What Does Behavioral Data Do for Marketers?
Behavioral customer acquisition data tells a marketer the likes of prospective customers – what brands they buy, where they go, and how they spend their money. Behavioral data is a great predictor of a match between a business and potential customers.
Marketing pixels are essential to digital marketing, as they help businesses track user behavior on their website or app. By gathering data from the “behavioral” category, marketers can gain valuable insights into user actions and preferences, which can be used to inform marketing strategies and improve overall business performance. This category isn’t as complicated as it sounds.
Really, “behavioral” equates to “lifestyle” – this category tracks what a site visitor likes – their favorite brands, where they go, and what they buy – as you can tell, this category is essential for any brand engaging in selling products or services online; with this information, a match can be determined.
So, what exactly can marketers do with the data gathered from the behavioral category? First, let’s take a closer look.
Personalized Promotions
One of the most significant benefits of tracking user behavior is that it allows businesses to personalize their marketing messages based on individual user preferences. For example, if a user has shown a preference for a particular product or service, marketers can use that information to tailor their marketing messages and promotions to that user’s interests.
Optimized Website (or App) Design
User behavior data can also help businesses optimize their website or app design. By analyzing user clicks, page views, and other actions, businesses can identify areas of their website or app that may be causing frustration or confusion and make necessary improvements.
Improved User Experience
Users’ behavioral data can also be used to improve the overall user experience. By analyzing user behavior, businesses can identify common pain points or areas where users are getting stuck and take steps to address those issues. For example, older users or users on cell phones will likely be looking for a streamlined experience.
Retargeting
Another critical use of user behavior data is retargeting. Retargeting involves showing ads to users who have previously interacted with a business’s website or app. By tracking user behavior, businesses can identify users who have shown an interest in their products or services and retarget those users with relevant ads. For example, if specific audience segments were visiting an e-commerce site when a specific sale for candles was on, the site could potentially draw the same traffic back in by running a separate candle promotion.
Cross-selling and Upselling
User behavior data can also be used to identify cross-selling and upselling opportunities. For example, if a user has shown an interest in a particular product or service, businesses can use that information to promote complementary or upgraded products or services – think of a site visitor who has recently bought plane tickets – an insurance company could be interested in selling travel insurance, and tour companies could be interested in showing the user relevant excursion packages.
Refine Customer Segmentation
The lifestyle data collected by marketing pixels can be used to refine customer segmentation. This allows companies to identify specific groups of customers with similar interests and needs and create targeted marketing campaigns for each group. It can also allow a business to understand correlations between customer segments – for example, if most site visitors who turn into customers are into pets, travel, and cars, there may be correlations with users who enjoy pet products, plane tickets, and motor oil – this can give keen marketers insight into potential future areas of expansion. Behavioral customer acquisition data is a proven driver of sales success in many business niches.
Improve Product Offerings
Lifestyle information gained from marketing pixels can also be used to improve product offerings. By understanding what products or services their customers are interested in, companies can develop new offerings or modify existing ones to meet customer needs and preferences better. For example, if a manufacturer of iron nails suddenly sees an uptick in traffic of users interested in antiques and building furniture, it would be a good idea for them to expand their product line of finishing nails and screws used in woodworking.
KPIs (Key Performance Indicators) to Track
When it comes to analyzing a site visitor’s lifestyle through behavioral customer acquisition data, there are several key performance indicators (KPIs) that a marketer could track. Here are some examples:
Conversion rate:
If a visitor turns into a customer by making a purchase or an interested customer by filling out a form, they become part of your site’s overall conversion rate. Savvy marketers can use the data behind conversion rates to judge the effectiveness of campaigns from the bottom up, looking to see if conversions are being made and at what stages.
Click-through rate (CTR):
This KPI measures the percentage of site visitors who click on a particular link or ad. By analyzing CTRs for different lifestyle interests, marketers can pick out what offerings are of the biggest interest to site visitors – for example, visitors interested in travel are likely to explore pages to do with vacation or plane ticket deals and probably will ignore anything to do with staycations.
Time on site:
This KPI measures how long a site visitor spends on a particular page or site. By monitoring this KPI, marketers can determine just how interested a site user is – if a user spends more time on a page, it is safe to assume they are reading, considering further action, or going to come back to the page for more interaction later.
Bounce rate:
This KPI measures the percentage of site visitors who leave a site after viewing only one page. For example, it could be expected that users interested in vegetarianism would quickly bounce from a butcher shop’s website. It seems like a pretty innocent KPI, but it is an important one. In the butcher shop example, it could be alarming for the site to suddenly start receiving a lot of visits from users interested in being vegetarian – it could indicate mismatched marketing campaigns or a traffic flow problem from outside links.
Average order value (AOV):
This KPI measures the average amount of money a customer spends on a single order. By tracking AOVs for different interest categories, a marketer can identify which segments are most valuable and adjust their pricing and promotion strategy accordingly, for example, raising or decreasing prices based on spending and order size.
Customer lifetime value (CLV):
This KPI measures the total value a customer brings to a business over the course of their relationship. Most marketers spend a lot of time determining which customer group will bring in the most profit for the smallest spend, making behavioral data a key driver of potential profit.
In conclusion, the data gathered from the “behavioral” category can provide businesses with valuable insights into user behavior and preferences. By leveraging this data, businesses can improve their marketing strategies, optimize their website or app design, improve the user experience, and identify new opportunities for growth. Ultimately, businesses that can use user behavior data effectively will be better positioned to achieve their marketing and business goals by knowing their customers before their customers know them.
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