As a business development person in a Machine Learning company, I get confronted every day with the changes happening in the role of sales.
As organizations evolve to become ready for the disruption of automation, there’s a growing need to understand what these technologies can and cannot do.
In this blog post, we’ll take a look at what these changes entail through the lens of the customer journey. More precisely what implies for a SaaS B2B company.
What is The Customer Journey?
It’s a framework that maps the experience of a typical consumer, from the moment it first encounters a brand till it becomes a paying customer. Excellent explanations of this model found here and here.
Following this framework we can identify three prototypical stages of the journey:
- Awareness stage: the potential customer becomes aware that it has a challenge.
- Consideration stage: it defines its problem and starts looking for ways to solve it.
- Decision stage: the customer selects a solution.
Through all of this process, marketing and sales teams try to align themselves with the objectives the buyer has in each of the different stages.
In the sales department, each of the stages of the journey maps to specific actions that are needed to help the buyer move forward in its path.
These actions usually are:
- Identify: use data collected from a variety of sources to try to single out users who are ready to buy.
- Connect: use personalized messages to establish a conversation with them.
- Explore: qualify the opportunity based on a qualification framework of your choice. Some good frameworks can be found here and here.
- Advise: translate the marketing message to the unique needs and context of the buyer.
So how’s the customer journey evolving due to the advent of Machine Learning?
Machine Learning is revolutionizing how teams approach these actions. Through automation of the most boring and repetitive tasks as well as helping to gather more and better insights.
1. Identifying customers personas is now easier than ever
Machine learning clustering algorithms can be used to identify the patterns in the customer demographics, buying habits, attitude, social sentiment, timing, location, goals etc.
2. Lead prioritization is being automated
Tools like Madkudu can take in-app behavior & demographic data to identify your best customers and predict which leads are ready to buy.
3. Identifying Leads in Social Media has become trivial
A perfect example is the Sales Navigator from Linkedin. It recommends leads based on several factors such as your sales preferences, search history, and profile interactions to help you identify new prospects.
4. Public web content has become a powerful tool for identifying leads
By scraping company websites, job boards, PR news, and other sources companies like Predictleads can create machine learning models to find triggers for potential leads.
5. Pre-call research is fairly simple now
Instead of having to go through all previous conversations, documents, and public data to prepare for a call. Sales representatives can now outsource this task to intelligent assistants like Nudge.ai, that deliver actionable insights right into your email or browser.
6. Email prioritization is getting smarter
Having to read and answer email responses can be a tedious and time-consuming task. With classification algorithms, you can now better prioritize the emails you receive and use a model to detect the level of interest in an email response to pay attention to those opportunities ready to close.
7. Sales calls are now being used to coach and help sales representatives in real time
8. Data entry is becoming obsolete (Finally)
With automatic data capturing models, companies like Clari can save countless of hours for sales representatives by updating CRM data on the fly.
9. Recommending the right sales collateral as an afterthought
Products like Zensight will guide any sales representative to offer the right content and message based on each lead characteristics.
10. Predicting the best next step to take is now possible
No more reactive solutions. Companies like Salesforce with its AI offering, Einstein, are now leading the way with proactive suggestions to move deals forward. From identifying when immediate responses are needed to automate follow up activities.
At MonkeyLearn we are focusing in processing natural language in the form of unstructured text with Machine Learning. By providing a horizontal tool to marketing and sales teams, they can leverage all of their text data across any department to understand their customer’s journey.
One thing you can try right away with MonkeyLearn
Social Media is an excellent channel to find new leads and opportunities, unfortunately, it’s full of noise and chatter. With this sales hack, you can dissipate the mist and identify and engage with real prospects at the right time: when they are unhappy with their current provider (competitor).
By building the following Zap, we’ll monitor mentions of competitors on Twitter, automatically analyze them with MonkeyLearn and detect whenever someone is complaining about the competition and alert us right away via Slack.
- Create a Zap.
- Select Twitter as Trigger App.
- Select Search Mention as Trigger.
- Input your competitor search query: this will trigger anytime someone mentions your competitor. In this case, we selected “IBMwatson nlp -filter:retweets” to filter noise about Watson not regarding natural language and to avoid grabbing retweets:
- Select MonkeyLearn as Action App.
- Select Classify Text as Action.
- Select a Sentiment Analysis Model. This will classify your competitors mentions into Negative, Neutral or Positive tweets. You can use a pre-trained model or eventually train your own custom model from scratch specifically for your use case.
- Select text to classify (Tweet text):
- Select Filter Action App.
- Filter tweets that are classified as Positive and Neutral and only continue if the tweets are classified as Negative:
- Select Slack Action App. This will trigger anytime a negative mention of a competitor is tweeted.
- Select Channel and complete template:
- Verify results.
- Wait for slack to warn you when someone talks negatively against your competitor on Twitter and seize the opportunity.
As we have seen, it’s fairly simple to start learning and making use of AI, even as a salesman like me. Hope you can get inspired by this post and come up with your own ideas on how to leverage machine learning.
Just sign up to begin your path on AI and start automating all the boring stuff you didn’t want to do in the first place.