At every funnel stage in a customer lifecycle any digital marketer would like to know who’s moving into the next stage and whom we should target in the current stage so that we can pull him to the next stage.
funnel stage customer lifecycle of digital marketer
Most of the analytics systems(Kissmetrics, Mixpanel, wizrocket) can tell you, how many users are struck in each stage okay great, but as a digital marketer I would like to know who’s going to move forward. When you handle 200-300 visits per day it is totally fine to do the analysis, permutations and combinations manually well it is a do-able job. However when you’ve more than 1000 visits/day and if you start using more than one marketing channel(Adwords, FB) things go little complicated. Especially if you are a single man show who’s handling adwords, FB, creatives, funnel optimisation welcome bro we are on the same boat. There are lot of things to be analysed, which keywords are performing better, which ads are performing better, which FB segment(Age=23, gender=male, city=london) is providing us higher conversion. With present technology machine can help you identify group of customers, individual customer, customer profiles, and start suggesting which channels are engaging whom. Of Course personalisation at E-mail level still needs to evolve, however you can’t have your sales rep’s to send personalised emails to every single signup that’s going to consume lot of time. Here machine learning can help you to prioritise, a) frequently identify your “ideal customer profile” b) Machine learning can help not only to score the leads but also visitors so you can save lot of money in re-marketing. c) whom to target in every single stage – out of 1000 visitors who visited my site, do we need to target everyone no, machines will analyse the data of perfect customers profile and target the visitors who has the same characteristics. d) You don’t have to manually come up with segments which infact takes lot of time. Creating segments which provides your higher conversions, LTV and targeting them is important. Defining those segments and understanding them holistically is hard, with lot of channels, lot of metrics you’ll need a dedicated team which should keep analysing this particular data. But the fact is, these things can be very well automated with the help of machine learning. In the following posts, we shall try to explain how to train the system and what is the best practices to follow along with the case studies. Signing off.

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