Data-Driven Personalization: Elevating User Experience
The B2B buying landscape has changed. According to Gartner® September 2023 research, The B2B Customer Life Cycle for Technology Products and Services, “It has never been more important to get beyond segmentation basics like enterprise size, industry and location. Moreover, you must refine your understanding of the organizational dynamics and the individuals who buy and influence within a given organization.”
This means that one of the key things all providers need to solve in order to deliver value in this new supplier-centric market includes a better understanding of the customer. Providers who demonstrate an understanding of the buyer's business situation have the most success. As such, data-driven personalization emerges as a pivotal strategy for brands. This article dives into this approach, outlining its benefits and offering actionable insights to implement data-driven personalization in your digital marketing strategy.
What is Data-Driven Personalization?
Data-driven personalization uses data and analytics to deliver insights that allow marketers to personalize customer experiences. Marketers use these data insights to tailor content, and recommendations to individuals based on their preferences, behaviors, and characteristics. It involves collecting and analyzing user data and customer insights such as demographics, browsing history, purchase history, and engagement patterns to create personalized and relevant customer experiences.
Through these personalization efforts, businesses can deliver targeted marketing messages, personalized product recommendations, customized website experiences, and personalized customer support, supporting buyers wherever they are in their buying cycle. It allows companies to better understand their customers and deliver highly relevant and customized interactions, ultimately improving customer loyalty and driving business growth.
Benefits of Data-Driven Personalization
Improved Customer Engagement
Using data to better understand what matters to your audience can improve customer engagement. Ascendly Marketing uses automation tools to scrape data from LinkedIn, websites, and media mentions. The data reveals their subscribers' hobbies and philanthropic activities, which they use to craft highly personalized emails. The result is an increase in customer engagement to the tune of 25%.
Higher Conversion Rates
Personalizing a user's journey can significantly boost conversion rates. Joe Kevens, Director of Demand Generation at PartnerStack and Founder of B2B SaaS Reviews leveraged Keyplay to personalize PartnerStack's sales and marketing outreach based on the custom signals that Keyplay revealed. They found that the higher scored accounts in Keyplay were twice as likely to close won.
HubSpot Solutions Partner, Vende Más, also saw similar results. Vende Más supported their B2B client in increasing their webinar invite conversion rates. When sending webinar invitations to their entire database, they found a 60-65% conversion rate to registrations. Vende Más knew they could increase that rate with better segmentation. The segment was designed based on a specific page visit and job title. and the conversion rate increased to 74%.
Cleartail Marketing supported a B2B client who was struggling with low engagement from their email marketing campaigns. Using data collected from website visits, chatbot conversations, and form submissions, Cleartail designed segmentations based on different parameters such as age, gender, location, etc. Various email campaigns were then deployed, matching the specific needs and behaviors of the segmented customers. The results? A 760% improvement in email engagement rates, and a 278% increase in revenue.
Key Elements of Data-Driven Personalization
Collecting Customer Data
Collecting customer data is the first step in data-driven personalization. Using B2B marketing tools like Google Analytics, marketers can glean insights into user behavior, preferences, and demographics. For instance, Google Analytics provides a comprehensive view of website interactions, from page views to click-through rates, allowing marketers to understand user engagement at a granular level. Social media analytics tools like Sprout Social enable the tracking of audience demographics, sentiments, and content performance across various platforms. Efforts like customer surveys and feedback forms add qualitative data to the mix, offering valuable direct insights into user preferences. The relevance of robust customer data lies in its ability to unveil patterns, discern user intent, and ultimately pave the way for hyper-personalized marketing strategies.
Segmentation strategies play a pivotal role in unlocking the true potential of data-driven marketing. Most commonly, marketers create demographic segmentation, categorizing audiences based on age, location, company size, etc. This data can usually be enriched easily in your CRM. However, in order to get deeper levels of personalization, segmentation needs to go beyond demographics. According to Gartner® in A Practical Guide to Market Segmentation for Technology and Service Providers,
“Some examples of segmentation approaches using traits beyond firmographics include:
- Technographics — Prospects running workloads in Microsoft Azure versus Amazon Web Services
- Business situation — Industries where supply chain disruptions create higher demand for locally produced materials versus those relying on global distribution networks
- Business and operating model — Companies that need help connecting internal systems versus companies that have workflows that cross enterprise lines
- Psychographics — Innovators that regard technology as a way to drive their digital business strategy versus laggards that are seeking a way to catch up to the competition
- Resources — In-house IT teams that have data science skills but no availability versus IT teams that need both data science skills and bandwidth”
Defining the Customer Journey
Defining the customer journey is a cornerstone of marketing effectiveness, offering marketers insights into the varied touchpoints a customer encounters. Tools like Google Analytics provide a comprehensive view, allowing marketers to track and understand user interactions across different channels. For instance, an e-commerce platform might identify a drop off point in the buying cycle, where visitors frequently visit product pages but abandon their carts. By accessing the data that shows the drop off, and than enriching that data with segmentation details, marketers can personalize communications, sending targeted emails with incentives to complete the purchase. In a SaaS business, understanding a customer's journey might involve looking into the product data to understand usage metrics, fall off metrics, and renewal triggers, and pairing that data with brand touchpoints like content consumed, website pages visited, and conversations with sales. By mapping out this data, marketers can begin to recognize patterns of engagement and tailor call to actions accordingly. Including full funnel data in the customer journey mapping is a key element to a successful SaaS marketing strategy. By effectively defining and analyzing the customer journey, marketers can implement data-driven personalization strategies that not only address specific needs at different stages but also makes it more likely to identify digital marketing ROI.
Creating Personalized Content
Dynamic content generation based on user segmentation is the output of data analysis, and can drive significant impact to your business. Mutiny leads the charge with playbooks that outline exactly how businesses can drive growth using unique and personalized content across your website.
Data-Driven Personalization and AI
AI can be leveraged in data-driven personalization in several ways:
Data collection and analysis
AI can collect and analyze vast amounts of data from various sources, such as customer interactions, demographics, and behavior patterns. This data can then be used to create personalized profiles for each user.
AI tools to support data collection and analysis:
- Google Analytics 360: This enterprise-level analytics solution from Google employs machine learning to analyze vast amounts of data, providing valuable insights into user behavior, demographics, and interactions. It enables businesses to create comprehensive user profiles for effective personalization.
- IBM Watson Studio: Offering robust data analysis and AI model development, Watson Studio enables businesses to collect and analyze diverse data sets. It's particularly useful for creating detailed customer profiles by integrating data from various sources.
AI can use sophisticated algorithms to analyze a user's preferences and make personalized recommendations. These recommendations can be related to products, content, services, or any other relevant offering.
AI tools to support recommendation systems:
- Amazon Personalize: Developed by Amazon Web Services, this service utilizes machine learning algorithms to create highly individualized product and content recommendations. It's designed to enhance user engagement and drive conversions based on user preferences.
- Sailthru: This AI-driven marketing automation platform incorporates predictive intelligence to offer personalized content recommendations. Sailthru analyzes user behavior to suggest products, articles, or other content, optimizing engagement.
Dynamic content creation
AI can generate personalized content, such as emails, articles, or advertisements, based on user preferences and behavior. It can adapt the content in real-time to ensure relevance and engagement.
AI tools to support dynamic content creation:
- Persado: Leveraging natural language processing and machine learning, Persado enables dynamic content creation for emails and other marketing materials. It analyzes user preferences and behavior to generate language variations that resonate most with individual users.
- Automizy: This AI-powered email marketing platform dynamically adapts email content based on user behavior and interactions. It ensures that each email is personalized in real-time, increasing relevance and engagement.
AI can use historical data to make predictions about future user behavior. This can help in tailoring personalized experiences and offers, predicting churn, optimizing marketing campaigns, and more.
AI tools to support predictive analytics:
- Predictive Analytics by Salesforce: Integrated into Salesforce's CRM platform, this tool uses AI to analyze historical data and predict future user behavior. Businesses can leverage these insights to personalize marketing strategies, predict churn, and optimize campaigns.
- WekaIO: Specializing in predictive analytics, WekaIO utilizes AI and machine learning to analyze patterns and predict future trends. This can be instrumental in tailoring personalized experiences based on anticipated user behavior.
Natural language processing
AI can understand and interpret natural language, allowing for personalized interactions with users through chatbots or virtual assistants. This enables AI to provide personalized recommendations or support based on individual needs.
AI tools to support natural language processing:
- Dialogflow (by Google Cloud): A powerful natural language understanding tool, Dialogflow enables businesses to build conversational interfaces and chatbots. It processes user input, understands intent, and provides personalized responses, enhancing user interactions.
- Chatbot.com: This platform utilizes natural language processing to create chatbots capable of personalized interactions. It understands user queries, preferences, and context to offer tailored recommendations or support.
Personalized pricing and offers
AI can analyze customer behavior and market trends to determine optimal pricing for individual customers. It can also generate personalized offers and discounts to maximize customer satisfaction and revenue.
AI tools to support personalized pricing and offers:
- Dynamic Yield: This AI-powered personalization platform helps businesses optimize pricing strategies based on individual customer behavior. It analyzes data to determine optimal pricing and generates personalized offers to maximize customer satisfaction and revenue.
- Zilliant: Specializing in price optimization, Zilliant uses AI algorithms to analyze customer behavior and market trends. It provides insights to determine personalized pricing and offers, ensuring competitiveness and customer value.
AI can enhance search functionality by understanding user intent and context, providing personalized search results. This ensures that users receive highly relevant information and recommendations.
AI tools to support personalized search:
- Elasticsearch: An open-source search and analytics engine, Elasticsearch integrates AI capabilities to enhance search functionality. It understands user intent and context, delivering personalized search results for a more relevant user experience.
- Coveo: This AI-powered search and relevance platform utilizes machine learning to understand user behavior and deliver personalized search results. It ensures that users receive highly relevant information based on their preferences.
Overall, AI enables businesses to leverage data-driven personalization at scale, providing tailored experiences that meet the unique needs and preferences of individual users.
Implementing Data-Driven Personalization
Below are eight examples of how to use data-driven personalization in your B2B engagement marketing strategy:
1. Dynamic website content: Customize website content based on user data. For example, if a customer frequently purchases skincare products, the website can display personalized skincare recommendations or offer discounts on related products when that customer returns to the site.
2. Email marketing: Segment email lists based on the segmentation strategies listed above. Send personalized emails containing relevant content, such as product recommendations, webinar invitations, etc. This helps improve engagement and conversions.
3. Personalized pricing: Offer individualized pricing or discounts based on customer behavior or value. For instance, VIP customers could receive special pricing or exclusive offers to incentivize them to renew.
4. Tailored customer service: Utilize customer data to provide more personalized customer service experiences. Agents can access customer profiles and history to understand their preferences and needs when handling inquiries or support requests.
6. Location-based targeting: Use location data to offer personalized recommendations or promotions specific to the customer's city or region. For example, marketers should use this strategy when looking to execute field marketing events.
7. Social media targeting: Leverage customer data to target specific demographics or interests with personalized social media advertisements. This ensures relevant content reaches the right audience and increases the likelihood of engagement and conversions.
8. Personalized loyalty programs: Use customer purchase history and preferences to personalize loyalty programs. Offer rewards, discounts, or exclusive perks relevant to individual customers' tastes and preferences, encouraging loyalty and repeat business.
Measuring Success with Data
To measure the success of data-driven personalization, adopt key performance indicators (KPIs) aligned with your objectives. Track metrics such as conversion rates, click-through rates, and customer satisfaction scores. Measuring your marketing performance is a critical step in evaluating the success of your personalization efforts. When implementing a multichannel marketing strategy, consider leveraging reporting models such as the first-touch attribution model to help you determine success.
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