If you are a web analytics implementation manager and have been managing google analytics or site catalyst implementation for several years and if you are planning to add few more skills on your badge; you could look into becoming a data analyst, customer insight analyst, customer experience analyst, research analyst, product analyst … there are so many career paths you choose in analytics space.  But I would like to throw one more option out there which I think will become exceedingly in demand over next few years - Big data implementation manager! With explosion of big data, before we can make use of the data for sophisticated analytics, we need to make sure we know the data is good, clean and meaningful. That’s where you come into picture. As a part of your web analytics manager job, you are skills at: working with product and marketing managers, data analysts and other business users to identify what data they need. working with the engineers to make sure the tags are customized to capture the much needed data using tools like Firebug to verify and QA the data sent to the web analytics companies is accurate verify the data in the reports and support users in utilizing the data Big data, more than even web analytics needs you! Most hadoop and log files are exploding with data that takes a long time to surf through to identify what we are looking for and the data quality of big data is one of the biggest sticky points that I have experienced so far. Big data also requires someone that’s comfortable: working with business users and data scientists to identify what information they need for analysis working with engineers to make sure the web/mobile applications are instrumented to capture the data that users need using tools like Firebug to verify and QA the data is captured in Hadoop (inhouse or in the cloud) verify the data in hbase or mongoDB or whatever database the analysts are going to use for analysis So you see, big data implementation management is not that different from web analytics implementation. You will need a couple of additional skills and you will be working with a lot bigger datasets, which could be a good career move. Wouldn’t you agree?

Nowadays with new big data platforms, companies are able to gather and retain huge volumes of data generated by users clicks/actions or even traced by devices such as mobile phones and tablets. Companies are looking for ways to utilize these massive volumes of data. The possibilities are endless! Below is one of the ways big data can be utilized to increase customer retention.   Marketers segment their customers by demographics (age, gender, location etc) and RFM (recency, frequency and monetary value) scores to create targeted segments for promotional offers. Recency means how recently did the customer purchase. Frequency means how often do they purchase. And Monetary Value means how much do they spend. But with big data, now we can store and analyze every single detail of every user's behavior beyond their RFM scores e.g. the time spent/engaged on the site, what areas of the site the user was engaged in, the depth of user engagement (products viewed, videos watched, mobile check-ins, interaction with activity feeds etc)   Marketers have used demographic segmentation and RFM scores for email/offline campaigns as well as real time personalized ads. However, demographics and RFM scores are static and/or slow changing indicators. Though those are powerful indicators, many researches have  shown that users current engagement is one of the most powerful indicators of user’s future behavior. In other words, user engagement goes down prior they drop off/stop visiting the site.   Drop in a user's engagement is an early predictor of her future attrition.   Now with big data, more than ever, it’s possible to identify the low engagement and intervene early, even in real time using machine learning. Don't’ wait for days for the recency or frequency metrics to show a drop in the scores. The best time to push engagement features in user flows or offer promotions is when user's engagement is going down but while they are still on the site! Many companies, including some of my clients, are leveraging big data and machine learning to improve customer retention.

Mobile Applications have changed how businesses are measuring user engagement. Many websites have typically measured user engagement by page views per users or time spent per user on site. Traditionally websites want users to spend as much time as possible on their site, as that mostly translates into increasing chance the user will spend $ or it generates more ad revenue (Of course there are exceptions like payment space or checkout flows, where the success is measured by how quickly and conveniently users and get the task done.) But Mobile Applications have changed the way we think about engaging users. Users are not going to spend hours or even few continuous minutes within the Apps. They are typically using the Apps on the go and they want instant gratification. More and more App developers are offering features/tools that can be accessed and completed with ease and speed. But they also want to bring them back as often as possible. So here are some of the sample KPIs for measuring engagements in Mobile Apps: Day part volumes - Softer peak hours and well distributed Actions throughout the day means users want to use App frequently. Don’t be discouraged if the visits are only few seconds long, it’s typical of App users. Velocity of Actions (most analytics tools prefer to tag them as Events) - The more clicks within the App, the more users are engaged (with of course exception of reading or video Apps). Feedback score volumes. The qualitative feedback is just as important. Always provide a feedback button within your App. In my experience, within App feedback volume far surpasses App store feedback. The more users are engaged, the more feedback you are going to get, even negative feedback is better than no feedback. At lease negative feedback provides you hints on what you need to change to retain the users!

Product development is driven by reaching out to internal and external users who provide feedback about the usage and value of each component of a proposed online product or service. Quite simply then, the web product analyst interprets this customer needs and/or wants input in order to deliver customer value.

If you have an online business, you either have a Mobile Application by now or you are in the process of building one. Most online businesses have a good knowledge of customer's click stream information, browsing behavior, conversion rates etc. However, Mobile Apps seem to be a different story.

We all have experienced errors on any transacting or even non-transacting web sites. As consumers we all find it annoying. And for those of us that are tasked with developing or supporting a site, strive to make the site running smoothly free of errors. But we know, our sites have issues.