- Dec 23, 2022
Plus a closer look at trends in the attribution realm, specifically.
It is difficult to predict specific trends in marketing technology for 2023, as the field is constantly evolving and new technologies are being developed all the time. However, some general trends that are likely to continue in the coming years include:
- Personalization: Marketing technology is increasingly being used to personalize the customer experience, including through the use of machine learning algorithms that can analyze customer data to tailor messaging and content to individual users.
Personalization in marketing refers to the practice of tailoring marketing efforts to individual customers based on their preferences, behaviors, and other characteristics. This can be done through the use of technology, such as machine learning and artificial intelligence, to analyze customer data and deliver personalized marketing messages, content, and experiences.
There are many ways in which personalization can be applied in marketing, including:
- Personalized emails: Sending emails with personalized subject lines and content based on the recipient’s past behaviors and preferences.
- Personalized website and landing pages: Customizing the content and layout of a website or landing page based on the visitor’s interests and past behaviors.
- Personalized advertising: Serving personalized ads to customers based on their interests and past behaviors.
- Personalized recommendations: Recommending products or services to customers based on their past purchases and browsing history.
Personalization can be an effective way to improve customer engagement and increase conversions, as it helps to make marketing efforts more relevant and personalized to the individual customer.
All of this personalization needs to be done with an eye on protecting consumer data and privacy. It’s good to provide a customized, personal experience; but don’t take it to creeper levels with too many emails and off-putting suggestions and offers.
2. Artificial intelligence (AI) and machine learning (ML): These technologies are being used to automate and optimize various marketing tasks, including data analysis, content creation, and customer segmentation.
AI and ML can be applied to marketing in a number of ways to improve various aspects of the marketing process. Some examples include:
- Customer segmentation: AI and ML can be used to analyze customer data and identify patterns and trends that can be used to segment customers into different groups based on their characteristics and behaviors. This can help marketers tailor their marketing campaigns and messaging to be more relevant to specific customer segments.
- Personalization: AI and ML can be used to create personalized experiences for customers by using data about their preferences and behaviors to recommend products or content that they are likely to be interested in. This can help improve customer engagement and increase conversions.
- Predictive analytics: AI and ML can be used to predict customer behavior and identify potential future trends, which can help marketers make more informed decisions about their marketing strategies and tactics.
- Content creation: AI and ML can be used to create and optimize marketing content, such as by identifying the most effective headlines or by generating personalized email subject lines.
- Social media monitoring: AI and ML can be used to analyze social media conversations and identify patterns and trends that can help marketers understand how their brand is perceived online and identify opportunities to engage with customers.
3. Omnichannel marketing: As more and more consumers use multiple channels to interact with brands, marketing technology is being developed to help businesses manage and coordinate their marketing efforts across different channels.
Omnichannel marketing is a strategy that aims to provide a seamless and consistent customer experience across all channels, including online and offline channels such as a company’s website, social media, email, physical stores, and mobile apps. Omnichannel marketing involves using data and technology to understand customer behavior across all channels and provide personalized and relevant marketing messages and experiences.
Some key components of an omnichannel marketing strategy include:
- Customer data: Collecting and analyzing data from all channels to get a complete view of the customer journey and identify opportunities to improve the customer experience.
- Personalization: Using customer data to deliver personalized and relevant marketing messages and experiences across all channels.
- Integration: Ensuring that all channels are integrated and connected, so that customer interactions and data can be tracked and used to provide a consistent experience across channels.
- Responsiveness: Being able to quickly and effectively respond to customer inquiries and requests across all channels, including social media and email.
By implementing an omnichannel marketing strategy, businesses can improve customer engagement and loyalty, increase conversions, and drive long-term growth.
- Customer experience management (CEM): Marketing technology is being used to improve the overall customer experience, including through the use of chatbots and other digital tools that enable businesses to interact with customers in real time.
CEM is the process of designing and optimizing the overall experience of customers as they interact with a company’s products, services, and brand. It involves understanding the customer’s needs and expectations, and using that understanding to improve all aspects of the customer journey, from the first point of contact to post-purchase support.
Effective CEM involves the following steps:
- Gathering customer feedback: This includes collecting data from various sources, such as customer surveys, online reviews, and social media, to get a comprehensive understanding of the customer experience.
- Analyzing customer data: This involves analyzing the data collected to identify trends, pain points, and areas for improvement in the customer journey.
- Designing and implementing improvements: Based on the insights gained from the data analysis, companies can implement changes and improvements to the customer experience. This might include revising processes, updating products or services, or enhancing the overall customer experience.
- Measuring and tracking results: It’s important to track the results of CEM efforts to see if they are having the desired impact and to identify any additional areas for improvement.
By managing the customer experience effectively, companies can improve customer satisfaction, loyalty, and retention, which can ultimately lead to increased revenue and business growth.
What About in the Attribution Space, Specifically?
Multi-touch attribution is a method of analyzing and attributing the impact of different marketing touchpoints on a customer’s journey and final conversion. Here are some trends in multi-touch attribution that may emerge in 2023:
- Increased focus on cross-channel attribution: As consumers increasingly use multiple channels to interact with brands, businesses are likely to place more emphasis on understanding the role that each channel plays in the customer journey and how they can optimize their marketing efforts across channels.
- Use of machine learning algorithms: Machine learning algorithms can be used to analyze large amounts of data and identify patterns that can help businesses understand the impact of different marketing touchpoints. As such, the use of machine learning in multi-touch attribution is likely to increase in the coming years.
- Greater integration with marketing technology: Marketing technology platforms are likely to continue to incorporate multi-touch attribution capabilities to enable businesses to analyze and optimize their marketing efforts in real time.
- Greater emphasis on privacy: As concerns about data privacy continue to grow, businesses may place greater emphasis on using multi-touch attribution methods that protect customer privacy and do not rely on the collection of personal data.
We’ll check in mid-year and later in the year to see how we did on our predictions, but it’s safe to say that data is still queen, and what businesses and their marketing teams do with it matters.