Consumer Intent Data: The Key to Unlocking Upselling and Cross-Selling Opportunities

Consumer intent data is a powerful indicator of a potential customer’s interest and buying intent. It reveals, directly and indirectly, where they stand in their buyer’s journey, whether they’re “just looking,” actively comparing options, or nearing a purchase decision.

When you purchase consumer intent data, you’ll gain access to essential items such as names, emails, zip codes, and other personal information shared during the online form submission process.

In this article, we’ll help you think like a data scientist to ‘connect the dots’ with your buyer intent data to deduce how prepared your prospects are to make a purchase.

Not only that, we’ll teach you how to analyze customer intent data to close more deals through upselling and cross-selling, because you never know when a new customer might need auto insurance and homeowners at the same time.

What does consumer intent data mean? How is it useful?

Interpreting consumer intent data will help you understand the customer’s past interests while showing you their current and future needs.

Intent data contains details such as email addresses, phone numbers, and zip codes, which are more than just contact points.

They are direct indicators of consumer interest and intent, providing subtle, and not-so-subtle clues about their sales-readiness to purchase.

For instance, consider consumer intent data that’s 60 days old.

The personal info submitted includes the individual’s age, current health issues, and existing insurance coverage.

These details give you an idea of the level of coverage they might need to help you assess their sales readiness and help you score them accordingly.

Similarly, a customer who showed interest in homeowner’s insurance three months ago can still offer relevant information (and more affordable than fresh intent data).

The data submitted might include the home’s location, value, and the homeowner’s current insurance status.

Using this data, you can infer if the prospect might be in a better position now to consider additional coverage options or even different types of insurance, like life insurance, providing you with an upselling and cross-selling opportunity.

Using intent data to cross-sell and upsell

Below we’ll provide scenarios to illustrate how salespeople can identify cross-selling and upselling opportunities that align with their customer’s current needs or lifestyle.

This approach allows sales agents to expand their offerings and provide more value to their customers while earning more sales.

1. Cross-selling in health insurance

• Customer details: John, age 35, inquired about health insurance 45 days ago. He provided information about his family, indicating he is married with two children.
• Cross-selling opportunity: Given John’s family details, an insurance agent can cross-sell life insurance or children’s health plans. The agent can reach out to John, explaining how these additional policies can provide comprehensive coverage for his entire family.

2. Upselling in auto insurance

• Customer details: Suzanne inquired about basic auto insurance for her sedan 30 days ago. She mentioned in the contact form that she frequently travels for work (100+ miles a day).
• Upselling opportunity: An insurance agent might offer Suzanne an upgraded auto insurance plan that includes additional coverage for roadside assistance and travel-related incidents, based on her extensive travel needs.

3. Cross-selling from homeowners to life insurance

• Customer details: Alex recently requested a quote for homeowners insurance and provided details of a new home purchase.
• Cross-selling opportunity: Given Alex’s recent home purchase, an agent can introduce life insurance policies, highlighting the importance of securing his family’s future and the home they have just invested in.

4. Upselling in mortgage protection insurance

• Customer details: Paula requested information on basic mortgage protection insurance two months ago, indicating she is a new homeowner.
• Upselling opportunity: An agent can propose an enhanced mortgage protection plan that includes coverage for job loss or disability, ensuring Sarah’s new home is protected under more comprehensive circumstances.

Breaking down intent data for 8 different industries

Understanding your customers’ needs is key to successful sales. Here’s how our Aged Lead Store can help.

We provide intent data for various industries. Analyzing this data can reveal more about your prospect’s needs according to their online behavior. 

By interpreting this data, you can tailor your sales approach to each specific prospect, maximizing your chances of success.

Additionally, we’ll provide examples of consumer intent data associated with different industries. These examples will help you identify upselling and cross-selling opportunities, further increasing your sales potential.

Auto insurance

• Consumer intent data provided: Data age type (15-85 or 86-365 days old), state, age, zip codes, current insurance carrier, and phone options (landline or cell), make, model, year, trim for 1-3 vehicles, gender, and other auto-related fields.
• Example 1: Data shows a 28-year-old from a high-traffic zip code looking to switch insurance carriers. This might indicate dissatisfaction with current rates or coverage, suggesting an opportunity to offer competitive pricing or more comprehensive coverage.
• Example 2: A 45-year-old in a rural area with a long-term relationship with their carrier might be more interested in loyalty discounts or bundled policies for multiple vehicles.

Homeowners insurance

• Consumer intent data provided: Data age (15-85 or 86-365 days), state, age, zip code, phone option, occasionally gender, square footage, year built, and details of make and model of the property.
• Example 1: A homeowner in a coastal area might be more susceptible to weather-related damages, indicating a need for enhanced natural disaster coverage.
• Example 2: An older prospect from an urban area could signal a homeowner looking for competitive rates or additional coverage for home renovations.

Health insurance

• Consumer intent data provided: Age of data (15-85 or 86-365 days), state, age, zip code, phone option, and occasionally gender or income details.
• Example 1: A young individual in a metropolitan area might be a first-time insurance buyer, indicating a need for basic, affordable plans.
• Example 2: An older customer might needmore comprehensive health coverage or interest in supplemental health services.

Life insurance and final expense insurance

• Consumer intent data provided: Age of data (15-85 or 86-365 days), state, age, zip code, phone option, coverage amount, occasionally gender, height, weight, policy type, term, smoking status (yes/no).
• Example 1: A middle-aged individual could be looking to secure their family’s future, indicating an opportunity for family coverage plans.
• Example 2: An older individual might be interested in final expense insurance, requiring a gentle approach focusing on dignity and security.

Mortgage industry

• Consumer intent data provided: Data age, state, filters (e.g., loan types such as FHA, conventional, reverse mortgage, VA, home purchase), credit rating (from excellent to poor), zip code, phone options, loan amount, property type, property value, LTV, and occasionally mortgage balance, mortgage rate.
• Example 1: A person with a high credit rating looking for a conventional loan might be interested in premium insurance plans that offer extensive coverage.
• Example 2: Someone interested in a reverse mortgage may benefit from additional financial planning services or insurance products tailored to retirees.

Solar installation

Consumer intent data provided: Age of data, state, zip code, phone option, electricity bill amount, electricity firm, and occasionally roof type, shade, or credit information.

Example 1: A solar panel consumer likely values energy efficiency, indicating potential interest in related home improvements or green energy incentives.

Example 2: This customer might also be open to insurance products that cover renewable energy installations.

Home improvement

• Consumer intent data provided: Age of data (15-85 days or 86-250 days), state, zip code, project types (solar, siding, roofing, flooring, plumbing, electrical, painting, basement remodeling, cleaning, doors, fencing, heating, windows, HVAC, kitchen remodel, outdoor projects, and garage) and phone options.
• Example 1: A prospect for a kitchen remodel could indicate a broader interest in overall home upgrades, suggesting opportunities to offer comprehensive home insurance covering renovations.
• Example 2: A roofing project prospect might open discussions about weather damage coverage in homeowners’ insurance.

Medicare supplement

Consumer intent data provided: Data age, state, age, zip code, phone options, and occasionally date of birth.

Example 1: An older individual might evaluate different Medicare supplement plans, indicating a need for detailed comparisons and personalized advice.

Example 2: This data could also suggest the opportunity to discuss additional health services like vision or dental plans that Medicare does not cover.

Effective strategies to ‘fill in the blanks’ from your buyer intent data

Sometimes you have to ‘fill in the blanks’ to infer information that wasn’t explicitly stated in the intent data you’ve received.

We’ll show you how to extrapolate the information given to give you more information before you even start contacting your new prospective clients.

Landline vs. cell phone differences

Pay attention to the phone type mentioned in the data. A landline often indicates an older demographic, which can influence the type and tone of your communication.

For example, if the customer has a landline listed, tailor your pitch to be more formal and straightforward. This older demographic may value reliability and security in their service offerings.

Using zip codes for localized offers

Research the lead’s zip code to understand their local market and potential customer needs. Look up their local news to learn about their region’s varying risks, lifestyles, and preferences.

For example, if the homeowner’s insurance customer is from a coastal area, emphasize policies that offer floor or hurricane coverage.

Timing and follow-up strategies for different lead types

Consider the age of the intent data, whether a few weeks to a few months to a year old, to determine the urgency and type of follow-up.

Older leads will require a different approach compared to newer ones.

For example, when reaching out to a prospect from 300-day-old auto insurance data, it’s important to acknowledge the time passed since they initially submitted their information. 

Start your conversation by recognizing this gap and offering an update.

Here’s an example of how you could phrase it: “We understand it’s been a while since you submitted your auto insurance inquiry (around 300 days). A lot can change in that time, so let me tell you what you need to know about today’s auto insurance market…”

Interpreting prospect age for personalization

Use the age of the intent data to gauge their position in the buying cycle. Older leads might be closer now to deciding to fill out their form, or they might have new needs that have come up since their initial inquiry (a few months ago).

For example, mortgage consumer intent data that’s several months old might now be ready to discuss home insurance options, having likely closed on the property.

Credit rating as a clue for financial products

For mortgage customers, pay close attention to the credit rating provided with the other consumer intent data. This can indicate the customer’s financial stability and willingness to invest in additional services for cross-selling opportunities.

For example, a customer with an excellent credit rating might be more receptive to premium insurance packages or investment opportunities.

Project type for home improvement prospects

Note the specific home project type for cross-selling opportunities. Each home improvement project can reveal a lot about the homeowner’s priorities and plans, and your homeowner will likely need multiple projects to be completed.

For example, a homeowner interested in solar panels might also be open to discussing energy-efficient home insurance policies or additional green home improvements.

Interpreting health concerns in insurance consumers

For health insurance consumers, consider the implied health concerns (if not specifically shared) and the lifestyle shown by their age and zip code. This information can help in suggesting the most relevant insurance plans.

For example, a customer indicating interest in family health plans may also be interested in life insurance or pediatric care options.

Personalizing communication based on the prospect’s age

If your prospect’s age information has been shared, use it to customize the communication style and product offerings.

For example, younger leads will typically respond better to text messages and emails or innovative insurance products, while older leads may prefer phone calls or direct mail.

How to overcome challenges of aged intent data

Aged Lead Store offers a vast selection of high-quality intent data, but they can present specific hurdles compared to fresh, real-time intent data that need addressing.

Here are our tips on how to overcome the challenges of working with aged customer inquiries.

Data relevancy and accuracy

• Challenge: Data may have outdated or incomplete information.
• Solution: Use Aged Lead Store’s Evergreen Lead Optimization Technology, which regularly updates key details like contact information and personal data. Always verify and, if necessary, update the details in your CRM before initiating contact.

Re-engaging cold leads

• Challenge: Prospects might not be in the same mental or purchasing space as when they first showed interest.
• Solution: Acknowledge the customer’s age in your approach. A message like, “I noticed you were interested in [service/product] a while back, and I wanted to check in to see if your needs have evolved” can reopen communication effectively.

Ensuring unique prospects (without duplications)

• Challenge: Avoid receiving duplicate data that you or your colleagues might have already used.
• Solution: Leverage Aged Lead Store’s system that tracks your previous orders and prevents sending the same data again. Share accounts in a team environment to reduce duplication risk.

Dealing with data gaps

• Challenge: Fields such as phone numbers or emails can sometimes be missing or unreadable.
• Solution: Before assuming data is missing, check for simple fixes like adjusting column widths in your spreadsheet. If genuine gaps exist, Aged Lead Store provides support to fix, re-pull, or replace this data.

The new tools of the trade: advanced techniques and software automation for handling consumer intent data

Use a CRM that can integrate aged intent data and enable easy segmentation and tracking. Tools like Salesforce, HubSpot, and Zoho can provide valuable insights.

Employ data enrichment software to fill in any gaps in your leads. Tools like Clearbit or ZoomInfo can add valuable context to your intent data.

Platforms like Marketo or Eloqua can automate your outreach, allowing for personalized communication at scale and saving time and effort.

Techniques for enhanced interpretation

Develop a consumer scoring system based on the age of their data, engagement levels and completeness of data. This helps prioritize efforts on the most promising prospects.

Segment your intent data based on the type of product an inquiry was submitted for, their geographic location, or other criteria relevant to your products or services.

For intent data with digital footprints (like IP addresses), tools like Google Analytics can offer insights into their online behavior, interests, and potential readiness to buy.

Regularly A/B tests different approaches in your email and direct mail campaigns to see what resonates best with your prospect’s demographic.

Best practices for contacting prospects in various industries

Successfully working with aged intent data involves a nuanced approach, emphasizing personalization, a multi-channel strategy, timing flexibility, and a patient, nurturing demeanor.

Let’s delve deeper with specific examples across different industries.

Personalization

Auto insurance

• Example: For a lead indicating interest in luxury vehicle insurance, personalize your message by discussing coverage options tailored for high-value vehicles.
• Key data points: Vehicle make, model, year.

Home improvement

• Example: If the lead shows interest in kitchen remodeling, your communication should focus on the benefits of your services specific to kitchen upgrades.
• Key data points: Project type (e.g., kitchen remodel).

Multi-Channel Approach

Mortgage

• Example: Begin with a direct mail piece offering insights on current mortgage trends, followed by a personalized email about your specific mortgage services.
• Key data points: Desired loan type, credit rating.

Life insurance

• Example: Send an initial email discussing the importance of life insurance, followed by a phone call to discuss policy options suitable for their age and family situation.
• Key data points: Age, coverage amount, policy type.

Timing

Solar installation

• Example: If a lead was generated in summer, reach out in early autumn to discuss how they can benefit from solar energy before the next high-usage season.
• Key data points: Lead age, geographic location.

Health insurance

• Example: Contact leads shortly before the annual open enrollment period, providing them with timely information on policy updates or new offerings.
• Key data points: Age, location.

Persistence and Patience

Medicare supplement

• Example: Regularly check in with leads, especially around key decision-making periods like turning 65, and offering information on Medicare supplement options.
• Key data points: Age, lead age.

Homeowners insurance

• Example: For leads that didn’t convert initially, periodically reach out with information on new policy features or discounts, especially if their geographic area has experienced recent environmental changes.
• Key data points: Property details, geographic location.

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