AI Marketing Use Cases to Improve Campaign Conversions and Transform Marketing Operations

Summary

  • For most marketers, the benefits of data-driven marketing remain limited by their ability to access, analyze and act on insights. Artificial intelligence (AI) applies automation to accelerate data analysis, create predictive models and feed recommendation engines so that marketers can predict customer behaviors, facilitate customer journeys, detect customer sentiment, treat each customer as an individual and otherwise receive and act upon data-driven insights. AI is most valued where speed is essential.
  • Marketers use artificial intelligence to better engage their target audiences, increase customer conversions, make more informed decisions and grow marketing-sourced revenue for their companies.
  • Customers expect companies to understand their needs and expectations. This cannot be done at scale without some form of AI.
Johnny Grow Revenue Growth Consulting

Artificial intelligence (AI) is no longer futuristic. It is in the mainstream and commonly used by marketing organizations. Enhancing the brand, improving offer conversions, acquiring more leads and growing customer share with AI technologies is no longer something for a later day. It is the here and now and it is delivering big results.

  • Capgemini research found that "3 in 4 organizations implementing AI increase sales of new products and services by more than 10%."
  • The same research also found “75% of organizations using AI enhanced customer satisfaction by more than 10%.”
  • Research by Forrester found that in just two years, businesses using AI to power data-driven insights in marketing would grow to $1.2 trillion combined.

See the research that advises "3 in 4 organizations implementing #AI increase sales of new products and services by more than 10% and enhance customer satisfaction by more than 10%."

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Many marketers have access to a suite of AI technologies as part of their CRM or marketing software. In fact, there's no shortage of marketing centric AI engines. Azure Machine Learning for Microsoft Dynamics, DaVinci from SAP, Einstein from Salesforce, Sensei from Adobe and Watson from IBM to name a few.

Nonetheless, many marketers struggle to find the right starting point.

It's important to recognize when implementing a marketing AI solution, technology takes a back seat to the business objectives and marketing use cases. The smart approach is to define the high value use cases that deliver quick-win pilot projects and deliver enough payback to continue the AI journey.

There are many AI marketing use cases. For this article I'll share some campaign performance examples that we have used repeatedly to achieve increased conversions and revenue growth.

Campaign Targeting

Many companies struggle to identify which target audiences are mostly likely to purchase which products or services. Failing to make this connection this will result in spending marketing budgets on prospects who won't buy or not focusing on prospects who will.

Personalization also drives payback. A study by Infosys found that 89 percent of customers say personalized offers have some impact on what they purchase, and 25 percent admit personalization "significantly influenced" their purchase decision.

AI can sift through large volumes of customer data in real-time to perform dynamic customer segmentation. It can score prospects or target audiences against your Ideal Customer Profile (ICP). It can make predictive recommendations based on historical data. It can even use data to create and deliver the optimal offer to the precise target audience at the right time and in the right channel. This creates relevancy and personalization which increases marketing conversions and leads for the salesforce.

This type of predictive modeling can also be used to feed a recommendation engine, and advise:

  • Next best offer, promotion or product
  • Next best content recommendation
  • Next best channel to engage
  • Next best day and time to send
  • Next best search keyword
  • Next best action in the buyer journey

Campaign Performance

Every marketing campaign relies on data. Or at least it should. But crunching volumes of data can be laborious and time consuming.

Fortunately, AI can process marketing data in short order, so marketers spend their time applying insights that achieve forecasted outcomes. Here are several marketing campaign benefits realized with AI.

  • AI can accelerate and improve conversion optimization, such as automated A/B or champion-challenger testing of landing pages, forms, emails and calls to action. AI becomes especially useful when measuring conversions across multiple channels and devices.
  • Email is an example of how AI can improve conversion results. Email marketing is challenged because emails are not precisely targeted, personalized, engaging or convincing. Marketers seldom have the time to derive insights related to email targeting, timing and testing.

However, AI can test, learn and derive conversion insights quickly. It can compare and model the ideal combination of target audiences, delivery timing, subject line keywords and the numerous variables that determine whether an email is received, read and acted upon. It can further test and recommend sentiment for individual recipients. Different types of messages resonate differently. Some people respond to humor, others to emotions and others to logic.

AI detection of customer sentiment preference is
fueling low double digit increases in email conversions.

  • AI can discover which content assets advance buyers in their sales cycles, and which don't. It can then recommend what content asset should be delivered at each stage in the buyer journey for each persona.
  • AI is a requirement to deliver hyper-personalization at scale. Machine learning can sift through large volumes of customer and social data to extract the data that drives relevance and personalization. It can then orchestrate message delivery so that each customer gets the right message at the right time on the right channel. It's helpful to understand that phrases such as "web personalization" or "email personalization" are obsolete. To achieve real personalization AI looks beyond any single channel to understand how to orchestrate multi-channel personalization.
  • AI can use text analytics or natural language processing to scour social networks, online communities, product review websites, blogs and other digital properties to monitor conversations about your brand and apply sentiment analysis to measure your brand or capture customer information that can be used to improve products or services.
  • Price optimization is elusive for most companies. However, AI can analyze price data from purchase history, discount data from sale opportunities or invoices, promotional incentives from quotes or orders, and other data that impacts customer pricing. The data can calculate price elasticity and generate predictive models to suggest optimal pricing or discounting by customer, product, territory, channel or other factors.