typical result of the AI tool will be as below:
- As soon as the sales lead or an inquiry is registered in the CRM, the AI tool will analyze different parameters.
- Based on the analysis, a score will be assigned to the sales lead. In simple terms, the tool will be comparing the profile of this new potential customer to a lot of your earlier customers for similar products and services.
- The sales lead or inquiry is allocated as category A which means that chances of conversion are high.
- The sales lead is allocated to category B which may mean that the inquiry is having some potential, but not urgent.
- The third category is category C, which may mean that the sales lead is not having any potential.
The AI tool will analyze a combination of parameters such as the product/service sought, country/city of origin, type of the customer, etc.
Steps of Machine learning for categorization & prediction of sales leads
The machine learning algorithm combined with data analytics works in the following manner.
- You feed the characteristics of your existing customers. The data will include customer name, website, city, country, product purchased, etc.
- Thus the AI tool will make a profile of your potential customers.
- Now when you receive a new sales lead, you will enter the details of this inquiry into the AI tool.
- The AI machine will generate a result indicating how close is these new sales lead to your desired customer.
- During the learning phases, you may have to reassign based on your experience. AI will learn from each of these sales leads.
- Slowly it will be able to improve the prediction score of each of the sales leads.
Process of predicting the potential of sales leads using Artificial Intelligence
The first step in the process is called data collection. This dataset will consist of qualified or disqualified companies in terms of quality leads. The qualified list will consist of your regular clients. You may already have done business with them at least once. And the disqualified list will consist of companies that do not really relate to the profile of your potential customer. This part is easy. Now there will be a lot more that are not in both of these categories. Our AI software will analyze these sales leads based on a lot more parameters.
There are several ways to collect this kind of data. Some opt for a manual technique while others prefer automated machine-aided analysis. Manual collection of data will result in more accurate and complete data in contrary to automatic analysis. In an automatic process, there is a chance that data will not be accurate and incomplete. However, the automatic analysis does offer the ability of time conservation. During the initial period of learning, the data collection will be mostly manual. This helps the AI tool to learn the process.
Now that the dataset is taken care of, we will have to move towards cleaning the data. This process is generally called data processing or data cleaning. Cleaning the data essentially means getting rid of irrelevant information. This can be achieved by writing a natural language process script. The idea is to make a proper dataset in a useful form. Our machine learning algorithm now uses this data to qualify the sales leads.
In our case, one of the cleaning techniques we applied was using regular expressions to get rid of non-alphabetical characters so that our model would only read words from the description.
Another problem was reducing the repetition of words in the data. To tackle this, we used a stemmer that would group the repeating words and simplify it to a single word.
The final problem was to get rid of the stop words such as for, I, it, etc. We used Natural Language Toolkit to solve this problem.
Finally, after processing the data we must transform our dataset into a machine-readable format. We used the Bag of Words (BoW) approach to turn the description into vectors. Machines can only read data as 1s or 0s. Therefore, to train a machine learning model, all the data is converted to binary format. This essentially aids the machine in reading data and learning from it.
All the aforementioned techniques fall under the topic of natural language processing. All of this was carried out using a python library called NLTK.
Now that our data is ready to be used in our machine learning algorithm, we split the data into two different sets. We slicked it into 70% training data and 30% testing data and ran in through a famous machine learning algorithm Random Forest. And after some fine-tuning, we reached a high accuracy on the test dataset which means the output would be helpful to our sales team.
Random Forests are a supervised machine learning algorithm. We use the Random Forest algorithm for classification. However, the algorithm can be used for regression problems too. The algorithm stems from decision trees. However, decision trees tend to overfit. To overcome this, Random Forests train multiple trees and take the majority decision to give an output.
Various other algorithms can be used to train the same model. To investigate the best model, multiple algorithms should be used, and the best performing models should be used. Decision Trees, Support Vector Machines (SVM), Liner classification, and K-Nearest Neighbor are some of the examples of good and efficient algorithms.
Pros & Cons of prediction of quality of sales leads using Artificial Intelligence
Of course, there are pros & cons to every tool. As AI is becoming an integral part of life, you can take a calculated decision based on your applications.
- When you have a large number of sales leads, you can use AI to some extent.
- You do not have to completely depend on human expertise.
- The AI can probably help you with 70 to 80% of the decision-making process. Based on these judgements, an expert can take further action.
- Training of new sales team members become easier since they can focus on important decision-making processes.
- If you have a large number of sales members, integrating the learning of each of them at a human level is difficult. Instead, each of them can help the learning process of the AI tool.
- The accuracy of results depends on the quantity and the quality of the data.
- As your business and customer evolve, you may have to modify the parameters of the data set. You cannot depend on the same parameters that you started with.
- At frequent intervals, human experts have to check the results and make adjustments. This process can never stop.
- If you introduce a new product or service, you may not have any prior data. In this case, you will have to assist the AI in the initial phases.
Vacker360 provides various tools based on Artificial Intelligence, Machine Learning, Data analytics, etc. mainly for the B2B segment.