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According to Constellation Research, businesses across all sectors will spend more than $100 billion per year on Artificial Intelligence (AI) technologies by 2025, up from a mere $2 billion in 2015. The marketing industry will be no exception.
AI holds great promise for making marketing more intelligent, efficient, consumer-friendly, and, ultimately, more effective. Perhaps more pointedly, though, AI will soon move from being a “nice-to-have” capability to a “have-to-have.” AI is simply a requirement for making sense of the vast arrays of data — both structured and unstructured — being generated from an explosion of digital touchpoints to extract actionable insights at speeds no human could ever replicate in order to deliver the personalized service consumers now demand.
Interestingly, in many cases, the sophistication of AI technologies has already advanced further and faster than most marketers’ ability to actually make use of them. On the one hand, there are the technical challenges of gathering and normalizing data inputs — the act of making different types of data comparable — connecting them to a unified view of the customer, and then aligning the AI-driven decisions to real-world actions. On the other hand, there are also real philosophical, ethical, or at least policydecisions to be made on the value exchange between marketers and consumers when data is shared and used to optimize marketing experiences.
The good news is that, as an industry, we are starting to see meaningful progress on both fronts. For businesses looking to keep pace with innovation and leverage AI, there are steps they can take today. But first, what are some examples of how AI can help make marketing more effective?
Using AI in Marketing
Smart marketers are developing, partnering to build, or integrating AI into their tech stacks to get better at what they do. AI is already being used in ad targeting and customer segmentation, but there are more possibilities in store such as:
- AI-powered chatbots use all the customer data at their disposal to answer questions and give advice to customers considering making a purchase. Take Sephora’s Kik bot, which quizzes customers about their makeup preferences and then follows up with specific product information.
- AI-enhanced image search allows users to upload pictures of products they are interested in, to find relevant shopping ideas. For example, companies such as CamFind let you snap a picture of something in the physical world, and get information related to it. Say you see a poster for a movie you’d like to see. Snap a photo, and CamFind will show you movie recommendations, times, and locations.
- Personalized training routines and nutrition information can be created based on data from other consumers with similar lifestyles. For example, UnderArmour leveraged IBM Watson’s AI to create a “personal health consultant” that provides users with timely, evidence-based coaching around sleep, fitness, activity, and nutrition.
- Optimized advertising uses AI to make decisions based on the full range of data available — including unstructured data such as sentiment and mood. For example, IBM, this time as a corporate marketer, teamed up with MediaMath (where I am the CMO/CSO) to activate true AI-driven programmatic marketing using its Watson Cognitive Bidder, to extract predictive signals from exposure to large amounts of data.
How to Put AI Into Practice
There are three key areas of consideration to help you get started leveraging AI for marketing today: affirm your consumer data policies, make your data actionable, and select the right AI partner. Let’s look at each in more detail:
Affirm your consumer data policies: Before getting started, it is important to confirm your policies regarding the handling of consumer data, transparency, and control. Your company needs to make sure that you’re complying with the EU’s new General Data Protection Regulation (GDPR). Consumers should be able to interact with connected devices — from web browsers to mobile phones to voice assistants — knowing that their data is being used in transparent ways, in a manner consistent with their preferences and expectations, to which they have explicitly consented.
Make your data actionable: There are three pillars here to consider:
- Common identifier: With the explosion of devices, digital identity today is deeply fragmented, leading to head-scratching experiences where, for example, a gym franchise might roll out a great new membership offer…to someone who is already a member. The first step is to establish a common identifier, usually an alpha-numeric string, across various touch points and data sets to help create a unified view of the customer. Emerging solutions such as DigiTrust are helping marketers tie together their various touchpoints in a safe, respectful, and scalable way.
- Data gathering: AI can make sense of all your data and extract insights from it, but only if you can collect and normalize it before activating it. Choose a data management platform (DMP) that incorporates the identity solution selected above and can handle data from a wide range of sources, so you can collect, organize, and centrally manage all your data, segment it into granular audiences, and activate it for marketing in real-time.
- Data end points: The best customer experiences cut across touchpoints, whether they are “paid” (online, Connected TV, or digital audio ads), “owned” (your stores, websites, call centers) or “earned” (PR, blog posts, social media). Although there are as yet few platforms that can deliver marketing across all of these touchpoints, technology such as IBM’s cloud-based Universal Business Exchange can build upon the common identifier and data gathered to drive consistent execution across a range of platforms and tools.
Select the right AI partner: With the policies, data assets, and pipes in place, the stage is set for AI to make optimal decisions and drive real business performance. For best results and fastest time to real return on your investment, be sure to choose a partner that has true AI, not simply rules-based decisioning, which is impossible to scale for the volume of data and combinations of interactions that marketers are managing today. In addition, be sure to choose partners with real experience addressing your particular use cases and working with your existing technology partners. Finally, as with any vendor relationship, be sure to check on your partner’s ethical and philosophical approach to AI, as this is still an emerging technology that demands thoughtful guardrails to produce effective results in a responsible manner.
AI is no longer just hype — it’s a “have to have” that you can start integrating into your marketing today with the steps outlined above.
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About The Author
Dan Rosenberg is CMO/CSO at MediaMath.