to the point: customer analytics 2020: moving beyond ......moving beyond traditional metrics...

To the Point: Customer Analytics 2020:

Moving Beyond Traditional Metrics

Customer Strategies & Technologies Summit

June 5-6, 2013

Lancaster London Hotel

London, UK

Gareth Herschel

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Gareth Herschel

CRM14I_103, 6/13


To the Point: Customer Analytics 2020: Moving Beyond Traditional Metrics

The way that organizations think about the customer analysis they do is transforming in many different ways.

These transitions are not consistent from industry to industry or even from organization to organization within

an industry. Identifying how changing the customer insight role can add the most value to the organization is

the key challenge for these groups and their management teams and is the focus of this presentation.

Gareth Herschel

CRM14I_103, 6/13



There are many analytical demands on modern organizations, and these demands will grow faster than the

ability of organizations to support them. For good or bad, much of this demand comes from customer related

domains such as marketing and customer service.

As organizations prioritize their analytic investments, much of the funding for data management and analysis

will come directly from these functions rather than a centralized IT or BI budget. In industries rich in

consumer data, the CMO will end up spending more on data and analysis than the CIO.

Customer analysis is by far the most popular category of analysis for organizations to invest

in.

Gareth Herschel

CRM14I_103, 6/13



Customer service sees the greatest disconnect between the opportunity of big data and

readiness to capture that benefit.

Many parts of the organization are unprepared for the advent of Big Data, and many parts of the organization

can benefit from its arrival. Two customer related functions stand out in this regard. A recent survey by the

Economist Intelligence Unit found that most people believe customer service to be the most unprepared

function for big data, while customer service and marketing stand to gain the most from adopting its concepts.

Gareth Herschel

CRM14I_103, 6/13



Marketing analysts spend a lot of time analyzing the customer across multiple dimensions; needs, value and

satisfaction being the three most critical high-level categories of analysis.

Although intended for traditional marketing purposes, this analysis can also be reused across the organization

for everything from prioritizing the waiting list for out-of-stock items in the logistics function to assessing

credit terms in the finance function or setting service levels for customer service.

The last decade has seen consistent growth in the concept of extending marketing and sales offers to customers

during customer service interactions. This concept is given a variety of names in the vendor market and within

organizations ("best next offer," "next best offer," "real-time decisions," "interaction optimization," "enterprise

decision management").

What you call it in your organization (Gartner also refers to it a number of ways, though "intelligent decision

automation" is our preference), this approach to improving customer interactions can deliver significantly

better business results than traditional ways of extending similar offers — a tenfold improvement is a good rule

of thumb, based on our observations.

Gareth Herschel

CRM14I_103, 6/13



Online streaming

Movie Recommendations

Movie Info

online retailer

Quality of analysis will become a significant differentiator in the quality of the customer

experience.

• Customers live in an age of ever more choices (referred to in the opening slide). Organizations that can help

them deal with this complexity are in a position to deliver compelling customer experiences.

• Some of this complexity can be addressed by providing portals and aggregation opportunities for customers

(such as mint.com for financial services). Others will rely on analysis to generate meaningful insights for

customers.

• This will result in a broadening of the competitive war, to not only be a competition between executives

around the business model or the marketing teams of who can build traffic volumes or a better user

interface, but also between competing teams of business analysts about who can provide the most

meaningful insight or recommendations for customers.

• In the above example, four different websites were asked for their recommendations for a consumer who

was interested in "Casablanca" (starring Humphrey Bogart and Ingrid Bergman). The four sites between

them generated 32 recommendations with zero overlap (none of them suggested the same movie as

another).

Gareth Herschel

CRM14I_103, 6/13



Where to draw the line? Pets — Kids — Criminals — Soldiers/Firefighters/Police — Doctors — Pilots —

Employees — Partners — SCARY

Strategic Planning Assumption: More than 50% of domestic "smart" objects and sensors

shipped in 2018 will be able to communicate directly or indirectly with a smartphone

Gareth Herschel

CRM14I_103, 6/13



Gartner survey data.

Dark shading is >66% penetration.

Light shading is >33% penetration.

Some data is ubiquitous, most categories are not and few industries have consistently high levels of adoption in

all cases.

Gareth Herschel

CRM14I_103, 6/13



Fewer than half of organizations have significant experience with any form of unstructured

data.

Although volume and velocity can be challenging to organizations, the issue of variety is likely to be the most

taxing from an analytic perspective. Organizations are familiar with a number of different analytic techniques

but most of these revolve around structured data and analysis. Organizations will struggle to adopt emergent

categories of analysis that deal with issues such as location, text, speech, video, and images.

Gareth Herschel

CRM14I_103, 6/13



Most organizations will find higher levels for demand for predictive and prescriptive analysis than they can

support. There are four approaches organizations can take singly or in combination to overcome this problem.

Using Analytic Service Providers allows the organization to leverage the domain expertise of their expert

users, providing a pool of talent that the organization would find difficult to duplicate internally or on-demand.

Purchasing Packaged Applications allows a higher degree of user self-service but relies on a high level of

trust from the organization that the vendor’s solution is truly appropriate for the particular circumstances in

which they operate.

Investing in architectures or tools to improve analyst productivity is often the most appealing way for

organizations to grow, but it may not be sufficient since it still relies heavily on their own limited pool of

expert users.

Using their own expert analysts to build applications for internal users to run as necessary (for example by

selecting from parameterized options) is the least mature approach, but may offer the greatest potential to

balance internal expertise with high levels of productivity.

Gareth Herschel

CRM14I_103, 6/13



Tactical Guideline: Emergent data mining tools do not eliminate the need for steps in the data

mining process; they shift the burden from the analyst to the business user or IT support.

Iterative Steps

Several industry process models can be used to leverage the power of the different technologies available to

data miners (a popular example is the Cross Industry Standard Process for Data Mining [CRISP DM]).

Regardless of the specific process model you choose, data mining projects follow a similar pattern from

problem definition to model deployment. The process is iterative and highly meshed.

Apart from extensions and refinements to underlying algorithms that perform the data mining, most

improvements in data mining applications have focused on the ease of use and, more specifically, the ability to

shift ownership and responsibility for certain stages of the modeling process away from a full-time, highly

skilled analyst toward either the business sponsor or the IT organization.

For example, data cleansing and formatting are increasingly performed in the underlying database before the

data or a sample of it is loaded into the modeling environment. Growing the ease of use of the modeling tools

and process control increasingly allows the business user to become the analyst when performing tasks, such as

the selection of modeling techniques or the validation of the model.

Action Item: Start with a well-defined problem and process. Keep in mind that data mining is not a linear

process. The preliminary models you build may provide insights that lead you to create new, different models

or even to redefine the initial question.

Gareth Herschel

CRM14I_103, 6/13



• Traditionally, predictions of customer behavior came from high-end tools used by a small minority of

dedicated users. This "craftsman" model for analytics will not scale to meet the growing demand for

customer-oriented predictions.

• This latent demand is increasingly being filled by one-touch analytic applications that automate elements of

the model-building process to deliver models significantly faster than traditional approaches.

• These "factory" analysis techniques are likely to capture much of the growth in the market, but the extent to

which these tools will actually displace the craftsman analysis in organizations (as opposed to being adopted

to complement existing solutions) is still in doubt.

Recommended Reading: How to Increase the Volume of Advanced Analysis; Gareth Herschel (G00225432)

Latent Demand

for Analysis

Organizations have no idea the level of latent demand for analysis from business decision

makers.

Gareth Herschel

CRM14I_103, 6/13



Analytic service providers come in an ever-increasing variety and this proliferation is likely to continue.

Although confusing, this flexibility does allow organizations to choose an analytic partner (or partners) that

meet their exact needs. For example the data that is to be analyzed may be data that the analytic provider has

collected directly, giving them an advantage over other providers, or it may be data that the organization has

already collected and now needs someone else to analyze, or it may be data that the provider obtains from

other providers, differentiating themselves on the quality of their analysis rather than the underlying data.

Similarly, the analysis an be run by the data provider or on company machines, the analysts may be those of

the service provider, your own analysts or may be crowd sourced (e.g., Kaggle). Once the analysis has been

performed the resulting insights may be provided back to the company to use or the service provider may

handle the execution of follow up actions directly. A variety of pricing models are also available although gain

sharing is generally less appealing than it often seems at first glance.

Gareth Herschel

CRM14I_103, 6/13



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