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Where is AI’s potential for personalization?
MarketingCharts staff
Through a special arrangement, presented here for discussion is a summary of articles from MarketingCharts, which provides up-to-the-minute data and research to marketers.
Marketers keeping an eye on trends will know that chatbots make frequent appearances in case studies for artificial intelligence (AI). But when it comes to hyper-personalization, a survey from Ascend2 shows open-question chatbots at the bottom of the list of the most effective AI-powered applications, with just 18 percent choosing it out of the tools given.
Instead, predictive analytics comes out on top, with more than three times as many respondents (56 percent) selecting it compared to the conversational tactic. This is followed by user experience (UX) applications at 46 percent.
The study shows that predictive analytics, however, is still far from being easy to implement, as nearly half (48 percent) consider it to be among the most difficult applications to deploy in a hyper-personalization strategy.
User experience (UX) applications are also some of the most challenging to deploy; 41 percent state that these technologies are difficult to put in place. This chimes with separate research sponsored by Adobe, which demonstrated only half of companies were confident in UX design.
The most difficult application to implement for hyper-personalization, though, is content creation and curation. This is likely a reflection of the time that it takes to personalize content at scale.
Other findings from the study:
- Only nine percent of marketing influencers surveyed say they have completed the development of a hyper-personalization strategy. The majority, totaling 62 percent, are either just talking about it or haven’t done anything around setting a hyper-personalization strategy.
- Top priorities for developing hyper-personalization include improving the customer experience (60 percent), applying data insights to decisions (51 percent) and understanding customers better (41 percent).
- The most challenging barriers to the success of a hyper-personalization strategy were seen as applying data insights to decisions, 53 percent; using more artificial intelligence, 40 percent; and attributing revenue to marketing, 38 percent.
- Predictive Analytics Considered Highly Effective For Personalization – Marketing Charts
- Hyperpersonalization Strategies – Ascend2
Discussion Questions
DISCUSSION QUESTIONS: What do you think are the most effective AI-powered applications for driving hyper-personalization strategies? Will the barriers around deployment for some or all soon come down?
Poll
BrainTrust
Cynthia Holcomb
Founder | CEO, Female Brain Ai & Prefeye - Preference Science Technologies Inc.
Michael Decker
Vice President, Marketing Strategy
With 51% of the respondents in the survey coming from B2B companies, I’m not so sure there’s sufficient data points to truly illuminate “hyper-personalization” strategies. The overall direction and shape of the results (from predictive analytics to open-question chat bots) probably does resonate with many practitioners.
Being human, we like to control and predict future events as much as possible and predictive analytics fulfills that need while reducing a lot of uncertainty. No doubt there’s an immense value and effectiveness in predictive analytics, but how we construct and imagine other opportunities, say open-question chat bots, artificially limits our ability to objectively assess the potential effectiveness.
To truly deliver on the promise of personalization, companies will need to combine multiple types of complementary technologies together: contact via chat bot, recommendation engines using artificial intelligence, robotic process automation confirms and completes the transaction and updates the customer’s profile and follow-on communications.
Just as in business we’re pushing to eliminate organizational and data silos, we need to take a wider view of technology and how to harness that power across the customer experience. Stop thinking point-to-point and embrace end-to-end.
The fact that predictive analytics tops the list is really not much of a surprise since that’s where most of the energy has been and — in terms of executable strategies — remains. As to whether the “barriers around deployment” will come down “soon” I think we need to remember a couple of things. First, AI is really in its infancy. Promising? Without a doubt. Mature? Not even close. Next, it all depends on what you mean by “soon.” If soon is 12 to 18 months, the answer is probably not. If on the other hand we are talking in the 24 to 60 month range, the answer is probably an unqualified “Yes.” Our problem is how we look at technology — in isolation, not calculating consumer recognition/acceptance factors well enough, and looking for instant payback or proof of concept. AI will play a critical role at retail, just not tomorrow.
Another name for hyper-personalization is “individual human preference.” Randomly fascinating, 95% of human emotion is managed by our individual, completely subjective, unconscious preferences. Giving us humans the extraordinary capability to know immediately if we like a dress, a car, a couch, a home. Daily, individual shoppers share their most personal product preferences with retailers. Retailers need a new way of looking at the same problem, the human aspect, so as not to waste time and resources. Leveraging AI inside of interactive intelligent knowledge platforms is key to eliciting individual human preference for “hyper-personalization.” Otherwise, just a new version of segmentation.
So we are surprised about chatbots? Finding out the most impersonal of technology can’t deliver a highly personal experience? Okay. No surprise there.
The caution we need take with the “hyper-personalization” is self-evident in the title. Because anytime we “hyper-“ things we fight diminishing returns — where the cost to do something outweighs the value of doing it.
A database of buyers broken into 30 or 40 sub-groups can deliver a tremendous step in helping profitability. Breaking those groups far smaller pots is unlikely to return enough additional profits to pay for the cost of the effort.
It’s not surprising that the tech folks like Adobe and Salesforce would preach the value of using their technology to do this. What’s surprising is that there aren’t more people ignoring them.
The people from the Great State of Missouri have it right … Show Me! Artificial Intelligence (never liked acronyms) excels as a tool to alleviate and expand human capabilities for mundane and tedious tasks. Big Data research tasks such as pouring over thousands of behavioral variables in ascertaining customer insights is PERFECT for AI. Talking to your customers in a meaningful, warm and human way is probably the WORST application of AI because it’s fake and easily exposed.
Hyper personalization has to be seen to be believed and the barriers come down when small companies figure out how SHOW us what works. (and what doesn’t). Theory is fine but eventually gets supplanted by implementation. We as retailers and retail brands need to see it and always play to our strengths!
Predictive analytics, Artificial Intelligence (AI), and personalization will all become ubiquitous in years to come as they continue to mature. They are all part of the “crystal ball” effect. Retailers and brands would like nothing more than to have a direct connection into everyone’s limbic system in order to “sell” products before we even know we want them!
I found the challenges around content creation and curation noteworthy. There are workflows and systems available to make the curation and automated cross-channel delivery of relevant content far more achievable than understood. That is an area worth exploring.
Just a few AI capabilities that are currently driving effective real-time personalization include natural language understanding, true machine learning, visual recognition, speech-to-text and vice versa, and others. The key is to differentiate perceived deployment difficulty versus realized deployment case studies.