Behavioral targeting is the standard for ad targeting and dynamic content serving, but in spite of good automation tools and easy-to-use CMS systems we still haven’t fully cracked the code. Re-marketing and triggered messaging delivers much better response and significant uplift in conversion; usually better than the original targeted message. Behavioral targeting gives us the ability to zero-in hyper-efficiently on those with a higher propensity to buy in real time. But there are no clear formulas to determine what inferences can accurately be drawn from demonstrated behavior. We’re still guessing wrong too often. We have to evaluate and engage anonymous surfers and clickers and must determine in seconds who is worth following and engaging and who isn’t. Consider 3 basic criteria for evaluating or scoring behavior: 1. Repetitive Behavior Someone who does the same thing again and again or visits the same content repeatedly is probably more interested than the average Joe. It’s reasonable to guess that someone returning for a 3rd click is probably interested, if not a real buyer. Repetition ratchets up intent to purchase. The vital questions are: How many visits signal intent and on which visit should we proactively prompt an interaction? How do we know how much repetition is sufficient to encourage conversion or at what point does a “Big Brother” intervention freak out a prospect? 2. Sequential Behavior By watching where customers went before and after visiting our brand, we get better insight. If she visits the same product at a competitor site, that signals active shopping. If she looks at a product that normally goes together and sells together with the first product we can infer serious consideration. Someone who accesses or responds in multiple ways or at different times is more interested and has a higher purchase intent than a person using only one media channel. If we collect data from multiple channels (cookies, email, search, logins, registrations, purchase history, coupon redemption, downloads, etc.) we see patterns that will suggest how to weight and model observed behavior. 3. Use of Multiple Response Devices More actions equal greater intent. If she fills in a form, signs up for an email newsletter, downloads a whitepaper, prints out a PDF, uses a zoom feature, puts data into a calculator or clicks a “contact me” button we have a semi-qualified lead. Most responders are generally interested but may not be ready-to-buy. The act of responding, while rarely more than 2% of those exposed to an offer, cues us to apply extra effort or TLC to prompt a buy. By watching what prospects do over time and across platforms, we can triangulate purchase intent and intensity. This applies particularly to high value, considered purchases like cars, stocks, diamonds, real estate. It works especially well in B2B marketing where the shopping cycle is longer and where the decision has more variables. The key to behavioral targeting is the data sets, analysis and observations that drive the business rules for serving up ads and content. It’s the thinking not the automation that matters. Getting these inferences and algorithms right helps us sell more things faster to those most likely to buy. If it doesn’t, it is just voyeurism.
Danny Flamberg, Managing Director of Digital Strategy and CRM at Publicis Kaplan Thaler (PKT) based in New York, has been building brands and building businesses for more than 25 years. In the US, Europe, the Middle East and South America, he has helped start-ups become important players in their markets and helped leading global brands extend their reach, market share and relationships with customers. Prior to joining PKT, he led a successful global consulting group called Booster Rocket, as Managing Partner. Before becoming a consultant, he was Vice President of Global Marketing at SAP, SVP and Managing Director at Digitas in New York and Europe and President of Relationship Marketing at Amiratti Puris Lintas and Lowe Worldwide. He has direct, digital, social, mobile, brand, CRM, experience in banking, credit cards, insurance, financial services, consumer packaged goods, automotive, technology, airlines, hospitality, professional services, telecom, computer hardware and software, automotive, luxury goods, food and beverage and retail. He earned an A.B, an M.A. and a Ph.D. in politics and economics at Columbia University. He lives with his beautiful wife, talented daughter and lovelorn dog on Manhattan’s Upper West Side.