Lyft Looks Like Something Went Wrong. Please Try Again.
Lyft has made a serial of great moves over the years in its advert and branding campaigns. The pop ride-sharing company, which originally capitalized on flubs made by competitor Uber, made headlines more than recently for steadily working to movement beyond the context of Uber and toward being recognized as of import in its own right. It started from shifting promotions from Uber-related reactions to edifice brand awareness by addressing the values of riders. They also looked into the data of their drivers, to guess their happiness gene to make necessary adjustments.
These were brilliant moves on their end, but in that location were a few others that didn't get the limelight they deserved, and they each revolved effectually their approach to user acquisition. Interestingly enough, there are many fundamentals to their approach that can easily carry over to how DTC brands can best take on user acquisition campaigns. Permit's dive correct into information technology!
What led Lyft to automation: The creation of Symphony
From a traditional standpoint, user acquisition is typically led by a data-driven cross-functional team that focuses on scale, measurability, and predictability. Lyft works with unlike partners, technologies, and strategies to make sure that Lyft is the tiptop choice for consumers. Over time, it became clear that there were too many cooks in the kitchen, and the all-time way for them to calibration efficiently was by creating a data-driven learning system. The visitor went on to do so, and named their new internal solution Symphony, "an orchestration system that takes a business objective, predicts future user value, allocates upkeep, and publishes that budget to drive new users to Lyft."
Some of the issues Symphony addresses include:
- Updating bids across thousands of search keywords.
- Turning off poor-performing display creative.
- Changing referrals values by market.
- Identifying high-value user segments.
- Sharing learnings from different strategies across campaigns.
Symphony is dynamic and continuously learning— which is exactly as predictive modeling should be. Of grade, their solution is non one size fits all. Lyft adjusts the models, and what is driven from them (budget and bids) co-ordinate to different channels and different entrada strategies.
One of the main components of Symphony is the LTV forecaster. This forecaster, which improves as the user progresses through the user journey, is based on characteristic importance of early user funnel information points, and products the likelihood to actuate, too as the lifetime value at different time intervals.
If you lot know us, our product, and our service. you probably understand why this piqued our interest.
As we dug deeper into Lyft's idea process into creating their LTV forecaster, we rapidly realized that there were some elements that DTC brands can learn from, and employ.
What DTC brands can acquire from Lyft'southward focus on LTV elements
Understanding the potential value of a user is critical for any business, regardless of the industry. When it came to forecasting LTV, Lyft's goal was to measure the efficiency of diverse acquisition channels based on the value of the users coming from those channels. Based on the findings, the budget can so exist allocated in relation to the expected value for users coming from a given aqueduct, and the price they are willing to pay in a particular region for those types of users.
DTC brands tin can also similarly allocate their budget towards customers that demonstrate college LTV, for greater ROI in the long-term.
Just how did Lyft summate a user's expected LTV? This is some other point DTC brands can hands replicate: by looking into historical information.
The more a user interacts with the service, fifty-fifty beyond different channels and touchpoints, the greater the LTV they demonstrate.
Benefits of using AI technology for UA campaigns
Using AI applied science to zero in on customers with greater LTV serves to exist ane of the greatest forms of churn mitigation for DTC brands. Equally we mentioned in a previous blog postal service, churn rates have a way of throwing a wrench into UA efforts, and reducing it not merely increases LTV, but ultimately leads to greater render on CAC.
Fortunately, we are living in times in which such advanced capabilities do not solely have to exist enjoyed by large brands similar Lyft that develop products, such as Symphony, internally. In another one of our previous posts, we talked almost how the Facebook conversions API and Google's Server-Side Tagging allow media buyers the integration that is essential to fire back server side signals, such equally LTV and offline conversions, in lodge to optimize campaigns based on them. Of course, it goes without saying that richer raw data yields more accurate models, which is why UA campaigns backed past AI for user-level LTV predictions are alike to uncovering buried treasure.
For most brands, software development for internal usage costs far likewise much in terms of resources, time, and of form finances. When factoring the preliminary project stage, application evolution stage, and post-implementation operating phase—the creation of internal use software isn't always feasible. They tin can also be a tough sell to the college ups. This is where SaaS solutions come up in, to help speed upwards the process for growth marketers and user acquisition managers that are looking to get on the path to exponentially greater ROI.
And let usa non forget, when artificial intelligence steps in to assist with loyalty marketing, it enables human counterparts to focus on more meaningful tasks, such every bit updating creatives, experimenting with new ad formats, and new goals. It'southward a win beyond the board!
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Source: https://www.voyantis.ai/blog/lyfts-growth-operations-a-great-lesson-in-how-to-approach-user-acquisition
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