Marketing automation has been around since the first CRMs enabled marketers to track their interactions with prospects and customers back in the 1980s. Since then, the discipline has evolved and become complex and effective, consisting of activities that range from email marketing and lead generation to customer retention, social media marketing and analytics.
And increasingly, AI and machine learning are helping marketers be more predictive and precise with these activities. AI helps marketers understand and predict customer behavior patterns, improve audience segmentation, and personalize interactions and content to deliver the greatest value and drive higher conversion rates.
Today’s popular marketing automation platforms like Marketo, Pardot and Eloqua are bolting on AI to augment the capabilities of their platforms. But off-the-shelf AI solutions for marketing automation are typically optimized for a single task or application and may not fully account for the specific requirements and attributes of a business and its target audience. As every business’s data is unique, using a custom AI solution is often preferable.
Let’s examine four areas in which AI and Marketing Automation meet, and why a custom AI solution can maximize a marketing team’s effectiveness in those areas.
Chatbots are an effective engagement solution for marketers. Powered by AI, they enable marketers to deliver human-like assistance and customer support. A recent Oracle survey found that 80% of decision makers globally are using or planning to use chatbots in their business by 2020.
Traditional rule-based chatbots answer questions on the basis of the predefined rules developers embed into them. Although chatbots are becoming smarter and more capable of behavioral recommendations, they often struggle to understand human contextual conversation because every individual has a unique style of communicating.
Custom AI solutions enable chatbots to learn from interactions with users, via advanced analytics platforms and API integrations to a business’s existing systems. The integrations are different from business to business, and a one-size-fits-all solutions may not work as well.
A custom AI solution can take into account all of the unique details and specifics of a business’s customer journey, enabling more precise, actionable insights and predictions about how that customer will converse with the chatbot -- and deliver more personalized, helpful customer service.
Personalizing marketing outreach and content leads to more conversions. For example, a Statista study shows that personalized emails had an open rate of 18.1 percent, compared with a 13.1 open rate for non-personalized emails. Marketers can use various data about customers -- behavioral attributes, past purchase data, demographics and more -- to better segment audiences and fine-tune their programs and messaging. This enables them to provide greater value and more relevant content and offers to customers by segment.
But as customers increasingly expect brands to understand and predict their specific preferences, personalization must evolve further. Hyper-personalized, one-to-one marketing speaks directly to a customer about their specific pain points and needs, and is possible only by applying customized machine learning algorithms to existing and incoming customer data.
Data captured in real time can be used instantly to develop and deliver personalized offers and targeted advertising when a consumer is likely to buy. And the algorithms and processing that’s needed for a given business will vary depending on the attributes of its target audience, products and services, and business model.
3. Propensity-to-buy, lead scoring
Powered by propensity-to-buy modeling, lead scoring enables marketers to predict with a high level of accuracy what a prospect will do next in response to an interaction with a brand. It leverages machine learning to spot trends and patterns in datasets and make educated guesses about what may happen next.
Taking into account data from CRM and MA platforms, social media and other data sources, predictive lead scoring models can identify shared traits among leads who convert or don’t convert, customers who purchased products or services, what time certain customers are likely to make a purchase, and so on. A custom solution built to spec will include specific details about a particular business’s products, market and target audience, and deliver more accurate predictions.
4. Retention and Customer Lifetime Value (CLV)
Marketing automation can be used to keep track of the actions and experiences of prospects and customers. But only AI can make predictions about what actions they might take in the future. These types of predictions enable marketers to hone their efforts and focus budgets on the activities that will yield the highest ROI:
By training models using data from marketing automation platforms, AI can be used to predict with a high level of confidence the potential for additional sales. It can identify what customers are likely candidates for and upsell or cross-sell, when they might buy, what they might buy, and even how much they’re likely to spend. This helps teams tailor content and outreach to nurture those potential leads more effectively.
AI can also be leveraged to understand a customer's lifetime value (CLV) more accurately than previous methods. For example, AI can uncover what types of customers will spend the most over time, or what customer attributes predict retention, then use those insights to implement the most persuasive acquisition and retention methods.
AI-powered churn prediction helps teams understand why customer engagement may have dropped within a channel or multiple channels, so they can take action to correct the problem.
Make Your Marketing Automation Work Smarter
AI-based programs are capable of processing user data at lightning speed and can be hyper-responsive to customer needs. And according to Accenture research, AI could lead to an economic boost of $14 trillion in additional gross value added (GVA) by 2035.