Companies can mine their historical pricing data along with data sets on a host of other variables to understand how certain dynamics from time of day to weather to the seasons impact demand for goods and services. Machine learning algorithms can learn from that information and combine that insight with additional market and consumer data to help companies dynamically price their goods based on those vast and numerous variables a strategy that ultimately helps companies maximize revenue.
The most visible example of dynamic pricing (which is sometimes called demand pricing) happens in the transportation industry:
Think surge pricing at Uber when conditions push up the number of people seeking rides all at once or sky-high prices for airline tickets during school vacation weeks.
Machine learning applications don’t just help companies set prices; they also helps companies deliver the right products and services to the right areas at the right time through predictive inventory planning and customer segmentation. Retailers, for example, use machine learning to predict what inventory will sell best in which of its stores based on the seasonal factors impacting a particular store, the demographics of that region and other data points such as what’s trending on social media, said Adnan Masood who as chief architect at UST Global specializes in AI and machine learning.
Customer Relationship management
Sales performance. Is there a way to understand why one middle-level sales executive brings twice as much lead conversion than another middle-level exec sitting in the same office? Technically, they both send emails, set calls, and participate in conferences, which somehow result in conversions or lack thereof. Any time we talk about what drives salespeople performance, we make assumptions prone to bias. A good example of ML use here is People.ai, a startup which tries to address the problem by tracking all the sales data, including emails, calls, and CRM interactions to use this data as a supervised learning set and predict which kinds of actions bring better results. Basically, the algorithm aids in developing a playbook for sales reps based on successful cases.
Retention. Similar tracking techniques, the use of text sentiment and other metadata analysis (from emails and social media posts) can be applied to detect possible job-hopping behavior among candidates.
Human resource allocation. You can use historic data from HR software sick days, vacations, holidays, etc. to make broader predictions on your workforce. Deloitte disclosed that a number of automotive companies are learning from the patterns of unscheduled absences to forecast the periods when people are likely to take a day off and reserve more workforce.
Customer recommendation engines
Machine learning powers the customer recommendation engines designed to enhance the customer experience and provide personalized experiences. In this use case, algorithms process data points about an individual customer, such as the customer’s past purchases, as well as other data sets such as a company’s current inventory, demographic trends and other customers’ buying histories to determine what products and services to recommend to each individual customer.
Here are a few examples of companies whose business models rely on recommendation engines:
- Big e-commerce companies like Amazon and Walmart use recommendation engines to personalize and expedite the shopping experience.
- Another well-known deployer of this machine learning application is Netflix, the streaming entertainment service, which uses a customer’s viewing history, the viewing history of customers with similar entertainment interests, information about individual shows and other data points to deliver personalized recommendations to its customers.
- Online video platform YouTube uses recommendation engine technology to help users quickly find videos that fit their tastes.
Sales and Marketing
Digital marketing and online-driven sales are the first application fields that you may think of for machine learning adoption. People interact with the web and leave a detailed footprint to be analyzed. While there are tangible results in unsupervised learning techniques for marketing and sales, the largest value impact is in the supervised learning field. Let’s have a look.
Lifetime Value. A customer lifetime value that we mentioned before is usually measured in the net profit this customer brings to a company in the longer run. If you’ve been tracking most of your customers and accurately documenting their in-funnel and further purchase behavior, you have enough data to make predictions about most budding customers early and target sales effort toward them.
Churn. The churn rate defines the number of customers who cease to complete target actions (e.g. add to cart, leave a comment, checkout, etc.) during a given period. Similar to lifetime value predictions, sorting “likely-to-churn-soon” from engaged customers will allow you to:
1) Analyze the reasons for such behavior.
2) Refocus and personalize offerings for different groups of churning customers.
Sentiment analysis. Skimming through thousands of feedback posts in social media and comments sections is painstaking work, especially in B2C after a new product or feature rollout. Sentiment analysis backed by natural language processing allows for aggregating and yielding analytics on customer feedback. You may play with sentiment analysis using Google Cloud Natural Language API to understand how this works and what kinds of analytics may be available.
Recommendations. Recommendation sections are something we can’t imagine modern eCommerce or media without. The common practice is to recommend other popular products or the ones you want to sell most. It doesn’t require machine learning algorithms at all. But if you want to engage customers with deep personalization, you can apply machine learning techniques to define the products that this customer is most likely to buy next and put them on top of the recommendation list. Also, Netflix, YouTube, and other video streaming services operate in similar way, tailoring their recommendations to a viewer’s lifetime behavior.