In the past, you knew your customer’s name and address at best. But as they move their interactions with you online, you now have access to more. You know what they’ve done in the past. You know their likes and dislikes. And now you can start to predict their wants and needs.
With MongoDB, you can now personalize the experience of millions of customers in real time. Faster. With less money.
|Personalization is Hard||MongoDB Makes it Easy|
Can’t Innovate. Customer data is more than names and addresses. Now it includes browsing habits and social media profiles. These diverse data types don’t fit well in the rows and columns of a relational database.
Do the Impossible. MongoDB can incorporate any type of data, no matter what it looks like or where it comes from, while providing all the features needed to personalize the customer experience, such as customized home pages, targeted offers and social network sign-on.
Can't Customize at Speed. You have an instant to target your customer. But the moment has gone, because you first have to transform your customer data before loading it into a data warehouse. Then you have to pull it out again. Only then can you build personalized profiles.
Faster Personalize in real time. You can run complex queries directly against your data. No more extracting, transforming and loading.
$$$$. Customer data is spread across multiple silos, because each application has its own identity management system. You can’t get a single view of your customer. And you have to maintain redundant data in multiple places.
$. Bring customer data into a single database shared by multiple applications, such as e-commerce, CRM, billing and more. Eliminate silos, and make life simpler for developers and ops.
Your customers’ expectations have gone up. But your database is still the same.
Rigid Schemas. You should be able to track new user attributes easily and build new features. Your teams should be able to work in sprints. But relational schemas are hard to change incrementally, especially without impacting performance or taking the database offline.
Scaling Problems.Analyzing customer behavior and delivering personalized experiences in real time requires a break from the familiar ETL and data warehouse approach to extract analytics. You don’t have time for lengthy load schedules, or to build new query models. You need to run complex queries against variably structured data, and you need to do it now.
Takes Too Long. Because each application is different, each maintains its own customer data. You want to bring customer data together into a single database, but a change in one application will break the data model. So you have to maintain redundant data across multiple systems. Life is harder for your developers. Life is miserable for your ops teams.
Organizations are using MongoDB for personalization because it lets them store any kind of data, analyze it in real time, and change the schema as they go.
Dynamic Schemas. MongoDB’s JSON document model makes it easy to store any type of data for any type of attribute. You can represent complex, hierarchical data structures – even geospatial data for location awareness – and evolve the schema instantly, without taking the database down or impacting performance.
Real-Time Analytics. With an expressive query language, secondary, geospatial and text indexes, the aggregation framework and MapReduce, MongoDB lets you run fast analytics across multi-structured data within the database. You get fast personalization at scale.
Lower Cost, Lower Complexity. Dynamic schemas and a flexible document model mean that a single MongoDB database can bring together diverse customer data and share it across multiple applications. There’s no need to maintain identity systems for each application. Pull in new data when you need to. MongoDB documents can vary in structure, which means documents from one system don’t need to have all the same fields as documents from another.