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What Is Machine Learning in Sales?
10 Ways Machine Learning Can Boost Your Sales
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Despite being among the oldest fields, sales are constantly evolving with the advent of modern technologies. From customer relationship management (CRM) systems to door-to-door sales software, virtual selling, and mobile sales apps, this industry has quickly adjusted to meet the demands of the digital marketplace.
Especially since the introduction of artificial intelligence (AI) and machine learning (ML), many companies have drastically changed how they approach their sales operations and witnessed great benefits. Statistics from Harvard Business Review show that AI for sales can result in over 50% increases in qualified leads and appointments, 40-60% cost reductions, and call time reductions of 60% to 70%.
So, how exactly can this transformative shift be realized thanks to machine learning in sales? Read on to find the answer!
Machine learning refers to a computer system’s capability to learn and adjust based on data patterns. It’s a subset of AI technology, where computers emulate human-like thoughts and behaviors.
In sales processes, this means an artificial intelligence system examining buyer interactions and offering suggestions on managing specific aspects of a deal, such as overcoming a sales objection. Essentially, machine learning in sales involves artificial intelligence interpreting recommendations’ results and autonomously refining its algorithm accordingly.
For instance, if you follow the AI’s advice to address a sales objection and successfully close the deal, a positive feedback loop will be established, reinforcing the AI’s learning that the recommendation was effective. Conversely, if the objection handling proves unsuccessful, the artificial intelligence will incorporate this feedback to enhance the accuracy of its recommendations next time.
However, the role of machine learning in supporting sales teams extends beyond objection handling. More on this in the next part!
From enhancing data-driven insights and refining forecasts to streamlining sales automation and minimizing cart abandonment, machine learning can profoundly improve sales effectiveness and performance.
There are different ways that machine learning can be employed to understand customers better.
First, by leveraging machine learning algorithms, sales teams can identify patterns within customer data and segment customers into groups with distinct preferences to enhance their products and services accordingly.
Additionally, machine learning can boost customer understanding by analyzing surveys. Sales teams can use machine learning to examine customer survey data and better understand customer desires and needs.
Moreover, e-commerce companies apply machine learning to analyze customer browsing and purchasing behaviors. This data can be instrumental in improving the overall customer experience on the website, such as by providing personalized product recommendations tailored to individual preferences.
Traditional forecasting tends to rely on subjective opinions about future events. While historical data offers some objectivity, past performance information doesn’t always yield accurate predictions.
This is where machine learning comes in handy.
It allows sales teams to enhance the accuracy of their sales forecasts through a comprehensive analysis of all relevant variables. This includes sentiment in prospect communications, red flags, and pipeline stage conversion rates.
The beauty of machine learning lies in its ability to iterate on forecasts, compare projections with actual numbers, uncover patterns to predict customer behavior and pinpoint prospects with the highest potential. This not only frees up sales reps to focus on closing deals but also expands the business’s pool of potential buyers.
Lead scoring is a powerful tool, helping reps prioritize leads, improve sales conversations, and route not-yet-sales-ready prospects to automated lead nurturing campaigns. However, the challenge is that many lead-scoring strategies depend on intuition.
Machine learning steps in to improve the effectiveness of your lead-scoring process before sales opportunities are available. It identifies buyer actions that signal qualification and constantly adjusts its algorithms based on the impact of its suggestions.
So imagine this: last week, you presented two proposals, one on Tuesday and one on Thursday. While traditional processes suggest following up with the Tuesday prospect first, your machine learning lead scoring system recognized that the Thursday prospect viewed the proposal five times, showing more interest and urgency. Thus, the AI system recommended you prioritize the Thursday over Tuesday prospect.
Customer Lifetime Value analysis proves crucial for companies to gauge how much they can invest in customer acquisition campaigns (CAC) while maintaining profitability. Understanding LTV enables informed decisions regarding expenditure on acquiring new customers.
There are numerous ways to calculate LTV, but generally, a fundamental aspect is to understand customer behaviors. What influences a customer’s value to your company? How long do they remain your customers? What and how frequently do they purchase? A thorough understanding of these questions will help you make more accurate predictions of LTV, allowing for strategic decisions on CAC campaigns and fostering profitable business growth.
Due to studying massive data sets, machine learning excels at recognizing patterns and trends that might elude human observation when forecasting LTV. It also continuously enhances LTV modeling precision by examining factors such as average contract length, upsell potential through NLP analysis, churn rates, and possible account growth.
AI and machine learning deploy advanced algorithms, such as K-means clustering, to identify patterns in customer data across demographics, interests, and intent. This knowledge facilitates more precise and detailed customer segmentation.
Since traditional segmentation is typically based on factors like age and location, more subjective identifiers, such as purchase intent, are usually inaccessible. With machine learning, sales teams can more easily segment prospects based on these criteria to deliver relevant experiences that enhance their buyer journey and increase successful closures.
Moreover, segmentation also facilitates personalized marketing campaigns. More on this later.
Machine learning enables marketers to discover past customer behavior to predict future actions. This enables them to craft unique campaigns tailored to resonate with each customer.
For example, in an email marketing campaign, machine learning systems identify which emails will most likely be opened based on recipients’ past interactions. By sending only the most likely successful emails, marketers can save both time and financial resources.
Besides, marketers can personalize marketing campaigns by segmenting customers to tailor messages for each group and utilizing natural language processing for feedback and customer sentiment analysis.
A keen understanding of pricing is essential for any sales rep. Traditionally, successful pricing strategies were discovered through trial and error. However, with the rise of the internet and the ease with which customers can compare prices and stay informed about market trends, relying solely on trials is never enough.
Machine learning shines bright here. It often singles out the optimal point on the demand-supply curve, quantifying the price elasticity of demand and maximizing revenue. Many salespeople have already utilized such tools; for instance, trade-in sales managers at a car dealership leverage information from mechanics, feeding it into a machine-learning app to determine their trade-in price.
Pricing of upcoming products factors in numerous aspects like competition, seasons, special events, operational costs, and broader economic situations. But with the help of machine learning algorithms, you can develop the most ideal pricing strategy that ensures both rapid product sales and profitability.
Sales is a demanding profession with high workloads, including mundane and tedious tasks. Typically, sales professionals allocate around 33% of their day to writing emails and scheduling calls, 17% to data entry, 12% to internal meetings, and only 17% to prospecting and researching leads.
Machine learning can be employed to automate repetitive tasks, thereby boosting overall sales performance. For example, it automates welcome emails to new leads, demo emails to new sign-ups, and follow-up emails to non-responsive contacts through natural language processing (NLP). This process, commonly used for text-based querying to provide real-time responses, can also understand spoken words through voice recognition applications. Notable examples of voice recognition in automation are Amazon’s Alexa and Apple’s Siri, which facilitates automated scheduling, replies, and data entry.
So, machine learning can generally reduce sales reps’ time spent on mundane sales activities by up to 50%, allowing them to redirect their focus toward more important tasks.
Cart abandonment translates to lost sales and revenue for online businesses. There are various reasons for cart abandonment, with unexpected costs like shipping or taxes being the most common. Difficulties in finding desired items, poor website design, and lengthy or complex checkout processes also explain this issue.
Fortunately, by analyzing historical data, the sales manager is able to predict when and why a shopper might abandon their cart. This insight enables the implementation of discounts or incentives to prevent abandonment.
Additionally, machine learning can help improve website design and streamline checkout processes, elevating the online shopping experience and decreasing cart abandonment.
Reducing churn is of great importance for subscription-based and SaaS businesses. Even if you’re securing 100 new deals each month, a high churn rate can undermine these efforts. Thus, the key is finding out what causes churn so that customer success teams can intervene early and save the relationship.
Some early indicators of potential churn are:
Machine learning analyzes signals and actions that previous buyers might have taken before churning. So, when these warning signs emerge, the system generates early notifications, enabling proactive measures to preempt churn and mitigate associated risks.
Sales is the lifeblood of any business. As of now, machine learning is the key gateway to staying ahead in this increasingly competitive game, and it’s not expected to cool down anytime soon.
By unlocking deeper customer insights, providing accurate sales forecasting, enhancing automation, and so much more, machine learning will transform your sales process, making it more efficient and effective.
As a leading AI company in Vietnam, Neurond promises to take your business to new heights. Our AI consultants will identify pain points in your sales process and validate a solution that suits your needs. Our end-to-end consultation service ranges from AI strategy development to product launch.
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