Boosting Sales With Machine Learning
How we use natural language processing to qualify leads
In this blog post I’ll explain how we’re making our sales process at Xeneta more effective by training a machine learning algorithm to predict the quality of our leads based upon their company descriptions.
Head over to GitHub if you want to check out the script immediately, and feel free to suggest improvements as it’s under continuous development.
It started with a request from business development representative Edvard, who was tired of performing the tedious task of going through big excel sheets filled with company names, trying to identify which ones we ought to contact.
This kind of pre-qualification of sales leads can take hours, as it forces the sales representative to figure out what every single company does (e.g. through read about them on LinkedIn) so that he/she can do a qualified guess at whether or not the company is a good fit for our SaaS app.
And how do you make a qualified guess? To understand that, you’ll first need to know what we do:
In essence, Xeneta help companies that ship containers discover saving potential by providing sea freight market intelligence.
More specifically, if your company ships above 500 containers per year, you’re likely to discover significant saving potential by using Xeneta, as we’re able to tell you exactly where you’re paying above the market average price.
This means that our target customers are vastly different from each other, as their only common denominator is that they’re somewhat involved in sea freight. Here are some examples of company categories we target:
- Freight forwarding
- Consumer & Retail
- Low paying commodities
Though the broad range of customers represents a challenge when finding leads, we’re normally able to tell if a company is of interest for Xeneta by reading their company description, as it often contains hints of whether or not they’re involved in sending stuff around the world.
This made us think:
Given a company description, can we train an algorithm to predict whether or not it’s a potential Xeneta customer?
If so, this algorithm could prove as a huge time saver for the sales team, as it could roughly sort the excel sheets before they start qualifying the leads manually.
As I started working on this, I quickly realised that the machine learning part wasn’t be the only problem. We also needed a way to get hold of the company descriptions.
We considered crawling the companies’ websites and fetch the About us section. But this smelled like a messy, unpredictable and time consuming activity, so we started looking for API’s to use instead. After some searching we discovered FullContact, which have a Company API that provides you with descriptions of millions of companies.
However, their API only accept company URL’s as inputs, which rarely are present in our excel sheets.
So we had to find a way to obtain the URL’s as well, which made us land on the following workflow:
- Using the Google API to google the company name (hacky, I know…)
- Loop through the search result and find the most likely correct URL
- Use this URL to query the FullContact API
There’s of course a loss at each step here, so we’re going to find a better way of doing this. However, this worked well enough to test the idea out.
Having these scripts in place, the next step was to create our training dataset. It needed to contain at least 1000 qualified companies and 1000 disqualified companies.
The first category was easy, as we could simply export a list of 1000 Xeneta users from SalesForce.
Finding 1000 disqualified was a bit tougher though, as we don’t keep track of the companies we’ve avoided contacting. So Edvard manually disqualified 1000 companies.
Cleaning the data
With that done, it was time to start writing the natural language processing script, with step one being to clean up the descriptions, as they are quite dirty and contain a lot of irrelevant information.
In the examples below, I’ll go though each of the cleaning techniques we’re currently applying, and show you how a raw description ends up as an array of numbers.
The first thing we do is to use regular expressions to get rid non-alphabetical characters, as our model will only be able to learn words.
description = re.sub(“[^a-zA-Z]”, “ “, description)
We also stem the words. This means reducing multiple variations of the same word to its stem. So instead of accepting words like manufacturer, manufaction, manufactured & manufactoring, we rather simplify them to manufact.
from nltk.stem.snowball import SnowballStemmer
stemmer = SnowballStemmer(‘english’)
description = getDescription()description = [stemmer.stem(word) for word in description]
We then remove stop words, using Natural Language Toolkit. Stop words are words that have little relevance for the conceptual understanding the text, such as is, to, for, at, I, it etc.
from nltk.corpus import stopwords
stopWords = set(stopwords.words('english'))
description = getDescription()description = [word for word in description if not word in stopWords]
Transforming the data
But cleaning and stemming the data won’t actually help us do any machine learning, as we also need to transform the descriptions into something the machine understands, which is numbers.
Bag of Words
For this, we’re using the Bag of Words (BoW) approach. If you’re not familiar with BoW, I’d recommend you to read this Kaggle tutorial.
BoW is a simple technique to turn text phrases into vectors, where each item in the vectors represents a specific word. Scikit learn’s CountVectorizer gives you super simple way to do this:
from sklearn.feature_extraction.text import CountVectorizervectorizer = CountVectorizer(analyzer = ‘word’, max_features=5000)
vectorized_training_data = vectorizer.transform(training_data)
The max_features parameter tells the vectorizer how many words you want to have in our vocabulary. In this example, the vectorizer will include the 5000 words that occur most frequently in our dataset and reject the rest of them.
Finally, we also apply a tf-idf transformation, which is a short for term frequency inverse document frequency. It’s a technique that adjusts the importance of the different words in your documents.
More specifically, tf-idf will emphasise words that occur frequently in a description (term frequency), while de-emphasise words that occur frequently in the entire dataset (inverse document frequency).
from sklearn.feature_extraction.text import TfidfTransformer
tfidf = TfidfTransformer(norm=’l1')tfidf.fit(vectorized_training_data)
tfidf_vectorized_data = tfidf.transform(vectorized_training_data)
Again, scikit learn saves the day by providing tf-idf out of the box. Simply fit the model to your vectorized training data, and then use the transform method to transform it.
After all the data has been cleaned, vectorised and transformed, we can finally start doing some machine learning, which is one of the simplest parts of this task.
I first sliced the data into 70% training data and 30% testing data, and then started off with two scikit learn algorithms: Random Forest (RF) and K Nearest Neighbors (KNN). It quickly became clear that RF outperformed KNN, as the former quickly reached more than 80% accuracy while the latter stayed at 60%.
Fitting a scikit learn model is super simple:
def runForest(X_train, X_test, Y_train, Y_test):
forest = RandomForestClassifier(n_estimators=100)
forest = forest.fit(X_train, Y_train)
score = forest.score(X_test, Y_test)
return scoreforest_score = runForest(X_train, X_test, Y_train, Y_test)
So I continued with RF to see how much I could increase the accuracy by tuning the following parameters:
- Vocabulary: how many words the CountVectorizer includes in the vocabulary (currently 5K)
- Gram Range: size of phrases to include in Bag Of Words (currently 1–3, meaning up until ‘3 word’-phrases)
- Estimators: amount of estimators to include in Random Forest (currently 90)
With these parameters tuned, the algorithm reaches an accuracy of 86,4% on the testing dataset, and is actually starting to become useful for our sales team.
The road ahead
However, the script is by no means finished. There are tons of way to improve it. For example, the algorithm is likely to be biased towards the kind of descriptions we currently have in our training data. This might become a performance bottle neck when testing it on more real world data.
Here are a few activities we’re considering to do in the road ahead:
- Get more data (scraping, other API’s, improve data cleaning)
- Test other types of data transformation(e.g. word2vec)
- Test other ml algorithms (e.g. neural nets)
We’ll be pushing to GitHub regularly if you want to follow the progress. And feel free to leave a comment below if you have anything you’d like to add.