How to Build Stronger Customer Profiles with AI

min read
How to Build Stronger Customer Profiles with AI

According to Forbes, 80% of enterprise companies are investing in artificial intelligence (AI) solutions today. AI is a machine’s ability to imitate intelligent human behavior by perceiving a set of inputs and processing that information in order to reach a desired outcome.

In the martech space, companies are utilizing AI to build customer profiles, resulting in more precise ad targeting as well as unprecedented customization. Below, we’ll cover the basics of customer profile creation, as well as some ways AI can help build them.

What Is a Customer Profile?

A customer profile is a detailed description of a company's ideal customer, based on demographics, psychographics, and behavioral data. Customer profiling involves gathering data from various sources such as transaction history, website activity, social media interactions, and survey responses to create a comprehensive view of a customer. Remember, with great power comes great responsibility, so it's important to act ethically with customer data. You also don’t want to put them off by revealing too much of what you know about them! 

The Benefits of Customer Profiling

  • Increased accuracy: Creating customer profiles allows a company to make sure the initiatives they launch are being targeted at the right people. It reduces wasted ad spend, and ensures you're reaching qualified prospects and customers. This accuracy compounds when you use artificial intelligence. AI can identify patterns and trends that we as humans would never find, and it can use them to improve ad targeting and increase conversions.
  • Personalization: Customer profiling helps businesses understand their current and potential customers, so they can tailor their marketing messaging to meet their needs. If you're using AI, you can easily scale personalization across thousands of customers, improving the customer experience and building brand loyalty in the process.
  • Targeting: By understanding customer behavior and preferences, businesses can better target their marketing efforts to reach the right audience at the right time, leading to more effective marketing campaigns and higher conversion rates.
  • More accurate predictions: By analyzing customer data, you can identify trends and patterns that can help you predict future behavior. This can include predicting which products or services are most likely to be popular, which customers are most likely to make a purchase, and which marketing campaigns are most likely to be successful.

What Data Do You Need to Create a Customer Profile?

The following are all the different types of information your business would want to collect in order to create a thorough customer profile: 

  • Age
  • Gender
  • Income
  • Location
  • Buying habits
  • Interests
  • Products/Services Used
  • Preferences

All of this is especially vital to your marketing, sales, and support teams to better understand your potential customers and increase the odds of keeping the ones you already have. 

Enrich Your Customer Profiles With Conversation Intelligence Insights

Capturing first-party data from the thousands of phone calls your organization receives every day can seem like a daunting task. Your team doesn’t have the capacity to listen to all those calls, let alone identify the common trends that are occurring in conversation.

That’s where conversation intelligence comes in — with Invoca, you can let AI do the listening for you. Invoca's platform can automatically transcribe and analyze phone conversations to identify key topics and insights. This includes information such as the products or services discussed, the customer's intent, and if they faced any barriers to purchase. Our solution automatically identifies trends at scale, so you can understand your callers’ needs. From there, you can drill down into each topic to get more detail and see specific instances where it was mentioned.

The next logical question is, “How can I use these insights in my existing workflows?” Don’t worry, Invoca turns conversation signals into structured data and pushes them into the martech platforms you use every day, including Salesforce, Google Ads, Adobe Experience Cloud, Search Ads 360, and many more.

Easily train custom AI models to unlock insights from phone conversations

Training an AI model may sound like a daunting task to take on, but not with Invoca! Our Signal AI Studio offers no-code UI that speeds you through the process of training a custom AI model. You simply tell it the insight you want to measure, and Signal AI Studio shows you transcribed examples from your calls that either fit or don’t fit that insight. It learns with every response, creating a new AI model in no time.

Custom AI models from Signal AI Studio can accurately detect virtually any insight or topic from conversations, including:

  • Caller Intent: Detect if the caller is a sales lead, current customer, new or existing patient, or job seeker
  • Caller Interest: Determine the specific product or service the caller is interested in, if they are looking for help with an online order, or need support with a product issue
  • Conversation Outcome: Detect if the caller made a purchase, booked an appointment, received a quote, or canceled a service
  • Call Events: Discover important events like if the caller asked to be called back or to speak to a supervisor
  • Voice of the Customer (VoC) Insights: Detect if the caller asked about pricing or a specific product feature, discussed a competitor, or lodged a complaint

You can use this tool to capture deep insights on your customers to build stronger profiles.

Learn more about how Signal AI Studio works here

Identify phone conversation themes with Topic Explorer

It can be difficult to sift through your mountains of voice-of-the-customer data to identify trends. That's where Invoca Topic Explorer comes in. This tool allows you to visualize the themes and related topics being discussed across thousands of your calls at once to surface unexpected insights. You can specify the topics or categories you want Topic Explorer to visualize, view GPT-powered descriptions of topic summaries, and review transcribed examples from actual calls. Topic Explorer is a unique and powerful way to surface new and actionable insights on customers, call experiences, agent performance, and digital marketing campaigns you weren’t using Signal AI Studio to actively analyze.

These insights can give you a deeper understanding of what your customers are interested in and how to target them.

Track customer sentiment as it changes throughout conversations

Another key piece of a customer profile is sentiment. This can be critical for future remarketing efforts, as it's clearly more effective to target satisfied customers with upsell campaigns versus frustrated ones.

When it comes to analyzing customer sentiment, most speech analytics tools only track the overall sentiment of a call, flagging a call as either “positive” or “negative.” But many marketers say that call-level sentiment isn’t particularly useful or actionable. We believe that the real opportunity lies in the ability to track caller and agent sentiment separately, and monitor how it changes throughout a conversation. So we built our sentiment analysis solution to not only track the individual sentiment of the caller and the agent separately, but also how it changes throughout the call. This makes it possible to monitor how effective agents are at turning a callers’ negative sentiment into positive, as an example.

3 Ways Marketers Use AI to Build Better Customer Profiles

Now that we’ve covered the basics, let’s dive into how AI can help you understand your audience on a deeper level. This, in turn, can improve your customer profiling strategy.

1. Teleflora Uses AI to Deliver Personalized Product Recommendations

This article in Direct Marketing News details how Teleflora, a leading floral arrangements vendor with more than 15,000 member florists in the US and Canada, uses AI to build customer profiles and provide a personalized touch.

When Tommy Lamb, Teleflora’s new director of CRM and loyalty, joined the company, he immediately realized their marketing strategy was underdeveloped. Since customers typically only used Teleflora at spread-out points in the year, the company needed strong remarketing and customer service to build brand loyalty. But rather than providing personalized offers, they only utilized a few generic holiday email campaigns.

A retail marketing platform called Bluecore gave Lamb and Teleflora the AI capabilities they needed in order to execute a three-pronged personalization plan:

  1. Teleflora first created more comprehensive customer profiles by combining their product data with their individual customer data.  
  2. Next, Teleflora paired Bluecore’s machine learning capabilities with these comprehensive profiles to anticipate the future purchases of various audience segments.
  3. Finally, Teleflora integrated advanced analytics, allowing them to identify best-selling items and other purchasing trends.

This AI strategy allows Teleflora to accurately anticipate customer needs. They can target customers who are ready to buy and make personalized recommendations, driving customer loyalty and ROI. Then, their analytics solution allows them to view the results and promote high- or low-performing products accordingly.

2. BMW Uses AI to Personalize Ad Spend and Lower its Cost-Per-Acquisition

In order to execute a recent campaign, BMW Mini worked to connect and organize its data into an actionable format. Its goal: to target adults searching for a premium vehicle who had shown interest in the BMW brand.

By partnering with ad agency Universal McCann, BMW was able to leverage its first-party data—which included people who had visited the BMW website or were already in their CRM system. BMW used this data to enhance its existing search strategy, ensuring its ads delivered relevant messaging to interested car shoppers.

BMW then utilized an AI solution to optimize the efficiency of its targeted ads. Over time, this solution optimized BMW’s ad targeting so the messaging would reach the right person, based on factors like time of day, previous searches, and BMW website visits. As a result of this strategy, BMW Mini’s conversions tripled and their cost per acquisition declined by 75%.

3. Comfort Keepers Uses AI to Target Caller-Ready Audiences

Comfort Keepers, one of the nation’s leading providers of in-home care for seniors, uses AI-powered conversation intelligence to understand what happens on calls to each of their 450+ franchisee locations. Since phone calls make up 70% of their marketing conversions, they analyze the calls to determine who each caller is, if they are a quality sales lead (vs. a non-sales call), and if they converted to an appointment or customer.

By using this conversation intelligence data, Comfort Keepers is able to fully gauge the success of their marketing efforts and prove it to each of their franchisees. Not only are they able to identify the quantity of the calls their campaigns drove, but they can also understand the quality. For example, rather than simply saying “we drove 2,000 calls this week,” they’re able to identify how many of those calls are potential new customers versus current customers. This gives them a full picture of the ROI from each of their campaigns to each location.

As a next step, once Comfort Keepers understands who converted on their calls and who did not, they can use that same conversation analytics data from AI to retarget their prospects with search, social, and display ads and use good callers in their lookalike campaigns.  

Additional Reading

Want to learn more about how Invoca can help you improve customer profiles and ad targeting? Check out these resources:

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