Mobilewalla Sees 225% Jump In ARR As Brands Tap Its Intelligence To Sharpen Predictive Modeling and Drive Growth

Mobilewalla, the global leader in consumer intelligence solutions, announced a 225 percent increase in annual recurring revenue (2019 ARR) over the prior fiscal year (2018). The increase in top-line growth points to a broader trend of brands committing to deepening their understanding of consumer behaviors and preferences and driving more value out of their existing artificial intelligence (AI) and machine learning (ML) investments.

While brands have spent the past decades collecting massive amounts of data, they are often challenged with turning all of that data into actionable insight that delivers ROI. Many brands are looking to AI and ML to maximize the value of this first-party data. While first-party data is important, it only encompasses direct customer engagement and is therefore largely inapplicable to modeling prospects or other consumers. Mobilewalla’s deep consumer data gives brands the critical additional insight they need to better understand their customers, target new prospects, improve the predictiveness of their AI and gain a competitive edge in increasingly commoditized markets.

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For brands to be successful with their AI efforts, the ML models they are building need lots of high-quality data. This data needs to have breadth, depth and a variety of attributes or features that are predictive of the outcome the model is trying to drive. Mobilewalla uses AI to create an extensive set of particularly valuable predictive features that are in high demand with data scientists solving marketing use cases. Among them:

• Householding: understanding the members of a household can be highly predictive. For example, mobile carriers have found that it is much easier for a prospective customer to switch to their mobile brand if someone in a prospective household is already a customer.
• Mobility: how often a person is seen at airports that are not their local airport, the average commute distance or distance someone travels daily, or if someone lives in a rural, urban or suburban area, can all be highly predictive indicators of certain outcomes such as the use of ride sharing services or the likelihood of ordering food online.
• Behavior: understanding how often a person visits shopping malls, if someone frequently attends sporting events, or if someone uses fitness apps or plays games on their mobile device can be highly predictive, as well as indicators of buying intent and brand propensity.

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“We’re seeing data science and marketing teams waking up to the fact that their first party data simply isn’t enough because it offers such a limited view of the consumer.  Without the right breadth and depth of data to power your predictive models, it’s extremely difficult to uncover impactful insights or increase predictivity,” said Anindya Datta, founder and CEO of Mobilewalla. “This collective ‘aha’ moment has been a big driver of our growth over the past year, as brands look to our data set and AI expertise to better understand, model and predict behavior.”

With the world’s largest consumer data set, Mobilewalla is powering the next generation of data-driven brands across industries such as retail, on-demand, telecommunications, consumer tech, fintech and media.  Enterprise data scientists, data-driven marketers, and advertising agencies use Mobilewalla to enrich their existing data, unlock consumer insights, build audience segments, better understand their customers and increase the precision and recall of their models.

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