Intent data refers to information that indicates a user’s purpose or goal when interacting with a digital product or service. It provides insight into why users take certain actions and what they are trying to accomplish. Some common examples of intent data include:
Search Queries
Search engine queries clearly convey a user’s intent. For example, searching for “hotels in Miami” indicates the user is looking to research or book hotels in that city. Analyzing search queries can reveal user goals and needs.
Clicked Links
Looking at the links and ads that users click on from search engine results pages and websites can shed light on their underlying intent. If a user clicks on a link to hotel booking sites, their intent is likely to research pricing and availability.
Browsing History
A user’s browsing history, especially across multiple sites and sessions, paints a picture of their interests and goals. Frequent visits to travel sites may indicate an upcoming trip being planned.
Transactions
Purchases and other transactions are a clear sign of user intent. If a user books a flight and hotel room, their travel plans are obvious based on those actions.
Social Media Activity
Posts, shares, likes, and other social media actions provide insight into a user’s mindset and interests. Someone posting about an upcoming wedding may be planning related purchases.
Submitted Forms
Form submissions like newsletter signups, lead gen offers, and contact request forms indicate users’ intentions. Submitting a “request quote” form shows intent to potentially buy a product.
Account Creation
When a user creates an account on an app or site, they are signaling an intent to engage further with the product or service. Signing up for a travel app, for example, indicates plans to book trips and manage travel.
Added to Cart/Wishlist
Adding items to a shopping cart or wishlist highlights purchase intent, even if the transaction is not completed immediately. Marketers can follow-up with these users via emails or ads.
Customer Service Interactions
Chats, calls, emails, and other customer service interactions capture intent as users ask questions and explain needs. Agents can analyze this content to improve products and services.
Location Data
Location-based services reveal user intent through mobility patterns. Frequenting home improvement stores may signify an upcoming remodeling project.
Voice Assistant Queries
Spoken queries to Siri, Alexa, Google Assistant and other AI helpers indicate intent. Asking for directions to a store location signals an in-person shopping trip.
Why is understanding intent data important for businesses?
Intent data is a goldmine for businesses because it enables more strategic and impactful marketing and product development. Some key benefits include:
- Personalizing experiences by tailoring messaging and offers to demonstrated user intent.
- Prioritizing development resources around features most desired by users.
- Crafting marketing campaigns that speak directly to customer needs and interests.
- Identifying gaps between user goals and available products/services.
- Predicting which users are most likely to convert and assigning lead scores.
- Building smarter recommendations and segmentation of users.
In summary, leveraging intent data allows brands to serve users better and maximize the value of interactions.
Common use cases of intent data
Here are some typical use cases showing how businesses harness intent data:
Search engine optimization (SEO)
Analyzing search queries and clicked organic results to inform keyword targeting, site content, and link building.
Paid search (PPC) ads
Bidding on keywords that indicate commercial intent and crafting relevant ad copy/landing pages.
Social media ads
Creating “lookalike” audiences matched to existing customers’ interests and social activity.
Email marketing
Triggering behavioral-based emails when users show signals like browsing products or abandoning carts.
Web push notifications
Prompting users to complete purchases when transaction intent is observed.
Chatbots
Using natural language understanding to discern user goals from conversations.
Recommendation engines
Suggesting relevant products and services tailored to inferred user intents.
Customer segmentation
Dividing audiences into groups based on common intent signals from their actions.
User journey mapping
Charting the steps users take to reach goals and identifying intent-driven opportunities.
New feature prioritization
Using voice-of-customer data to guide roadmaps and prioritize desired functionality.
Churn prediction
Pinpointing at-risk customers based on digital body language indicating declining engagement.
Next-best action modeling
Crafting personalized recommendations to match inferred next intent steps.
Examples of how businesses use intent data
Let’s look at some real-world examples of how brands leverage intent data:
Expedia
The travel booking site taps search queries, browsing history, and trip-related purchased to recommend relevant hotels, flights, and vacation packages.
Target
Target analyzes purchase history and browsing behavior to determine pregnancy status and send customized coupons and offers.
Amazon
Amazon’s recommendation engine considers past purchases, browsing, and cart adds to suggest products that align with demonstrated customer intent.
Netflix
By tracking viewing behavior and ratings, Netflix personalizes the content suggested to match each member’s taste preferences.
Spotify
Based on listening patterns, liked songs, and playlist additions, Spotify recommends music aligned with user intent.
Uber
Uber looks at frequent destinations and mobility patterns to predict rider intent and recommend relevant promotions.
Nike
Nike analyzes athletic activity data from its apps and gear to customize product recommendations for runners.
LinkedIn observes member profile updates, engagement, and other actions to recommend relevant jobs and people to connect with.
Challenges with leveraging intent data
While intent data delivers tremendous value, it also comes with some potential pitfalls to be aware of:
Privacy concerns
Collecting and harnessing user data raises ethics issues around transparency and consent management.
Data silos
Intent data often resides across disparate sources both online and offline, making consolidation difficult.
Signal accuracy
Inferring intent isn’t an exact science – the derived insights may misrepresent some users’ goals.
Bias
Patterns detected from small or skewed data samples may lead to experiences that alienate certain user groups.
Creepiness
Ultra-personalized experiences driven by intent data may cross comfort boundaries for some customers.
Changing intent
User goals evolve rapidly, so models must incorporate new signals to stay relevant over time.
Regulations
Evolving data privacy laws add compliance complexity when managing and activating on intent data.
Best practices for leveraging intent data
To harness intent data successfully and ethically, brands should follow these best practices:
- Be transparent and allow user choice for data collection.
- Anonymize intent signals that aren’t necessary to retain individually.
- Customize models and experiences while avoiding discrimination.
- Continuously update modeling with new data to reflect changing intent.
- Balance personalization with some degree of randomness to maintain serendipity.
- Obtain consent before using intent data for new purposes.
- Allow user overrides of recommendations driven by intent models.
- Explain recommendations derived from intent data when possible.
- Leverage first-party data from owned channels rather than relying solely on third-party data.
- Evaluate predictions for bias before activation to avoid skewed outcomes.
The future of intent data
Looking ahead, here are some emerging trends around intent data:
- Rise of new intent signals from expanding IoT ecosystems and ambient computing.
- First-party behavioral data replacing third-party cookies and mobile ad IDs.
- More accurate intent predictions from AI, ML, and neural networks.
- Natural language processing to extract granular intent from unstructured text.
- Predicting future customer intent earlier in the journey.
- Next-best-action recommendations optimized in real time.
- More consumers rewarding brands that use intent data to provide value vs. annoyance.
Conclusion
Intent data is transforming digital experiences by enabling highly tailored personalization and optimizations. As methods for inferring intent continue advancing, consumers will enjoy more delightful, frictionless engagements with brands. But responsible data practices must remain front and center to maintain user trust. Overall, harnessing signals of user goals and desires presents huge opportunities for businesses to improve their products, outmaneuver competitors, and build lasting customer loyalty.
