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Graph Advantage: Business Recommendation Engines

Business Recommendation Engines with Neo4j on GraphGridThe most common interactions we have with recommendations today involve social, the people we may know, and retail, the products we may also like, but some of the more interesting recommendation engines are the ones that operate internal to an organization providing business recommendations around strategy, direction and execution. Designing and building business recommendation engines that leverage a comprehensively connected data view within an enterprise can provide many competitive advantages. These advantages can include

increased efficiencies for how subject matter experts on the business domain should prioritize their daily efforts as well as helping the organization transition to being a more data driven enterprise with these insights guiding internal business use cases that go deep in offering a business-based direction on a holistic data view.

Business Recommendation Engines Guide Engagement

Whether it involves leveraging direct or indirect customer feedback through social media platforms, business supply chain details from the manufacturing plant to the logistics network, or inferring relationships according to an activity utilizing the network to determine the confidence in that assertion, the Neo4j graph database offers the significant advantages when it comes to making an enterprise data driven through business recommendation engines.

A known strategy for business recommendations internal to business is the design of pattern-based recommendation algorithms. Such recommendations are dynamic in nature as data flows through the system and aid business analysts that need help to prioritize their time to filter data by reviewing them in order of those that rank highly enough to be focused on first.

For instance, if you’re intent of finding insurance, medical, or financial fraud, there are a number of understood patterns associated with fraud within the data, which can be used to proactively pause transactions and rank them by priority for closer examination. Such detection is effective for analysts because it can order them by a confidence score to not waste the time of a high value resource examining all transactions. Other internal business recommendation engines involve:

  • Suitable product groupings to maximize revenue on a certain time period according to product and agreement attributes.
  • Adjacent areas of research and concentration according to current and previous performance.
  • Customer feedback response and support to increase engagement and satisfaction of support interactions by prioritizing those that are most critical to respond to personally first.

Making Businesses Personal with Real-Time Recommendations

Business recommendation engines are all about uniting how your business operates and your main business drivers with what consumers need around all the points of interaction with customers to engage more personally with them. Neo4j is an important technology for allowing real-time recommendation. Our experts at GraphGrid can design business recommendation engines that aid in making connections with your customers and enhancing engagement through your points of interaction.

Whether it involves utilizing social connections to making recommendation or trying to join the dots between unrelated facts to infer interests, the Neo4j native graph database on GraphGrid can provide you with tremendous advantages business through insightful business recommendations.