How to Outsource Analytics
Yes, that dirty work includes data conversion, edit/hygiene, categorization/tagging, data summarization and variable creation, encompassing all kinds of numeric, character and freeform data (refer to "Beyond RFM Data" and "Freeform Data Aren't Exactly Free"). It is not the most glorious part of this business, but data consistency is the key to successful implementation of any advanced analytics. So, if a model-ready environment is not available, someone had better know how to make the best of whatever is given. I have seen too many meltdowns in "before" and "after" modeling steps due to inconsistencies in databases.
So, grill the candidates with the following questions:
- If they support file conversions, edit, categorization and summarization
- How big of a dataset is too big, and how many files/tables are too many for them
- How much free-form data are too much for them
- Ask for sample model variables that they have created in the past
3. Track Records in the Industry: It can be argued that industry knowledge is even more crucial for the success than statistical know-how, as nuances are often "Lost in Translation" without relevant industry experience. In fact, some may not even be able to carry on a proper conversation with a client without it, leading to all kinds of wrong assumptions. I have seen a case where "real" rocket scientists messed up models for credit card campaigns.
The No. 1 reason why industry experience is important is everyone's success metrics are unique. Just to name a few, financial services (banking, credit card, insurance, investment, etc.), travel and hospitality, entertainment, packaged goods, online and offline retail, catalogs, publication, telecommunications/utilities, non-profit and political organizations all call for different types of analytics and models, as their business models and the way they interact with target audiences are vastly different. For example, building a model (or a database, for that matter) for businesses where they hand over merchandise "before" they collect money is fundamentally different than the ones where exchange happens simultaneously. Even a simple concept of payment date or transaction date cannot be treated the same way. For retailers, recent dates could be better for business, but for subscription business, older dates may carry more weight. And these are just some examples with "dates," before touching any dollar figures or other fun stuff.
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.