How Smart Brands Use Web Data to Power Predictive Analytics?

In an ever-changing and highly competitive modern marketplace, the phrase “knowledge is power” has never rung more true. For brands seeking not only to survive, but thrive, understanding their customers and market is essential. Understanding isn’t limited to post-purchase surveys, anecdotal experience, and more; it is being driven more and more by the intelligent use of predictive analytics, fed by the vast ocean of web data at their fingertips.

A future where brands understand their customers’ needs before the customers even express them. Where brands structure their marketing campaigns (messaging, timing, channels), ideally, based on the ability to anticipate trends before they occur, operational inefficiencies are predicted and then addressed before they affect the bottom line. It isn’t science fiction, but the world that innovative brands are continuously building today, one building block at a time, using web data as their building block and predictive analytics as the blueprint.

What Is Web Data?

When businesses hear “web data,” they tend to think about web analytics like page views, bounce rates, conversions, and other metrics. Although useful, web data encompasses much more than this definition. Web data is a complex mosaic of digital experiences that reveals customer interactions, behaviors, and purchase journeys. Web data also indicates new market trends and competitive landscapes.

Types of Web Data:

• Web Analytics: Traditional analytics are about sources, user paths, page views, click-throughs, and conversion funnels. But they also include intricate aspects of how users are navigating content, forms, and product pages.
• CRM Data: Although CRM isn’t exclusively web data, it often is connected (web usage data) and shows an overview of all the interactions customers have with a brand: website visits, emails opened, customer service inquiries, etc.
• E-commerce Transactional Data: The ongoing purchase, abandoned, product view, or search data from an e-commerce interaction provides analysis on customer preferences, price sensitivity, and buying behavior.
• Social Media Data: Likes, shares, comments, mentions, and sentiment can give a real-time indicator of public opinion, brand-oriented conversations, trends, and patterns that are materializing.
• Search Data: Aside from direct website traffic, understanding search data (branded and unbranded) can uncover customer intent, trending topics, and competitors’ activities.
• Mobile App Data: If a brand has a mobile app, identifying and collecting some user behavior as it relates to feature usage and purchase behavior may provide another distinct viewpoint of user interaction.
• 3rd Party Data Aggregators: These sources can add demographic data to your 1st party data, and capabilities on interest, values, and lifestyle segmentation can add nuances to customer profiles.
• Competitive Intelligence Tools: Some tools can scrape and analyze competitor websites and mobile app content, pricing, products and services offerings, and marketing campaigns to gain insights into the market landscape.

While the volume, variety, and velocity of web data can be overwhelming, it represents a tremendous and unique opportunity. The hard part is figuring out how to distinguish the signal from the digital noise. The positive aspect is that these signals can be utilized to fuel advanced predictive models, potentially allowing for accurate predictions of future behavior.

How can Predictive Analytics Help You See the Future of Your Business?

Predictive analytics leverages historical data to predict future events using various statistical techniques, such as data mining, predictive modeling, and machine learning. Savvy brands leverage predictive analytics to gain a competitive edge in several different business functions.

1. Hyper-Personalized Customer Experiences: The Holy Grail of Customer Engagement

Customers today expect personalized experiences. They need innovative brands to recognize their preferences and anticipate their needs. Predictive analytics operates effectively on the web, primarily utilizing web data.

• Predict product needs: Analyzing a customer’s browsing history, past purchases, search queries, and products viewed but not purchased enables predictive models to suggest products or services relevant to the customer. If an online bookstore, for example, analyzes a customer buying books on the mystery genre, it can confidently recommend a new thriller to the same customer.

• Personalized Content Recommendations: Most media companies today employ predictive analytics models, using web data, to more effectively engage with users while increasing content consumption and session time. A news website may curate the homepage for a given reader based on articles the reader is most likely to engage with.

• Personalized Marketing Campaigns: Instead of mass mailings, brands can now organize their audience into groups based on their predictive analytics model’s likelihood of responding to an offer. It helps organizations achieve higher open, click-through, and conversion rates.

• Proactive Customer Service: Brands can proactively speak with customers when they see repeated troubleshooting efforts without any satisfactory solution, overused visits to a help page, social media comments complaining about the brand, etc. Brands can identify customers who need assistance and reach out promptly.

Example: Amazon’s recommendation engine uses predictive analytics models. Amazon can use huge swathes of customer interactions, past behavior, and the similar behavior of previous customers to suggest products that may interest the customer.

2. Optimized Marketing & Sales Strategies: Maximize ROI

Predictive analytics enables brands to use resources more wisely and achieve more return on investment (ROI) by:

• Predict Demand: Predictive modeling can also examine a new marketing campaign’s prospective success based on historical data, including the last campaign(s) to run, the audiences for each, and current market demand. Brands can refine campaigns to deliver bigger results.

• Dynamic Pricing: For e-commerce businesses, the prices can change for each customer based on demand, competitor pricing, inventory levels, and price sensitivity. Implementing these strategies can maximize revenue for the brand while also ensuring its competitive position.

• Lead Scoring and Prioritization: Predictive lead scoring models correlate customer web behaviors to assign a score for the likelihood of new leads converting. A sales team can use this model to target people with the highest score to prioritize sales efforts for leads that are more likely to convert.

• Churn Prediction and Prevention: Predictive models scan web data, including identifying trends in a customer whose engagement with a website diminishes or negative behavior from their customer service experience. From that, the brand can proactively begin addressing the decline in engagement among those customers by presenting them with a direct offer or personalized support.

• Attribution Modeling: Predictive analytics goes beyond last-click attribution to build comprehensive attribution models. These models assign weight to all customer touchpoints encountered when engaging with a brand. With a more informed attribution model, brands can properly allocate media and advertising budgets.

Example: A SaaS company could use predictive analytics tools to create a list of identified trial users who are most likely to convert to a paid subscription. Therefore, the SaaS company can provide them with a different level of personalized support than the customers who are further along in the trial and evaluating the options.

3. Enhanced Product Development & Innovation: Building What Customers Want

Web data and predictive analytics are informing product development in many ways:

• Identifying gaps and opportunities: By evaluating search queries, social media posts, customer reviews and discussions, and competitor product specs (with some limitations), brands can identify unmet needs and trends that are just starting to emerge.

• Prioritizing Features: Identifying which features generate the most discussion or are frequently used online will help prioritize product development for brands.

• Forecasting demand: Combining web data with sales data and seasonality can help brands forecast demand.

• Identifying product issues early on: Using web data and social media sentiment can help brands identify problems with products or services early on in the process.

Example: A company involved in consumer electronics may want to look more closely at the reviews of its products currently available on the market. After identifying the problems being discussed frequently in the reviews of that product, it may move forward with product development by addressing those issues in the next generation of devices.

4. Optimized Operations & Supply Chain: Driving Efficiency and Cost Savings

Predictive analytics spurs operational efficiency and cost savings:

• Inventory Optimization: Brands can utilize traffic patterns and seasonal trends to build a forecast of website demand, therefore optimizing inventory levels and associated costs.

• Fraud Prevention: Brands can also use predictive models that analyze transaction data to identify transactions that may have fraud in real time, therefore discouraging losses due to fraud.

• Resource Allocation: Brands can develop staffing optimizations in response to anticipated demand, based on internet activity or a promotional campaign.

• Website Capabilities: Brands can use predicted traffic patterns and server load predictions to proactively scale their server capabilities to ensure their website is operating, or in case of a shutdown.

Example: A fashion retailer can rely on predictive analytics to forecast demand for a newly launched clothing item, allowing them to adjust their inventory order in advance of peak demand, or utilize their advanced forecasting to collect product returns, further confirming their prediction of demand.

What Are The Methodologies To Drive Force Behind Predictions?

The prediction process of raw web data into usable predictions involves analytical methodologies mainly from Artificial Intelligence (AI) and Machine Learning (ML) techniques.

ML Methods/Machine Learning: ML implies that algorithms are trained with large datasets to discover patterns and form predictions about outcomes.

Supervised Learning: Models are trained on labeled data to predict future values or outcomes. Within this category, use of Regression (predicting continuous values such as total sales volume) and Classification (predicting categorical outcomes such as likelihood of customer churn).

Unsupervised Learning: Finding and recognizing hidden patterns and structures in unlabeled data. Customers can be segmented based on their online behavior using clustering algorithms without any predefined categories.

Reinforcement Learning: Use this method in dynamic environments, like optimizing A/B tests on a company’s website.

Deep Learning: Deep learning is a neural network learning architecture, enabling the discovery and processing of complex patterns in data, performing very well in image recognition, natural language processing (NLP), and similar areas.

Natural Language Processing (NLP): NLP is when a computer can understand and generate human language. It is vital for extracting usable insights from unstructured web-based data, such as that obtained from customer reviews and social media posts.

Predictive Modelling: Develop predictive models to predict future values or outcomes using a variety of statistical techniques.

• Time Series: For predicting ongoing trends over time, such as website traffic driven by SEO, or leads or sales volumes resulting from paid ads.
• Decision Trees & Random Forest: For classification and regression methods that develop a flow-chart structure to predict an outcome from a series of decisions.
• Gradient Boosting Machines (GBM): Robust ensemble learning techniques that build a series of weak prediction models to make the final model..

The appropriate methodology will depend on the business context of the problem, the type and amount of available web data, and the range of accuracy and interpretability expected.

What Are The Challenges and Considerations?

Navigating the Digital Minefield, Brands need to address several challenges before they can take full advantage of using web data for predictive analytics efforts:

Data Volume, Velocity, and Variety (Big Data): the scale of web data often contains complexity; investing in the infrastructure and data processing capacities to manage, identify, clean, and analyze this information takes skilled data scientists.

Data Quality and Accuracy: If the web data is incomplete, inaccurate, or inconsistent, the predictions will also be flawed. Brands need to invest in and leverage data governance best practices that will validate the data as complete and actionable.

Data Silo’s: Web data often exists in an array of systems that are siloed from each other. Disrupting the data silo’s as an organization is critical to develop a full view of the customer and create valid predictions about them.

Data Privacy and Security: Brands need to ensure that how they collect, store, and use web data is ethical, legal, and compliant. It is crucial to be transparent with customers about how their data is utilized.

Algorithmic Bias: Predictive models can inherit biases that exist during the training phase. Brands need to ensure they continuously identify and mitigate bias in the models they deploy.

Interpretability vs. Accuracy: Complex ML models can produce perfect predictions, but they may require expert-level interpretation. Oftentimes, balancing the accuracy of the prediction with the interpretability is needed.

Technological Infrastructure & Talent: Undertaking and operationalizing sophisticated predictive analytics requires investment in technology and talent to deploy and maintain.

Resistance to Change: Organizations must be mindful of the cultural shift required to transition to using data and predictive analytics. Change management efforts need to combat resistance to the use of new processes and technologies.

Conclusion: The X-byte Era

We have reached the initial moments of the X-byte era, which guarantees an exceptional amount of digital data that will require not only storage but also intelligence, speed, and clarity in terms of strategy. In the X-byte era, brands will be distinct in that they will not just collect web data, but will synthesize, learn from, and act in real-time on the digital data collected.

When predictive analytics receives a broad spectrum of digital asset data sources, it becomes much more than a forecasting tool; it is a predictive instrument that is an actual competitive engine. Brands that survive will have successfully made the shift from reactive reporting to proactively predicting and from disparate web insights to cohesive intelligence that is measurable and actionable.

In the X-byte era, success will be defined by:

• Extreme personalization based upon real-time behavioural patterns.
• Exponential insights produced by self-learning AI models.
• Execution speed occurs when companies are cognitively data-driven by quickly delivering ideas based on the authority of data.
• Extensive cross-platform intelligence where every digital touchpoint is connected and can become a single strategic brain.

Smart brands are not waiting to see what the future brings; they are building it. They use the noise of the web and enact clarity from it, foresight, and precision towards their objectives. The X-byte world belongs to brands that see ahead, not because they look harder, but because they can adeptly predict smarter.