B2B Use Case: Qualifying Leads for Memory Chips

As a B2B use case, let’s say that you’re the Demand Generation manager for an enterprise product that your company, Pro Processors, sells: memory chips for servers. You don’t sell to consumers but to companies like Google, Facebook, and Amazon. A lot is at stake, since a typical deal could bring in tens of millions of dollars and take your salespeople three to six months to close.

Keep the CPA under $500

In terms of demand gen, the marketing manager has tasked you with generating qualified leads and to keep the cost per acquisition (CPA) under US$500, and herein lies the problem. You interpret cost per acquisition to mean cost per lead, but it would be much more business-focused to think in terms of cost per qualified lead.

In any case, your memory chips have just been reviewed very favorably in an industry report, so you decide to leverage the report as a multi-channel marketing campaign. The research institute that produced the report does not offer the report directly on its own website, but you have secured the right to post it for download on proprocessors.com.

You work with your design team to create a dedicated landing page as shown in Figure 15.1 that features the report, and you set up the following campaigns to drive traffic:

• AdWords search ads
• LinkedIn sponsored ads
• Twitter promoted tweets
• Facebook paid ads

CPA Based on Total Leads, Qualified and Unqualified

When you’re setting up and initially evaluating your campaigns, you do everything you’re supposed to. For starters, you make sure to add GA campaign parameters in Table 15.1 to the inbound campaign links (and, for AdWords, you’ve enabled auto-tagging). For example, the link from your LinkedIn campaign is formatted as:

Campaign Channel	utm_medium	utm_source	utm_campaign
AdWords	cpc	google	Memory Chip Report
LinkedIn	social-paid	linkedin	2016q2-chip-li
Twitter	social-paid	twitter	2016q2-chip-tw
Facebook	social-paid	facebook	2016q2-chip-fb

http://www.proprocessors.com/chip-report?utm_source=linkedin&utm_medium=social-paid&utm_campaign=2016q2-chip-li

You have also set up a goal for the lead submission. You specify /chip-report-thank-you as the destination of your Chip Report goal and /chip-report as the preceding funnel step.

You launch the campaigns. After they’ve been humming along for a month, you analyze the results first results. Because you have used campaign parameters, the GA Campaigns report shown in Figure 15.2 cleanly displays the lead generation goal completions for each of the campaign channels.

Remembering that the Campaigns report is based only on last-click attribution, and noting two assists for the Chip Report goal in the Multi-Channel Funnel ˃ Top Conversion Paths report shown in Figure 15.3, you split the credit for two of the last-click LinkedIn conversions with Facebook and Google AdWords.

You also notice that a Google AdWords campaign clickthrough later resulted in a conversion from a Google organic return visit. Since this return visit likely resulted from a branded, navigational search prompted by the initial paid visit, you give Google AdWords full credit for this lead as well.

You open a spreadsheet and plug in the cost data and the lead conversions for each, as in Table 15.2. It seems like you’ve achieved your objective: the cost per lead is below $500 for each channel, but don’t celebrate yet. (As a note, all data within this chapter is fictitious and does not reflect on the comparative performance of any marketing channel referenced as part of this use case.)

Campaign Channel	Cost (US$)	Leads	Cost (US$) per Lead
AdWords	$1,458	8.5	$172
LinkedIn	$1,678	10	$168
Twitter	$908	6	$151
Facebook	$1,193	6.5	$184
All	$5,237	31	$169

Correlating Campaign Channels with Qualified Leads

To associate the campaign and lead qualification data, you can take one of two approaches:

• Record the campaign data directly in the CRM. When you submit a lead from your website to your CRM, you can pass your campaign information to be stored directly with each lead in the CRM.
• Store a visitor ID in both GA and the CRM. You can store a unique but non–personally identifiable ID in GA and your CRM. This ID can then serve as the key on which your exported GA and CRM data. This approach, discussed in more detail later in the chapter, allows you to integrate other types of GA data (in addition to acquisition) with your CRM data.

The Salesforce example provided by Allaedin Ezzedin takes a hybrid approach: we pass campaign data into the CRM directly, and also pass the visitor ID to enable further integrated analysis of GA and Salesforce data. While the example focuses on leads, we could use the same procedure to record campaigns or other traffic sources for completed Ecommerce transactions.

Let’s say that in the case of the memory chip leads, you’re now directly storing campaign data in your CRM—which is Salesforce in our example. Now that your CRM records contain both marketing and lead qualification fields, you can begin to calculate marketing ROI based on qualified leads instead of all leads.

You let the campaign run for another month, and then you pull a new report from your CRM, as represented in Table 15.4.

You now tabulate your qualified leads and calculate a much more meaningful cost per acquisition for each marketing channel, as shown in Table 15.5.

You can now report to the marketing manager that the overall CPA, based on qualified leads, is below $500, and you can consider steering more of your ongoing budget toward the campaigns that are showing better ROI.

One element, however, is missing from our previous analysis. Because the campaign values that we stored in Salesforce are based only on last-click attribution, no credit is given for earlier visits that eventually led to a lead submission on a return visit from a different campaign channel, but we’ll address this gap below.

Exporting Google Analytics Data

You can export as many as 5,000 rows (as comma-separated values [CSVs] and other data formats) directly from any GA table report. If you need to export more than 5,000 rows, you can export in batches, starting at row 1, and then row 5001, and then row 10001, and so on.

If you use the Google Analytics Spreadsheet Add-on, you can export up to 10,000 rows at one time into a Google sheet document, from which you can easily export as CSV. The add-on also allows scheduled automation.

As two additional options for more automated export from GA, you can use ShufflePoint or Analytics Canvas, both discussed in Chapter 16, “Advanced Reporting and Visualization with Third-Party Tools.”

Importing Google Analytics Data into CRM

Once you have imported GA data into your CRM, you can join any GA dimensions with any CRM columns. In the previous lead qualification example, instead of writing the Google campaign data directly to Salesforce with each lead submission, we could have instead just written the visitor ID to the CRM (and also stored it in GA as a custom dimension) and then joined the GA campaign data and the Salesforce lead qualification data, as shown in Table 15.6.

If we have been storing client ID as the Visitor ID custom dimension even for unauthenticated sessions, as outlined later in the chapter and in the implementation checklist at www.e-nor.com/book, we could run a report to display previous sessions, from the same campaign or different campaigns, that preceded the returning sessions in which the lead submission occurred. Table 15.7 indicates that one of our qualified leads first clicked through on our LinkedIn campaign before returning the next day from the AdWords campaign and converting. We’re thus getting a more nuanced understanding of our campaign performance, and we’re in fact emulating the multisession scope that the GA Multi-Channel Funnel reports provide, but in this case we’re also benefitting from the additional perspective that the CRM qualification field offers.

Merging Google Analytics Behavior and Audience Data with CRM Data

Medium, source, and campaign are certainly not the only types of GA data that you can merge with CRM data. As other examples, you could merge pageview, event, or custom dimension data with CRM data to measure its impact on lead quality or order value. You could also merge GA audience characteristics such as device category.

What if we wanted to correlate CRM lead quality with video interactions on the memory chip landing page shown previously in Figure 15.1? As long as you have captured the video interactions as GA events, you could merge the event data with the lead qualification data as in Table 15.8 to measure potential impact.

The video interaction seems to correlate strongly with lead qualification. Of course, the visitors who were most qualified may also have been the most likely to view at least 50% of the video, but it still seems that it would be logical for you, as an analyst and optimizer, to recommend that your designers make the video more prominent on the memory chip landing page pictured in Figure 15.1.

The example in Table 15.9 shows order value by device category—desktop, tablet, or mobile (smartphone)—for leads who later finalized a transaction through the sales team. Tablet is showing the highest average order value, so it would make sense to target more of your AdWords and Facebook ad budgets to tablet. (You could, of course, also add source, medium, and campaign columns for further analysis.)

Using CRM ID as Visitor ID in Google Analytics

In the preceding examples, we generated the visitor ID on the client end and populated it into GA as a custom dimension and into the CRM as a custom field. For the visitor ID value, we actually didn’t create anything new; we just read in the client ID that GA stores in the _ga cookie.

As another option, we could retrieve the lead or customer ID from the CRM an pass it back to the page—probably as a GTM data layer variable or a plain JavaScript variable—for you to record into as a GA custom dimension with the next pageview or event. This option is also illustrated in Figure 15.5.

For instance, in the memory chip example, instead of passing a client-generated visitor ID to the CRM as a hidden field in the lead form, you could have instead read the CRM lead ID as soon as the lead was created, saved it to the page as a variable, and populated this value into GA as the visitor ID custom dimension.

If the lead submission redirected to a separate thank-you page, you could populate the visitor ID as a custom dimension in the new pageview hit—just make sure to set the variable value higher up on the page than the GTM container.

If, however, the lead submission did not redirect to a new page but instead just displayed a confirmation message on the same page without a page reload, you could still pass the visitor ID as a custom dimension in a GA Event tag in Google Tag Manager (GTM) as follows:

1. When the lead is created in the CRM, retrieve the lead ID value from the new CRM record as outlined in Listing 15.1, and set that as a GTM data layer variable while also writing the other data layer variables that our catch-all event tag, first discussed in Chapter 6, depends on.
2. Set up Visitor ID as a custom dimension, as described in Chapter 12.
3. Reconfigure the catch-all event tag to check for visitor ID as a data layer variable (Figure 15.9) to populate into the custom dimension (Figure 15.8). (If the visitor ID is not present in the data layer the other times we use the catch-all event tag, the custom dimension will just not be populated.)

With the CRM lead or customer ID now recorded in GA as a custom dimension, we’ll be able to perform all of the same joins demonstrated earlier in the chapter when we instead generated the visitor ID generated from the client side and passed it into the CRM. Starting from either the website or the CRM, the objective is to store a common visitor identifier in both data sets.