December 14, 2018
Machine Learning Marketing
Machine learning in marketing is making every customer experience unique. Through data collection and analysis, machine learning can personalize your customer’s experience on each website, their interaction with support, the ads they view on Facebook, and the journeys they take with companies they’ve subscribed to.
According to an IDC report, companies who adopt advanced and predictive analytics, including machine learning, will grow 65% faster than apps without predictive functionality.
But machine learning is not just for large companies and data scientists. Your average marketer, working in a smaller company (yes, even in a startup) can employ the same machine learning tactics used by Netflix and Amazon. You don’t have to build your own machine learning algorithm from scratch either. Instead, you can integrate cloud apps that have in-built machine learning capabilities into your marketing strategy.
3 ways the average marketer can use machine learning (without starting from scratch)
1. Personalize customer support
Nearly 60% of enterprise executives believe that machine learning will have the most significant impact on customer experience and support (Artificial Intelligence: What’s Possible for Enterprises in 2017).
As of 2018, machines haven’t quite taken over yet, and there is still a strong human element to customer support. While chatbots are a great way to answer frequently asked questions, they’re restrictive when it comes to specialized support. This is where software like LiveChat comes in. LiveChat gives support staff the ability to chat in real-time with their customers and solve the hard-to-answer questions.
LiveChat has incorporated machine learning into its platform to help marketers actively solve and predict customer problems. In using a machine learning algorithm, LiveChat automates the manual tagging and categorization process of customer conversations. Based on previous chat history, LiveChat offers smart tag suggestions that align with your customer experience objectives. These tags might be sales, support, success, marketing, etc.
With automated tags, you can easily categorize customer facing conversations based on topic, and start to understand common problems their customers face. This information is extremely useful for marketers in particular, who can use it as a basis for answering common concerns and questions in the form of blog articles, videos, eBooks, and more.
2. Customize Facebook advertising
Facebook is already capturing a vast amount of data from its users. When creating a Facebook ad to showcase your latest products and promotions, customer data will help you personalize your campaigns.
But you don’t have to sort through this customer data all by yourself. Facebook has a sophisticated machine learning algorithm that will help you leverage data and improve the performance of your ad campaigns.
By 2020, the targeting accuracy, context, and precision of real-time advertising across digital platforms like Facebook will accelerate (Can Machines be Creative? How Technology is Transforming Marketing Personalization and Relevance, by IDC).
We’re already seeing the effects of Facebook advertising and some ads are so targeted that users often question if Facebook’s algorithm is reading their mind.
When creating a campaign on Facebook, marketers can target ads to specific groups of users, who have traits and buying behavior that aligns with their business. By selecting a campaign objective, the Facebook algorithm can determine the best audience to place the ad in front of. You can also set a conversions objective so your ads are delivered to people that are most likely to complete a conversion, i.e., make a purchase.
By integrating Facebook ads into your marketing strategy, you can reach an audience that sits outside your organic reach.
3. Data-driven decisions with CRM systems
The amount of customer data collected by CRM systems on a daily basis is huge. So much so that extracting insights can be daunting for any marketer. If companies want to make data-driven decisions, they’ll need to rely on machine learning.
Integrating a CRM system like Pipedrive or Salesforce into your marketing strategy allows you to track, maintain, and segment your customer data. A machine learning algorithm works underneath it all to transform your customer’s data into actionable insights.
Salesforce uses machine learning to generate predictive lead scoring, capture interactions between users, and sift through information in a contact’s email and calendar accounts. All this information helps you understand who your customers are and what they want. With all this in mind, Salesforce predicts that by 2020, 57% of customers will depend on companies to know what they need before they ask for anything.
With machine learning, marketers can watch their CRM work in the background while on the forefront they can put energy into making smarter marketing decisions. Integrated CRM solutions are instrumental in helping marketers analyze data and gain insight into the behavior, status, and position of their contacts within the customer journey.
Machine learning helps marketers optimize and improve the customer journey. Each of the cloud apps mentioned above adopt this technology to help marketers make data-driven decisions and ultimately improve customer experiences.