In recent times, big data, artificial intelligence, and machine learning are in limelight. Thanks to the new tools of highly effective marketing teams, collectively and individually, today, there are more data points than ever before. Rather than traditional high-expenditure campaigns, the results of these emerging tools are highly personalized. The advantages of these tools are deleterious in the marketing field. This is because of the contribution of these tools to track and record every single interaction a consumer has with a product. Whether through a website, email, or social interaction. Machine learning algorithms are used to collect this data in real-time, and immediately personalize experiences unique to each visitor, eliminating the need for static profiles based on outdated data sets.
Since this advanced technology is rich in data storing, mining and efficient processes in place. Marketing teams can efficiently focus on identifying strategies to effectively use this technology to optimize operations and output. The major thing while you are working on with machine learning is to have a well-planned strategy and with AI is to possess a highly advanced resource. Thus, marketers need to take the time to contemplate the ideal outcomes and plan accordingly as it is not. The time or place to jump into processes without considering goals.
The following statistics show the contribution of these advanced technologies in the marketing field.
Let’s get an insight into three main implications of big data, machine learning, and AI marketing:
1. Comprehensive Consumer Profiles
Machine Learning and AI are the latest technologies that would help you to gain highly personalized data. Better knowledge of customer and prospect audiences makes marketers deliver the right message, to the right person, at the right time. During every single possible consumer interaction, including CRM. The key is for marketers to capture the data automatically, even when a person is offline in order to build a comprehensive profile. To attain a highly personalized and relevant content, marketing teams can take this a step further with scoring and analytics, which prompt refined strategies.
2. Enlarge Engagement Rates
When it comes to marketing automation, Big data, machine learning, and AI influence consumer engagement to a great extent. Marketers make proactive changes to their digital marketing strategies with deeper insight into consumer demographics, socio-economic data, and geographical patterns. To influence online behavior and email interactions, understand the numbers behind the actions. Yes! Numbers never lie. It has an indispensable part for marketers to remember that personalized email marketing is now expected by individual consumers and B2B audiences alike. To increase engagement rates and win business, leverage these data to work for a brand in the smartest way possible.
3. Extend Retention Rates
Every coin has two sides. Likewise, using advanced technology to win new business is only one side of the coin. To increase current retention rates, they can use these newfound metrics and consumer insights. These technologies will provide the richest data a marketer can ever collect as if the consumer purchased something or engaged with a product offering, they still warrant communication. The more the current customers feel relaxed and comfortable with a certain e-commerce brand, the more they will provide information about themselves in exchange for promotions or deals. Create extremely personalized experiences that make a product so valuable using this data.
4. Tackle challenging marketing problems
Thanks to AI and Machine Learning, 58% of enterprises are tackling the most challenging marketing problems. It prioritizing personalized customer care, new product development. Since these are the “need to do” marketing areas, they have the highest complexity and highest benefit.
According to Capgemini’s analysis, Marketers haven’t been putting as much emphasis on the “must-do” areas of high benefit and low complexity.
5. Price optimization and Price Elasticity
To define more competitive, contextually relevant pricing, all marketers are increasingly relying on Machine Learning. To encompass product and services pricing scenarios, Machine Learning apps are scaling price optimization beyond airlines, hotels, and events. Moreover, this technology is being used to determine pricing elasticity by each product. Then factoring in channel segment, sales period and the product’s position in an overall product line pricing strategy.
6. Price optimization and Price Elasticity
To define more competitive, contextually relevant pricing, all marketers are increasingly relying on Machine Learning. To encompass product and services pricing scenarios, Machine Learning apps are scaling price optimization beyond airlines, hotels, and events. Moreover, this technology is being used to determine pricing elasticity by each product, factoring in channel segment, sales period and the product’s position in an overall product line pricing strategy.
6. Optimizing Marketing Mix
Above all these features, last but not the least. Machine learning is revolutionizing marketing by optimizing the marketing mix by determining which sales offer. Incentives and programs are presented to which prospects through which channels. Specific sales offers are supported by contextual content, offers, and incentives. To predict the best combination of marketing mix elements that will lead to a new sale, up-sell or cross-sell, these items are made available to an optimization engine which uses machine learning logic. To exemplify this context, Amazon’s product recommendation feature is an ideal one.
In a nutshell, in order to stay competitive and relevant to consumers. These organizations must continue innovating to improve the journey for those that interact with their brand—even if just starting to test personalization strategies. Acquire the most advanced technologies for better retention & engagement rates and to create comprehensive consumer profiles.
