If your company builds or uses machine learning models, you need to be paying attention to Mobilewalla. The company helps brands get more out of their AI investments by enriching their predictive models with third-party consumer data.
Machine learning models that help companies better understand, acquire and retain customers.
We sat down with Laurie Hood, SVP of Marketing at Mobilewalla, to learn more about the company and its solutions.
In a single sentence or statement, describe your company.
Mobilewalla is a leader in consumer intelligence solutions, combining the industry’s most robust data set with deep artificial intelligence expertise to help organizations better understand, model and predict customer behavior.
How does your company use artificial intelligence in its products?
At Mobilewalla we are significant users of AI within our own business, but also, with our rich insights into consumer behavior, our proprietary solutions help organizations get more out of their AI investments by making more informed business decisions and effectively acquiring, understanding and retaining their most valuable customers.
As a company, Mobilewalla has deep expertise in both big data management and artificial intelligence, and our ability to innovate in these areas has allowed us to differentiate our solutions in a highly competitive market. It's taken us well beyond being a data provider and increased our ability to provide our clients with highly valuable business insights. Aggregating and processing huge volumes of consumer data is a key core capability. We have also developed sophisticated machine learning-based fraud and anomaly detection techniques that identify and remove fraudulent data.
We apply our award-winning data science expertise to this data set building best-in-class demographic and behavioral segments. These segments are not just limited to demographic data, but also include life stage, employment, interests and many more behavioral attributes. We have further leveraged our data science expertise to build a significant set of consumer-centric features that allows for even more powerful and finely grained predictive model building and data segmentation.
What are the primary marketing use cases for your AI-powered solutions?
Our data is used by organizations to support the key marketing goals of acquiring new customers, retaining existing customers and growing the business through cross-sell and upsell.
Our data is available as audience segments used in programmatic and social marketing campaigns. Many large organizations enrich their existing data with Mobilewalla third-party data, increasing the understanding of their existing customers and using predictive modeling for use cases such as high-value customer identification, offer acceptance, churn modeling, householding and look-alike modeling for audience targeting and attribution.
What makes your AI-powered solution smarter than traditional approaches and products?
With the adoption of AI in many organizations to address marketing use cases, marketers and data scientists are often relying only on their first-party data to build an understanding of their customers and to try to predict and influence consumer behavior.
First-party data lacks the breadth and depth to produce the most predictive models and to understand non-customer behavior. Mobilewalla data has the scale to enrich and improve modeling efforts increasing predictive accuracy and business results.
Are there any minimum requirements for marketers to get value out of your AI-powered technology? (e.g. data, list size, etc.)
We have a variety of products that can be used across different sized organizations. Any size organization can use our audience segments and enrich their existing data. To use Mobilewalla data more deeply and as part of AI initiatives, the marketer needs to be partnered with their data science team.
Who are your ideal customers in terms of company size and industries?
Our ideal customer is typically a larger, data-centric, B2C company that is multi-channel and fairly reliant on digital channels for consumer engagement.
Our ideal customer has made investments beyond CRM to understand their customers, has customer types that can be differentiated (high-value vs. low-value) and has a data science team working with marketing to better understand their customers and target their best prospects.
Key industries include retail/e-commerce, on-demand, financial services/fintech, media, quick-service restaurants, travel and hospitality and telecommunications.
What do you see as the limitations of AI as it exists today?
AI can be very effective when implemented properly. Many organizations face challenges in hiring the right skills, building an understanding of AI and how it can be used and having enough training data and features to build models that are going to deliver solid results.
What do you see as the future potential of AI in marketing?
Increased adoption over time as the number of skilled resources grows and as the concepts are embraced and understood across an organization.
Any other thoughts on AI in marketing, or advice for marketers who are just starting with AI?
Marketers need to first look at the use cases they are trying to solve—where can they apply AI within their business to produce meaningful results?
Then they can determine the necessary skills, resources and data they need. From a data standpoint, they need to assess the data they own and have access to, understand what other data is available across the organization, investigate third party data sources that will provide additional benefit and then key and link this data to build a single customer view.
Paul Roetzer is founder and CEO of Marketing AI Institute. He is the author of Marketing Artificial Intelligence (Matt Holt Books, 2022) The Marketing Performance Blueprint (Wiley, 2014) and The Marketing Agency Blueprint (Wiley, 2012); and creator of the Marketing AI Conference (MAICON).