Every Figure Must Be Good, Bad or Ugly
To list a few examples of typical baselines:
- Industry norm
- Overall company norm
- Other brands
- Other products/product lines
- Other marketing channels (if channel-driven)
- Other regions and countries (if regional)
- Previous years, seasons, quarters, months, weeks or year-to-date
- Cost factors (for Return on Investment)
Then, involved parties should get into a healthy argument about key measurements, as different ones may paint a totally different picture. Overall sales figure in terms dollars may have gone down, but the number of high-value deals may have gone up, revealing multiple challenges down the line. Analysts must create an environment where multi-dimensional pictures of the situation may emerge naturally.
Some of the obvious and not-so-obvious metrics are:
- Counts of opens, clicks, visits, pages views, shopping baskets, abandonments, etc. Typical digital metrics.
- Number of conversions/transactions (in my opinion, the ultimate prize)
- Units sold
- # Unique visitors and/or customers (very important in the age of multichannel marketing)
- Dollars — Total paid, discount/coupon amount, returns (If we are to figure out what type of offers are effective or harmful, follow the discounts, too.)
- Days between transactions
- Recency of transactions
- Tenure of customers
If we conduct proper comparisons against proper baseline numbers, these raw figures may reveal interesting stories on their own (as in, “which ones are good and which ones are really ugly?”).
If we play with them a little more, more interesting stories will spring up. Simply, start dividing them with one another, again, considering what the users of information would care about the most. For instance:
- Conversion rates — Compared to opens, visits, unique visitors (or customers), mailing counts, total contact counts, etc. Do them all while at it, starting with the Number of Customers, divided by the Number of Total Contacts.
- Average dollar per transaction
- Average dollar per customer
- Dollar generated per 1,000 contacts
- Discount ratio (Discount amount / Total dollar generated)
- Average units per transaction
- Revenue over Cost (good, old ROI)
Why go crazy here? Because, very often, one or two types of ratios don’t paint the whole picture. There are many instances where conversion rate and value of the transaction move in opposite directions (i.e., high conversion rate, but not many dollars generated per transaction). That is why we would even have “Dollar generated per every 1,000 contacts,” investigating yet another angle.
Stephen H. Yu is a world-class database marketer. He has a proven track record in comprehensive strategic planning and tactical execution, effectively bridging the gap between the marketing and technology world with a balanced view obtained from more than 30 years of experience in best practices of database marketing. Currently, Yu is president and chief consultant at Willow Data Strategy. Previously, he was the head of analytics and insights at eClerx, and VP, Data Strategy & Analytics at Infogroup. Prior to that, Yu was the founding CTO of I-Behavior Inc., which pioneered the use of SKU-level behavioral data. “As a long-time data player with plenty of battle experiences, I would like to share my thoughts and knowledge that I obtained from being a bridge person between the marketing world and the technology world. In the end, data and analytics are just tools for decision-makers; let’s think about what we should be (or shouldn’t be) doing with them first. And the tools must be wielded properly to meet the goals, so let me share some useful tricks in database design, data refinement process and analytics.” Reach him at firstname.lastname@example.org.