Practical AI for Growth Marketers

Today’s customers are tethered to their devices 24/7 and switch between them seamlessly. Advances in Big Data technologies like Hadoop have made it easy to capture raw data in diverse formats and store them across several different data stores usually called data lakes spanning SQL systems, NoSQL systems, flat files and excel sheets. As a growth marketer this is the raw gold mine you are working with and you should prioritize data capture in any format over shoehorning it to a particular data store or schema. A.I tools that you invest in should adapt to this mix of structured and unstructured data.

The half life of consumer intent is getting shorter with each passing year, and customers expect “on-demand” experiences that are contextually relevant and personalized to them across every device. Growth marketers should prioritize simpler AI algorithms and processes that can adapt well to real time data than more complex batch mode solutions that may need several hours or days to execute. Pay close attention to training time it takes to build and deploy A.I models and how fast can they incorporate new data.

While it’s ideal to have every attribute and preference known about all users, in reality you will end up with incomplete or partially known data fields despite your best efforts. B2C growth marketers in particular should expect this from day one and invest in tools and solutions that adapt well to incomplete data. Take for example a user location, there may be a mix of user given location data, with device lat/long, ip to geo, inferences from content viewed or searches done and more. As a growth marketer you should prefer A.I tools that can adapt well to the mix of all this data and output best effort answers for widest user base than on few users with complete and clean data.

Most A.I algorithms expect training data to be fed to them and the size and availability of training data is big obstacle to overcome to use them effectively. Certain class of A.I algorithms like Boosted Random Forests are better at adapting to the size of training data than Convolutional Neural Networks aka Deep Learning. Growth marketers should prefer those algorithms that can work with limited training data and have in-built sampling techniques to deal with disproportionate class sizes.

A.I algorithms come in many forms, from easy to understand decision trees to black box complex ones like Deep Boltzman machines. Navigating the black boxes can be tricky, what works today cannot be said of tomorrow and need very careful tuning to yield short term results. Growth marketers should prefer AI algorithms that explain their outputs, and helps marketer understand various factors and weights given to them in realizing that output. Tools that iterate quickly and incorporate domain specific knowledge much more easily are likely to work better in the long term than hyper optimized black boxes with enticing short term yields.

When it comes to the nitty gritty of it all remember that A.I is no magic bullet but a practical tool to achieving your custom goals.