Successful data-driven marketing strategies are motivating brands to modify their martech stack with new and improved AI and ML solutions.
Brands worldwide have accepted the fact that AI and Machine Learning integrated into marketing campaigns and strategies can achieve a higher if not new level of data quality and customer retention results.
The sixth edition of Salesforce’s State of Marketing research reveals that 70 percent of high-performing marketing teams claim to have a holistic AI strategy. CMOs who lead such marketing teams value marketing analytics skills and the curiosity to learn as their major requirements. With hopes of improving their customer experience, they choose to work towards new marketing strategies that are fully integrated with AI and Machine Learning skills.
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A combination of data sources and customer sentiment analysis that uses Machine Learning algorithms, AI-based demand sensing and demand forecast accuracy can be determined. Marketers use this AI-based technique to predict unique buying patterns regardless of geographic regions. With outcomes of reducing stock-outs and back-orders, this insight can help retail industries to save almost $50 billion in outdated inventory each year.
A McKinsey report, A Technology Blueprint for Personalization at Scale, claims that personalization at scale can add $1.7 to $3 trillion in new value. To reach these numbers, the AI-enabled feature calls for a martech stack upgrade. The stack modification would require the support of the ownership of channel and customer results. A combination of a modified martech stack with clear accountability will deliver results. With the right personalization-at-scale and a unified CDP that works with ML algorithms, brands uncover new customer data patterns that will provide results overtime.
According to a Forrester study, such successful AI personalization, including personalizing individual channel experiences, can help companies achieve over 5 percent sales revenue and over 10 percent increase in order frequency. Customer satisfaction metrics can also scale higher than what was possible. Delivering customer retention and Net Promoter Scores that surpass marketing techniques, the study also indicates a 13.25 percent improvement in cross-selling and up-selling outcomes.
Another personalization approach is using predictive analytics and ML that can amplify a brand’s user interaction. They create a real-time personalization that merges with customer account information and contextual data.
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Additionally, major brands like Disney are leveraging AI modeling techniques. For fresh insights into brands’ media mix models, they gather data from across the organizations and then use it in a model. Such varieties of models help them achieve their target budget and optimize their media mix.
Experts suggest that marketers also utilize intelligent technology to automate their remote and routine tasks. With time to spare, they can focus on AI-inspired marketing campaigns, testing and the optimization of mobile applications. Personal productivity tools do not do the trick anymore. A Salesforce research reported that the use of AI in organizations skyrocketed from 29 percent in 2018 to 84 percent in 2020. Most CMOs have realized the need for more AI and ML solutions to improve their marketing strategies and provide better business growth.
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