Value Proposition design is even better when it also supports the transition to drill-down
discussions, possibly involving our own subject matter experts. Those conversations are more
effective in building customer consensus when detailed build-up of summary variables and the
sources of information are accessible even if they were not visible on the surface. Great cars have
simple controls that the average driver can use confidently but also have great engineering under
the hood that is appreciated by good mechanics.
Product Claim Data. Scientists and management consultants love well-designed statistical research.
Studies engage their intellect. Evidence-based decisions are their mantra. Study design is an
integral part of their training. Performing extended studies can be part of their full employment
plan. Extreme examples can be found among drug developers, conditioned by FDA approval
requirements, who only find comfort in randomized, multi-center, double-blind, placebo
controlled, clinical trials. Trials that take years to perform, cost millions of dollars and sometimes
deliver ambiguous results.
For value conversations, product claims data rarely need to be this rigorous or this expensive. Start
with an initial hypothesis of quantified product benefit. Even the most meticulous development
studies start with quantitative hypotheses before they test anything.
Next have an internal conversation with the development team or the technical team to test and
refine the hypothesis. Then talk to a beta customer or an early adopter. These conversations
usually transform an initial hypothesis into data “based on anecdotal evidence” at a minimum. With
a modest amount of additional process and presentation, the data become “based on case
studies,” “based on customer interviews” or “the results of a survey.” More detail on the sources of
the data (even if they are anonymous) bolsters credibility. If needed, these data can help inform
well-designed market research or outcomes studies.
None of these data sources adheres to the exacting methodology of scientific development
studies. Even in a medical context, the vast majority of customer value claims don’t require an
expensive study. Yet these data sources support a product claim and support good conversations
about a product’s differentiation and what that is worth. More importantly, using hypotheses and
approximate data as a basis for conversation helps to refine the data themselves through
feedback, identify credible data sources and build the quality of the Value Proposition with
experience.
Economic and Operating Data. It doesn’t take an MBA or a CPA to quantify the effect of a product
on a customer’s business. Initial economic, market and operating data can almost always be
generated quickly through some combination of publicly available sources, sales rep knowledge
and expert knowledge. Where there are gaps in the data from these sources, a plausible initial
hypothesis is a good place to start. Testing v1 with beta customers and early adopters helps
improve the quality of the data. Often it also helps identify customer segments, situations and
competitors where a modified version of the Value Proposition with distinct data makes sense.
Public sources, interviews, surveys and case studies can be usefully supplemented with more
formal market research and outcome studies. But waiting for formal studies to start the customer
dialog misses an opportunity. Most customers are willing to engage in conversations specific to
their needs and situation. That includes providing specific data. Customers are often trying to
figure out whether to invest time and internal political capital in a purchasing process. Having a
better understanding early of value specific to a customer’s business helps the customer make
better decisions. It also helps the sales rep qualify the account as a target.
In practice, there are two barriers to having conversations about customer business specifics:
Too many empty blanks. Some Value Propositions are designed with a long list of
questions before any estimated Customer Value is available for a conversation. These
Value Propositions are rarely successful. Sales reps have to spend extra prep time getting
answers before their customer call. Worse still, reps have to spend scarce customer time in
cycles of discovery without knowing the payoff. Customers get tired of answering
questions without understanding why. Some customers stop answering questions
altogether, perhaps out of concern for confidentiality or perhaps because they get
suspicious as to why the rep is asking.
The best Value Propositions make it possible to have a value conversation with no customer
specifics, usually based on a combination of benchmark data and other customer experience. They
start with a value answer and highlight a few pieces of benchmark data where it is easy and natural
for the customer to react by providing their own specifics. This process of incremental tailoring and
customizing in a series of conversations allows for gradual customization of a Unique Value
Proposition without creating the customer experience of a trip to the dentist to for a complete
check-up. It also allows the conversation to go only as deep as necessary. And it facilitates early
value conversations by the sales rep that work seamlessly with the later involvement of technical
pre-sales or subject matter experts in the customer value conversation.
Insufficient confidence. The true causes of this problem are harder to diagnose. Confidence
shortfalls could arise from any or all of the following concerns by a member of the
customer-facing team:
o Data quality
o Data relevance to the specific customer
o How to find and defend the data source
o How to change the data
o Whether changing the data will destroy the value
o How to use value based on benchmark data in a conversation
o How to get more specific with the customer about their data
o General lack of skills and confidence
Apart from the last concern, these are all addressable with good Value Proposition design, good
content, good training and well-communicated motivation coming from other reps’ experiences
and successes. Value selling can become a viral habit of sales with good Value Propositions and a
good adoption plan.
With customer engagement comes feedback, including customer data that can help improve the
general Value Proposition and customer-specific data that can be useful to other reps in other
sales situations. Ultimately, easy access to general value propositions and relevant customerspecific
value propositions make the next sales conversation better. An agile approach makes this
repository of useful tools better and better.
Great Value Propositions help sales teams win. Value Propositions are useful in a number of ways
to accelerate sales and improve profitability. Not only are perfect data unnecessary, but requiring
exact data can become an excuse, creating a barrier to implementation and improved
performance. Sales professionals use Value Propositions in some ways and circumstances that
don’t require specific customer data at all. Where customer-specific data and strong product
claims support the sales process, agile methods make it possible to start with plausible
approximate or benchmark data and continue to improve the quality of the Value Proposition and
underlying data quality based on customer conversations. Supporting those conversations and
learning from them helps to translate specific customer experience into general sales success.
Representing Data in Charts20
You’ve got your data, you’ve made some sense of it, and now it is time to communicate your
results. Great! This article will provide examples of many types of charts and graphs and explain
how to pick the best one for your data depending on the message you want to convey.
Choosing a type of chart depends first and foremost on what kind of data you have and what you
want to express. I find that charts and graphs are typically used to convey one of the following:
comparisons/relationships, distribution, trends, composition, flow/process, or location.
- Comparison/Relationship Charts – Pretty self explanatory, right? You have data on two or more
variables and you want to show them together, probably to show a correlation or pattern of some
type. Examples might include MPG of three different cars, average heights according to race,
etc. Bar charts and line charts, or combinations of the two, are very commonly used for the
purpose of comparison.
Line Graph Comparing Happiness and Age, Source: The Economist
20 Source: My Market Research Methods, as at http://www.mymarketresearchmethods.com/types-ofcharts-
choose/, as on 4th September, 2017.
Bar Chart Comparing Presidents and Executive Orders, Source: I Love Charts
Venn diagrams are especially useful to show relationships. These are typically qualitative in nature.
Funny Venn Diagram, Source: I Love Charts - Distribution Charts – These types of charts aim to convey “what is the distribution?” of my
data. For example, let’s say you did a survey and you asked everyone their age. A distribution
chart would be usefeul to visualize the distribution of ages among respondents. Column and Line
Histogram charts are probably the most common forms of distribution charts. Scatter plot charts
are also great for this purpose.
Column Histogram Chart
scatter plot chart
Scatter Charts in MS Excel
Word clouds are an interesting way to visualize the frequency distribution of words with textual
data. Here’s an example of a word cloud from the 2011 Academy Award acceptance
speeches. While these aren’t the solution to the world’s problems, they can be useful from time to
time in quickly analyzing open-ended comments from surveys.
Word Cloud Oscars 2011 - Charts that show Trends – While the chart categories mentioned above can certainly show
trends, I think it is deserving to identify this as a category of its own. The most common way to
show trends over time is with a line chart. Nowhere is this more common than in showing stock
price trends over time. The chart below is a “candlestick” chart.
Stock Candlestick Chart
If you have a few extra minutes, here’s an incredible video of Hans Rosling showing charts in
motion, demonstrating both relationships and trends all at once. - Composition Charts – The next category of chart types is “composition” charts, which attempt to
show viewers “this is how my data is composed.” By far, the most common “composition” chart is
a pie chart. A pie chart might show that 60% of my survey respondents were composed of women
and 40% were men.
Funny Pie Chart
Original: http://tongodeon.livejournal.com/583338.html This work is licensed under a Creative
Commons Attribution-Noncommercial-Share Alike 2.5 License
http://creativecommons.org/licenses/by-nc-sa/2.5
Pie Chart Option found in MS Excel
I like using doughnut charts as a variation on the all-too-common pie chart.
Doughnut Charts - Flow/Process Charts – Flow charts are used to show–you guessed it–the flow of a
process. These are often used to guide a decision. In fact, they are often called “decision
trees.” Here’s a couple of examples:
Flow Chart
Funny Flow Chart - Location Charts (Maps) – Geographical maps and data overlays on maps cannot be left off this
list. Of course, everyone knows about basic maps, but take a look at some of these map-inspired
charts and graphs:
World Map Based on Population, Source: WorldMapper
Crime Map Oakland, CA
Advanced Charts and Data Visualizations
Most of the charts and graphs shown above are pretty traditional. Data visualization is a hot field
and there are many new cool forms of “charts” emerging. Calling them “charts” doesn’t do them
justice. I’ll dedicate more time to this in another article, but here’s a sample of what I’m talking
about.
Advanced Data Visualization, Source: Information is Beautiful
Infographic, Source: Information is Beautiful
That’s about it. I’ve leave you with some links to additional information.
Additional Links and Information
The best guide for choosing a chart that I’ve found to date is this one. They’ve narrowed down
the categories of charts down to four.
Chart Types, Source
“Juice Labs” has a nice tool they call the “chart chooser.” It allows you to filter based on your
needs.
Finally, here is a nice infographic from online-behavior.com (snipit below) explaining a wide variety
of chart types.
Chart Type Infographic, Source: online-behavior.com
Activity 7
How would you represent customer satisfaction data? What measures would you use? What
graphs and charts would you use? Why?
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