How to Improve User Acquisition & Conversion using Location Analytics
For app-based delivery + logistics + mobility companies where “location” is critical.
An app-based delivery company delivers whatever users order through their app right at the user’s location, anytime. On-demand companies such as food delivery companies, supply chain companies like e-commerce companies, hyperlocal delivery companies, and mobility companies all come under this category.
With all these companies, there is one thing in common: they have operations and moving assets on ground. Hence, location becomes very important as a parameter to include in strategies and decisions.
But, how can we use location analytics to increase user acquisition, conversion, and retention? Believe it or not, geographic patterns about these metrics can tell us a lot about our business and users. For example:
Not everyone who installs your app uses it or opens your app orders. In that case, is there a location where the density of churn is very high?
In this piece, we are going to discuss how we use location analytics to understand how we can improve our user acquisition, conversion, and retention.
Case 1: User Acquisition
Acquiring users in locations where there is already demand will help us reduce our CAC (customer acquisition cost). Hence, in choosing the next location where you want to expand, a good question to ask is:
Where are the locations that I already see a lot of latent demand in terms of app installs, searches, orders placed etc.?
We can use this insight to decide which area we need to establish partnerships with stores or restaurants, or where to open up a new vehicle or charging station, or which location to become serviceable in.
Similarly, if you are a mobility company with a significant percentage of power users, the next question that is crawling in your mind is:
How do these power users move and where do come from and go to?
Understanding these characteristics can determine what kind of locations and user personas we need to double down on. For example, are these users office goers or students?
Case 2: User Conversion
At the core of conversion, is the fact that there is churn. Today, when people do churn analysis, they only look at the persona and the step of the funnel. Every user that didn’t convert is revenue lost in front of us.
However, analyzing where the churn is happening is critical because that helps us identify where we need to improve operations, provision supply, or do offline marketing.
Between when users open the apps to when he finally makes the payment for the order, there are a large number of steps that occur in between in the app.
To book a ride:
Enter destination → searching the nearest bike → selecting the bike → starting the trip.
To order food:
Select cuisine → Select restaurant → Select the dish → Checkout → Make payment
For example, a grocery company that we are working with realized that people were churning out from the ordering process once they saw the SLAs and they realize they can get better SLAs somewhere else! When we plotted the churn data, we noticed some high-density clusters in cities, and the team started focusing on provisioning more delivery partners in those areas.
Case 3: User Retention
Once you convert a user, how do you ensure repeatability and retention? After all, we all know that retention is one of the most important metrics for a company as it ensures consistent revenue. But, in a world where users can switch across multiple competitors at the tap of a button and with their demands of the highest quality service, it’s harder and harder to ensure retention.
Different companies have different strategies for retention. Some companies do up-selling: Getting users to order the same catalog at a higher frequency while some companies do cross-sell: Getting users to order across different categories and catalog.
Location can help in retaining users as well be pushing very targeted promotions based on the user’s location and behavior.
For example, let’s go back to our mobility company example. Suppose we figure out that our most valuable users are students who take our vehicles from hostels to colleges. We can run a campaign subsidizing these routes!
Out of home (OOH) advertising is also a very popular marketing strategy to nurture and retain your users and the success of this strategy depends on the right messaging and the location.
How can Locale.ai help?
Locale is a location analytics platform, which means we convert your raw lat-long data across all databases and formats into meaningful insights so that decision-makers can make very precise, data-driven decisions about their ground operations.
Web analytics tools already help you improve these metrics, using clickstream data. However, with companies that have moving assets on the ground, not looking at things geospatially can be extremely detrimental! Let’s dive deeper into how you use can use Locale to increase user acquisition, conversion, and retention.
Acquisition
Demand Mapping lets you overlay your search data and install data that you can use to analyze the spatial and time-wise trends for different areas. Growth Heads can use these insights to identify which areas are untapped and for the business for different categories.
Conversion
Churn Analysis helps you analyze where different events happen geospatially and where are people churning out the most at a very granular level. Whether you need to open a vehicle station hub to provide more stations or call the manager of the restaurant with the highest prep time or do some offline events — the decision is yours.
Retention
Workflows module helps you take actions in cases when certain metrics and thresholds are met. Companies use this to send very targeted geo-promotions to their users or get alerts in case their KPIs observe anomalies.
At Locale, we are creating examples of companies who are improving their unit economics, cost per delivery, utilization by doing geospatial analytics. If you are a growth manager looking to improve user acquisition, retention, and conversion, then get in touch for a demo here or get in touch with me on LinkedIn or Twitter.
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Originally published at https://blog.locale.ai on April 27, 2020.
