Having lots of customer data is not enough; organizations must create an Insights Engine to integrate data from various sources and generate customer insights that improve engagement. The document outlines a process where organizations: 1) Identify all potential customer data sources; 2) Score and prepare each for integration; 3) Infer relationships and segment customers to develop unique profiles. This Insights Engine provides a deeper understanding of customers, uncovers competitive advantages and disadvantages, and informs targeted messaging to customer segments.

1. Crea%ng an Insight Engine for Improved Marke%ng
Having lots of data about your customers is not a compe66ve advantage,
how customer insight is used to engage them is the difference
Background:
1 Primary research acquired
to address specific
ques4ons/topics, o8en
shelved once used,
however could enrich
customer engagement
when integrated with
other customer data
sources
2 Customers expect a
posi4ve interac4on, the
beAer your data-rich
insights, the easier it will
be for self-service tools or
front line staff, to deliver
the interac4on customers
were expec4ng
3 Consistent successful
interac4ons lead to higher
expecta4ons, trust and
ul4mately to loyal
customers
4 Con4nuously improving
customer insight will lead
customer interac4ons to
evolve as marketplace
shi8s to stay ahead of
changing customer, media
and lifestyle trends
Challenge:
Most organizations have customer data, plenty
of data about its customers. However, few have
accomplished a complete 360 degree customer
view offering transactional data, behavioral
input from unstructured interactions, social
media and online communities, and attitudinal
insight from traditional market research
(qualitative and quantitative). The task is to
synthesize a complete customer view reflecting
insight both transactional and probabilistic,
across an omnichannel landscape.
Process:
The process begins with a workshop to identify
all potential data files to be considered. Each
was scored for potential value as well as level of
difficulty to import and integrate. Teams were
assigned responsibility to format, modify and
integrate required data. Inferred data was
assigned statistical probabilities. After majority
of data feeds were completed, segmentation
organized unique marketable clusters.
Extensive testing and validation applied to prep
insight engine for trial.
Outcome:
Application of the Insights Engine created
deeper understanding of customers. Emerging
benefits include a new perspective on brand
value, uncovered competitive advantages not
clearly recognized as well as disadvantages
better addressed by competitors.
Erik LaPrade Case Study 2016

2. Segment #1
• Message A
• Message B
• Message C
Segment #2
• Message D
• Message E
• Message F
Segment #3
• Message G
• Message H
• Message I
Segment #4
• Message J
• Message K
For more informa4on contact:
Erik LaPrade
https://www.linkedin.com/in/eriklaprade
eriklaprade@gmail.com
cell: (913) 319-9757
Integrated Data Files Driving Insights Engine
Transac6onal, behavioral and probabilis6c
data files all blended into one Insights Engine
Erik LaPrade Case Study 2016