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We already know that artificial intelligence and machine learning technology will shift the paradigm of what work looks like in the future. Some are frightened by it, and others are excited for the potential that machine learning brings. Politicians have cited upcoming AI/ML capabilities as the primary reason that the countries throughout the world should implement a universal basic income policy. This technology will no doubt have a big impact on the future of work, leaving many to question “how will it affect my organization?” The American Enterprise Institute reminds us that only 53 companies from the fortune 500 in the year 1955 are still in the fortune 500 today. One of the primary reasons that the other 447 companies have fallen is because of their inability to modernize and utilize technology to deal with changing business climates.
The primary purpose of the marketing department is to increase revenue by driving pipeline. Marketing teams have gotten more sophisticated in their strategies and tactics as a way to stay “ahead of the competition” and optimize resources. Omni-Channel and Cross Channel Marketing, Retargeting, and many other methods have become the norm. Over the past few years marketers and technology enthusiasts have been asking:
“How can AI/ML empower the work that we are doing? How will it be able to extend the capabilities and results from my team?”
Machine learning is the answer. In order for a business to see the power of machine learning, basic marketing data is all that is needed. This can come from a marketing automation system or a CRM, as most existing martech stacks are more than enough to start seeing some fantastic results. The more data that a machine learning engine has to consume, the more powerful the results will be. Below are some of the main use cases of machine learning technology in 2019 for marketers.
How do sales reps and marketers prioritize who they should be targeting with their marketing and sales outreach? With billions of people, and millions of companies out there, it is easy to get lost. Many sales organizations employ analysts who are manually parsing and measuring different variables, evaluating the type of person and company that is most likely to become a customer. The challenge of doing this manually is that the human brain can only take into account a few factors at a time. Whereas a machine learning algorithm is able to measure every quantifiable variable simultaneously, making the possibilities endless and extremely efficient. This allows marketers to focus on the right people and companies with the highest probability to become customers.
Both B2B and B2C brands are finding that prospects usually engage with their brand multiple times across devices and touchpoints before actually converting into a customer. Most mature enterprises are gathering vast amounts of customer identifiable information and activity in efforts to forecast and predict conversions. Customer segmentation based on this data allows organizations to more accurately define and refine their target customer segments. With these actionable data insights, account based marketing and marketing personalization become empowered with more precision.
With historical data in both the marketing automation and CRM platforms, machine learning engines are able to accurately predict which marketing assets are the right fit for a given account or contact, and are able to automate the delivery of these assets. These insights provide recommendations to sales and marketing teams for how to engage with either prospects or existing customers.
Marketers are constantly in search of improving the effectiveness of campaigns to improve open rate metrics and other KPI’s. With AI, research is released on a regular basis recommending which days and times are optimal for email marketing delivery. Rather than waiting for past performance to be manually analyzed, machine learning capabilities can easily provide tailored suggestions based on the industry, company, or contact level details. A C-level executive might be most reachable at 6:00 pm on Thursday, but an operations manager could be most reachable at 7:30 am on Tuesday. Getting this timing correct leads to massive improvements in results.
A machine learning algorithm is able to predict a percentage of likelihood for which individuals or companies are most likely to engage in marketing campaigns before they are activated. If your company knew that there was a 60% likelihood of me becoming a paid customer, would anything change about the way that the business approaches me? The answer is yes. Contacts that are highly likely to engage and convert are placed into higher value campaigns, as an example, say the likelihood would then increase conversion from say 60% to 90%. As this happens, lead conversion rates as well as total lead quantity increase dramatically.
The harvard business review reminds us that, depending on the industry, acquiring a new customer can cost anywhere between 5X and 25X the cost of what it would take to retain an existing customer. As all variables are considered simultaneously, leading machine learning technology will predict which customers are good targets for upsell and cross sell opportunities. Also which specific products they are most likely to purchase.
These six benefits of machine learning for marketers provides a compelling case to introduce this technology, supported with significant data on how to improve key metrics. In most cases, a person will still need to take the data, interpret it, and act on it. One of the reasons that leading fortune 500 companies have started to leverage Put It Forward’s machine learning capabilities is because our engine can automatically uncover the secrets to successful customer acquisition.
Now that you know the benefits of implementing machine learning into your marketing arsenal, what is stopping you from using this technology to grow your business exponentially?
Contact us to try out this technology and see for yourself
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