Josh asks, “I work at a company that doesn’t sell direct to customers. We sell to distributors who sell to big box stores who sell to the customer. How do I set up goals in Google Analytics?”
Terrific question – and a challenging one. This is similar to setting goals for a massive organization which has business units that sell to customers, but the corporate marketing team doesn’t sell anything directly.
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Notes and Transcript
The first question to ask is, what are you measured on? For example…
- If you’re measured on brand awareness, branded organic search traffic (traffic to pages that rank near the top of search results for your brand names) is a good goal.
- If you’re measured on audience reach, net new users to your website is a valid goal.
- If you’re measured on store assists, meaning helping customers get to stores, measure the traffic to pages like Store Locator or Where to Buy pages.
- If you’re measured on product interest, consider measuring product information page traffic, product branded searches, or coupon redemption (especially if you issue the coupons digitally).
The harder part of goal setting in B2B2C is assigning economic value to your goals. No matter what you do, any goal is going to be estimated and directional, because there’s no concrete way to assign transactional data values like purchases directly to your digital marketing and digital properties. At the end of the day, you don’t sell to your customers – you sell to your customers’ customers.
What you’ll need to do is create a datamart (basically a giant spreadsheet) of all the customer data you have available:
- Ads and ad performance
- Google Analytics data
- Social media data
- Email data
- Sales data to VARs, distributors, etc.
- Purchase data from retailers
- Coupon data
- Warranty data
- Customer service data
Assemble this in the datamart by time. Day-level data would be best if you have it (some retailers like Amazon and Walmart report exceptionally good day-level data to manufacturers). Then run a statistical analysis to determine which combination of metrics leads to total sales. For the average non-data scientist, I recommend using IBM Watson Analytics for this task. Use the Driver Analysis/Predictive Model feature.
Once you know what metrics have the greatest statistical relevance, ascertain whether the “winning” metrics are measured in Google Analytics. If they are, then distribute the sales data across those metrics by conversion ratios. For example, if you have a million dollars in sales and one hundred million email clicks, assign an email click’s value to be a dollar.
If you end up without a statistically significant or relevant set of metrics and sales, then assign an arbitrary, small value to your goals in Google Analytics and slap a warning label on every report and dashboard that you create that the goal value is arbitrary and used only for directional tracking.
Re-tune your goals and goal values every quarter, since changing economic conditions will affect B2B2C more. Consumer habits and spending patterns affect B2B2C goal tracking more because you have less control over what your retailers sell and how they sell it. You’ll also want to account for seasonality in your goal setting; whatever goal values you settle on, set a quarterly percentage modifier to account for things like the holiday retail season. In no reality should Q3 goal values be identical to Q4 goal values for any product sold to the consumer.
Disclosure: I am an IBM Champion, which means I receive non-financial incentives from IBM such as apparel and conference/event sponsorships in exchange for recommending appropriate uses of their products and services.
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