The proliferation of various enterprise systems and tools has
created multiple versions of the customer data. Every client-
facing and back-office systems including ERP, marketing
automation, CRM, website, and store systems contains a
slice of the customer data. This includes transaction data
such as orders, policies, payments, claims, and also the
channel interaction data such as customer service calls,
website and physical store visits. Many of these repositories
also hold master data such as name, address and phone
number. There are a lot of duplicates and inconsistencies in
all of this data leading to over 41% B2B marketers citing it as
their biggest challenge.
It’s better and cheaper to find and fix
the problems earlier in the process as illustrated by the 1-10-
100 rule: it takes $1 to verify a record as it is entered,
$10 to cleanse it, and $100 if nothing is done. The
impact of poor data is evident in the outcome:
marketing leaders estimate that 26% of their budget
is spent on ineffective channels and tactics due to
lack of clean, consistent and connected customer
data. Now, more than ever organizations are looking to
create a real-time customer 360 data hub with the
transaction, interaction and master data to drive
effective marketing campaigns and increase revenue.
Traditional Customer 360 solutions are
missing the mark
Traditional customer 360 and MDM solutions are built upon
relational databases, which store information such as account,
contact, lead, campaign, and opportunity in separate tables, one for
each type of business entity. The relational databases are great
tools for indexing and searching for data, as well as for supporting
transactions and performing basic analysis; however, the relational
databases are poorly-equipped to connect across the tables or
business entities and identify hidden relationships and patterns
going across multiple leads, campaigns, and opportunities.
to find the potential customer engagement and attribution patterns
that lead to opportunities and revenue, analysts need to join a
number of large tables to run the queries and collect the data for
analysis. Such queries could take hours or even days to run,
rendering any meaningful analysis of the patterns for customer
engagement and attribution among leads, contacts, campaigns, and
opportunities practically impossible.
Why TigerGraph, a Native Parallel Graph Database for Real-time Customer 360/MDM?
Graph Based Customer Entity Resolution
Merging customer data from data sources is not always easy, however. One challenge, in particular, is the entity resolution, deciding when multiple entities from different data sources actually represent the same real-world entity and then merging them into one entity. Consider the example, where there are three data sources containing following types of customer information:
Source1 (SSN, Email, Address)
Source2 (SSN, Phone, Name, Age)
Source3 (Email, Phone, Gender)
Let’s assume that SSN, Email, and Phone are each sufficient to uniquely identify an individual (that is, they constitute PII, personally identifiable
information). The problem is that the different sources use different identifiers, and that individual records might be missing some information. Over time, missing PII of a customer may show up later in another data source. The goal is to use whatever PII we have about a customer to find all information (attributes) of a customer across all data sources and build a unified record with the following attributes:
Customer (SSN, Email, Phone, Name, Age, Gender, Address).
Graph databases are purpose-built to connect across multiple sources to create a single record. In this case, TigerGraph creates a customer Vertex for each customer, connected to various PII vertices such as SSN, Email, Phone. Next, multiple customer entities or vertices with identical SSN, email and phone number are merged with business rules applied to reconcile differing values of fields or attributes such as age and address. TigerGraph can use the last updated dates for addresses or other rules to populate the address for the consolidated record for the customer vertex U1 and also manage a list of known addresses along with the source information for regulatory compliance such as European Union’s General Data Protection Regulation or GDPR and for corporate information governance.
Finding the Customer Engagement &
Attribution Patterns with Graph
Consider the example of a TigerGraph opportunity with GMD Corporation, an internet, and eCommerce giant with diversified operations including the mobile wallet and payments. Digging deeper into the graph for a “real-time fraud detection” opportunity for their payment division, we can see that there were three stakeholders that were involved in this opportunity - Sam Eisenberg, an architect with GMD payments, who came in via free trial on tigergraph.com website on June 11, 2018, followed by Jamie Walters, Sr. manager of credit and fraud decision support who signed up for a test drive of the TigerGraph AntiFraud demonstration on June 21, 2018 and finally, Joshua King, Data Engineer who signed for the free trial of TigerGraph on June 23, 2018.
An opportunity was created on June 12 by the sales representative focused on GMD Payment account for the fraud detection.
As we click on and drill down to look at the customer engagement for the architect, Sam Eisenberg in TigerGraph’s GraphStudio, we can see that Sam had a sales meeting with
TigerGraph on June 14, 2018, following his request for TigerGraph free trial on June 11. He also signed up for the test drive on June 11 to watch the AntiFraud demonstration for TigerGraph. This was followed up with the webinar, GSQL part 1 on June 21, indicating increased interest from the prospect in TigerGraph’s portfolio. This was followed by the download of TigerGraph’s benchmark report on August 6, 2018, comparing the performance of TigerGraph with Amazon Neptune, Neo4j, ArangoDB, and JanusGraph. There were two more sales meetings with Sam and other GMD payments executives following the benchmark download. First sales meeting following the benchmark download was on August 13, a little more than a week after the download. This was followed by another sales meeting on September 19, 2018, which led to the final signed deal of 295,000 USD on September 26, 2018, for GMD payments fraud detection opportunity.
TigerGraph analyzes the customer engagement data for all prospects, comparing them to the customer journey of Sam and other prospects that led to the signed deal. These insights allow marketers to design nurture streams and identify prospects that are prime for sales conversations around specific topics based on their journey maximizing probability of converting a lead into an opportunity and an opportunity into a signed deal.