There is a lot of talk about how machine learning, AI, and big data can be used to help B2B companies improve their efforts. While there’s no disputing this, there is a large misconception that B2C businesses cannot be helped in the same way.
This is, of course, quite false.
B2C companies operate quite differently from B2Bs because of who their customers are. B2C focuses on the individual, rather than an organization. In B2B, processes like sales, marketing, customer service, etc. are designed for multiple people and decision makers, whereas B2C is focused on just one.
So, can machine learning technology still fit into this category of B2C?
Absolutely. Here are three key reasons why.
1. Creates Opportunities to Know Customers on a Deeper Level
With big data comes lots of information and insight, allowing marketing and sales teams to really get to know their customers on a personal level. Knowing what your customers want and why they act the way they do can be far more powerful than just knowing who they are.
Data-driven systems that are powered with AI and machine learning technology can take things a step further than just audience demographics by uncovering the most important data: consumer intent.
Machine learning technology can be used to track each consumer experience from start to finish and predict the kind of engagement they need to move to the next step of the funnel. As these systems collect more data, the process is fine-tuned and perfected, allowing B2C companies to create more effective marketing content in real-time that understands exactly what a customer is looking for.
2. Intelligent Automation for Faster Processes
Ever since the idea of artificial intelligence came on the scene, businesses have seen it as an opportunity to automate simple, yet time-consuming tasks. Boosting productivity and output is one of the clearest ways to make a company more profitable, so automating these processes with machine learning is an obvious benefit for B2C companies.
This is why robotic process automation (RPA) is often a great option for B2C businesses. RPA “learns” how to complete a process when an employee shows it how to complete a task, such as filing and sending an invoice or fixing an IT issue. This level of machine learning makes it much more practical and allows B2C brands to automate lots of day-to-day tasks.
This technology can be used for all kinds of processes within a business, from filing to handling customer service inquiries. The benefits of intelligent automation are clear; 80% of companies who have automated their marketing systems have reported more leads, and 77% reported sales increases as well.
Additionally, automating customer service processes with the help of machine learning enabled chatbots to provide quicker answers to customers. Although these bots are fairly new, Business Insider reported that nearly half of consumers they surveyed agree that they would rather talk to a bot who can instantly answer their questions than wait to speak with an actual human being.
3. Fuels Marketing Personalization
The buyer’s journey for B2C companies is typically much shorter than B2Bs, as it is generally a product or service that is sold to a single consumer, rather than an organization. Therefore, the entire experience can be tailored and personalized to the individual customer. Personalization is exceptionally important for the everyday consumer, with 6 out of 10 agreeing that it has a strong influence on their decision to purchase. However, tweaking the entire process for each customer is incredibly difficult for most marketers.
This is where machine learning can make an exceptional difference in marketing ROI. Machine learning can be used to properly segment your audience through data collection from POS systems. From here, you can provide better shopping recommendations and unique offers that are custom-made for each customer.
Big data personalization tactics have been especially helpful for B2C retail companies, such as Macy’s, which gathers consumer data for personalized incentives based on each customers’ shopping habits.
Amazon has been using this kind of technology for years, evidenced by their hyper-personalized and strategic product recommendations embedded onto every page. Their data-driven algorithms compare each shopper’s search history and past purchases to promote relevant products with targeted recommendations.
The only way that B2C companies can truly get to know their customers is by collecting and analyzing the right data and translating that information into actionable marketing strategies. Data driven systems are able to gather consumer data points while machine learning technology can take this information and learn what it takes to incentivize shoppers towards action.
Over to You
Machine learning technology is certainly not perfect, at least not yet. But it makes a great contribution to a more customer-focused approach by breaking down key data points.
Only time will tell just how big of an impact machine learning will have in business operations, but it is clear that it is already changing the way that B2C companies can approach their most important tasks.
About the Author
Pratik Dholakiya is the founder of Growfusely, a content marketing agency specializing in content and data-driven SEO. He regularly speaks at various conferences about SEO, Content Marketing, and Entrepreneurship. Pratik has spoken at the 80th Annual Conference of the Florida Public Relations Association, Accounting and Finance Show, Singapore, NextBigWhat’s UnPluggd, IIT-Bombay, SMX Israel, SEMrush Meetup, MICA, IIT-Roorkee, and other major events. As a passionate SEO and content marketer, he shares his thoughts and knowledge in publications like Search Engine Land, Search Engine Journal, Entrepreneur Magazine, Fast Company, The Next Web, YourStory, and Inc42, to name a few.
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It’s interesting that 80 percent of companies that have automated their marketing systems have more leads. My brother runs a marketing company and wants to automate most of their system for next year. I’ll have to show him this so he can know the ins and outs of automation.