Getting freemium users to become paid users is a nuanced process that varies from person to person.
Users on the brink of turning their free trial into a paid subscription need only a little nudge. Others, admittedly a much smaller number of people, will quickly pay for subscriptions. But neither group is the ideal target.
It’s the users who are less certain about buying that arguably make the best prospects, with campaigns designed to meet them wherever they are in the funnel and strategically lead them to the other side.
“The idea is to guide your users through certain actions and features that are available in your product,” said Anil Kaul, CEO of analytics/consulting firm Absolutdata. “As they move through, you can get them closer and closer to the point where they’re willing to pay.”
Historically, Absolutdata has provided more of a managed service centered on campaign spend optimization, customer acquisition, market research and loyalty tracking to its roughly 45 clients, including Adidas, Ancestry.com, Autodesk, Burlington Coat Factory, ETrade, KIA, Royal Caribbean and Sprint.
More recently, Absolutdata has begun to turn its attention to what Kaul called “decision engineering,” a process that uses a combination of predictive analytics and CRM to provide companies with action plans beyond the initial insights.
Although Absolutdata has its roots in consulting, the company is in the process of expanding its decisioning tool to include some self-serve functionality.
“We’ve worked with clients that started out with conversion rates as high as 50% and some that had conversion rates as low as 5%, but it’s possible to get results in both of those scenarios,” Kaul said. “We’ve seen conversions increase by up to 34% simply by refining the message and paying attention to the user journey.”
Headquartered in San Francisco, Absolutdata also maintains offices in New York, Los Angeles, London, India, Dubai and Singapore. Founded 12 years ago, the company, which has about 400 employees, raised $20 million in Series A funding from the private equity arm of Fidelity Growth Partners India in 2012.
AdExchanger caught up with Kaul.
AdExchanger: How does the decisioning engine play out from a practical standpoint?
ANIL KAUL: Say you’re running an ad campaign. Traditionally, data and analytics are used to figure out who the campaign should be targeted to. That’s still true. But data and analytics should also help you design the campaign itself. They should help you figure out what the campaign should look like, what the offer should be, what the message should be. All of that can be decided based on the data you already have available to you.
We make recommendations and then we also do tracking.
What data do you look at to make these recommendations?
We have three data sources: user data, such as product usage and demographics; data from previous campaigns, including testing, segments targeted, results, messaging used, etc.; data derived from social media.
So, how do you turn people from casual users of a free version into paid subscribers?
Of the people who are using your product for free, there is a subset who will eventually pay for that product – but they’ll only be ready to pay for it in return for the value they receive. Organizations need to actively guide their users through that journey, because that’s what will result in free customers agreeing to become paid customers.
The first step is to find out more about your persuadable users. You shouldn’t reach out to people who are unlikely to ever pay or worry too much about the people who are going to pay anyway. It’s about identifying that middle group.
Having done that, we create microsegments and figure out which set of actions they need to go through to get them closer to conversion. We look at users in subsegments from the past to see which of their activities were highly correlated with an ultimate willingness to pay. From there, it’s about figuring out what channel, messaging, timing and offer will push particular users to action and guiding those users through particular actions and features of your product.
What’s an example of this in action?
One of our clients, a file-sharing company [named] Hightail, which used to be called YouSendIt, realized that within their segments there were people who would always zip files. That group was never going to be monetized. Based on their actions, they’re not willing to pay. They were zipping as a way to reduce the number of files they sent because free customers have a limit on the number of files they can send every month.
But Hightail had an ample enough opportunity enticing the users that only needed to go through a one-step process [in order to become paid users], so that was the group to tackle first. In the short term, you don’t need to worry about the people that are really difficult to monetize.
Are some users simply not monetizable other than through a straight-up advertising model?
Yes, certain people will be very difficult to monetize, [but] we don’t look at anybody as unmonetizable. We look at how many actions it will take before a person is ready to be monetized. Certain people will need to go through 10 steps, other may just need to go through one. The idea is to get a true understanding of how that applies to the customers in your customer base.
We look at each person and say, “This is the number of actions he or she will have to take before they’re reading to be monetized.” You end up creating fairly detailed campaigns.
How did that shake out in the Hightail case?
We were able to show them about 18 microsegments among their users, all of whom had very different behaviors – but that didn’t mean that there were 18 campaigns. There were maybe six or seven campaigns in total that looked at the difference in terms of tenure of users, the types of files sense, the volume of files sent, etc. Certain things are more important to certain users than others, like, for example, being able to track if another person has downloaded a sent file or not.
There are different actions to take [in terms of] monetization depending on who a user is within a group.
Is there any value in keeping free users around?
In my direct marketing experience, the cost of serving those users could be tremendously high, but for most app developers or companies with digital subscriptions, the cost is very low, so they don’t mind keeping them.
In some situations, companies might get value out of users if they’re not paid users. If you’re a company with a network effect like LinkedIn or Facebook, where the size of your base is very important, those users might never pay, but they’re still contributing to your company’s overall value and they’re likely being monetized through advertising.
