Where is AI’s potential for personalization?
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: 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?