How Real-Time Analytics can Help You Recognize Hesitant Buyers

As an eCommerce professional you are painfully aware of the fact that only a fraction of all your web store visitors will actually buy. Still, getting visitors to engage and follow a path to conversion is at the core of driving value to your web store.

Ecommerce manager working on laptop to convert website visitors

While most companies track web store conversion, the diversity of possible factors affecting conversion, and its evil twin, abandonment, makes it a difficult process to manage.

A variety of online customer service tools exist today, but knowing which customers to approach is still difficult. Also, providing live customer service is valuable in many cases, but not in all.

As customer service is also costly, the effort should be directed in a way providing the best value.

So, how can you target your sales efforts better?

By utilising machine learning and real-time analytics!

This may sound like something out of a science fiction movie but the process is actually quite similar to human comprehension.

Machine learning tools use data to detect patterns from extremely large data sets and then adjust actions accordingly. By doing this they also constantly learn more and improve their own understanding.

How to identify and find your best customers?

You can use machine learning to find underlying patterns that predict purchase behaviour and estimate the buying probability of each visitor in real-time. Your store data contains shopping patterns so the process begins by collecting usage data in your online store.

What you are looking for is to understand what separates customers that purchase from the ones that don’t, and also, what early actions predict purchasing. Based on the data collected, you can divide customers into three groups based on their buying probability:

  1. Safe buyers: customers that are likely to make a purchase regardless of any contacts or sales activities,

  2. Hesitant buyers: customers that may need a gentle nudge and can be guided toward purchasing, and

  3. Window shoppers: customers that are unlikely to make a purchase no matter what you do.

The most interesting group for increasing sales is the second one, the hesitant buyers. This group consists of people that can be influenced. They might want more information or need assistance, or might simply be unfamiliar with online shopping to the extent that exchanging a few words with a real person gives them the confidence to proceed.

Using real-time analytics to target sales efforts

With machine learning tools you can predict the purchasing probability of each and every web store visitor. The estimates are based on visitor behaviour on your site and on every site in your extensive network.

You can use this information to categorise visitors in real-time and apply the action visitors need in order to make a decision. As long as customers stay on the right path, all is good and well.

But once customers fall off this path, you are alerted and the first contact or action is automatically initiated.

There are several options you can choose from, including chat conversations, on-site banners, pop-ups and site redirect. But these are only examples, you can create any kind of customised action you want.

The prediction model is continuously updated to reflect new changes to the online store and changes in customer behaviour.

By identifying high potential customers, you can target your actions better and approach the right person at the right time with the right message. - Imagine what this could mean for your online sales!

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