What AI can do to help your business grow.
Machine learning is making major headlines in industries ranging from healthcare to cybersecurity. From identifying eye disease to detecting credit card fraud, this quickly evolving technology has the potential to revolutionize the way we interact with each other and with the digital world on a daily basis.
While the term ‘machine learning’ is often used interchangeably with artificial intelligence, it’s really a subset, branch or application of AI (where a machine learns on its own without programming).
One less-heralded area of change that will impact organizations of all sizes is sales and marketing. Machine learning is set to enhance every aspect of the sales process, from initial product mix and pricing to ongoing customer service.
Indeed, high-performing sales teams are 4.1 times more likely to use artificial intelligence and machine learning applications than their peers, according to a report published by Salesforce.
Most sales managers begin each quarter with a sales forecast, which can range from wishful thinking to highly complex forecasting models, but these can suffer bias from the people preparing them.
Large enterprises are already using machine learning techniques to create more accurate, timely forecasts using massive data sets. For firms managing big data, but without in-house expertise, platforms such as Microsoft Azure and Amazon Web Services offer forecasting services based on machine learning.
Product mix and ad targeting
Machine learning will also help with retail product mix — from predicting which new fashions people will buy to altering prices for airline and concert tickets to ensure they sell out. Uber is one of the best-known examples of dynamic pricing, but there’s much broader potential for machine learning models to determine customer responses to pricing and then set optimal prices for each customer.
One of the first steps in selling a product is creating awareness and educating consumers through advertising. Historically, advertisers reached out to broad audiences through newspapers, radio and TV — but anyone who uses social media knows this is changing.
Advertisers are starting to use machine learning for ad targeting based on highly specific criteria — the ads we see may even be influenced by machine learning models that perform sentiment analysis.
And the traditional practice of cold calling — which involves calling broad swaths of phone numbers in search of a few interested prospects — is also being transformed by machine learning models. A Harvard Business Review article says sales teams that adopt AI, for example, are seeing an increase in leads of more than 50 per cent — and reducing calls by 60 to 70 per cent.
These models can be integrated into customer relationship management (CRM) systems such as Salesforce, incorporating data from each new prospect to continuously improve recommendations. It can tell salespeople who to call and when to call, for example, and provide insight into the prospect to help customize each call for better engagement.
There are several new vendors entering this market. One such company is InsideSales.com, which uses Neuralytics (a predictive self-learning engine) to examine the context of a sales call using CRM platforms — taking factors into account such as weather, traffic and even recent sports teams results to help drive when and where calls should be made.
Throughout the sales cycle, machine learning can be used to develop better chatbots for ongoing customer service. These chatbots can answer basic inquiries and create cost savings by reducing the need for call centres. They’re also being used early in the sales cycle when customers are still researching and comparing products or services.
Machine learning can also be used to analyze and reduce customer churn, improving on traditional backward-looking statistical models and helping identify customers who might leave so steps can be taken to increase retention.
The world of sales and marketing is poised for dramatic changes, thanks to machine learning and other forms of automation. As mentioned, these changes are currently limited to firms with large data sets and the ability to hire in-house expertise — but as the field grows and as vendors make their products more user-friendly (with better algorithms that require less data), opportunities will grow for smaller businesses.
Machine learning is already showing its potential to strengthen sales leads, target marketing efforts and determine the lifetime value of customers — all of which optimize a salesperson’s time and energy. While it’s an emerging area, eventually more out-of-the-box tools will become available, offering new possibilities to businesses of all sizes.