Practical ways B2C marketers can use data to personalize websites

B2C marketers have a wealth of readily available information that is valuable for personalizing websites. This video outlines the 3 primary types of data used in B2C website personalization and provides some common use cases for each.

B2C marketers have three great sources of data to help personalize the consumer experience on their website, contextual data, demographic data, and behavioral data. Contextual data is great for B2C marketers because it’s readily available, and it really helps you meet your consumer where they are. You can look at location and speak to the local sports team, local landmarks, or local source of city pride. You can speak to them on the go on a mobile device, or in a different mindset where they’re at home, or at work on a desktop. For example, if you’re offering is related to food, knowing the time of day can be great. You can speak to breakfast, lunch or dinner. If you know the date, now you’ve got a whole treasure trove of things that happen every single year that you can speak to, whether it’s the holidays, back to school, or mothers day. There’s an endless supply of useful contextual data.

What’s really interesting about predictive personalization is that it can discover pockets of opportunity in performance that we as marketers would never have discovered. To find out that a particular message performs well in New York City on Tuesday afternoons, I don’t know a marketer that would take the time to do that, but a machine can. And so there’s great opportunity sitting there.

Demographic data can help you speak to your consumer where they are in life, to know their age, to know their gender, to know their socio-economic status. Maybe you can know what kind of car they drive, or how many kids they have. Think about how you would speak differently to a teenage girl versus an older man with two teenage girls. You talk to them totally differently, and demographic data can help you do that.

Behavioral data can also be a good source of information. You can look at parts of your site that somebody might have visited before. That might tell you about what they’re interested in. It might tell you something about who they are. Or if they’ve purchased before, now you have great data to offer them a complimentary product. Ask them about their experience with your products. Suggest to them that they post a review about that product so you get more coverage. You can even tailor future offers based on that behavioral data.

Our hope is that you can use contextual, demographic, and behavioral data to better personalize your consumer website experience and make more revenue today.

Video: 3 primary types of data used in website personalization

Audience data allows us to create more personalized experiences for our audiences which leads to higher engagement, loyalty, and conversion. This video outlines the 3 primary types of data used in website personalization and provides some common use cases for each.

There are three kinds of data you can use to personalize your website: contextual, demographic and firmographic, and behavioral.

Contextual data tells you everything around the visitor that’s coming to your site: where are they, what time of day/day of week, what kind of device they’re on. Where were they before they visited your website?

Demographic and firmographic data is very different for B2C and B2B marketers. Demographic data is things like: age, gender, income, what kind of car somebody owns. Firmographic data, on the other hand, is about a company. What’s the company? What industry are they in? How much revenue do they have? How many employees do they have? Each of these help you better personalize your site.

The last category, behavioral, tells you about what somebody’s been doing before, which can help you better personalize later. For example, they might have visited a certain section of your site multiple times, telling you about something they’re interested in or what segment of your customer base they are.

They may have also purchased things before, which might make it easy for you to suggest to them cross-sell or upsell on complimentary products.

Another dimension to consider as you think about these three types of data is whether you have them yourself, which is often called first-party data, or you’re gonna go buy them from somebody else, often called third-party data.

Our hope is that using contextual, demographic and firmographic, and behavioral data help you better personalize your site and drive more revenue today.

Webinar: Practical uses of AI in marketing

Artificial Intelligence (AI) is more than just a buzzword. Marketers are applying AI in a variety of ways to help deliver better and more relevant experiences for users. In turn, it’s helping them drive more revenue, faster and with less work.  This 45 minute webinar is designed for marketers who are new to AI. Topics covered include:

  • The major types of AI and how they relate to each other
  • The types of AI best suited to different marketing problems
  • How you should think differently to get the most out of AI

This video is an overview for a four-part course taught by Intellimize CEO, Guy Yalif on the practical uses of AI in marketing presented by the CXL Institute.

5 best practices for using data for website personalization

Data is the fuel that powers personalization.

Here are 5 best practices for using data to personalize your website to drive more conversions.

Data is the fuel that powers personalization. Yet most marketers have a lot of questions when it comes to how to best use data in their personalization efforts. We’ve been fortunate enough to work with leading marketers across a variety of industries and in this post I’ll share some best practices for using data for website personalization.

Tip One: Make Full Use of Data You Can See on Every Impression

A common question I hear is, “What data do I need to personalize my site?” Most marketers are surprised when I answer, “You don’t need to deliver any data to get started.” It’s easiest to start with data that is commonly available on every impression. This includes data such as device type, time of day, day of week, IP-based geography, and more.

To most marketers’ surprise, high performing segments are often there to be found in this routinely available data. When you’re considering personalization, make sure the tools you are using can efficiently make use of this data.

For example, one of our clients, Chime, reaped significant gains by personalizing based on this data. They saw a 23% lift above their winner-take-all variation by personalizing for mobile, a 30% improvement during evening hours, and significant lift based on geography.

Tip Two: Use Paid Media Data to Enhance Personalization

If you are using paid media campaigns to drive traffic to your site, you can use UTM parameters to pass the targeting information you used in advertising to further enhance personalization. Data such as the creative content of your campaigns, keywords, and the referring site can be quite useful in creating a seamless experience across your paid media and your web site. One of our clients recently saw a 43% improvement in conversions as a result of personalizing headlines for visitors who came from specific campaigns they ran on YouTube.

Tip Three: Use Your First Party Customer Data to Personalize on Lifecycle

Your most valuable customers expect to be treated differently than a first time visitor to your site. Recognizing loyal and valuable customers with special, limited offers and unique offerings reinforces their special relationship and can increase conversions (and loyalty). For first-time customers, providing incentives for trial make sense and testing several versions makes sense. Of course, first party data can be used beyond the lifecycle as well.  You can use your customer data to drive specific behavior, for example, one of our clients recently drove a 400% increase in app downloads by personalizing for existing customers.

Tip Four: Use Third Party Data for More Relevancy

Third party data can be particularly valuable when used to create relevancy for visitors to your site. There are many data providers offering a wide variety of consumer and B2B data. The most commonly used categories include:

  • Demographic data such as age and gender can be particularly useful when personalizing for consumer applications. For example, banks might use age data to personalize promotions for investment products based on the lifestage of customers. Retailers might use gender and age data to highlight popular items.
  • Behavioral data such as past purchases, hobbies, or interests can be useful in a variety of applications. For example, credit card companies may personalize their offerings based on frequent travelers or sports fans.
  • Finally there is a rich set of firmographic and technographic data B2B marketers can use for deeper personalization. One example is using reverse IP lookup to identify the company of visitors to personalize your offerings or deliver Account Based Marketing (ABM) driven experiences.

Tip Five: Use Predictive Personalization to Put it All Together

Given the rich set of data and number of potential segments based on the data above, using an AI based tool is particularly valuable at automating segment discovery and simultaneously learning which variations perform best for each segment. For example, it is routine for our customers to optimize millions of versions of a page across millions of segments with <5,000 page views per day. Predictive personalization can help you deliver performance you simply could not achieve through manual or rules-based personalization approaches.

Want to learn more about how data can help you drive more conversions?

We would be happy to show you some best practices and examples of how you can use data in your company to personalize your site to improve conversion rates. Simply click the “Request Demo” button on our site.

Video: How Perkville drove 13% improvement in loyalty program registrations

3 Questions Video Blog Series: Sunil Saha, Perkville

We had an opportunity to sit down recently with Sunil Saha, Chief Executive Officer at PerkvilleHe shared that using predictive personalization enabled them to drive a 13% lift in new loyalty program registrations. 

The team at Perkville initially focused on two flows: customer referrals and loyalty program registrations.  He noted that at first he was skeptical they would see results given their relatively small traffic and was excited to see a 13% lift in registrations and a 70% lift in customer referrals. His advice for other marketers is to use predictive personalization to test more ideas.  For example, he notes, personalizations related to device type and geography can help drive results. “There’s probably a lot you’re not testing today. There’s a lot of value you can get from using technology like this.”

Video: Key lessons for internet retailers on driving sales lift with predictive personalization

3 Questions Video Blog Series: Meera Bhatia, Stella & Dot

We sat down recently with Meera Bhatia, Chief Product Officer at Stella & DotMeera shared some lessons for internet retailers using predictive personalization.

Her team used predictive personalization to increase shopping cart conversions by 52% and engagement with product pages by 400%.  She notes that sometimes small changes can make a big difference and that it’s difficult to predict which ideas will work for various audiences.  Her advice for other internet retailers is to move beyond traditional A/B testing methods and consider predictive personalization to accelerate testing and drive revenue lift faster.

Video: Shane Steele: How Chime uses predictive personalization to drive new customer signups

3 Questions Video Blog Series: Shane Steele, Chime

We sat down recently with Shane Steele, VP Marketing at Chime to discuss using predictive personalization on financial services sites.  Shane shared how it helped her lean marketing team better focus their efforts on the ideas that mattered most.  She shared advice for other financial services marketers on predictive personalization and how it can help create different messages for different kinds of banking customers.