Depending on who you ask, there is a long-running, and possibly fabricated, statistic that consumers see hundreds of brand messages each day. Regardless of the actual numbers (some say 200, others say 20,000) we can all agree that each day we are bombarded by brand messages in all forms. So what is a media planner or a statistician on the Measurement & Analytics team at AMP Agency supposed to do when trying to determine just which one of those messages led to an actual purchase? Getting to an answer typically relies on developing complex attribution models that requires more math than I'd like to discuss. But before we get into any of these complex scenarios, let's first discuss what attribution modeling is. In its simplest form, it's merely giving credit where credit is due. Let's say you're trying to buy a pair of sneakers. You first see a TV spot about a pair you are considering during your favorite TV show. Two days later you check out the website to get more information. The next day, you see a Google search ad about the sneakers so you end up clicking on it and purchasing that day. What gets the credit for your purchase? Do you give credit to the TV placement, the sneaker website or the search ad? How much credit do you ATTRIBUTE to each? It's a common marketing question that has several possible solutions. There's last click attribution, where the last step gets all the credit (the search ad). There's first click attribution, where the first step would get all the credit (the TV placement). There's also linear attribution where you give equal credit to each touch point in the process. Avinash Kaushik put together a wonderful post detailing some of the various types of attribution models. Each of those various types of attribution models has its benefits. They can be cost effective, easy to understand or easy to implement, but how accurate are they really? It's almost as if you're just arbitrarily giving credit based on nothing concrete. This is where econometric attribution modeling comes in. It is a customized statistical approach in order to assign credit leveraging large amounts of data (some may even go as far as to describe it as Big Data'?¦ gasp!) using modeling techniques not unlike those seen in predictive or media mix modeling. Now you're saying 'Hey Chichi, I've heard about that kind of stuff, but it's too complicated for me. I want to use something I understand.'? Don't worry, I've got you covered. The title of this post implies I'm going to explain how it works, so let me give it a shot. This econometric approach essentially looks at trends over a long period of time and can attribute impact based on fluctuations in both the channels and the final conversion. Have I lost you already? Let's look at some pretty pictures with fake data. The graph below shows display investment, TV investment and POS data (Point of Sale or Sales data) trended over the days of a year. The goal of this exercise is to accurately attribute sales credit to TV and display investment. I've purposely broken the chart up into four quadrants to explain the basics of how econometric attribution modeling works. Now, for my fellow statisticians and the like out there, this is merely an example for illustrative purposes. There are many more factors that can influence sales that are not accounted for in this image, but the methodology is still very similar. Channels that are heavily 'correlated'? with success should get the majority of the credit for that success. Just because a channel was first (or last) doesn't mean that it is best and deserves all the credit. A statistical approach like this can accurately give credit based on real data. Econometric attribution modeling has its limitations because you need a good amount of data on your customers to even begin. So here at AMP we use it with our clients more for strategic planning than in-market optimization. However, despite its limitations, it is still more accurate and actionable than most of the standard attribution approaches out there. So when your boss asks you who he should thank for improving his business by introducing econometric attribution modeling, don't forget to attribute some of that credit to this post. Interested in learning more about analytics and attribution modeling? Register for our upcoming "Always Make Pudding" Analytics webinar here.
Any true quantitative approach requires a large collection of relevant data points which can then be leveraged strategically and potentially statistically in order to create actionable insights. In the case of event marketing, a strategic approach is required. Data must be collected from numerous sources across many events throughout the year and beyond in order for it to be actionable. These data points should come from a combination of sources: Pre-Post Surveys The most obvious is the event survey. If the survey questions are structured strategically, they can be used to collect important quantitative data. Questions that measure brand health, recall, purchase intent, attitudes and impact can be merged with other data sets to create an actionable database. RFID Tracking/Check-in RFID codes as well as check in points can quantify attendees at certain areas of an event. Other tracking tools such as multi environment tracking in Google Universal Analytics (in which you map offline actions into various Google Analytic fields) can also be implemented. The task of introducing and implementing RFID codes and Google Universal Analytics for offline events is not easy and can be expensive, but the data may be invaluable. Social Media and Web Analytics Measuring social media and web metrics such as 'likes'?, 'follows'?, 'shares'? and 'retweets'? as well as website traffic, bounce rates, content popularity and online download rates are a quick and effective way to quantify things such as lift and increased engagement, potentially attributed to the event activation. Data Analysis Once data from the actual event is collected, quantitative researchers can then layer on data from other sources, such as website and sales data, to begin to determine relationships through attribution and predictive modeling techniques. Depending on the validity and robustness of the data set, researchers can use these advanced statistical concepts to not only determine that an event was successful in terms of its impact on sales but also how each part of that event contributed to that success. This information can then be used to optimize future events. Stayed tuned for part 3 of the series on event measurement tips and tricks, in which we will discuss a tactical approach to obtaining qualitative insights from your brand's event.
*Warning, this post will improve your life. It's 3PM, and you're tired. Those 5-hour ENERGY commercials creep you out, so you don't have any. You took a 10 minute nap in the bathroom stall, but it didn't work. You should walk some stairs. 'The smallest act is greater than the greatest intention'?-Kahlil Gibran AMP's Stair Walks derived from a group of coworkers that were tired from a long day, but still had a large end of the day pile of work to go. They needed a quick break, but also a boost to finish the day strong. Stair Walks seemed to be the perfect solution. Before I break it down for you, let me introduce myself. My name is Chichi or Chi2 and I work on the Analytics team, so I take my statistics seriously. Some people talk the talk, but I talk the numbers. After realizing the benefits of adding stairs to a work day, it was easy to use numbers to convince my coworkers of what they were missing out on. The ROI is just something no one can ignore. Here at AMP, we're on the 8th floor. Going up and down is 16 flights of stairs with a total of 309 stair steps There are also some gaps in between each flight that total 128 steps, so now there is a total step count of 437 It takes me about 2min and 29secs at a casual pace to do all 16 flights (9.3 seconds per flight, 0.3 seconds per step) In a typical work week, I spend 12 min and 25 seconds walking 80 flights of stairs and taking 2,185 steps doing them Men's Health says if you walk 2 flights of stairs a day, you can lose up to 6 lbs a year. Assuming I don't take the stairs on weekends, and taking into account holidays and vacations, I can lose up to 32 lbs a year for a 3 minute break every day. How's that for ROI? 'An escalator can never break; it can only become stairs'?¦ Sorry for the convenience'?-Mitch Hedberg Some people see the glass as half empty and can't open their eyes to all the positives. Here are a couple responses I get from time to time. Q: Hey Chichi, won't I sweat doing it? The stair walk is going to ruin my fabulous-ness. A: First of all, this isn't a decathlon, you're walking up some stairs. Secondly, so what? Sweat is the new black; wear it with pride. Q: Yo Chi, I do Wii Fit. So, aren't these stairs going to be a waste of my time? A: I know you're lying because no one's used a Wii since '09. But regardless, stairs give more than health benefits. It allows you to interact with coworkers, give the mind a mental break from work and get the blood flowing in the body. Q: But, but'?¦ I'm so busyyy. A: Don't think I didn't see you on Facebook 20min ago. And, even if I didn't, a three minute break in the action has more benefits to work production than negatives. Research shows taking a step back from your work at 3pm improves work output.(Sample size is small) The equation below summarizes the message of this post: Walking Stairs = Being Awesome Stair Walks are just one of the reasons why AMP's culture is so awesome. So you should probably apply to one of the positions on the career page because we don't just do awesome work, we also take steps to making ourselves more awesome.1 1You see what I did with the title and the last sentence of this post? Pretty clever huh? You only get that clever by walking the stairs.