With maya.ai, enterprises are able to personalize at never before scale and speed
100,000+ workforce rely on maya.ai to run the business. Why? It helps them get more done in less time without all the chaos and confusion.
Enterprises switch to maya.ai when things aren’t working. See how maya.ai helps each team member
Anna is responsible for everything from improving portfolio metrics to making the best use of her allocated resources and finding solutions to increase value realized from each customer.
She’s having some trouble because the right people and tools are not available and the portfolio is stagnating. She’s deeply rooted in the many systems and processes imposed on her. She wants to revolutionize the way her team works. But solutions available are disjointed, time-consuming and expensive.
She has one unified portal to understand her customers, track merchants and discover where opportunities lie.
maya.ai helps Anna grow her portfolio considerably, and also saves cost for a task that would take her team months to complete. It’ll now take just a few days, making them more productive.
Thanks to maya.ai, Anna’s team can complete tasks that traditionally took days or weeks in a matter of minutes. They can step away from repetitive work and start innovating instantly.
No one preaches digital as hard as maya.ai. So by subscribing to maya.ai, banks and payment service providers can have a digital presence in just 7 days. What’s more is that maya.ai’s digital capabilities can be made available to all customers, across the web and mobile.
Juan Carlos is responsible for creating a comprehensive digital strategy that can support all bank products. Not only is he responsible for faster roll-out of assets, but he needs to also be up-to-date with the latest in digital engagement technology.
The technology and user interfaces are changing too fast for Juan Carlos to keep up with and all the product owners have their own opinions on what digital assets they should offer their customers. Moreover, all of Juan’s digital assets are disjointed, which leads to sub-optimal user experience.
maya.ai provides a unified digital strategy that enables personalization on every digital asset while integrating with only one API, cutting away the need to work with multiple vendors.
With maya.ai, Juan Carlos has access to APIs that are easy-to-use and flexible in the assets he wishes to integrate them. The flexibility of APIs leveraged ensures minimal time and effort investment is needed.
maya.ai comes with APIs which ensure that each interaction the customer makes is fed into a machine learning loop that improves the efficacy of the engagement. These APIs also capture a vast amount of user behavior data without the customer spending a single penny.
Andrew is responsible for ensuring his portfolio is actively engaged and constantly growing. He needs to identify and execute a set of actions to ensure he meets his monthly, quarterly and annual targets.
Andrew has three big challenges – understanding his customers beyond their transactional behavior, staying relevant with these customers and having limited control on the execution of engagement opportunities that he identifies.
With maya.ai, Andrew can understand the tastes of every individual in his portfolio. He can understand how these tastes change with seasons, locations, categories, as well as global and local trends. This allows him to understand how to engage customers individually.
maya.ai allows users like Andrew to piece together engagement opportunities centered around taste. These opportunities come with accurate sizing of the monetary impact they will create. With this information, Andrew can stack up campaigns that total up to his targets in a matter of minutes.
Andrew can execute campaigns on all digital assets directly from his maya.ai console in a simple 3 step process, and go live in 30 minutes. He is no longer a slave to the campaign queue. He can actively control the growth of his portfolio.
maya.ai has exhaustively mapped and modeled traditional card-related use cases such as spend increase, spread increase, customer re/activation, cross-border spends, attrition management, etc. All these use cases come as off-the-shelf models that Andrew can initiate at the click of a button.
Kate’s responsibilities include creating a set number of campaigns every month. She also needs to take the models built by different teams, layer them with appropriate creatives, load them on to the campaign execution engine and launch.
For Kate, the campaigns take too long to execute and there’s far too much menial work, including generating reports. There are also multiple departments to coordinate work with, which typically slows things down.
With the help of maya.ai, creating and starting campaigns doesn’t take much time. The campaigns are pre-curated and can be launched based on the priorities set by Kate.
maya.ai’s recommendation engine is built on best-in-class algorithms. The generated campaigns are context-sensitive. They balance individual, hyperlocal and global trends with portfolio growth targets. All of this ensures the highest possible returns on investment on each campaign.
More if not better. maya.ai has proved that one taste-centric and context-aware campaign can have the same impact as 5 traditional campaigns. This helps reduce the number of campaigns a bank needs to run to realize the same impact. This reduces the load on the campaign team. And cuts down the amount of spam to customers.
A key part of the campaign execution process is tracking the results. It’s often a manual and time-consuming process. maya.ai automates this and tracks campaign performance with every incremental data refresh. It automatically refreshes the results and neatly presents them on a shareable UI.
Yolanda is tasked with identifying and acquiring the right offer partnerships. She has to ensure that the partnerships give the bank an edge over competitors.
50% of her offer portfolio provides no ROI. On the other hand 90% of the value generated comes from just 5% of the merchants. Her biggest challenge is ensuring relevance of these partnerships to the customers. She also needs to balance the costs of acquiring each offer with the quantum of offers needed to cater to every single customer. Tying up with offer aggregators give her scale, but at the cost of relevance. She also lacks a single intelligent system to onboard and distribute offers.
With maya.ai, Yolanda gets a monthly list of top recommended merchants for offer partnerships. This list can be viewed at various levels of abstraction across geographies, categories and customer segments. Each recommended partnership also comes with accurate sizing of the future value the merchant can generate.
maya.ai centralizes the performance tracking workflows to provide a neat dashboard tracking the performance of the offer portfolio across individual merchants, categories, geographies, and customer segments. Yolanda can make data-driven decisions on which offer partnerships to keep and which ones to let go.
Having a digital onboarding portal that captures the requisite information needed, maya.ai ensures the quality of data with a centralized offer management system. Once on-boarded, Yolanda can make the offer live across all assets and ensure the right customer is given the right offer at the right time, on the right channel.
Ashwin creates models that support the business requirements of various functions within the enterprise. His models need to be best in class and should help improve the metrics across different lines of business.
Typically, Ashwin is overloaded with multiple modeling requests of varying complexity. As a result, he finds it difficult to spend time on perfecting each model. He also needs to constantly learn and improve his own skills as an analyst. The advent of new data modeling techniques and technologies has made it hard for Ashwin to stay ahead of the curve. This leads him to reusing the same models he's worked on before. With little time left for innovation.
Under Access to the latest in big data technology, change description to “Thanks to maya.ai Ashwin now has access to the latest AI and ML models that delivered through easy-to-use interfaces. All without the need to learn new languages or use multiple platforms.
Typical modeling techniques that are not industry focused rely on the accuracy and strength of a model. Banks need an accurate sizing of monetary impact. With maya.ai, Ashwin can get accurate estimates of monetary outcomes while iterating on model quality parameters.
maya.ai takes on the burden of curating run-of-the-mill campaigns automatically with every data refresh. This gives Ashwin time to work on high-impact use cases and models. He can on-board custom models into maya.ai and create an ensemble models that mixes off-the-shelf models with custom-built ones.