Conversion Engineering- From art to science and back again1
A typical SaaS/subscription business which offers a freemium/free-trial option to the user and waits for them to convert into a paid subscriber. The length and type of trajectory that characterizes the conversion to paid subscriber varies by each user and marketing stimuli form an integral part of this trajectory. Some of the approaches an organization may employ to nudge users along to conversion are:
Conversion Engineering1
Some SaaS executives rely heavily on their experience and intuition to focus on a certain set of users. They focus on developing features that are appealing to their chosen target population and send specific marketing messages. While success is debatable, the scalability and sustainability of such an approach is certainly a challenge
Conversion Engineering2
Some organizations are extremely confident about their product features and their efforts in making the product even more exciting; they focus on making better features, and wait for the free users to naturally graduate towards the paid options. This ‘one size fits all’ approach is likely to backfire with a diverse user base not only in terms of lack of conversion into paid users but also in terms of completely disengaging from the system
Conversion Engineering3
This approach is based on drawing the user into the system with a limited number of features to begin with. Adding incremental features to the system while simultaneously applying one or more of several marketing stimuli such as e-mail blasts, in-app messages and automatic updates, serves to not only keep the user engaged but increases the level of engagement as well. Before long users have expended or reached the ‘free’ limit of the service and have no option but to convert to a paid user to continue. This approach can sometimes cross the fine line between engaging with the customer vs. pushing aggressively, often leading to a 50/50 chance of success
Conversion Engineering4
Organizations have moved on to more sophisticated ways to determine what works and what does not when it comes to converting free users into paid customers. This approach is entrenched in mining the user data already available to the organization. As Analytics Professionals, it strengthens our conviction that organizations are getting increasingly data-driven with their decisions. While data scientists are the driving force behind this, our premise is that these statisticians and data scientists are not being leveraged most efficiently in addressing the “freemium to premium” conundrum, much less the data. Data is often used to generate hindsight. How is your data being leveraged?
Can you identify the persuadable (to convert) user? Do you know which messages carry the “biggest bang for the buck”? What is the best way to reach your persuadable guest? What about the future? What is your user likely to do next? Granted, that these are all daunting questions. However, the data and data scientists, if leveraged correctly are already well-equipped to answer all these questions and more
Conversion Engineering5
The SaaS market is beginning to see some tools which bring all the above solution elements together. The business user gets to quickly identify the users who are most likely to convert, is suggested options of the most likely messages that would work, as well as the ideal platforms to send those messages through. API plug-ins in the ecosystem of your existing Marketing Automation tool make this a few-click-process, literally. These tools are backed by robust advanced analytics at the back-end with the ability to integrate the analytical models built by your current in-house analytics team(s). The tool provides information that is not only hindsight, but insight and foresight as well, which are essentially future decision options that are engineered based on your data.
While the tools are firmly entrenched in data and analytics in so far as engineering decision option is concerned, they also allow the experienced marketing manager the freedom to choose the right option.
Further, the tool based approach is cost-effective to implement, easier to scale and decision options are faster to revise
While all roads may lead to Rome (i.e. the Paid Subscriber), some roads may be simpler, faster and cheaper. Which road do you want to take today?
Authored by – Lavanya Ramanan – SME Analytics & Products at Absolutdata