Use the Benchmarks report to understand how long it takes for your Cold Traffic (new visitors and new leads) to become customers and how effectively your marketing strategies are converting them.
Benchmarks report used to be called Predictive Behaviors. A few changes have been made to this report, along with renaming it.
Why would I use this report?
This report provides key insights into how long it takes for new visitors to become customers and how effectively your marketing strategies are converting them. By analyzing benchmarks such as Conversion Lag Time and Conversion Rate at Lag Time, you can:
- Set realistic expectations for the impact of new campaigns.
- Identify opportunities to improve your customer acquisition process.
- Align marketing strategies with your audience’s behavior.
Use this report to make data-driven decisions and optimize your marketing efforts for faster and more efficient growth.
Benchmark Metrics
Benchmark metrics are broken into two categories. New Visits and New Leads. See benchmarks metrics around how long it takes for New Visits to become customers and if you have a lead gen strategy, how long it takes New Leads to convert to customers.
New Visits Conversion Lag Time
This metric calculates the average time it takes for at least half of your new customers to move from their very first click on your website to making their first purchase. It provides a clear picture of how quickly new visitors convert into paying customers.
How to Use It:
Understanding the lag time helps you set realistic expectations for how long it takes to see results from top-of-funnel marketing efforts. For example:
- If the lag time is long, you may want to focus on nurturing campaigns (retargeting ads, etc.) to keep prospects engaged during the decision-making period.
- If the lag time is short, it indicates that your marketing and sales funnel is efficiently converting visitors, which may allow you to allocate resources toward acquiring more traffic.
Avg. New Visits Conversion Lag Time Conversion Rate
This metric shows the conversion rate of new visitors to new customers at the point when half of your customers have made their first purchase. It reflects how effectively your website and marketing strategy are turning new traffic into paying customers.
How to Use It:
This metric can help you evaluate the effectiveness of your acquisition strategies and optimize conversion rates. For example:
- If the rate is lower than expected, consider testing landing page optimizations, streamlining the purchase process, or offering clearer value propositions in your ads and content.
- If the rate is high, this indicates strong performance, which can be used as a benchmark when testing new strategies or expanding your campaigns.
New Lead Conversion Lag Time
This metric calculates the average time it takes for at least half of your new customers to go from becoming a new lead (opting in) to making their first purchase. It gives insight into how efficiently you’re converting engaged prospects into paying customers.
How to Use It:
Understanding the lag time from lead to purchase helps you assess the performance of your mid-funnel and bottom-funnel strategies. For example:
- If the lag time is long, consider nurturing leads with targeted email campaigns, personalized offers, or retargeting ads to guide them toward a purchase.
- If the lag time is short, it indicates a well-optimized lead-to-customer journey, which may allow you to invest more in lead generation strategies.
Avg. New Lead Conversion Lag Time Conversion Rate
This metric shows the conversion rate of new leads to customers at the point when half of your customers have made their first purchase. It evaluates the effectiveness of your lead conversion process.
How to Use It:
This metric highlights the quality of your leads and the effectiveness of your sales funnel. For example:
- If the conversion rate is lower than expected, you might need to refine your messaging, improve your sales follow-ups, or remove friction from the purchase process.
- If the conversion rate is high, it reflects strong lead engagement and conversion performance, providing a benchmark for future campaigns.
Strategic Insights for New Visit Benchmark Metrics
These metrics together offer critical insights into the health and effectiveness of your top-of-funnel marketing efforts:
- They help you gauge how long you need to wait to assess new campaigns for effectiveness.
- They provide clarity on where prospects might be dropping off in the customer journey, allowing for targeted improvements.
- They assist in aligning marketing timelines and budgets with the actual behavior of your audience.
By regularly monitoring these metrics, you can make informed decisions about how to allocate resources, set performance expectations, and drive more efficient customer acquisition.
Strategic Insights for New Lead Benchmark Metrics
These metrics help you:
- Determine how long you need to wait before evaluating the success of your lead acquisition efforts.
- Identify bottlenecks or opportunities for improvement in your sales funnel.
- Align your marketing and sales strategies to better meet the needs of your leads.
By regularly analyzing these metrics, you can optimize your mid-to-bottom funnel strategies to convert leads into customers more effectively.
FAQ:
- What timezone is the Sales by Hour in the Benchmarks report?
The timezone for Sales by Hour is based on the purchase timezone of the customer. - Why does the Revenue/Order Count not match with my other reports?
This report is for analyzing leads and customer buying cycles. As such, the date range provided in this report is applied to the lead creation date, not specific order dates.
It is possible that the lead creation date, of the customers who purchased during the selected date range, is earlier than the date range selected. As a result, they are not included in the customer/lead analysis of a report for the past 365 days.
This report should not be expected to match the numbers on your attribution reports because it is a different report performing different types of analysis on your data.