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How to identify your customer's purchase intent using AI

Ashmita Tamta

July 22, 20224 min read

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Businesses spend a significant portion of their money on marketing. A company, on average, spends 11% of its total budget on marketing, yet nearly 80% of marketers aren't satisfied with their conversion rates.

The graph shows the total percentage of the marketing budget allocated by various industries, with consumer packaged goods and services in the lead.

Why do marketers struggle with low conversion rates? 

There could be many reasons for the drop in conversion rates, such as not truly understanding your customers, not identifying and addressing their exact pain points, not reaching out to them at the right time on their preferred channel, etc.  

One of the key challenges companies face when improving conversion rates is identifying customer purchase intent, i.e., your customer’s willingness to pay for your product/service. Customers could stay on your website for long, even browse multiple pages, yet not make a purchase.

However, the solution lies in understanding your customer’s purchase behavior and reaching out at the right time and on the right channel. 

How complex can a customer’s purchase behavior be?

Since 2021, the average online conversion rates have remained significantly low. Less than 4% of desktop users make purchases, and the conversion rate of tablet and smartphone users is even lower (3% and 1%, respectively). However, offline retail stores enjoy conversion rates between 20% and 40%. Why do we have such a disparity in conversion numbers between online and physical stores?

The chart shows why customers prefer shopping offline to online.

When shopping, trust plays a crucial role. Customers want to know if they can trust the company/brand before investing their hard-earned money into their product. 

According to a Ripen study, about 30% of customers expressed that they want to see or feel a product in person before purchase. Besides, customers experience instant gratification while shopping in physical stores. 

Here are a few other factors that make predicting customer purchase behavior difficult:

  • A Harvard study identified that when consumers have to make high-risk decisions, they rely more on intuition than deliberation. Online shoppers often ignore logical factors (return policy, data privacy policies, etc.) and get influenced by design and visual cues, such as website color and font, to make purchase decisions. 

  • Micro-moments: a typical day in a consumer’s life is filled with hundreds of moments such as texting friends, replying to emails, checking time and weather, replying to DMs on Instagram, etc. But there are these other moments — I want-to-learn, I want-to-buy, I want-to-go, and I want-to-do — that really matter. These moments are called micro-moments. These are intent-driven moments of decision-making and preference-shaping that occur throughout the entire customer journey. For instance, 69% of leisure travelers who are smartphone users search for travel ideas during spare moments, like when they're standing in line or waiting for the subway. Nearly half of those travelers go on to book their choices through an entirely separate channel. 

These are but a few factors that shape customers’ buying behavior. The complex nature of customer behavior makes it difficult for you to develop a holistic and integrated marketing plan. AI is one of the tried-and-tested techniques modern marketers are using to bring order into chaos while identifying customer’s purchase intent.

Identify your customer's purchase intent using AI

Detecting purchase intent without the help of technology can be daunting. You need to leverage advanced tools to obtain an in-depth understanding of your customers. That’s where AI can make a big difference.

Modern AI tools can listen to your customers, recognize the sentiment associated with their messages, and gauge their needs and pain points. They also identify your customers' purchase intent by analyzing messages and categorizing them based on types — leads, complaints, inquiries, and compliments. After categorization, messages are prioritized, and the relevant teams alerted. 

Let’s look at the four types of message categories mentioned above.

1. Leads: includes messages with purchase intent or that show customers’ interest in procuring a particular product, e.g.,

  • I want to buy this.

  • I'm interested in getting this phone.

  • I would love to buy these shoes.

2. Inquiry: Includes queries regarding a brand, its products and services, e.g.,

  • Where can I purchase this? 

  • When is this launching in the UK? 

  • Are these refundable?

3. Complaints: includes issues regarding products or services, and generic complaints regarding customer service, e.g.,

  • Constant blue screen on my new laptop.

  • Your customer care team isn't helpful at all.

  • My shoe is torn after one week of use.

4. Compliment: includes messages showing brand love or any positive comments regarding their product/services, messages showing customer satisfaction, and generic appreciation of the brand/product/service by customers/users, e.g.,

  • Loved these new shades.

  • The best phone ever.

  • Dan from your customer services department was really helpful, he did a thorough job!

  • Did a really thorough job!

This categorization empowers customer-facing teams (including marketing, sales, and customer service) to prioritize customer messages — and address messages that require an immediate response. For example, when a customer posts a product-related query, AI tags it as an “inquiry,' thus alerting customer-facing teams to attend to such messages instantly. 

Conclusion 

Understanding your customers’ buying intent is the key to dissecting their online purchasing behavior and thus increasing conversion rates. With the help of AI, you can cut through the noise and identify buying signals from customer conversations and messages that are relevant to your business.

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