Artifical intelligence (AI) is one of 2017’s hottest topics. And for good reason. AI presents a number of opportunities for advances in marketing applications’ use of consumer data. At the core of AI is machine learning. These repeatable processes can “train” or be automated to enable the processing of vast amounts of data in sub-second time. Feeding both 1st and 3rd party consumer data and consumer data segmentation models through machine learning is the basis for AI. Similarly, segmenting this data to uncover identifiable commonalties in demographics, psychographics, ownership, behavior, and other attributed is the basis for targeted, personalized marketing. Combined, there are powerful use cases on the horizon. Here are 4 ways lead marketers should be tangibly looking at AI to drive their programs of the future.
1. Look-Alike Modeling. Identifying common characteristics that your best customers share can help identify high-propensity leads and prospects. AI allows marketers to take a granular look at customer data to gain more accuracy and detail in this identification process, with the ultimate goal of creating “segment of one” modeling for truly personalized marketing. Improvements in accuracy and personalization translate to improved lead acqusition. This requires accurate, interlinked, up-to-date customer data as errors in the underlying data will result in bad targeting models. More granular data analysis requires robust attribute data to deliver accurate, nuanced modeling results and one-to-one marketing.
2. Lead Verification and Scoring. Correctly linked identity data helps marketers verify and identify each inbound lead regardless of the channel they entered through. This verification ensures correct and complete contact data is associated with each lead. Often during verification, additional consumer behavioral attributes are added, providing AI with the data needed to create powerful consumer segments. Segmentation models can then be used to score each inquiry and orchestrate the proper response across all marketing channels. AI tracking and machine learning ensure that lead communications become more and more effective, and adapt automatically to changes in the marketplace. For this to happen in real time, on-demand access to verified identity data and attributes is essential.
3. Outbound Lead Targeting. Delivering relevant content and communications in real time is essential to interest, engage and ultimately convert consumers from leads to customers. Automation helps scale the process while gaining accuracy in targeting and improving results over time as the AI learns from lead actions. With AI driving outbound marketing, lead marketers can reach more and more finely targeted segments at greater scale, bringing personalized, one-to-one marketing closer. AI can also manage versioning in both copy and imagery to match recipient needs and interests, simplifying the administrative work of scaling campaigns. This type of personalized targeting and messaging requires up-to-date attribute data to drive stronger response rates.
4. Inbound Lead Engagement. The same kind of customization that has long characterized outbound marketing can now be adapted to serve personalized content in real time with inbound leads. For example, it enables lead marketers to change website or programmatic content (imagery, copy, offers), based on the source of the click. Depending on the lead’s interests and motivations, targeted offers and imagery can increase lead stickiness and conversion potential. Machine learning in this instance drives continuous improvement in matching content to consumer segments. Correctly recognizing and identify incoming prospects, who use multiple channels to connect, sometimes in a single day, is key. It’s also the only way to ensure accurate addressability for follow-up. Comprehensive and timely attribute data – covering as many dimensions as possible – is equally essential to effective engagement.
AI is here – and its truest marketing applications are just starting to peak from the horizon with tangible marketing opporuntities across the lead lifecycle – from lead identification through targeted acquisition and engagement. This has the potential to change lead marketing programs in both predictable and unpredictable ways with many far reaching implications. What is certain is that AI will make lead marketing more personalized, more responsive and more effective and therefore the quality and accuracy of lead identity and attribute data to “ground” AI learnings has never more important.