Airbnb’s first data scientist, Riley Newman, characterizes data as “the voice of the customer.” For marketers, this data has become their most valuable resource.
But data analysis is a highly specialized job. The “voice of the customer” is encoded in data points and analyzed with specialized languages like R and Python. Few marketers are capable of such hard data analysis. Instead, marketers should look to data scientists. Here are four ways data experts can help.
Pinpointing preferences and interests
It’s easy to zero in on the preferences of any one customer, but much harder when trying to infer what more significant samples of a thousand, or even a hundred people like.
Data scientists can tone down the deafening chatter of social media to find common topics regarding your app. They can highlight features users like the most, and which are getting ignored. Clickstream data analysis is one such way of uncovering user preferences -- tracing the massed journeys of users through your app, click by click.
Assigning value to customers
Data scientists can look at spending behavior of current users to estimate the lifetime value of future customers. And while having a massive user base is always good, not all customers are equal. Data science can help you segment by user LTV and get a more accurate estimate of ROI (return on investment) and ROAS (return on advertising spend). Both these metrics tell you how well your campaigns are performing.
Seeing into the future
Predictive analytics give data scientists a crystal ball. They can tease out patterns in how users behave up to the moment they churn. Once the patterns of past behavior are unveiled, future actions, from engagement to spending to churn, can be predicted for users following the established patterns.
For marketers, these predictions extend beyond customer reactions to their own priority within the app development team. Revenue forecasts gives executives an estimate of how much their marketing team can work with, allowing them to plan ahead for the next campaign.
Properly attributing conversions
Developers are partnered with countless ad networks and channels, and often have multiple ad variants running at once. Having each click, view, and purchase mapped out across networks helps marketers boost high performing ads and adjust or lower bids for underperforming ads or keywords. This is where tools like Tenjin help -- but are best used in conjunction with a knowledgeable expert inside the company.
Data science is one of the most in-demand teams (or individuals) at any development company today, so it isn’t always easy to pull resources to help with marketing. But the effort is well worth it: improvements made in marketing reduce spend and improve performance across the entire company.