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Viewpoint: |
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companies measure and seek to understand their customers’
attitudes towards and perceptions of the services they
deliver. The most
successful companies go still further—they benchmark their
performance relative to other leading companies providing similar
services. The
CustomerSat Benchmark Programs enable participants to compare their
performance with benchmark satisfaction and loyalty indices
representing the aggregate performance of other leading companies
measuring similar performance attributes.
Benchmarking can be a valuable competitive tool when used to improve operations; recognize and leverage strategic advantages; anticipate customer requirements; and identify potential vulnerabilities. Benchmarking helps prioritize areas of improvement by allowing a company to compare their confidential customer satisfaction performance scores with the aggregate scores of comparable companies. Additional benefits are derived by combining timely results from a comprehensive feedback program with an ongoing Benchmark Program. Specifically, firms benefit in five ways by applying this approach: Technical/Customer Support. Results allow the support department to:
Marketing.
Input from customers is the lifeblood of Marketing, helping to:
Product Development. Customer feedback about products allows companies to focus on product development that meets real customer needs, and to bring those products to market faster. Sales.
A more informed sales force is a more effective sales force. Knowing
the areas in which the company excels, particularly in comparison to
other players, allows for hard-hitting sales presentations,
leading to increased sales. Loyal
Customers.
Assuming that companies devise prioritized action plans based on
survey results—and then re-measure to ensure that the changes they
implement result in higher customer satisfaction—customers will
benefit from having their unmet needs addressed. The more satisfied
the customers, the more likely it is that they will:
Segmenting
Customers by Loyalty: The Harvard Business School Apostle Model
CustomerSat’s Benchmark Program helps companies utilize the powerful Harvard Business School “Apostle” model. By plotting Satisfaction versus Loyalty on the X- and Y-axes, one can segment customers according to their attitudes about satisfaction and loyalty. The four basic segments are:
The
authors of the model suggest that immediate attention be focused on:
Apostles—understand their characteristics, keep up the
good work, and communicate with them about how pleased you are with
their business); Near Apostles—what will it take to
increase their satisfaction and make them an Apostle?; and Terrorist—is there some small effort that will turn them around.
Vendors who have a significant Hostage population should also
address their concerns, as they are exhibiting a “false
loyalty.” CustomerSat’s
eCEM system can identify customers by each segment, individually or
by aggregate. Furthermore,
by using the Filter capability, as well as the Quadrant Analysis
functionality, we can learn what characterizes each customer segment
in order to better meet its needs.
When this model is used in conjunction with Benchmarking,
companies have a powerful weapon in the battle to stay ahead of the
competition. |
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Customer & Prospect Scorecarding
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Introduction To
maximize revenue, salespeople need to focus time and attention on
their best prospects.
To maximize customer retention, support people need to focus
time and attention on the most "at risk" of their
profitable customers.
In both cases, quickly determining the right prospects and
customers on which to focus is a key challenge.
For
companies with a large prospect and customer base, sales and support
people typically lack sufficient account knowledge to prioritize
their efforts. A
few comments in a customer database may be all the guidance that is
available. People
are forced to use a crude form of pattern recognition: prospects
that "look like" past successes are given high priority.
Similarly, customers that "act like" ones that were lost
are thought to be at risk. This approach applies both conscious and
subliminal knowledge of sales and support people.
But it also has limitations and dangers: Mistaken
similarities. Superficial
cues can be misleading.
For example, a prospect in the health care vertical market
segment, where sales people have had past successes, could in fact
be more similar in size, channel, and geography to prospects where
most sales have been lost. Past
successes were at high cost.
Past sales and retention successes may be due to inordinately
high allocation of resources, while lower hanging "fruit"
-prospects and customers in other segments - have gone unrecognized
and unharvested.
Or, the few customers who complain loudest may be less at
risk of switching, or less profitable than other less-vocal
customers, causing support resources to be sub-optimally allocated. Markets
and customers change rapidly. Past
sales successes decline rapidly in value over time as guides into
the future. For example, telecommunications suppliers and
business-to-consumer e-commerce sites showed strong financial health
in the late-1990s, and were prime prospects for many companies.
By 2002, both sectors’ financial performance had declined,
making them weaker sales prospects while putting them
"at-risk" as customers. Rigorous techniques that accurately determine the highest-potential prospects and most valued at-risk customers offer companies very high returns on investment. Such techniques result directly in improved sales productivity and revenues. Enter scorecarding. Scorecarding
Prospects and Customers Scorecarding
means scoring and ranking customers and prospects by their expected
purchases, or scoring customers by the degree to which revenue or
profit they represent may be “at risk.” Scorecarding
replaces crude "looks like," "acts like"
associations with a reliable, analytical process that confidently
and consistently guide the sales and support staff. Scorecarding
allows:
How
Scorecarding Works CustomerSat
scorecarding uses key information currently in a prospect or
customer database, adds prospect and customer attitudes and
intentions gathered through online surveys, and builds a robust
model that predicts (scores) prospect and customer potential (Figure
1). Prospect scores
typically project likelihood of buying or expected value of
purchases, over the next twelve months. Customer scores project
purchases, willingness to serve recommend, or risk of defection to a
competitor. Sales people
focus on the prospects with the highest scores; support people focus
on prospects with either highest or lowest scores, depending upon
the metric.
Building
the Model A
prospect or customer database contains a profile of each individual
or organization: a set of variables such as a title of individual,
organization size, vertical market, location, channel, and products
purchased. Some variables
are categorical, such as sales region; others are continuous, such
as past spending; still others are binary, such as whether a
prospect or customer attended an annual users' conference. We
want to use these variables to predict future behavior, namely
purchases. Since we cannot
know a future behavior in advance, we use a known substitute -
either historical purchases or stated purchase intentions. Using
historical purchases alone is problematic.
Past sales may have required excessive time and effort, and
included unprofitably hefty discounts.
Also, as requirements and technologies change, past sales
become increasingly irrelevant in predicting future purchases.
As one sales director explained, "Trying to predict
future purchases from history is like looking in a rear-view
mirror." Purchase
intentions, however, are unbiased by these effects and provide a
more reliable indicator of future behavior.
Surveying
for Purchase Intentions To assess purchase intentions, we select a representative sample of the prospect or customer base for surveying. If there are well-defined customer segments, we may select a representative sample for each segment. A survey is composed of three question types: objectives, qualifying questions, and profiling questions (Figure 2).
Objectives such as purchase intentions or estimates of spending are the keys to developing the predictive model. Applying multivariate techniques to the survey respondents' profile and purchase intentions, we develop formulas that predict the purchase intentions (scores) of all prospects and customers, based on their profiles (Figure 3). Prospects or customers are then sorted from highest to lowest score.
Reports
are Powerful Management Tools Well-designed,
actionable reports allow management, sales and support staff to view
the highest potential prospects or most "at risk"
customers at a glance. Several
valuable reports are:
Fine-Tuning
the Model After
scorecarding has been in use for several months, the model can be
fine-tuned based on actual experience and new survey results.
Reports are updated regularly with new scores to keep guidance to
sales and support staff fresh. Conclusion Scorecarding
has a direct, immediate impact on a company’s bottom line.
With consistent use of up-to-date scorecard data, sales and
support organizations have the data they need to focus resources on
opportunities instead of dead-ends. Sales obtains the information
that turns good prospects into sales; support has fast access to the
information that will allow them to retain your most valuable and
profitable customers. Click
here to download a PDF file of this paper (registration
required). |
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Real-Time Feedback Analytics: Make
the Right Decisions and Investments Faster
A
CustomerSat White Paper |
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Managers continuously need to
make decisions and investments based on estimates of customer,
market, and employee reactions.
For example, investments in service quality require
predicting the resulting changes in customer satisfaction, loyalty,
and revenues. Investments
in production capacity require projecting market demand.
Investments in raising employee productivity require
estimating productivity gains. Managers usually have only crude systems or have to rely on
guesswork to make these projections and guide these decisions.
At the same time, rising capital costs and budget cutbacks
are forcing managers to more closely scrutinize and justify every
expense, headcount, and capital investment than ever before.
Lack of the right customer and employee intelligence at the
right time costs enterprises trillions of dollars per year. CustomerSat analytics uniquely
enable managers to make the best-informed decisions and take
immediate action based on real-time customer and employee feedback.
These analytics can be combined in a virtually unlimited
number of ways to determine priorities, inform trade-offs, and
better grow profits, revenues, customer loyalty and satisfaction.
CustomerSat analytics are
built on a foundation of online surveying, reporting, interactive
design tools, and automated actions and alerts, all integrated with
CRM and other enterprise systems and databases.
As a result, CustomerSat analytics readily support virtually
any management decision or action that depends upon customer,
market, or employee responses. |
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(c)
2008 CustomerSat
500 Ellis Street., Mt. View, CA 94043
Mail
to: expert@CustomerSat.com
http://www.CustomerSat.com