2. Share The Customers, Share The Benefits
How about making an extra $500 million in two years time with what you already
have? Central data hubs and data marts have generated significant benefits for
group companies that have utilized them. More and more group companies in
the Middle East are taking heed and are launching similar initiatives to reap the
benefits.
What?
Customer data hubs and data marts, which bring together customer data from all
key operational systems and structure them in a more manageable and
informative manner, have been popular among most leading companies for
years, serving as a sound base for analytically driven customer strategies and
tactics. Central data hubs and data marts are the next step for conglomerates
wanting to take the concept to the next level, bringing together customer data
from across all group companies, to facilitate group-level understanding of
customers and opportunities.
Central data hubs and data marts create the data foundations for facilitating
synergies, enabling customer data sharing in a controlled and well-governed
way, while increasing the data quality and quantity for all. Although it is heavily
supported by data and technology, companies shall avoid considering this as yet
another technology solution, and make it a business-driven initiative, as the
business implications and impacts are vast.
But, Why?
Various leading companies in North America and Europe, such as RBC Canada,
Hilton Hotels Corp, and Harrah’s, have been using central data models to
facilitate sharing and making use of customer information compiled from all
individual business lines and group companies for years, and have realized
handsome returns for their investments. The concept has caught on in the
Middle East more recently; one regional conglomerate attributed over $500
million of its revenues earned over the past two years to the use of its central
data mart, leveraged through its group loyalty program.
Here are a couple of reasons why such a relatively low cost initiative can
generate such impact on the bottom-line:
1) The first and most obvious benefit of central data models is the
opportunities created to cross-sell to group customers across companies. A
central data hub and data mart brings together detailed information about
customers, collected across various industries, building a very solid lead list, with
details on customer value and potential, needs, and behavior. Such a central
data model presents all the untapped potential within the customer and non-
customer base for all group companies. This benefit extends to the new business
development areas for conglomerates, building a strong lead list for a quick and
successful launch in new ventures.
3. 2) Similar to understanding the opportunities in the group customer base, a
central data model can also facilitate better understanding of risks – in both
financial and relational terms. For example, a customer who has defaulted or
delayed payments in one of the business lines can be flagged as high risk in other
group companies. Or, similarly, another customer churning from one of the
group companies may require special attention in other lines, as the churn might
indicate disloyalty to the group brand as a whole.
3) Another important benefit from a central data hub is the availability of
most up-to-date and accurate customer information for all group companies.
More often than not, when group companies bring together their customer data,
they realize that some of the other group companies have more recent contact
information or more complete demographics data on their customers.
Centralizing customer data is one of the cheapest and most effective means for
enriching the data for all. It is even possible to take this one step further and
direct group companies to gather information for not just their own benefits, but
for the other companies in the group. A simple example would be a car rental
company, which can easily collect information on customers who rent
automobiles for a relatively long-term and identify those who do so because of a
recent relocation. Although this information has limited benefits for the car
rental company, it could become a valuable piece of information for one of the
other group companies; say, a home appliances retailer.
4) It is a known and fairly common case; one of the highest value customers of
a company visits a store of its sister company. Expecting that he would receive
the same special treatments he has enjoyed in other company, the customer
becomes frustrated when no one in the sister company even recognizes him. For
the first company, this means dissatisfying a high value customer, whereas for
the sister company, this means a customer with relatively high potential gone
unnoticed. It is simply a lose-lose situation for all parties involved. Without
merging the customer data from all group companies, such cases will continue to
exist in any given conglomerate.
5) Another common case in conglomerates without central data and customer
relations management is the overwhelming amount of communications their
customers get. Without knowing how much each group company sends to a
specific customer, these groups end up sending tens of messages to the same
customer, and sometimes with conflicting propositions or even offering
competing products. Of course, resolution of such issue goes beyond just
centralizing the data and calls for more organized approach to communications
with customers as well.
Nowadays, everyone talks about synergy within group companies, and managing
the total customer relationship and experience across a group, but very few take
solid steps towards building them. Central data hubs and data marts are one of
the few quick win steps and major enablers for such objectives, which shall
become one of the building blocks for any conglomerate.
4. So, How?
There are six main steps that need to be taken towards building and leveraging
central data hub and data marts in group companies:
1. Design of the Data Model: The first step in design is understanding of the
business requirements across the group, to come up with a comprehensive
listing of the critical data elements and customer information expected from the
central data model. The key challenge at this stage is to support business teams
in thinking outside of their own boxes (own business lines) and envisioning what
they could get from other lines of businesses. Benchmarks and best practices
definitely help at this stage, but for best results, cross-industry experts and group
level brainstorming sessions should be part of such a process. Based on the
business requirements, the ideal data model design should be prepared, which
requires flexibility to cater for not only existing business lines’ data needs, but
also potential future ventures of the group as well. The output from this step is
usually a relatively large-scale data model, which shall be prioritized for
implementation as well. One of the leading conglomerates in the Middle East has
started with over 7,000 data elements for its central data mart design, which
included only the first priority fields.
2. Data and Customer Governance: Although the governance model does not
get into action before implementation of the data model, being one of the most
challenging and make-or-break aspects of the central data models, companies
need to start discussing the governance model as early as possible in the process.
The governance model not only affects how much customer satisfaction or
dissatisfaction the central data model can create, but also assigns ultimate
power and responsibilities, making it a highly political and long-duration
discussion topic. Companies need to take into account various policies within this
step, including:
Customer Data Feed: Whether or not to include all sensitive customer
information (e.g. including private data such as medical details, as well as
sensitive customers such as VIPs of companies.
Customer Data Access: To what extend the group companies and external
parties can have access to combined customer data (e.g. summarized data
only, details for only their own customers, details for all customers and leads)
Customer Communications: How frequently, with what priorities and
through what means the customers can be reached out to (e.g. via a central
CRM team, with 1 message per month from each group company.
Customer Permissions: Whether opt-in or opt-out option will be used for
sharing customer data across group companies and taking actions
Customer and Data Ownership: Who are the owners and guardians for the
shared customers and customer data (e.g. central CRM and IT teams, shared
ownership between companies, one company acting as the guardian)
Although there exist various rules of thumb and proven methods for each of
these aspects, various conglomerates opt for different decisions in them, based
on their market and business conditions, calling for a well-thought out solution
instead of a one-size-fits-all solution. For example, one European conglomerate
uses a centralized newsletter to integrate communications with customers,
5. which limits the number of communications a customer could receive, while
giving opportunity to each group company for reaching out to him. On the other
hand, a North American conglomerate prefers centralizing the campaign
management for the whole group, selecting the best offer for each customer
from across the group and communicating them, following a truly customer
centric approach. Companies need to analyze their own requirements and
customer expectations and come up with a solution of their own, while studying
such best practices.
3. Preparation of the Data: Of course, the actual work of bringing the data
together, merging separate data sources from across the group companies, is a
major step of its own. This step involves mapping of sources’ system fields with
the central data model, extraction of vast amounts of data from them, cleansing
and standardizing, transforming and loading into the central data hub and data
marts. One of the key activities within this step is unification of customers – and
possibly households – which have records with varying levels of accuracy and
completeness across the group. Here, companies need to set up customer
matching rules, which define what constitutes a unique customer (e.g. records
with same name, similar address and same phone number). Additionally, it is
necessary to define the rules for specifying the golden records when performing
the merge (e.g. if a customer has two different age information in two different
business lines, which record should be used).
4. Data Improvements across the Group: Central data hub and data mart
development activities always demonstrate substantial gaps across the group
companies, as it gives the opportunity to compare quality of data between them,
and identifies additional requirements to support each other. As a natural
consequence, central data model development process requires data
improvements across most business lines. Although the journey for ideal
customer data is a continuous one for most companies, conglomerates need to
take this into account when planning for implementation of their central data
hubs and data marts, assigning ownership and responsibilities for data quality
improvements.
5. Group-Level Customer Analytics: Once the central data model is in place, the
next step is analysis of compiled customer data, running various customer
analytics models at the group level. Similar to applications at individual company
levels, customer analytics can reveal critical insights for the group as a whole. For
example, companies can identify their high value and high potential group
customers, and can use bits of information across the group to identify customer
segments. As the variety of channels, products and services offered to the
customers increase, so too do the opportunities to understand them better (such
as understanding their lifestyle or socio-economical status). For example, a
conglomerate, with a customer who has recently purchase a Ferrari from its
automotive business line can easily cross-sell from its luxury retail lines,
identifying such customer as trendy and high net-worth.
6. Business Use of Data: Last, but not the least, companies need to put the
customer insights and leads into use, incorporating them into their day to day
operations (e.g. prioritizing the high value group customers in their call center,
even if they are not the high value customers for their own business line), and
6. come up with numerous campaigns to tap into the available potential for
benefits. These activities shall bring the group companies closer, with task-forces
working to come up with cross-company campaigns and opportunities, making
the synergy myth a reality for all.
Each of these six steps requires detailed planning and involvement from various
stakeholders, requiring a well-structured program management office.
What Next?
Building a central data hub and data mart, and establishing a group level
customer and data governance model around this involves a lot of convincing
and company politics, and requires top level buy-in and sponsorship. As a result,
we recommend starting with some proof of concept, such as bringing data on
sample lists of customers across group companies and testing cross-company
pilots to demonstrate the benefits up-front. This has worked extremely well in
various cases, such as one of the leading groups in the North America, where
contributions to the central data model was initially voluntary for group
companies, but once the results started to come in, no business line wanted to
stay out.
7. About Forte Consultancy Group
Forte Consultancy Group delivers fact-based solutions, balancing short and long term
impact as well as benefits for stakeholders. Forte Consultancy Group provides a variety
of service offerings for numerous sectors, approached in three general phases -
intelligence, design, and implementation.
For more information, please contact
info@forteconsultancy.com
Forte Consultancy Group | Istanbul Office
www.forteconsultancy.com
