As a lead-gen or B2B advertiser, you should definitely be utilizing backend data from your CRM in some fashion. Whether it is identifying what paid channels drive the majority of leads or matching qualified leads and opportunities with cost, knowing which parts of your paid efforts are actually driving quality leads is of critical importance. Plus, it provides an immediate advantage over other advertisers who are not utilizing this data. Below are analyses we recommend running on a regular basis in order to ensure you’re making decisions based on backend data.

1. Ad Group Level Analyses

Pair up platform-level cost data with CRM lead quality data. This is likely the most basic backend analysis but arguably the most important. It enables you to make sure that your top-converting ad groups and campaigns on the frontend are also driving qualified leads on the backend. For example, it’s better to have an ad group with three conversions, two of which became qualified leads than an ad group with 50 conversions but zero were qualified.

If there is enough data, we’d recommend running this analysis weekly. Otherwise, feel free to run it on the campaign-level. This critical analysis helps ensure that any recent initiatives in the account are driving toward improving both lead quality and quantity. Performing this analysis weekly allows the account to accrue enough data to determine actionable results. But it isn’t too sporadic to where a seemingly minor change in the account, like changing ad copy, that is having a negative impact on lead quality goes unnoticed.

2. Landing Page Analyses

When assessing landing pages, the LP with the highest conversion rate and lowest CPA is typically declared the winner. However, what if landing page A has a 10% conversion rate compared to a conversion rate of 7% on landing page B, but landing page B has a lead-to-qualified-lead rate of 60% compared to landing page A’s 5%. In this case, landing page A is certainly getting more form-fills through the door, but if the business is focused on closing deals, landing page B is the clear choice. Make sure the backend data corroborates the final choice on which page to continue using.

3. Time of Day or Day of Week Analyses

If your CRM has a field with a lead form fill timestamp, this is a very interesting and insightful analysis to run. For most advertisers, not all times of day or days of week drive the same quality of leads. Maybe Saturdays or the hours from midnight to 4 am have a lower CPA and would be bid up, but are these leads of the same quality as a lead that comes in Tuesday at 2pm? Usually not.

In order to execute this analysis, take the timestamp of when the form was filled out and separate the day and the hour the lead came in. By using the formula =TEXT([cell with date],”dddd”), you can easily turn any date into its respective day of the week, and match up cost by day to lead quality in your CRM to see which dates have the most qualified leads and the lowest cost per qualified lead. For time of day, take the hour of day (0-23) that the lead came in, and match that up with the hour of day report in Google Ads or Bing and evaluate similarly to the day of week analysis. Make sure that the timezone of the channel and the CRM are similar; if not, just use simple match to make sure the exact same times are being compared.

4. Job Title or Industry Analyses

Fields like job title, industry, number of employees, etc. are often found in form fills depending on the business type and can provide valuable insight via backend analyses.  For the sake of this example, let’s use industry, but the same can be said of any additional self-provided, qualitative information in the form.

If your CRM can pull a report of ad group or campaign, as well as industry by lead, then you can match up this data with ad group or campaign level cost data from channel platforms. With this, you can see cost per lead, lead to qualified lead rate, close rates, and cost per close by industry. Also, by matching it up at the ad group or campaign level, you can see which areas of the account are driving leads in which industries.

Say your company has a goal to increase construction leads by 10% in the coming month. More often than not, people don’t self-identify their industry when performing a search. Say someone is looking for review management services, they will likely type in just that instead of “review management services for construction industry.” By matching up ad group level data with platform data, you may find that a seemingly innocuous non-brand ad group, or even just your brand name in exact match, drives an above-average amount of construction leads. Then, the only thing left is to increase investment in those specific areas and watch the construction leads grow.

5. Lead Owner Effectiveness

You can also evaluate lead owner (salesperson) effectiveness by campaign type, time of day, industry, or day of week. Most CRMs show the sales person in charge of working and nurturing a lead, and this is data that can be used just like any other. By analyzing all of the above metrics, you may find that Leonard is 50% better at closing deals from branded campaigns than anyone else, but that he is particularly weak at closing deals from individuals in the education sector. If that is the case, whenever a lead comes in from a brand campaign, it might make sense to break the round-robin sales order to give the most effective individual in that category the best chance at closing the deal.

To Sum it All Up

Tying back backend data to channel data is essential to improving lead quality. These analyses can help you gain valuable insight into what levers you can pull to improve quality. If you’d like to learn more, contact our B2B and lead gen experts at Metric Theory.