Micro(up)selling-the data advantage

Nalpeiron Technology Blog, September 2013 http://Nalperion.com

Micro(up)selling: the data advantage

 

If you were one of the unlucky PC users who experienced the Windows upgrade to Vista, you may well remember the high levels of frustration when the new release arrived: there were 1500 changes to the software. The new graphic interface ran the battery down faster.   The new attractive Windows shell was beautifully organized but the most used buttons had disappeared! On top of the annoyance at not being able to find basics such as where the Startbutton was, there was the added anger at the sense that someone at Windows was laughing all the way to the bank, and here we were just trying to get the day’s work done! Microsoft product managers and engineers spent years of hard work and planning the Vista rollout and were proud of their achievements, but their customers gained a lot of unwanted headaches.

 

Nalpeiron’s customers never have to worry that their customers will experience this unnecessary level of frustration when upgrades roll out. These businesses use software analytics to ensure that end-users are installing the software easily, adopting the most significant features, and using the product to its maximum efficiency.  They don’t need to plan for the future; they simply observe what customers are doing in real-time using software usage monitoring.   

 

At the earliest stages of the business relationship, the customer may be using a freemium or low-cost version of the software. There’s nothing new under the sun about bringing a customer in the front door with a loss-leader. And it is during the first 30 days of software use that is the tech enterprise’s most valuable time to work with end-users.

 

During this first 30 days, the end-user is offering the tech enterprise the opportunity to listen in as to how the software is being used by a beginner on the software.  Every click and keyboard stroke provides data to our own servers and it is our job to follow along with the most sophisticated analytics possible.  Ideally, the end-users are having a great experience with the features, their day-to-day workload has been reduced, simplified, and enhanced and their businesses are enjoying increasing success.  

 

Before the end of that first 30 days, we need that end-user to have made the decision to sign up for the premium version or continue with the monthly service.  These are the most basic of all business transactions.  Nalpeiron Software Analytics also tracks users’ feature adoption rates.  As end-users adopt features, this drives revenue to the business when each feature has a price tag, no matter how small. These monthly and micro-sales are exactly what the data provided by sophisticated user tracking is so useful for.  The goal is not the make grand sales as to make trends towards user feature adoption.  

 

Tech businesses have many options for up-selling once the customer is having a positive experience.  Users can be offered a chat window to find out more about a product.  They can be offered more or similar products, special deals for more users or for a greater amount of monthly usage.  Customers can be provided with helpful videos teaching a more advanced feature they haven’t tried yet.

 

When thousands, tens of thousands, hundreds of thousands of users begin spending seconds or minutes longer in a session, software analytics data converts that information into potential sales. Nalpeiron Software Analytics tracks user sessions and duration and converts that end-user behavior into data that can be easily analyzed and searched.

 

Nalpeiron software analytics adds another layer of sophistication by tracking user loyalty/churn. Here analytics data goes a step further than watching and listening to users’ behavior, by predicting their future decision-making regarding your products. Armed with this level of data and information, product managers and engineers now have clear direction to develop new features, provided not from their own ideas and planning, but based directly on data provided by customer behavior.

 

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