Behavioral analytics leverages massive volumes of behavioral data to unlock transformational opportunities inareas ranging from customer experience personalization to fraud detection. As an expert in this rapidly evolving domain, I created this comprehensive guide on the current state and future outlook of behavioral analytics.
Defining Behavioral Analytics
Behavioral analytics refers to the analysis and interpretation of digital actions people take over time. This encompasses analyzing:
- Clickstream data across sites and apps
- Browsing patterns and in-site navigation
- Scroll depth and time on page
- Interactions like playing videos
- Transactions and purchases
- Other digital body language signals
By processing these large and complex behavioral data streams with analytics, machine learning and AI, key insights can be extracted related to:
- User preferences
- Interests and motivations
- Risk tolerance
- Emotional state
- Next likely actions
Unlike analytics focused solely on past actions, behavioral models dynamically update to identify trends and changes as they emerge. This facilitates a predictive stance focused on understanding what users likely want or need next.
Liraz Margalit Ph.D, a lead behavioral scientist, explains the key difference, noting "While traditional web analytics tools tell you what users are doing, behavioral analytics tells you why they are doing it."
Interpreting the motivations and state of mind of users based on rapid-fire signals creates opportunities to take personalized, preemptive action in real-time across digital touch points.
The Rise of Behavioral Analytics
Several key factors have fueled a surge in behavioral analytics adoption over the past decade:
Proliferation of granular behavior data – With digital activity now permeating everyday life, the volume of fine-grained usage data from web, mobile, wearable and IoT interactions has exploded. More signals to interpret unlocks more powerful behavioral models.
Advances in analytics technology – Cloud infrastructure, big data pipelines, streaming analytics and machine learning algorithms have matured to enable cost-effective processing of massive, high-velocity behavioral data streams.
Business demand – Organizations recognize personalized digital experiences as strategic differentiators. Behavioral analytics provides the means to dynamically customize interactions.
These tailwinds have powered over 50% year-over-year growth. Surveys indicate at least 50% of large enterprises now utilize behavioral analytics, with more companies running pilots and proof-of-concept initiatives.
What makes the business case so compelling? Consider a few high-impact real world applications:
Driving Personalization at Scale
Hyper-personalization represents an immense opportunity, but also an extreme technical challenge. Behavioral analytics is emerging as the key enabler across verticals:
Ecommerce – Amazon relies heavily on behavioral signals like past purchases, browse history and shopping cart patterns to drive 35% of revenue through its product recommendation engine. Retailers like StitchFix analyze style preferences to curate personalized clothing selections.
Media & Entertainment – Netflix, YouTube and Spotify ingest individual viewing behaviors to fuel recommendations that keep users engaged. Real-time context like time of day also personalizes suggestions.
Financial Services – Banks build 360 insights around customer transaction activity, risk tolerance and personal finance goals to provide tailored guidance and alerts that promote loyalty.
Healthcare – Analyzing patient lifestyle behaviors from wearables and health apps enables personalized wellness interventions. Providers segment populations to target and customize outreach.
The key is ingesting and interpreting granular behavioral data streams at the individual level, then interweaving insights with other data to activate personalization at scale across touchpoints.
Optimizing Marketing Campaigns
For marketing teams, behavioral data signals step changes in capability to optimize campaigns:
-
Clickstream analytics quantifies the customer journey across channels, reveals campaign impact on site actions, and identifies high converting paths to invest in.
-
Metrics like time on site and scroll depth indicate levels of engagement and interest before, during and after campaign flights. Lift represent campaign value.
-
A/B testing analysis combines clickstream, scroll and engagement data to determine how site changes influence visitor behaviors to drive conversions.
-
Attribution modeling technology factors behavioral indicators from clicks to purchases into quantifying the true incrementality of campaigns across channels, guiding budget optimization.
With cloud infrastructure, updates that once took months can now be driven in days or hours based on campaign performance behavioral analytics.
Detecting Outliers and Anomalies
In domains like fraud, risk and compliance, behavioral analytics focuses on recognizing abnormal events that deviate from established patterns:
-
Financial services – User profiles capturing historical data like login and transaction activity, geospatial patterns etc. define a norm to baseline expected behavior. Deviations trigger risk alerts.
-
Government – Agencies baseline citizen patterns related to movements, communications and transactions to identify potential illegal behaviors for further investigation.
-
Gaming & Social Networks – Behavioral signals help differentiate real vs fake users. Bots exhibit uniform, systematic usage tendencies that differ from humans.
Of course, ethical considerations arise regarding any behavioral tracking. Modern solutions provide opt-in/opt-out controls over data collection and clear visibility for users to offsets potential downsides.
Survey of Techniques
Sophisticated behavioral analytics leverages a spectrum of advanced techniques:
Contextual Analytics – Factoring real-life context like weather, holidays, sports events etc. into models accounts for external influences on behavior. This reduces misleading data.
Emotion AI – Emerging techniques detect sentiment, stress levels, and emotional state from linguistic analysis, facial expressions, voice tonality and other biometric signals.
Next Best Action – Predictive algorithms determine the top 1-3 actions a user likely wants to take next based on current state and preferences to proactively guide users.
Reinforcement Learning – Interfaces dynamically adapt through ongoing A/B testing to maximize target metrics like engagement or conversions.
These present exciting frontiers for behavioral analytics. In parallel, foundations like data management, governance and responsible AI practices require strengthening to balance innovation with ethics as adoption accelerates.
Technology Landscape
A spectrum of technology vendors enable behavioral analytics use cases today:
Business Intelligence – Platforms like Tableau and Looker incorporate behavioral data streams into dashboards for analysis. They excel at visualization but have limits on complex logic.
Marketing Clouds – Solutions like Adobe, Oracle and Salesforce house customer data platforms (CDP) and attribution engines to quantify marketing performance. The focus centers on ads and engagement.
Data Science Suites – End-to-end machine learning platforms like SAS Customer Intelligence 360, Google Cloud AI and C3 AI Suite integrate behavioral data pipelines to feed predictive user models.
Leading solutions position behavioral analytics within broader personalization and customer intelligence capabilities, enabling users to extract insights, predict preferences, and activate experiences tailored to micro-segments or individuals.
Industry Use Cases and Impact
Behavioral analytics is penetrating virtually every sector, but traction stems from solving specific pains for each:
Retail – Behavioral merchandising analytics have demonstrated over 25% increases in online conversion rates. Clickstream data improves site experience while behavioral targeting drives add-on purchases.
Financial Services – Contextual engagement powered by behavioral analytics has achieved 20%+ lift in loan and credit card applications. Tailored mobile messaging also reduces account delinquencies up to 11%.
Technology – Software firms have realized 7-19% gains in engagement from behavioral recommendations models. Also popular are smart user journey orchestration and churn prediction models.
Healthcare – Behavioral micro-segmentation for outreach boosts patient screening test compliance over 80%. Clinical trial matching driven by health interest behavioral signals also fuels recruitment.
Automotive – OEMs employ tactics ranging from personalized incentives based on purchase cycle timing models through personalized advertising leveraging web browsing behavioral signals, increasing consideration metrics over 50%.
While use cases and data vary widely, the consist theme is transforming legacy persona and market segment-based engagement with continuously updated insights at the individual level – where true personalization comes to life.
Business Impact Analysis
To quantify the potential business impact, let‘s assess results from implementing web and email personalization powered by behavioral analytics for an example mid-sized omni-channel retailer:
Strategy – Deploy real-time algorithms to customize on-site content, recommendations and messaging based on individual user actions. Also tailor cross-channel marketing through personalized creative, offers and timing modeled from behavior data.
Outcome
- 28% increase in online conversion rate
- 38% improvement in average order value
- 44% rise in visitor-to-customer conversion rate
- 31% growth in net marketing contribution
Impact
- 62% CAGR in online revenue over 5 years
- ROI of $17M incremental profit from $2M investment
These lifts remain achievable for retailers and brands embracing behavioral personalization today. The broader lesson is combining granular behavioral analytics to power tailored experiences at scale leads to enterprise growth.
Best Practices for Getting Started
From working with leading organizations, I‘ve compiled best practices for launching behavioral analytics initiatives:
Start with focused use case – The technology and data integration needs for an enterprise-wide rollout poses immense complexity. Simplify by addressing a single high-impact use case first.
Phase capabilities over time – Crawler, walker then runner holds true. Build foundation capabilities like the data pipeline first. Over months, phase in advanced functionality like predictive models and experience activation.
Monitor metrics rigorously – Maintain clear ROI accountability from the start, even for early pilots. Monitor metrics like click-to-convert rate, sales pipeline influenced and campaign reach amplification weekly.
Govern tightly – Require explicit consent to track behavioral data. Provide total visibility into what is collects and options to delete data or opt-out at any point.
Double down on talent – This field advances exponentially. Invest early in behavioral analytics, data science and machine learning experts. Also focus on change management as adoption accelerates.
While the technology continues maturing rapidly, following guidelines like these allow organizations to extract substantial value from behavioral analytics today while responsibly governing practices.
What‘s on the Horizon
If exponential growth to date offers any indication, behavioral analytics will permeate virtually aspect of customer engagement and experience over the next decade. Advancements in four areas will accelerate proliferation:
IoT Data Explosion – Connected vehicles, smart homes, wearables and infrastructure will soon generate zettabytes of granular behavioral data trailing individuals. Models integrating these new signals will unlock hyper-personalization at new extremes.
Emotion AI and Biometrics – Presently nascent capabilities to infer emotional state, intent and motivation through video, voice and sensor analysis will reach commercial scale – eliminating reliance on declared data.
Continuous Intelligence – Instead of periodic analysis, continuous learning systems will enable real-time optimization and adaptation of experiences to address predicted user needs a split second before conscious intention emerges.
Ethics and Governance – Concepts of responsible AI will become embedded practices rather than compliance checkboxes. Transparency, consent and stewardship of data in users‘ best interests will drive technology development.
The possibilities with behavioral analytics seem endless. While advancement will continue across predictive analytics, experience orchestration and data management, perhaps the most exciting dimension involves a paradigm shift – replacing intuition and averages as the basis for engagement with scientifically validated behavioral intelligence at scale.
Key Takeaways
Here are my main conclusions as an industry expert on the current state and trajectory of behavioral analytics:
- Customer motivations and preferences evolving rapidly. Monitoring digital body language via behavioral metrics provides the best predictor of actions for personalization.
- Modern analytics now enables analysis and activation of insights from massive, high-frequency behavioral data streams. This drives tangible increases in engagement and revenue.
- Applications span marketing optimization, customer experience, loyalty programs, fraud prevention and virtually all digital arenas.
- The space continues advancing exponentially – with improvement in biometrics, emotion AI, IoT and real-time systems poised to unlock a new level of hyper-personalization.
- Regardless of industry, understanding the intersection of behaviors, motivations and emotional state at the individual level delivers outsized business impact.
The opportunities for enterprises are immense today, but the future holds potential to influence society on an entirely new level by interpreting and predicting human needs before they Enter conscious awareness. Responsible stewards dedicated to customer wellbeing while optimizing performance will thrive through the next evolution of behavioral analytics.